Technology-Enhanced Learning and the Virtual University 9789819941704, 9819941709

Citation preview

University Development and Administration Series Editor: Fernando F. Padró

Michael David Sankey · Henk Huijser  Rachel Fitzgerald Editors

Technology-Enhanced Learning and the Virtual University

University Development and Administration Series Editor Fernando F. Padró, University of Southern Queensland, Queensland, QLD, Australia

It is a generational work designed to take a comprehensive and utilitarian look at higher education in the first decade of the twenty-first century and to provide a glimpse of potential developments as the century progresses. In this regard, it combines many of the intentions found in the three antecedent works previously described. This series provides basic and (per force) historical perspectives on topics covered that touch upon the impact and approach within universities regarding issues of social justice; designing and fostering a climate and structure that promotes the provision of high skills and new knowledge (transmitted and created); creating schema that ensure the quality and integrity of all programs across the campus; issues and approaches toward establishing and maintaining good external and internal governance; the effective management of financial and human resources; the importance of purpose in generating a viable university structure; the impact of changing paradigms in learning, teaching, and student engagement (the changing toward a learner-centered environment); capacities in relation to human and financial resources available to higher education; and the expectations from and performance of higher education institutions.

Michael David Sankey • Henk Huijser • Rachel Fitzgerald Editors

Technology-Enhanced Learning and the Virtual University With 91 Figures and 20 Tables

Editors Michael David Sankey Director Learning Futures and Lead Education Architect Charles Darwin University Darwin, NT, Australia

Henk Huijser Queensland University of Technology Brisbane, QLD, Australia

Rachel Fitzgerald The Faculty of Business, Economics and Law University of Queensland Brisbane, QLD, Australia

ISSN 2522-5626 ISSN 2522-5634 (electronic) University Development and Administration ISBN 978-981-99-4169-8 ISBN 978-981-99-4170-4 (eBook) https://doi.org/10.1007/978-981-99-4170-4 © Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Paper in this product is recyclable.

Preface

Australia is currently undergoing what many see as the most important or significant review of its higher education sector since the 2008 Bradley Review, which was the basis for the formation of a risk-based quality assurance regulatory scheme. The recently released Australian University Accord Interim Report (2023) envisions a system in 2035 which “will be an integrated tertiary system, with a commitment to access for everyone with the potential and application, achieving significant growth in pursuit of ambitious national skills and equity targets” (p. 20). Of interest regarding this volume’s topic, technology enhanced learning through the presence of a virtual university, “[l]earning and teaching will be transformed, with an ambitious commitment to student experience and use of technology” (p. 20). Section 2.4.2 advocates for “[i]nclusive and high-quality teaching that embraces technological advancements” (p. 71) that broadens access to education through hybrid and online education learning arrangements. Their view is that more needs to be done in terms of attractiveness (meeting student, social, and national needs) to partake higher education study (especially in regional and remote areas) and quality of design, development of approaches (curricular and learning support) and materials, and deployment to ensure active student engagement. Furthermore, “[i]mproving online learning capability also presents an opportunity for Australia to expand its teaching footprint and reach new students and overseas markets” (p. 73) as a means to leverage international education to advance national interests. “The overall goal of reform must be growth for skills through greater equity” (p. 1). In the last chapter of the volume, Sankey, Huijser, and Fitzgerald point out that “the principles of social equity are in its very DNA” (p. 623). Ashford-Rowe, Russell, and Press in their chapter point out how the volume’s proposed evolution of the “virtual university” will serve to increase equitable access. Social equity promotes mitigation of structural socioeconomic disparities impacting the prospects for access, opportunity, and outcomes of different social groups (Gooden 2015; Johnson et al. 2011). Equity in its strictest sense is when the ratio of one person’s outcomes to input ratio is the same as that of a second individual (Walster et al. 1975). Specific to education, Australia’s Gonski Report (Gonski et al. 2011) reviewing the appropriateness of funding of its primary and secondary educational system defined equity in education as students having “access to an acceptable international standard of education, regardless of where they live or the school v

vi

Preface

they attend” (p. 105). In higher education as well as lower levels of education, however, social equity is bounded by a regional dimension in many countries impacting access, affordability, and attitudes regarding the benefits of education (Bowen 2013; Brennan et al. 2008; Bunce 2022). Technology itself is not an answer to successful enrolment and engagement in universities. Pedagogical practices suited to active learner engagement and student support to navigate the institutional environment, institutional culture and values, policies and procedures are important in establishing agency and student identity (Ostini et al. 2020; Padró 2022). Technology enhanced learning (TEL) is a catalyst in getting individuals who cannot physically access institutions for whatever reason to become students to pursue learning opportunities that will allow them to enhance their capability to achieve personal and professional goals. There is an efficiency benefit present in TEL for individuals and institution (Atherton 2021). There is also an effective benefit in terms of at least perceived – and recurrently actual – quality of performance (cf. Cameron 1986) based on findings that online learning at least does as well as face-to-face (FtF) interaction, even if a blended learning approach may provide modest advantages to either (Means et al. 2014). For the remainder of this essay, the discussion will focus on access as a means of achieving social equity, in a way testing the premise of the statement made by Sankey, Huijser, and Fitzgerald in the volume’s last chapter. Access is parsed into five facets, with each facet influencing the other to explore the relationship between HEIs, current and prospective students, government, industry, and the community (this last mostly by implication).

Access Access is a central and not peripheral concern when it comes to equity concerns in the provision of online education (Atherton 2021), and has many facets requiring some unpacking. Levesque, Harris, and Russell (2013), for example, proposed a five-dimension framework for access in healthcare that seems suitable for higher education institutions (HEIs) with some adaptation: 1. Approachability (potential students identifying the existence of a program that meets an individual’s needs and having the capacity to engage with the HEI) 2. Acceptability (consonant with relevant cultural and social factors making TEL an attractive proposition – cf. Kano 1984; Huijser et al. 2022; Kek et al. 2017) 3. Availability and accommodation (from the perspectives of availability of internet services, quality and consistency of connectivity, capacity to enroll, actively engage, persist, and complete coursework, and seek out learning support and other necessary institutional services) 4. Affordability (capacity to meet the direct and indirect costs of attending higher education plus having the necessary time to successfully pursue coursework) 5. Appropriateness (fitness of and for purpose in meeting institutional, personal, and social needs; personal level of risk aversiveness or tolerance to partaking

Preface

vii

economic burden, perception of enhanced capability vis-à-vis personal satisfaction regarding employability and social goal attainment – cf. Avery et al. 2012; Oliver 1997; Padró in press) This framework is reminiscent to Landecker’s (1951) social integration framework based on cultural integration (consistency of TEL with social standards of appropriateness), normative integration (conformity with the social standards), communicative integration (ability to legitimize what an HEI provides via its online offerings and thus make it attractive to current and potential students), and functional integration (the extent of usage of TEL and oversight or regulatory recognition of TEL learning). The link between these two frameworks is worth noting because the public administration literature links programming to contemporary social values (Guy et al. 2012; Hart 1974), meaning that there can be a transitory component to normative referencing regarding quality and social equity as well as providing a viewpoint regarding the acceptance of TEL-based education as part of degree recognition by national oversight and regulatory schemes that is noted in the recognition in the international education offering arena.

Approachability: Communicating Access and Benefit Some time ago, Elliot Eisner (1979) commented that it is sad that university academics, school teachers, and administrators in both systems have seldom tried to explain the complexities in the process of education to the public. Instead, there has been “a willingness to accept assumptions about teaching, curriculum, and evaluation that have at least questionable validity” (p. 13). Value-add to individuals and society (this includes communities, employers, and government) is a concern because of its provision of legitimacy to the existence of higher education; however, value-add is dependent on having similarities in viewpoint. Chan (2016) said it best: If [HEIs] and students do not have aligned goals and aims for completing a bachelor’s degree, then there is likely to be disappointment on both sides. On one hand, academics and staff may be disappointed if students do not go beyond the minimum requirements in their engagement with learning tasks. On the other hand, students may balk at learning outcomes that have little connection with vocations. (p. 3)

Approachability as proposed here is more than the openness of academics and staff to engage with students, and it is an important key to learning and teaching success (Cox et al. 2010; Denzine et al 2000; Hagenauer et al. 2023). In terms of Landecker’s (1951) framework, approachability is also about the ability to link through the exchange of meanings, that is, establishing a clear exchange of what the HEI means regarding their offerings and what the potential student (and let us not forget politicians and regulators) seeks. One area of cognitive dissonance between the values or beliefs of HEI marketing and social references to HEI benefits to individuals and potential and actual students (cf. Festinger 1957) is the current view of

viii

Preface

students as consumers by both parties (Bunce et al. 2017; Padró 2022). Like Bunce et al. (2017) found, “consumer orientation mediated traditional relationships between learner identity, grade goal and academic performance, and found that a higher consumer orientation was associated with lower academic performance” (p. 1958). Dissonance results from HEIs providing a limited view of capability enhancement, often presented by HEIs as an implied view that ability, intelligence, and potential are innate elements of the educational process (Burke et al. 2016), and external stakeholders like politicians, regulators, parents, and potential and actual students viewing the educational process as transactional in nature based on utility (Finney et al. 2010; Padró 2022).

Sensemaking Poylanyi (1962) wrote that “the capacity to perform a useful action presupposed some purely intellectual control over the circumstances in which the action is to take place” (p. 174). Key is establishing the understanding of the context of personal circumstances and establishing a clear framing and understanding of context in relation to online programs can be understood from the perspective of sensemaking. Online course offerings have become commodified, often sold as a market product, with the sales pitch consisting of four elements: (1) student-centered learning, (2) flexibility, (3) being interactive, and (4) digital inclusion (Cunha et al. 2020). Sensemaking promotes a causal, explanatory situational awareness for individuals that converts belief into action (Klein et al. 2006a, b; Pirolli et al. 2011). When it comes to TEL, sensemaking is framed by the lens of mediality – the “technoanthropological means of representation and communication, as developed and developing through mutual interaction, such as to give rise to a particular environment in which certain forms (of sense) can be distinguished” (Müller 2018, p. 49). Sensemaking provides the capacity of the potential student to figure out the multiple social perspectives regarding the benefits accrued from of a higher education credential (personal and professional) and the quality of content, experience, and outcomes, that is, the capability to achieve what is wanted (Fig. 1). This is done by not just the sharing of information between actual and potential student and the HEI but the reciprocal sharing of intents and interpretations of expectations framed by experience. As Astin (1985, 1993) noted, a successful higher education experience – meeting expectations – is the basis of who the student is (what Buchler (1951) called proception) and the capability to achieve personal, institutional, and social expectations mediated by the whole of the HEI environment (Padró 2022). Panke’s chapter on the use of social media frames this discussion because as she writes, social media plays a major role in the lives of millennials, ergo the interest of HEIs to use social media to reach out to students and from a learning and teaching perspective, how to use it effectively to enhance student engagement and learning, not to mention visioning alignment regarding the value of the subject matter communicated through the curriculum and qualification being sought. The question she asks is an important one: “How can virtual universities leverage social media effectively, responsibly, and purposefully?” (p. 623).

Preface

ix

Fig. 1 Personal meaningmaking sensemaking of context and decisions. (Source: Padró, in press)

Sensemaking has an immanent communication/dialogic-based process that constantly attempts to find out what the story is and what needs to be done about it (Dervin 1999; Weick et al. 2005). Weick’s (1995, p. 17). The sensemaking framework based on seven characteristics explains the process of how individuals figure things regarding their environment to create context: (1) grounded in identity construction, (2) retrospectiveness (and future-focused – Colville et al. 2016), (3) enactive of sensible environments, (4) based on social interactions, (5) ongoing, (6) focused on and by extracted cues, and (7) driven by plausibility rather than accuracy. While a general process must do, the results can vary because of different personal identities based on proceptual differences (i.e., the difference of individual summation of beliefs and experiences – cf. Buchler 1951, pp. 5, 6), difference in ability to extract cues and their meaning (especially if limited to self-confirmation bias), and, effectively, risk aversiveness, or tolerance to act on the interpretation and evaluation of available information. Kek, Padró, and Huijser’s (2022) interconnected university ecology model (Fig. 2) provides an institutional perspective on sensemaking. The model shows the many interconnections with which HEIs deal with directly or indirectly in its assessment of mission attainment as part of its triple-helix relationship with government and general external environment (cf. Etzkowitz et al. 1998). Risk assessment and strategic planning are based on prioritizing interconnections in relation to identified institutional viability preferences and sense of mission. For example, in the Australasian Council on Open, Distance and e-Learning (ACODE) Benchmarks for Technology Enhanced Learning (Sankey et al. 2014), Benchmark 1 – Institutionwide policy and governance for technology enhanced learning – illustrates this well. The various governance structures within HEIs distribute different aspects of online learning environments (OLEs) responsibilities (core technologies, supported technologies, allowed technologies, and emerging technologies – Sankey et al. 2013) as

x

Preface

Fig. 2 An interconnected university ecology on which HEI sensemaking is made. (Source: Adapted from Kek et al. 2022, p. 881)

seen in Fig. 3. The relationships within the institutional bureaucracy “must be understood from formal sources, such as bylaws, charters, faculty handbooks, or other documents” (Stroup 1966, p. 77). Decisions of marketing, course and program content, and quality emanate from appropriate academic and enterprise-related units and coordinated by management through strategic policy and risk management units that are then ultimately responsible to the head of the HEI supported by the executive committee or council, academic board, or faculty senate and the external governance body (Board of Trustees, Council). The challenge here, as highlighted in Smallman and Ryan’s chapter, is making the governance and managerial aspects as transparent as possible in order to engender trust. The distinction between governance and management is related to the complexity of HEI organizational structures because of the roles staff (primarily academic) and students have within the academic community (Millett 1980). The difference is that governance focuses on decision-

Preface

xi

Fig. 3 University committee structure related to online learning environments (OLEs). (Source: Sankey et al. 2013, p. 4)

making “about purposes, policies, programs and resources” whereas management concentrates on “work planning and work performance” (p. 147). In sum, sensemaking in terms of approachability is a situation of establishing a commonality of understanding regarding achieving mutuality of benefit. Like Derrida (1996, p. 7) noted, there are two seemingly contradictory propositions at play: 1. We only ever speak one language. 2. We never speak only one language (italics in the original). The idea here is that the socially constructed space and the individual perceived space have to be worked out to create a double contingency to achieve consensus in meeting need-dispositions (Parsons 1951) that can only be worked out through communication to the extent possible because of the differences inherent in the self-referential determination of the two parties (Luhmann 1995). The importance of HEI education is one aspect of the equation, the other aspect being the affordances TEL provides to make HEI offerings attractive to society as a whole. Sankey, Huijser, and Fitzgerald’s first chapter in the volume points to studies showing that students accept online-based learning as a legitimate alternative to their higher education learning experience in their argument for the development of the virtual university. What has been described here are some of the steps and processes individuals partake in to make TEL a legitimate alternative to the traditional FtF learning. What HEIs do to make the case for their legitimacy of purpose and offerings is discussed in the ‘Appropriateness’ subsection below, where accountability drives sensemaking as part of solving double contingency bridging between HEIs and students, a point analogous to the one Marshall makes in his chapter about how sensemaking can be translated into a quality framework.

xii

Preface

Acceptability: Alignment with Socially (Culturally) Appropriate Alternatives Cultural factors affecting acceptability are multifaceted. The chapter in this volume by Ashford-Rowe, Russell, and Press argues that digital literacy has become a socially recognized means of achieving success. This acceptance generates ethical and moral concerns accompanying considerations of ease of access and the worth of pursuing a higher education degree. Brey (2006) identified four social and ethical concerns: (1) the value transfer in higher education (addressed in the above section), (2) academic freedom, (3) equality and diversity, and (4) ethical staff and student behavior. These are discussed below as part of the cultural integration process (Landecker 1951) acceptance of OLE offerings by the public.

Academic Freedom Academic freedom, among other things, can be considered a set of “[f]ar-reaching individual rights to expressive freedoms for members of the academic community (both staff and students) mainly as free enquirers, including the freedom to study, the freedom to teach, the freedom of research and information, the freedom of expression and publication (including the ‘right to err’), and the right to undertake professional activities outside of academic employment” (Vrielink et al. 2011, p. 117). Brey’s (2006) concern about academic freedom in TEL is based on academic values immanent in the performance of academic work. By extension based on tasks performed, academic freedom and its values also permeate institutional student support efforts and related enterprise interactions between an HEI and its students (Padró 2018, 2022). Academic freedom concerns apply to both staff and students. TEL has been an acceptable prospect within academia for many decades, so from an intra-institutional perspective resistance is limited to an academic’s preference in the use and approach of technology in the teaching of subject matter. For some academics, this can be an objection or preference based on the individual exercise of academic freedom on autonomy on what can be taught and how to teach (Badamchi 2022). For others who teach at HEIs, the concerns may be more of comfort, institutional recognition of effort, time, and/or access to assistance in course design in the online space well as support when issues arise. Also, as Brey (2006) pointed out, there could be freedom of speech issues for staff as well as students. His focus was on third-party restrictions (possibly even censoring) of emails and acquisition of some information. An example of this is the sharing of comments provided by students in the student evaluation of a course and/or teacher, usually for reasons of confidentiality, protection of teaching staff pertaining career progression, and shielding of staff from abusive comments (Heffernan 2023). It is worthwhile to remember that what could be deemed as good practice in staff development, appraisal, and counselling of staff in other private and public sectors can undermine academic freedom (National Committee of Inquiry into Higher Education 1997). Technological developments in computer hardware and software from the last quarter of the twentieth century to the present have made learning and teaching a better, more active learning experience for academics and students because of the

Preface

xiii

interactive opportunities within TEL (cf. Chickering et al. 1996). These developments reflect what Bowen and Lack (2012) observed: Far greater access to the Internet, improvement in internet speed, reduction in storage costs, the proliferation if increasingly sophisticated mobile devises, and other advances have combined with changing mindsets to suggest that online learning, in many of its manifestations, can lead to at least comparable learning outcomes relative to face-to-face instruction at lower cost (p. 45).

Advances in hardware and software, and more particularly cloud-based platforms, have allowed those born into the Millennial generation onwards to not only be used to computers and other devises but to prefer and thrive via online activities for many aspects of daily life. Discomfort and resistance seem to be more prevalent with older students (Williams et al. 2014). COVID-19 showed the benefits and advantages of online learning and OLEs. Downsides were technical issues, teachers’ lack of technical skills, improper adaptation of teaching style to the online environment, lack of interaction between teachers and student, and poor communications (Coman et al. 2020). Other downsides include the intensification of social and digital inequality, especially for those from households (Bashir et al. 2021). Upsides included institutional agility to adapt its learning modes into the online format and student adaptability to this type of learning experience, although with mixed student views of their online learning experience (Martin 2020). Student academic freedom concerns can be the protection of their freedom of expression, improper academic evaluation, improper disclosure of student information, freedom of association, participation in institutional governance, and overreach of institutional limitations on rights of citizenship (American Association of University Professors 1967).

Equality and Diversity Clark (1983) wrote that four values shape institutional and sector behavior: social justice, competence, liberty, and loyalty. HEI actions in support of these values “often clash [and] even contradict one another, necessitating accommodations that soften conflict and allow simultaneous expression” (p. 241). He also noted that for students, equality: is taken to consist, in ascending order of stringency, of equality of opportunity in the sense of access, equality of opportunity in the sense of treatment once admitted, and equality of outcome or reward (p. 241).

The concern is the notable level of evidence that OLE supports the widening participation and equal treatment of traditionally under-represented marginalized groups rather than creating additional or new hurdles inducing new inequalities (Brey 2006; Simpson 2005). Anderson and Simpson (2007) also noted approachability concerns based on a lack of interest in computer access and use due to their not being as important as other immediate issues relating to day-to-day living concerns.

xiv

Preface

An educational philosopher in the USA, a colleague of the author, was uncomfortable with the programs of which we were a part to pursue technology enhanced learning through online instruction because of the fear that such practice provides an “inferior” learning experience to students. His concern was specific to the type of students the institution was serving, the mostly under-represented by culture and/or race, disability, first-in-family, gender, SES, and sexual identity/orientation. The fear was based on concerns that online education could resurrect the “separate but equal” premise of the 1896 U.S. Supreme Court case Plessy v. Ferguson (163 U.S. 537) that was later rejected in the 1954 case of Brown v. Board of Education (347 U.S. 483). At issue was the quality of experience and practice, in this case that online education would not provide as appropriate, complete, and robust learning experiences and personal development as face-to-face (FtF) classroom teaching. Ironically, the institution was moving toward using a rudimentary approach toward internet-based course content and communications management to meet students located in different states during this time as a matter of practicality. This colleague’s concern, however, has been one that has been in the forefront of educators, accreditors, and regulators since at least the beginning of the twenty-first century. As an individual who has been active in the quality assurance (QA) of higher education environment as an external and/or internal examiner or reviewer of programs and institutions from 1997 onwards, preparation for reviewing online programs has been from the get-go based on online education having to meet the same quality criteria as FtF. Accountability through sound evaluation practices has been critical in demonstrating for the most part for some time that blended and online instruction are at least as good as FtF (Crowley 2012). Consequently, QA has an equity component to it through its assurance that course/program offerings and their instructional modalities are reasonably the same in terms of content, assessment, and pedagogical acumen.

Ethical Staff and Student Behavior Already alluded to in the Academic Freedom sub-subsection above were the issues surrounding freedom of expression for both staff and students, harassment and hate speech, and freedom of inquiry for both. Brey (2006) identified other issues: digital plagiarism, copyright infringement and software theft, hacking, improper use of computer and online resources, and breaches of information privacy and confidentiality. And just recently, the potential for the use of recent developments in artificial intelligence for breaches of academic integrity has added to the list of issues. It can be argued programs such as ChatGPT have raised the concerns of ethical behavior of staff and students to new levels of concerns; yet there is also a paradoxical issue present as well given how in some professions the use of ChatGPT is becoming a “must have” skill requiring a rethinking of teaching ChatGPT as part of skill acquisition. The chapter by Mason, Lefrere, Peoples, Lee, and Shaw describes the more positive aspect of artificial intelligence that seems to be overwhelmed with the academic integrity fears currently prevalent in electronic and print media and have become the bane of HEI policies and procedures as personal experience can attest.

Preface

xv

These recent developments in artificial intelligence represent a normative shift because of this paradox. The key point here is reconciling the paradox so that ethical constraints co-exist with necessary skill development in numerous subject areas. Traditional assessment practices have become questionable, requiring HEIs to consider a broad and holistic approach to academic integrity (Bretag et al. 2011, 2016; Daumiller et al. 2019). This more global approach should demonstrate accountability by formally considering (a) purpose, (b) audience, (c) use, (d) effect, and (e) sustainability based on (i) defensibility of method, (ii) demonstration of a holistic perspective to assessment and related concerns, (iii) prioritization (identify what is most critical to know), (iv) collaborative in the development and execution of the assessment system, and (v) adaptability of application (Ellis et al. 2020). Furthermore, the International Center for Academic Integrity’s [ICAI] (2021) fundamental values of academic integrity should guide ethical use considerations for both staff and students: (a) honesty, (b) trust, (c) fairness, (d) respect, (e) responsibility, and (f) courage. “Without them, the work of teachers, learners, and researchers loses value and credibility” (p. 4). The temptation is to focus primarily on students; however, it applies to academic staff as well as defined by the disciplinary standards of practice regarding the use of artificial intelligence software in terms of research publications and course content inclusion of materials from already or soon to be published research and/or previous course specifications or syllabi.

Affordability Quality improvement of all HEI offerings is indelibly linked to student expectations (Al-Makssoossi 2022). One of the key expectations is the ability to afford enrolling at an HEI and earning the desired academic credential. It is definitely a “must have” requirement on the part of prospective and current students, a baseline demand majorly influencing enrolment decisions for new students and one element of persistence concerns for continuing students. Looking at affordability from the lens of a Kano analysis is an approach that allows cost to act as a baseline and other available offerings as “attractive” or desired considerations. The strength of the Kano perspective is the capacity for HEIs to identify and distinguish the impact different criteria have on the decision of current and potential students (e.g., look and feel of LMS and course sites within the LMS, pedagogical approaches, reputational perceptions, synchronicity, etc. – Al-Makssoossi 2022). “Attractive” quality criteria are those that satisfy when they are perceived to be met in their entirety but do not create a negative view (dissatisfaction) of the HEI if not met because they were not expressly expected (Kano et al. 1984). What is affordability? The literature explains it as the net price a student pays relative to family or personal income (e.g., Hill et al. 2006). Affordability has direct costs and indirect cost components influencing the utility of their educational experience vis-à-vis overall benefit to the well-being of the individual thinking about pursuing a higher education credential either in FtF or through TEL opportunities. There are the direct costs such as tuition fees, service fees, and hardware and

xvi

Preface

software costs to pursue online education if not provided by the HEI. Those are easily understood as core components of costs. Indirect costs or opportunity costs are a different matter because they are contextually driven and thus sometimes difficult to identify and quantify. For example, the costs required for taking courses beyond tuition and fees. Examples of these indirect expenses are books, equipment (computer hardware, software, other technological devises and resources – as discussed in Mishra’s and Bossu and Ellis’ chapters), and supplies (Kelchen 2015). There are room and board costs, transportation costs, and other living costs beyond food like connectivity costs associated with reliable access to internet services and the cost from using more electricity. Then add the deferred costs of interests of school loan repayments and associated issues related to slow or non-payment. A more challenging indirect cost to conceptualize is the idea of forgone income, that is, how much money is being lost due to un- or under-employment (being in a lower-level job or part-time employment) resulting from pursuing a higher education credential (ranging from a micro-credential to a full degree at the undergraduate or post-graduate levels). These indirect costs are personal in nature and potentially exacerbated by location and cost of living concerns that may vary (e.g., regional versus urban locations). Indirect costs have to also be viewed from the perspective of a trade-off between equity and efficiency. The question posited by Conner and Rabovsky (2011) is of critical importance: “What is the optimal balance between efficiency and affordability versus quality, and how should society balance these concerns against long-standing attempts to maintain and increase access?” (p. 98) Studies of indirect costs typically are based on a human capital that estimates the potential loss production as a consequence of decisions such as participation in higher education (Koopmanschap et al. 1995). This utility perspective reflects how the perceived value of a degree is ultimately evaluated in terms of the “premium the degree provides over what [the person] would otherwise have earned, some of which reasonably be devoted to repaying student loans” (Baum 2018, p. 4). HEIs feed into this utility perspective through the emphasis on employability and work integrated learning (WIL) experiences as described in Dean, Campbell, Shalavin, and Eady’s chapter on preparing students for the future of work in the virtual world.

Appropriateness: Demonstrating Accountability Through Normative Alignment with Standards of Practice Vickers (1973) defined norms as being tacit standards expressing in “ought to be” terms what is acceptable. Appropriateness viewed through normative alignment represents the extent of how TEL conforms to expectations derived from standards of practice (cf. Landecker 1952). “There is an inside–outside differentiation between values and norms; values (I live my values) are inside the person, whereas norms and cultural practices are perceived to be outside the person (I conform to the norms)” (Frese 2015, p. 2). An appropriateness outcome thus is when a person’s values align with the opportunity presented them. As discussed so far, alignment tends to be done in terms of pursuit of personal capability as defined by Sen (1992) as a freedom to

Preface

xvii

achieve through the exercise of personal agency. The perspective of instrumentality plays a role in determining a positive view toward engaging an HEI as a student and the choice of which HEI and program to pursue. While part of the decision on the part of the potential student may be transaction in approach, HEIs – as hinted at in the narrative so far – should consider moving from the temptation of competencybased skill development to “a more dynamic concept of capability, embracing learning, culture and values” that focuses on educating the whole person in meeting professional and social responsibility “through critical, reflective learning experiences” (O’Reilly et al. 1999, p. 3). This author wholeheartedly agrees with Marshall when, in his chapter, he states that the “[e]xamination of quality is often used as a proxy for political and economic interests in the place universities occupy in society” (p. 623). Alas, two issues are at play here: fitness OF purpose and fitness FOR purpose (Padró et al. 2019). Each provides the basis for QA, the main determinant of appropriateness in terms of course content, program structure and alignment within the program and degrees offered, pedagogical practice, staff and student support pertinent to TEL issues, and identification of risks. Fitness FOR purpose signifies the fulfillment of expectations via conformance to standards (Jung et al. 2016). Standards formation typically represents a two-step consensus building process as exemplified by the International Organization for Standardization’s (ISO) process: forming consensus among experts and national or international level consensus regarding best opinions of how market or sector needs are met (ISO 2010). Fitness OF purpose is a different concept altogether. It is about how “the university should be doing the right things and in the right way” (Swan 1998, p. 273). The frames of reference for fitness OF purpose are institutional mission and program objectives, serving as a means of evaluating “whether the quality related intention of an organization [is] adequate” (Vlӑsceanu 2007, p. 72). Lester’s (1999) distinction between fitness FOR purpose and fitness OF purpose clarifies the difference and importance of both: The limitation of fitness for purpose is that it operates within the boundaries set by the purpose itself, . . . totally dependent on how well [purpose] . . . has been framed. . . [F]itness for purpose is essentially a single-loop test of validity which . . . has no ethical, moral or spiritual dimension, but can be. . . narrowly pragmatic or instrumental. . . To move beyond this limitation points to considering the fitness of the purpose, or how well it has been framed in terms of wider contexts and issues. Fitness of purpose represents a double- or multipleloop test of validity, as it asks the learner to consider the congruence of his or her objectives in broader contexts and question the assumptions on which they are based: effectively, move out of the logic or frame or reference in which the purpose is based, and question its congruence in a wider context. (p. 104)

There are a number of international and national professional bodies or regulatory agencies in Australasia, Europe, and the USA who have developed benchmarks, guidelines, or standards pertaining to TEL practice ranging from operational issues to learning and teaching frameworks (Marshall et al. 2017; Padró et al. 2018). Most of these documents have complementing aspects to them, although as Marshall and

xviii

Preface

Sankey (2017) noted, outside of face validity, validity of these benchmarks, guidelines, and standards can be a concern, probably due to the consensus approach normally taken in their development, as already noted. ISO standards have been used by HEIs in a number of countries in lieu of accrediting bodies or regulatory agencies overseeing the quality assurance of the higher education sector. Thus they have been directly or indirectly shaping institutional behaviors through accreditation practices and/or regulatory compliance practices from different regulatory bodies with which HEIs have to interact for legal reasons. Standards such as the ISO 9000 series of standards for organizational quality management systems, ISO 14000 series standards for voluntary environmental regulation, ISO 26000 standard on social responsibility, and the ISO 31000 standard on risk management are wholly or in part embedded into national regulatory frameworks, in part of New Public Management thinking by governments and consonance with quality management systems thinking and practices (Padró et al. 2020). For example, third-party partnerships between HEIs (particularly in the international arena), international student recruitment through agents, and other forms of contracted agreements often follow processes set forth by the ISO 9000 series. Standards such as ISO 31000 may not have been intended as compliance standards; yet are used for alignment and improvement of institutional process and have become part of the regulatory world overseeing higher education (Padró 2015; Padró et al. 2015). ISO has developed two standards that are clearly specific to HEI institutional level managerial processes, ISO 21001 and 40180:2017. Standard 21001:2018 (https://www.iium.edu.my/media/59850/ISO%2021001%20Educational%20Organi zation%20Management.pdf) is a standard for management systems of educational organizations based on providing products and services that can meet learner requirements based on eleven principles linked to quality norms. The standard enables an understanding and consistency in meeting requirements, generating a value-add for HEIs and students, and achieving effective process performance and process improvement through better evaluation of data and information (p. vii). ISO 21001:2018 has similarities to the ISO 9000 series of standards and ISO 31000. ISO also has Standard 40180:2017 (https://cdn.standards.iteh.ai/samples/62825/ e14cb25debf0459b8369c9b0a28a2aae/ISO-IEC-40180-2017.pdf) for information technology quality for formal educational experiences training. This standard represents the current global consensus on TEL practice (Stracke 2019). ISO 40187: 2017 replaced the ISO Standard 19796-1:2005 on information technology in e-learning, online education, and training and is compatible with ISO 9001 and ISO 14000 (environmental regulation). ISO 40180:2017 is not a quality management system; rather, it is to guide and support of a quality management system and the improvement of learning quality. Figure 4 provides the overview of the quality reference framework’s (QRF) steps (macro-, meso-, and micro-levels – Stracke 2019) required for each of the seven processes making up the framework. If there is a question regarding these ISO standards, it is one of whether the emphasis is too much on procedural efficiency rather than effectiveness of results.

Preface

xix

Fig. 4 Overview of ISO 40180:2017 quality reference framework. (Source: ISO 40180:2017, p. 8. https://cdn.standards.iteh.ai/samples/62825/e14cb25debf0459b8369c9b0a28a2aae/ISO-IEC40180-2017.pdf)

A quality system is supposed to enhance both effectiveness and efficiency (Durakbasa et al. 2018), but as Cameron (1986) noted, effectiveness criteria change over time and diversify between stakeholder groups based on specific stakeholder interests due to effectiveness being a problem-driven construct – in the case of TEL, the context of making online learning offerings a legitimate and attractive alternative to personal capability enhancement through degree attainment. ISO 40180:2017 provides the overview of TEL processes that follow a typical Shewhart Plan-Do-Check-Act (PDCA) cycle. The planning aspects are the needs and framework analyses and conception/design elements. The do parts of the cycle are the development and implementation elements. Checking is comprised of the learning process/realization and evaluation. Then it is a return to the planning phase to act on improvement propositions. The process reflects Mintzberg and Waters’ (1985) description of the difference between intended strategy and that which was realized. What is learned is the basis of improvement. The completion of the cycle allows for learning to be coupled with strategic thinking and subsequent implementation (Mintzberg 1987). Professional associations or regulatory bodies provide a more granular set of expectations regarding design, implementation, and institutional inter-relationship between units tasked to provide content and format of courses, online support to staff and students, broader technical support of learning management systems, and managerial oversight. Self-evaluation is a standard approach regarding how well an HEI is performing in relation to their benchmarks or standards. One example of a professional association formation of a TEL quality assurance scheme is the Australasian Council on Open, Distance and e-learning’s (ACODE) benchmarks. It is one of the earliest and accepted schemes available for HEI use. Their eight benchmarks are designed to support continued improvement (Sankey et al. 2014). These benchmarks include a scoping statement, a good practice statement, and performance indicators (PIs) rated using a Likert 5-point scale (Padró et al. 2018). An example from accrediting bodies comes from the USA. The Council of Regional

xx

Preface

Accrediting Commissions (C-RAC) composed of the nation’s seven accrediting commissions recognized by the Federal Government initially came out with guidelines for distance education in 2002 and updated in 2011, which consisted of nine guidelines. A more recent update occurred in 2021 in wake of the COVID-19 experience (https://nchems.org/project/21st-century-distance-education-guidelines/). A third example is the European Association of Distance Teaching Universities’ (EADTU) benchmarking approach to e-learning quality assessment (2016). It is an institutional network composed of universities actively involved in online, open, and distance education (https://eadtu.eu/index.php/about/general). The manual it has created represents an update based on the changing environment and practice in TEL, institutional experience of network members, and engagement with HEIs and agencies (EADTU 2016). Its focus has been to develop “a focus on the parameters of quality assurance relevant to e-learning” (p. 11). The manual provides QA considerations on six areas, each with benchmarks, detailed indicators, and guidance notes (p. 13). Table 1 below provides an overview of the titles of the major components of the ACODE Benchmarks, the C-RAC guidelines, and EADTU benchmarking manual. Table 1 E-learning QA topics covered by three international nonregulatory bodies ACODE benchmarks (2014) B1. Institution-wide policy and governance for technology enhanced learning (8 PIs) B2. Planning for institutionwide quality improvement of technology enhanced learning (5 PIs) B3. Information technology systems, services and support for technology enhanced learning (8 PIs) B4. The application of technology enhanced learning services (9 PIs) B5. Staff professional development for the effective use of technology enhanced learning (7 PIs) B6. Staff support for the use of technology enhanced learning (9 PIs) B7. Student training for the effective use of technology enhanced learning (8 PIs) B8. Student support for the use of technology enhanced learning (10 PIs)

C-RAC 2021 Institutional capacity (7 guideline statements)

EADTU benchmarking approach (2016) Strategic management (7 detailed indicators)

Institutional transparency and disclosures (2 guideline statements with subparts to each of the two) Academic programs (4 guideline statements)

Curriculum design (4 detailed indicators)

Student support for students (4 guideline statements)

Course delivery (2 detailed indicators)

Program review (2 guideline statements)

Staff support (4 detailed indicators)

Course design (5 detailed indicators)

Student support (5 detailed indicators)

Preface

xxi

Industry

Society/ Community

University Purpose

Government

University

Four-helix interconnected university model

Fig. 5 Four-helix interconnected university model. (Source: Kek et al. 2022, p. 876)

Approach may be different; however, areas of interests are similar, and these can be slotted to fill in many of the elements found within ISO 40180:2017. Prescriptiveness of approach is not desired by any of these agencies or most accrediting or regulatory bodies in general for that matter. It is up to each HEI how to use all of these within their required internal and external QA mandates in order to evaluate how well they are doing in meeting educational, enterprise, policy, and student expectations and requirements. At the core, QA practice and goals are reflective of the compacts made under the Etzkowitz and Leydesdorff (1998) triple-helix model between governments, HEIs, and industry and more broadly the reflection of the potential for additional dimensions or helices as indicated by Leydesdorff (2012) to explain the nonlinear inter-relationships that are part of HEI outcome development and implementation, which in the case of higher education can be described as a quadruple helix mode as envisioned by Kek et al. (2022, Fig. 5). “The key message is that the university is only one part of an overall learning ecology, and that there are many other factors that impact on the various communities within the university and beyond (in industry, government, and society)” (p. 876) (Fig. 5).

Concluding Comments Tay in his chapter on a social equity-based framework toward the development of the virtual university, following on Drok’s (2020) study, wrote that:

xxii

Preface while the adoption of technologies has led to positive learning experiences, whether that translates to improved outcomes and opportunities and more equitable access, in particular for students from disadvantaged backgrounds, is an ongoing debate (Drok 2020). (p. 623)

Drok (2020) noted that online education brought with it its own challenges pertaining to student engagement, persistence, and success. As a number of the chapters in the volume indicate, the new “virtual university” should not attempt to merely replicate the FtF experience. It was noted that the basic tenet of QA is to treat the various forms of TEL and “open education” opportunities to the same level of rigorous practice and outcomes as FtF, but not merely as a mirror experience, implicitly because of apprehensions of what Drok documented. For example, Chickering and Gamson’s (1987) “Seven Principles of Good Practice in Undergraduate Education” proposed that good learning and teaching practice in FtF higher education includes (1) the encouragement of contacts between students and teachers, (2) the development of reciprocity and cooperation between students, (3) the use of active learning techniques, (4) the provision of good feedback, (5) an emphasis on time on task, (6) communicating high expectations, and (7) the recognition and respect of diverse talents students have and approaches to learning used. Chickering and Ehrmann (1996) subsequently opined that TEL can actually promote these learning and teaching practices, especially when technology is not used for its own sake (Chizmar et al. 1999). These have been turned to an evaluative framework for evaluating online learning and teaching (e.g., Bangert 2004; Grant et al. 2007) that is often used to determine effective of online courses (Tanis 2020). While this approach has proven appropriate and useful in determining the effectiveness of online teaching in comparison to face-to-face learning experiences, Bangert’s (2008) findings demonstrated some contextual difference needing consideration: . . . the dimensions of effective teaching originally described for face-to-face classroom settings by Chickering and Gamson’s framework have different causal relationships when applied to online learning environments. Contextual influences such as student characteristics, course content, and instructor skills manifest themselves differently in online courses, implying that they will have different relationships to the processes and activities required for quality Internet-based instruction. (p. 43)

Returning to Australia’s interim Accord Report (2023), the belief is that success will require changes to its higher education system. One viewpoint regarding system change was #6: Learning and teaching will be transformed, with an ambitious commitment to student experience and use of technology. (p. 20)

Dean et al.’s chapter on WIL reflects the Accord Report’s view that “[p]lacement and WIL is essential to ensure that graduates are ready and employable” (p. 28). On page 28, the Accord’s authors note concerns over barriers preventing the uptake of WIL. The role of technology in this regard is not discussed; however, what this volume has to offer will provide some potential recommendations on how to

Preface

xxiii

proceed. Similar to Mason et al.’s chapter on artificial page, it states on pages 82 and 84 a belief that “[i]nclusive and high-quality teaching that embraces technological advances” like artificial intelligence will allow universities to adapt more quickly and improve their learning and teaching environments. A limited approach toward change has meant consistently lower levels of satisfaction in engagement for online students, which is a concern. The chapters here will provide guidance in how to overcome these concerns to the satisfaction of at least the triple-helix agreements and cooperative agreements between government, the HEI sector, and industry. The approach taken in this essay has provided a different yet congruous approach to the claims of a strong claim toward achieving social equity. Discussions about approachability, acceptability, availability and accommodation, affordability, and appropriateness provide a different light to many of the concepts the different authors had; yet the endpoints are similar. Not discussed are the deeper implications regarding HEI governance and management, namely because these would take a more detailed and lengthy discussion and would lead to points outside the scope of this volume, particularly to the realms of policy formation and the cybernetics of higher education (cf. Birnbaum 1987). To conclude, Australia’s interim Accord Report sees advances in technology and TEL as an important means to make constructive change to the Australian higher education sector. One of the key goals for this change is to widen participation in the name of social equity. The focus on employability and practices such as WIL, however, creates an inherent contradiction in the Accord Report’s lack of consideration of communal and social benefits emanating from being an educated citizen who gives back to the community and is a knowledgeable participant in democratic processes. A number of the chapters in the volume raise the concern that there is a need to do more than focus on competencies and think more in terms of lifewidening participation. This is why this essay argues for a consideration to base education, especially that performed through the evolving “virtual university,” from the perspective of Sen’s capability framework as this allows for enhanced agency to access those elements within the workforce and the community that are desired or preferred. Queensland, Australia October 2023

Fernando F. Padró

References Al-Makssoossi, A.M.M. 2022. Developing an integrated Kano-QFD SWOT approach to recommend prioritised institutional requirements based on social and academic expectations of Arabic international students at three Queensland universities. PhD dissertation, University of Southern Queensland.

xxiv

Preface

American Association of University Professors [AAUP]. 1967. Joint statement on rights and freedom of students. AAUP Bulletin 53 (4): 365–368. https://www. aaup.org/report/joint-statement-rights-and-freedoms-students Anderson, B., and M. Simpson. 2007. Ethical issues in online education. Open Learning: The Journal of Open, Distance and e-Learning 22 (2): 129–138. https://doi.org/10.1080/02680510701306673. Atherton, G. 2021. Perspectives on the challenges to access and equity in higher education across the world in the context of COVID. National Education Opportunities Network (NEON). https://www.iesalc.unesco.org/wp-content/uploads/ 2021/09/DRAFT-WAHED-24th-Sep-2021-2.pdf Avery, C., and S. Turner. 2012. Student loans: Do college students borrow too much – Or not enough? Journal of Economic Perspectives 26 (1): 165–192. https://doi.org/ 10.2307/41348811. Badamchi, D.K. 2022. Academic freedom: How to conceptualize and justify it? Philosophy and Social Criticism 48 (4): 619–630. https://doi.org/10.1177/ 01914537211072888. Bangert, A.W. 2004. The seven principles of good practice: A framework for evaluating on-line teaching. Internet and Higher Education 7: 217–232. https:// doi.org/10.1016/j.iheduc.2004.06.003. ———. 2008. The development and validation of the student evaluation of online teaching effectiveness. Computers in the Schools 25 (1–2): 25–47. https://doi.org/ 10.1080/07380560802157717. Bashir, A., S. Bashir, K. Rana, P. Lambert, and A. Vernallis. 2021. Post-COVID-19 adaptation; the shifts towards online learning, hybrid course delivery and the implications for Biosciences courses in the higher education setting. Frontiers in Education 6: 13 pp. https://doi.org/10.3389/feduc.2021.711619. Baum, S. 2018. Understanding college affordability. Statement of Dr. Sandy Baum before the Committee on Health, Education, Labor and Pensions, United States Senate. Urban Institute. www.urban.org/sites/default/files/publication/96351/ understanding_college_affordability_4.pdf. Birnbaum, R. 1987. How colleges work: The cybernetics of academic organization and leadership. Jossey-Bass. Bretag, T., S. Mahmud, M. Wallace, R. Walker, C. James, M. Green, J. East, U. McGowan, and L. Partridge. 2011. Core elements of exemplary academic integrity policy in Australian higher education. International Journal for Educational Integrity 7 (2): 3–12. https://doi.org/10.21913/IJEI.v7i2.759. Bretag, T., and S. Mahmud. 2016. A conceptual framework for implementing exemplary academic integrity policy in Australian Higher Education. In Handbook of academic integrity, ed. T. Bretag, 463–480. Springer Science+Business Media Singapore. Bowen, W.G., and K.A. Lack. 2013. Higher education in the digital age. Princeton University Press. Bradley, D., P. Noonan, H. Nugent, and B. Scales. 2008. Review of Australian higher education: Final report. Department of Education, Employment and Workplace Relations. https://apo.org.au/node/15776.

Preface

xxv

Brennan, J., and R. Naidoo. 2008. Higher education and the achievement (and/or prevention) of equity and social justice. Higher Education 56: 287–302. https:// doi.org/10.1007/s10734-008-9127-3. Brey, P. 2006. Social and ethical dimensions of computer-mediated education. Journal of Information, Communication and Ethics in Society 4 (2): 91–101. https://doi.org/10.1108/14779960680000284. Bunce, L., A. Baird, and S.E. Jones. 2017. The student-as-consumer approach in higher education and its effects on academic performance. Studies in Higher Education 42 (11): 1958–1978. https://doi.org/10.1080/03075079.2015. 1127908. Bunce, L.T. 2022. The student “experience” in commercialized higher education. In Student support services, ed. H. Huijser, M.Y.C.A. Kek, and F.F. Padró, 35–51. Springer Nature Singapore Pte Ltd. Burke, P.J., A. Bennett, C. Burgess, K. Gray, and E. Southgate. 2016. Capability, belonging and equity in higher education: Developing inclusive approaches. Report submitted to the National Centre for Student Equity in Higher Education (NCSEHE), Curtin University. The University of Newcastle. https://nova. newcastle.edu.au/vital/access/manager/Repository/uon:32939. Cameron, K.S. 1986. Effectiveness as paradox: Consensus and conflict in conceptions of organizational effectiveness. Management Science 32 (5): 539–553. https://doi.org/10.1287/mnsc.32.5.539. Chan, R.Y. 2016. Understanding the purpose of higher education: An analysis of the economic and social benefits for completing a college degree. Journal of Education Policy, Planning, and Administration 6 (5): 1–40. http://www.jeppa.org. Chickering, A.W., and Z.F. Gamson. 1987. Seven principles of good practice in undergraduate education. AAHE Bulletin 39 (7): 3–7. https://doi.org/10.1002/tl. 37219914708. Chickering, A.W., and S.C. Ehrmann. 1996. Implementing the seven principles: Technology as lever. AAHE Bulletin 49 (2): 3–6. Chizmar, J.F., and M.S. Walbert. 1999. Web-based learning environments guided by principles of good teaching practice. Journal of Economic Education 1999: 248–264. https://doi.org/10.1080/00220489909595985. Colville, I., A. Pye, and A.D. Brown. 2016. Sensemaking processes and Weickarious learning. Management Learning 47 (1): 3–13. https://doi.org/10.1177/ 1350507615616542. Coman, C., L.G. Tîru, L. Mesesan-Schmitz, C. Stanciu, and M.C. Bularca. 2020. Online teaching and learning in higher education during the Coronavirus pandemic: Students’ perspective. Sustainability 12: 24 pp. https://doi.org/10.3390/ su122410367. Conner, T.W., and T.M. Rabovsky. 2011. Accountability, affordability, access: A review of the recent trends in higher education policy research. The Policy Studies Journal 39 (S1): 93–112. https://doi.org/10.1111/j.1541-0072.2010.00389_7.x. Council of Regional Accrediting Commissions [C-RAC]. 2021. Proposed 21st century distance education guidelines. Author, NCSARA, NCHEMS. https:// www.c-rac.org/post/c-rac-statement-on-nc-sara-distance-education-guidelines

xxvi

Preface

Cox, B.E., K.L. McIntosh, P.T. Terenzini, R.D. Reason, and B.R. Lutovsky Quaye. 2010. Pedagogical signals of faculty approachability: Factors shaping facultystudent interaction outside the classroom. Research in Higher Education 51: 767–788. https://doi.org/10.1007/s11162-010-9178-z. Crowley, A. 2012. Supporting online students: A guide to planning, implementing, and evaluating services. Jossey-Bass. Cunha, M.N., T. Chuchu, and E.T. Maziriri. 2020. Threats, challenges, and opportunities for open universities and massive online open courses in the digital revolution. International Journal of Emerging Technologies in Learning 15 (12): 191–204. https://doi.org/10.3991/ijet.v15i12.13435. Daumiller, M., and S. Janke. 2019. The impact of performance goals on cheating depends on how performance is evaluated. AERA Open 5 (4): 1–10. https://doi. org/10.1177/2332858419894276. Denzine, G.M., and S. Pulos. 2000. College students’ perceptions of faculty approachability. Education Research Quarterly 24 (1): 56–66. https://www. proquest.com/scholarly-journals/college-students-perceptions-faculty/docview/ 216181530/se-2 Derrida, J. 1996. Monolingualism of the other or the prosthesis of origin (P. Mensah, Trans.). Stanford University Press. Dervin, B. 1999. On studying information seeking methodologically: The implications of connecting metatheory to method. Information Process Management 35: 727–750. https://doi.org/10.1016/S0306-4573(99)00023-0. Drok, K. 2020. Improving student outcomes in online learning: Tracing the impact of Opportunity Through Online Learning. National Centre for Student Equity in Higher Education, Curtin University. https://www.ncsehe.edu.au/wp-content/ uploads/2020/11/ImpactReport_CathyStone_FINAL.pdf Durakbasa, N., G. Bas, and J. Bauer. 2018. Implementing education vision in the context of Industry 4.0. IOP Conference Series: Materials Science and Engineering 448: 012045. https://doi.org/10.1088/1757-899X/448/1/012045. Eisner, E.W. 1979. The educational imagination: On the design and evaluation of school programs. MacMillan Publishing Co, Inc. and Collier Macmillan Publishers. Ellis, D.E., V.M. Brown, and C.T. Tse. 2020. Comprehensive assessment for teaching and learning centres: A field-tested planning model. International Journal for Academic Development 25 (4): 337–349. https://doi.org/10.1080/1360144X. 2020.1786694. Etzkowitz, H., and L. Leydesdorff. 1998. The endless transition: A “triple-helix” of university-industry-government relations. Minerva 36 (3): 203–208. https://doi. org/10.1023/A:1017159001649. European Association of Distance Teaching Universities [(EADTU]. 2016. Quality assessment for e-learning: A benchmarking approach. 3rd ed Author. https://excellencelabel.eadtu.eu/e-xcellence-review/manual. Festinger, L. 1957. A theory of cognitive dissonance. Stanford University Press.

Preface

xxvii

Finney, T.G., and R.Z. Finney. 2010. Are students their universities’ customers? An exploratory study. Education and Training 52 (4): 276–291. https://doi.org/10. 1108/00400911011050954. Frese, M. 2015. Cultural practices, norms, and values. Journal of Cross-Cultural Psychology 46 (1): 1327–1330. https://doi.org/10.1177/0022022115600267. Gonski, D., K. Boston, K. Greiner, C. Lawrence, B. Scales, and P. Tannock. 2011. Review of funding for schooling—Final Report. Department of Education, Employment and Workplace Relations. https://www.education.gov.au/schoolfunding/resources/review-funding-schooling-final-report-december-2011 Gooden, S.T. 2015. PAR’s social equity footprint. Public Administration Review 75 (3): 372–381. https://doi.org/10.1111/puar.12346. Grant, M.R., and H.R. Thornton. 2007. Best practices in undergraduate adultcentered online learning: Mechanisms for course design and delivery. MERLOT Journal of Online Learning and Teaching 3 (4): 346–355. Guy, M.E., and S.A. McCandless. 2012. Social equity: Its legacy, its promise. Public Administration Review 72 (1): S5–S13. https://doi.org/10.1111/j.1540-6210. 2012.02635.x. Hagenauer, G., F. Muelbacher, and M. Ivanova. 2023. “It’s where learning and teaching begins – Is this relationship” – Insights on the teacher-student relationship at university from the teachers’ perspective. Higher Education 85: 819–835. https://doi.org/10.1007/s10734-022-00867-z. Hart, D.K. 1974. Social equity, justice, and the equitable administrator. Public Administration Review 34 (1): 3–11. https://doi.org/10.2307/974395. Heffernan, T. 2023. Abusive comments in student evaluations of courses and teaching: The attacks women and marginalised academics endure. Higher Education 85: 225–239. https://doi.org/10.1007/s10734-022-00831-x. Hill, C.B., and G.C. Winston. 2006. Access: Net prices, affordability, and equity at a highly selective college. Economics of Education Review 25: 29–41. https://doi. org/10.1016/j.econedurev.2004.11.004. Huijser, H., M.Y.C.A. Kek, and F.F. Padró. 2022. Introduction: Student support services in an overall ecology for learning. In Student support services, ed. H. Huijser, M.Y.C.A. Kek, and F.F. Padró, 3–10. Springer Nature Singapore Pte Ltd. International Center for Academic Integrity [ICAI]. 2021. The fundamental values of academic integrity. 3rd ed. https://academicintegrity.org/images/pdfs/20019_ ICAI-Fundamental-Values_R12.pdf. International Organization for Standardization [ISO]. 2010. Guidance for ISO liaison organizations: Engaging stakeholders and building consensus. Author. https://www.iso.org/publication/PUB100270.html Johnson, N.J., and J.H. Svara. 2011. Social equity is American society and public administration. In Justice for all: Promoting social equity in public administration, ed. N.J. Johnson and J.H. Svara, 3–25. M.E. Sharpe. Jung, I., T. Sasaki, and C. Latchem. 2016. A framework for assessing fitness for purpose in open educational resources. International Journal of Educational

xxviii

Preface

Technology in Higher Education 13(3): 11 pp. https://doi.org/10.1186/s41239016-0002-5. Kano, N., N. Seraku, F. Takahashi, and S. Tsuji. 1984. Attractive quality and must-be quality. Journal of the Japanese Society for Quality Control 14 (2): 39–48. https://doi.org/10.20684/quality.14.2_147. Kek, M.Y.C.A., and H. Huijser. 2017. Problem-based learning into the future: Imagining an agile PBL ecology for learning. Springer. Kek, M.Y.C.A., F.F. Padró, and H. Huijser. 2022. Towards an interconnected university ecology for a hypercomplex world. In Student support services, ed. H. Huijser, M.Y.C.A. Kek, and F.F. Padró, 871–883. Springer Nature Singapore Pte Ltd. Kelchen, R. 2015. Exploring the topic of indirect costs to today’s higher education students. American Council on Education Center for Education Attainment & Innovation.. https://www.acenet.edu/Documents/Quick-Hits-Indirect-Costs.pdf Klein, G., B. Moon, and R.R. Hoffman. 2006a. Making sense of sensemaking 1: Alternative perspectives. EEE Intelligent Systems 21 (4): 70–73. https://doi. org/10.1109/MIS.2006.75. ———. 2006b. Making sense of sensemaking 2: A macrocognitive model. EEE Intelligent Systems 21 (4): 88–92. https://doi.org/10.1109/MIS.2006.100. Koopmanschap, M.A., F.F.H. Rutten, B.M. van Ineveld, and L. van Roijen. 1995. The friction cost method for measuring indirect costs of disease. Journal of Health Economics 14: 171–189. https://doi.org/10.1016/0167-6296(94)00044-5. Landecker, W.S. 1951. Types of integration and their measurement. American Journal of Sociology 56 (4): 332–340. https://doi.org/10.1086/220757. ———. 1952. Integration and group structure: An area for research. Social Forces 30 (4): 394–400. https://doi.org/10.2307/2572339. Lester, S. 1999. Assessing the self-managing learner: A contradiction in terms? In Developing the capable practitioner: Professional capability through higher education, ed. D. O’Reilly, L. Cunningham, and S. Lester, 99–108. Kogan Page. Levesque, J.-F., M.F. Harris, and G. Russell. 2013. Patient-centred access to health care: Conceptualising access at the interface of health systems and populations. International Journal for Equity in Health 12 (1): 18–27. https://doi.org/10.1186/ 1475-9276-12-18. Leydesdorff, L. 2012. The triple helix, quadruple helix, . . ., and an N-tuple of helices: Explanatory models for analyzing the knowledge-based economy? Journal of Knowledge Economy 3: 25–35. https://doi.org/10.1007/s13132-0110049-4. Luhmann, N. 1995. Social systems (J. Bednarz, Jr. & D. Baecker, Trans.). Stanford University Press. Marshall, S., and M. Sankey. 2017. The ACODE benchmarks for technology enhanced learning. In Proceedings of the THETA 2017 conference, connecting minds. Creating the future, 1–13. https://www.researchgate.net/publication/ 320216099_The_ACODE_Benchmarks_for_Technology_Enhanced_Learning Martin, L. 2020. Foundations for good practice: The student experience of online learning in Australian higher education during the COVID-19 pandemic. TEQSA.

Preface

xxix

https://www.teqsa.gov.au/guides-resources/resources/sector-updates-and-alerts/ foundations-good-practice-student-experience-online-learning-australian-highereducation-during-covid-19-pandemic Means, B., M. Bakia, and R. Murphy. 2014. Learning online: What research tells us about whether, when and how. Routledge. Millett, J.D. 1980. Management, governance & leadership: A guide for college and university administrators. AMACOM. Mintzberg, H., and J.A. Waters. 1985. Of strategies, deliberate and emergent. Strategic Management Journal 6 (3): 257–272. https://doi.org/10.1002/smj. 4250060306. Mintzberg, H. 1987. Crafting strategy. Harvard Business Review 65 (4): 66–75. Müller, R. 2018. “Mediality”. Some fundamental questions on the theory and history of media. History—Theory—Criticism 1: 49–68. https://www.academia.edu/ 44543304/_Mediality_On_Some_Fundamental_Questions_of_the_Theory_ and_History_of_Media National Committee of Inquiry into Higher Education. 1997. Higher education in the learning society: Report of the National Committee [Dearing Report]. London: HMSO. Retrieved from http://www.educationengland.org.uk/docu ments/dearing1997/dearing1997.html. O’Reilly, D., L. Cunningham, and S. Lester. 1999. Introduction. In Developing the capable practitioner: Professional capability through higher education, ed. D. O’Reilly, L. Cunningham, and S. Lester, 1–5. Kogan Page. Ostini, J., H. Partridge, K. Kelly, S. Owen, and S. Jeffries. 2020. Narratives of access: A critical exploration of how institutional interactions with students affect regional student participation in higher education. Student Success 11 (2): 60–71. https://doi.org/10.5204/ssj.v11i2.1334. Padró, F.F., N. Winwood, and M.F. Hawke. 2015. Utilizing qualitative components of risk management as evidence on how university strategies meet QA criteria and standards. Paper presented at INQAAHE Bi-annual Conference, Chicago, IL. https://core.ac.uk/download/pdf/211499094.pdf. Padró, F.F. 2015. Which is better for embedding risk management in higher education quality assurance: ISO 31000 or the COSO framework? In Proceedings of the 18th QMOD-ICQSS international conference on quality and service sciences 12–14 October 2015, Seoul, Republic of Korea, ed. S.M. Dahlgaard-Park and J.J. Dahlgaard. Lund University Library Press. Padró, F.F., and M.D. Sankey. 2018. Benchmarking as an instrument for continuous improvement in a regulated higher education quality assurance environment. In Cases in quality initiatives for organizational longevity, ed. A. Krishnan, 35–73. IGI Global. Padró, F.F., M.Y.C.A. Kek, N. Press, K. Trimmer, J.H. Green, M. Hawke, and L. Hawke. 2019. How about professionalism, professions and standards: The creation of acculturated professionals. In Ensuring quality in professional education: Engineering pedagogy and international knowledge structures, ed. K. Trimmer, T. Newman, and F.F. Padró, vol. 2, 1–28. Palgrave McMillan.

xxx

Preface

Padró, F.F., K. Trimmer, H. Chang, and J.H. Green. 2020. TQM’s presence within legal systems: Example of impact on Australian higher education. The TQM Journal. https://doi.org/10.1108/TQM-12-2019-0307. Padró, F.F. 2022a. Preface. In Professional and support staff in higher education, ed. C. Bossu and N. Brown, v–xxii. Springer Nature Singapore Pte Ltd. ———. 2022b. Preface. In Student support services, ed. H. Huijser, M.Y.C.A. Kek, and F.F. Padró, iv–xxxviii. Springer Nature Singapore Pte Ltd. ———. in press. Sen’s Capability Framework as an immanent element of higher education learning and teaching. In Widening participation in higher education, ed. F.F. Padró, J.H. Green, and D. Bull. Springer Nature Singapore Pte Ltd. Parsons, T. 1951. The social system. Free Press. Pirolli, P., and D.M. Russell. 2011. Introduction to this special issue on sensemaking. Human-Computer Interaction 26 (1–2): 1–8. https://doi.org/10.1080/07370024. 2011.556557. Polanyi, M. 1962. Personal knowledge: Towards a post-critical philosophy. University of Chicago Press. Sankey, M., H. Carter, M. Marshall, R. Obexer, C. Russell, and R. Lawson. 2014. Benchmarks for technology enhanced learning. Australasian Council on Open Distance and e-learning [ACODE]. Retrieved from: https://www.acode.edu.au/ mod/resource/view.php?id¼193 Sankey, M.D., and F.F. Padró. 2013. Using distributed learning as a measure to capture additional data on the quality of technology enhanced learning (TEL). Paper presented at the INQAAHE 2013 Biennial Conference, Taipei, Taiwan. https://www.researchgate.net/publication/272495767_Using_distributed_learn ing_as_a_measure_to_capture_additional_data_on_the_quality_of_technology_ enhanced_learning_TEL Simpson, O. 2005. E-learning, democracy and social exclusion: Issues of access and retention in the United Kingdom. In Global perspectives on e-learning: Rhetoric and reality, ed. A.A. Carr-Chelman, 89–100. SAGE. Stracke, C.M. 2019. Quality frameworks and learning design for open education. International Review of Research in Open and Distributed Learning 20 (2): 180–203. https://doi.org/10.19173/irrodl.v20i2.4213. Stroup, H. 1966. Bureaucracy in higher education. Free Press, Collier-Macmillan Limited. Tanis, C.J. 2020. The seven principles of online learning: Feedback from faculty and alumni on its importance for teaching and learning. Research in Learning Technology 28: 2319–2345. https://doi.org/10.25304/rlt.v28.2319. Vickers, G. 1973. Values, norms and policies. Policy Sciences 4: 103–111. https:// doi.org/10.1007/BF01404936. Vlӑsceanu, L., and Grünberg, & Pârlea, D. 2007. Quality assurance accreditation: A glossary of basic terms and definitions. 2nd ed. UNESCO. Vrielink, J., Lemmens, P., Parmentier, S. & the LERU Working Group on Human Rights. 2011. Academic freedom as a fundamental right. Procedia Social and Behavioral Sciences 13: 117–141. https://doi.org/10.1016/j.sbspro.2011.03.009.

Preface

xxxi

Walster, E., and G.W. Walster. 1975. Equity and social justice. Journal of Social Issues 31 (3): 21–43. https://doi.org/10.1111/j.1540-4560.1975.tb00001. Weick, K.E. 1995. Sensemaking in organizations. Sage. Weick, K.E., K.M. Sutcliffe, and D. Obstfeld. 2005. Organizing and the process of sensemaking. Organization Science 16 (4): 409–421. https://doi.org/10.1287/ orsc.1050.0133. Williams, C.J., J.J. Matt, and F.L. O’Reilly. 2014. Generational perspective of higher education online student learning styles. Journal of Education and Learning 3 (2): 33–51. https://doi.org/10.5539/jel.v3n2p33.

Acknowledgments

The editors of this volume would like to acknowledge the generous contributions of a wide range of people across the global higher education community. Engagement of this nature, around such a timely topic, has clearly demonstrated that there is way more that unites us as a sector than separates us. The nature of the Virtual University, as discussed in this volume, is that it crosses many boundaries and spans many continents, and it is through the affordances that technology can provide us that we see and hopefully embrace an opportunity for a more holistic approach to knowledge creation and sharing. A sharing that would seek to unite, rather than divide. The three editors of this volume all reside in Australia but originate from three different lands, as such we wish to acknowledge and pay our respects to the First Nations peoples on whose unceded lands we live. We continue to benefit greatly from a very long tradition of learning, teaching, and connecting across time and space, including in the production of this volume. Michael would like to also acknowledge the support of Henk and Rachel in this journey. We have worked really well as a team, despite coming from different institutions and not seeing each other as often as we might like. I would also like to acknowledge my partner Kim, for her support and encouragement to push through with this important project. Henk would like to acknowledge the many colleagues, both current and in the past, who have contributed to this volume with their ideas, critiques, and engagement; some literally, in the form of the many excellent chapters in this book, and some indirectly in the form of many discussions and conversations about the virtual university. As always, Henk would also like to thank Trish for both inspiring and grounding him; love and gratitude always. Rachel extends her sincere gratitude to Michael and Henk for their exceptional leadership and friendship during this journey. She also expresses deep appreciation to all the authors for their invaluable contributions and their patience. Rachel sends heartfelt thanks and love to Graeme, Jasmine, and Aidan for their unwavering support always. In the early days of this project, it was not as immediately obvious to potential authors as to the urgency for this publication. Then COVID-19 hit and all of a sudden, the Virtual University concept became very real to many people very quickly. It is hoped that lessons shared here in this volume will benefit many as we together chart a productive future. xxxiii

Contents

Part I 1

Introduction

.......................................

1

The Virtual University: Moving from Fiction to Fact . . . . . . . . . . Michael David Sankey, Henk Huijser, and Rachel Fitzgerald

3

Part II Laying a Solid Foundation: Policy and Governance Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Aligning the Vision for Technology-Enhanced Learning with a Master Plan, Policies, and Procedures . . . . . . . . . . . . . . . . . Kevin Ashford-Rowe, Holly Russell, Nona Press, and Judith Smith

21

23

3

Transparency in Governing Technology Enhanced Learning . . . . Clive Smallman and Peter Ryan

43

4

Laying and Maintaining the Foundations for Quality . . . . . . . . . . Stephen Marshall

59

Part III 5

The Virtual Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . .

77

A Social Equity–Based Framework Toward the Development of the Virtual University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhiqiang Amos Tay

79

6

Academic Engagement in Pedagogic Transformation Rachel Maxwell and Alejandro Armellini

..........

93

7

Innovation and the Role of Emerging Technologies . . . . . . . . . . . . Polly K. Lai and Lina Markauskaite

109

Part IV 8

Supporting Staff and Students . . . . . . . . . . . . . . . . . . . . . .

Models of Professional Development for Technology-Enhanced Learning in the Virtual University . . . . . . . . . . . . . . . . . . . . . . . . . Kwong Nui Sim and Henk Huijser

127

129 xxxv

xxxvi

9

10

11

Contents

Peer Observation of Teaching in the Virtual University: Factors for Success . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Martina Crehan, Morag Munro, and Muireann O’Keeffe

147

Transition Techniques When Introducing Change: A Sociomaterial Approach to the Virtual University . . . . . . . . . . . Hilary Wheaton and Sherman Young

165

The Role of Analytics When Supporting Staff and Students in the Virtual Learning Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hazel Jones and Rachel Fitzgerald

187

Part V 12

13

14

Learning Theories and Application of TEL

.............

201

The 3C Merry-Go-Round: Constructivism, Cognitivism, Connectivism, Etc. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chris Campbell and Tran Le Nghi Tran

203

The Role and Application of Learning Theories in the Virtual University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Iwona Czaplinski and Henk Huijser

227

Adapting and Creating New Theories Through the Ongoing Research of Technology-Enhanced Learning . . . . . . . . . . . . . . . . . Nathaniel Ostashewski

245

15

Social Media: Friend and Foe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stefanie Panke

16

The Future of the Learning Management System in the Virtual University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stephen Marshall and Michael David Sankey

Part VI

New and Emerging Forms of Assessment

.............

263

283

305

17

Making Online Assessment Active and Authentic . . . . . . . . . . . . . Mathew Hillier

307

18

Peer and Collaborative Assessment . . . . . . . . . . . . . . . . . . . . . . . . Tiffany Gunning, Chie Adachi, and Joanna Tai

353

19

Preparing Students for the Future of Work and the Role of the Virtual . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bonnie Amelia Dean, Matthew Campbell, Courtney Ann Shalavin, and Michelle J. Eady

20

Authenticity, Originality, and Beating the Cheats . . . . . . . . . . . . . Sheona Thomson, Alexander Amigud, and Henk Huijser

375

393

Contents

Part VII 21

22

xxxvii

The Role Openness Plays in the Virtual University . . . . . .

Open Educational Practice as an Enabler for Virtual Universities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Carina Bossu and Darren Ellis The Affordances of Openness for the Virtual University . . . . . . . . Sanjaya Mishra

Part VIII

New and Alternate Forms of Credentialing . . . . . . . . . . .

23

Micro-credentialing Models and Practice . . . . . . . . . . . . . . . . . . . . Ratna Selvaratnam

24

The Opportunities and Challenges in the Portability and Authentication of Micro-credentials and Short Courses in a Post-COVID Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rachel Fitzgerald and Henk Huijser

Part IX 25

26

28

......

Developing and Quantifying Intrinsically Motivating Instruction: Models and Principles of Gameful Design, Adaptive and Online Experiential Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kevin Bell The Role of Adaptive Learning Technologies and Conditional Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kate Thompson, Anna Charisse Farr, Thom Saunders, and Gavin Winter

Part X 27

Gamification, Adaptive and Conditional Learning

The Rise and Rise of AI, VR, AR, MR, and XR . . . . . . . . . . . .

Emerging, Emergent, and Emerged Approaches to Mixed Reality in Learning and Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . Stephen Marshall Artificial Intelligence and Evolution of the Virtual University . . . . Jon Mason, Paul Lefrere, Bruce Peoples, Jaeho Lee, and Peter Shaw

Part XI Quality, Benchmarking, Learning, and Educational Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Using Institutional Data to Drive Quality, Improvement, and Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sarah Dart and Samuel Cunningham

417

419 433

449 451

465

479

481

501

525

527 547

569

571

xxxviii

30

Contents

The Role of Standards and Benchmarking in Technology-Enhanced Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595 Stephen Marshall and Michael David Sankey

Part XII

Concluding Thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

617

The Virtual University in Practice . . . . . . . . . . . . . . . . . . . . . . . . . Michael David Sankey, Henk Huijser, and Rachel Fitzgerald

619

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

641

31

About the Editors

Michael David Sankey Professor Michael Sankey is from Charles Darwin University in Australia, where he is the Director of Learning Futures and Lead Education Architect. In addition to this role, Michael is President of the Australasian Council on Open, Distance and e-Learning (ACODE) and a Fellow of the Australasian Society for Computers in Learning in Tertiary Education (ASCILITE). He specializes in emerging technologies, technology enhanced learning, curriculum and assessment renewal, eLearning quality and benchmarking, multimodal design, digital, visual, and multiliteracies. Michael has worked in Higher Education for 30+ years, at five universities and is particularly interested in how constructively aligned and aesthetically enhanced learning environments can better transmit concepts to students, particularly those from diverse backgrounds and those who study at a distance. More information at: https://orcid.org/0000-0001-5516-7631 Henk Huijser Associate Professor Henk Huijser holds a PhD in Screen and Media Studies, and has been an Academic Developer involved in Learning and Teaching in Higher Education since 2005 at the University of Southern Queensland, Bahrain Polytechnic, Bachelor Institute of Indigenous Tertiary Education, and Xi’an Jiaotong-Liverpool University (XJTLU) in Suzhou, China. Henk has been a Curriculum and Learning Designer in the Learning and Teaching Unit at Queensland University of Technology since 2017. Henk has published extensively in the field of learning and teaching in higher education, including (with Megan Kek) Problem-Based Learning into the Future: Imagining an Agile PBL Ecology for Learning (2017, Springer), and xxxix

xl

About the Editors

as the Lead Editor of Student Support Services: Exploring Impact on Student Engagement, Experience and Learning (2022, Springer). He is an Associate Editor of the International Journal for Academic Development, the Australasian Journal of Educational Technology, and a Co-Editor of the Journal of Peer Learning. More information at: https://orcid.org/0000-0001-9699-4940 Rachel Fitzgerald She (PhD, SFHEA) is Deputy Associate Dean (Academic) for the Faculty of Business, Economics and Law at the University of Queensland. Rachel has extensive experience driving futureorientated higher education. With comprehensive expertise in online and blended learning, Rachel has achieved global recognition for her contributions to the innovation of education through digital learning and curriculum design. She has published widely on digital learning innovation and online education and is deeply immersed in research that harnesses technology for enhancing learning. More information can be found at: https:// orcid.org/0000-0003-2905-6895

Contributors

Chie Adachi Queen Mary University of London, London, UK Alexander Amigud Tecnológico de Monterrey, Monterrey, Mexico Alejandro Armellini University of Portsmouth, Portsmouth, UK Kevin Ashford-Rowe Queensland University of Technology, Brisbane, QLD, Australia Kevin Bell AWS World Wide Public Service, Sydney, NSW, Australia Carina Bossu Institute of Educational Technology, The Open University, Milton Keynes, UK Chris Campbell Division of Learning and Teaching, Charles Sturt University, Albury, NSW, Australia Matthew Campbell University of Queensland, Brisbane, QLD, Australia Martina Crehan Health Professions Education Centre, Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Dublin, Ireland Samuel Cunningham Learning and Teaching Unit and Faculty of Engineering, Queensland University of Technology, Brisbane, QLD, Australia Iwona Czaplinski Curriculum Standards and Quality, Queensland University of Technology, Brisbane, QLD, Australia Sarah Dart Learning and Teaching Unit and Faculty of Engineering, Queensland University of Technology, Brisbane, QLD, Australia Bonnie Amelia Dean Learning, Teaching and Curriculum, University of Wollongong, Wollongong, NSW, Australia Michelle J. Eady School of Education, University of Wollongong, Wollongong, NSW, Australia Darren Ellis Centre for Air Transport Management, Cranfield University, Cranfield, UK xli

xlii

Contributors

Anna Charisse Farr School of Teacher Education and Leadership, Queensland University of Technology, Brisbane, QLD, Australia Rachel Fitzgerald The Faculty of Business, Economics and Law, University of Queensland, Brisbane, QLD, Australia Tiffany Gunning Deakin Learning Futures, Deakin University, Geelong, Australia Mathew Hillier Office of the Pro-vice Chancellor of Learning and Teaching, Macquarie University, Sydney, NSW, Australia Henk Huijser Learning and Teaching Unit and Faculty of Engineering, Queensland University of Technology, Brisbane, QLD, Australia Hazel Jones Griffith Business School, Griffith University, Brisbane, QLD, Australia Polly K. Lai Centre for Teaching and Learning, Southern Cross University, Gold Coast, Australia Jaeho Lee Department of Computer Science, University of Seoul, Seoul, South Korea Paul Lefrere CCA-research, Milton Keynes, UK Lina Markauskaite Sydney School of Education and Social Work, The University of Sydney, Sydney, Australia Stephen Marshall Centre for Academic Development, Victoria University of Wellington, Wellington, New Zealand Jon Mason Faculty of Arts and Society, Charles Darwin University, Darwin, Australia Rachel Maxwell Community Manager, Solutionpath Ltd, Leeds, UK Sanjaya Mishra Commonwealth of Learning, Burnaby, BC, Canada Morag Munro Office of the Dean of Teaching and Learning, Maynooth University, Maynooth, Ireland Muireann O’Keeffe College of Arts and Tourism, Technological University Dublin, Dublin, Ireland Nathaniel Ostashewski Open, Digital, and Distance Education Program, FHSS, Athabasca University, Athabasca, AB, Canada Stefanie Panke School of Social Work, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Bruce Peoples Innovations LLC, Kissimmee, FL, USA Nona Press Queensland University of Technology, Brisbane, QLD, Australia Holly Russell Queensland University of Technology, Brisbane, QLD, Australia Peter Ryan Consult Ed, Sydney, NSW, Australia

Contributors

xliii

Michael David Sankey Director Learning Futures and Lead Education Architect, Charles Darwin University, Darwin, NT, Australia Thom Saunders Visualization and Interaction Solutions for Engagement and Research (VISER) Group, Queensland University of Technology, Brisbane, QLD, Australia Ratna Selvaratnam Centre for Learning and Teaching, Edith Cowan University, Joondalup, Australia Courtney Ann Shalavin School of Education, University of Wollongong, Wollongong, NSW, Australia Peter Shaw Oujiang Laboratory, Wenzhou, China Kwong Nui Sim Sydney International School of Technology and Commerce, Sydney, NSW, Australia Clive Smallman Kingsford Institute of Higher Education, Sydney, NSW, Australia Judith Smith Queensland University of Technology, Brisbane, QLD, Australia Joanna Tai Centre for Research in Assessment and Digital Learning (CRADLE), Deakin University, Melbourne, Australia Zhiqiang Amos Tay Queensland University of Technology, Brisbane, QLD, Australia Kate Thompson School of Teacher Education and Leadership, Queensland University of Technology, Brisbane, QLD, Australia Sheona Thomson Learning and Teaching Unit and Faculty of Engineering, Queensland University of Technology, Brisbane, QLD, Australia Tran Le Nghi Tran Learning Futures, Griffith University, Brisbane, QLD, Australia Hilary Wheaton Education Portfolio, RMIT University, Melbourne, VIC, Australia Gavin Winter Visualization and Interaction Solutions for Engagement and Research (VISER) Group, Queensland University of Technology, Brisbane, QLD, Australia Sherman Young Education Portfolio, RMIT University, Melbourne, VIC, Australia

Part I Introduction

1

The Virtual University: Moving from Fiction to Fact Michael David Sankey

, Henk Huijser

, and Rachel Fitzgerald

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Laying a Solid Foundation: Policy and Governance Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Virtual Learning Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Supporting Staff and Students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning Theories and the Application of TEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Using Social Media in the Virtual University and the Future of the LMS . . . . . . . . . . . . . . . . . . . . . . The Role Openness Plays in the Virtual University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . New and Emerging Forms of Online Assessment and Alternate Forms of Credentialing . . . . . . Gamification, Adaptive and Conditional Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Rise and Rise of AI, VR, AR, MR, and XR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quality, Benchmarking, Learning, and Educational Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concluding Thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 5 7 7 8 9 10 11 14 15 16 17 19 19

Abstract

As the online component of university business continues to increase, particularly in the area of learning and teaching, the “virtual learning environment” has M. D. Sankey (*) Director Learning Futures and Lead Education Architect, Charles Darwin University, Darwin, NT, Australia e-mail: [email protected] H. Huijser Learning and Teaching Unit and Faculty of Engineering, Queensland University of Technology, Brisbane, QLD, Australia e-mail: [email protected] R. Fitzgerald The Faculty of Business, Economics and Law, University of Queensland, Brisbane, QLD, Australia e-mail: rachel.fi[email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_1

3

4

M. D. Sankey et al.

become as important as the “physical learning environment.” This was clearly demonstrated over recent years, as was the imperative that as much care, if not more, is taken in how the virtual environment looks, feels, and responds to students and staff. It is evident that many institutions are seeing this as not just a means by which they can better support their current students, but as a way to value add and expand their reach. In the current higher education environment, the ability to provide an equivalence of experience in the online space makes the value proposition of the virtual university very attractive. This chapter provides a window into the wealth of information that, when deliciously applied, may lay a solid foundation for the creation of an enduring virtual university, one that is both desirable and productive. Like all good building project, this process starts by first laying a solid foundation. In the case of an entity like a virtual university, this means providing a clear consistent policy and governance framework that, when established, provides an alignment to the vision and strategic plan and has its roots firmly laid in quality ground. As we work our way through the concepts contained within this volume, it is hoped that a picture will start to form that points to not just the validity of the virtual university, but also the limitless opportunities it may provide. Keywords

Virtual university · Technology-enhanced learning · Higher education · TEL · Online

Introduction As the online component of university business continues to increase, particularly in the aftermath of COVID-19, the notion of a “virtual learning environment,” as a means to mediate and facilitate student learning has now become as important as the “physical learning environment” (Champagne and Granja 2021). There are plenty of studies which demonstrate that learners accept online learning as a legitimate alternative, across multiple national jurisdictions (Paul and Jefferson 2019). That said, it is imperative that as much care, if not more, is taken in how responsive the online environment is to the needs of learners and staff, which transcends the look and feel, and more importantly, how staff engage with their students (Pratiwi et al. 2021). It is evident that many institutions are seeing online learning as a means to better support learners, and as a way to add value and expand their reach (Keegan and Bannister 2021). In the current higher education environment, the ability to provide an equivalent experience in the online space makes the value proposition of the virtual university very attractive. This chapter seeks to provide an initial helicopter view of a range of considerations in implementing a virtual university. Various elements will be further explored by a range of experts from around the globe, particularly focused on how one might conceivably create a holistic virtual university, mindful of the ever-expanding range of technologyenhanced learning applications. These diverse elements will then crystalize, in the final chapter of this volume, into a series of recommendations and considerations.

1

The Virtual University: Moving from Fiction to Fact

5

The concept of a virtual university is not a new one, by any means, and has its genesis in distance and open education movements, dating back decades (Anderson and Simpson 2012). In fact, back in 1996, Carol Twigg and Diana Oblinger (1996) wrote of the affordances of the virtual university, stating, “The concept of the virtual university represents a multifaceted response to consumer demand for increased access, improved quality, and reduced cost of higher education” (para. 91). Yet, this is now something that all universities need to consider, rather than it being an optional extra, or being left to specialist institutions and external providers. One thing COVID has taught us is that learners are now, more than ever, taking learning into their own hands and looking for ways in which higher education can fit their lifestyle, rather than the other way around (Broom 2021). A serious consideration of this requires an honest look at the pros and cons and viability of establishing a fully virtual university, looking particularly at how to address the many challenges of translating on-campus experiences into virtual environments, including the challenge of equitable access. Thankfully, many have considered such challenges, which is the knowledge base we share in this volume. Very distinctly, this book is not interested in models of blended learning, hybrid learning, or flipped classrooms, all of which have been written about at length. Instead, this book is designed solely for promoting the idea that it is possible, with the right strategies and combination of tools and techniques, to develop a comprehensive virtual university through the innovative use of technology to enhance learning and teaching.

Laying a Solid Foundation: Policy and Governance Models Aligning the vision for technology-enhanced learning with plans for a virtual university is closely aligned with the practical elements one must have in place to ensure such a venture can be successful. A virtual university is a major undertaking and those planning to progress must develop university-wide strategies, engage in consultation with stakeholders, and ensure that policy, procedures, and budgets have been considered (bureaucratic realities). Having clear goals and plans in place can empower any digital solutions you might choose and enable technologies to challenge and extend existing paradigms around learning and teaching. As we have seen over the last 25 years of online education, leadership in online learning often comes from the academic champions who create networks and codesign spaces in which we engage the learning and teaching community. While this has led us to a particular point, to go beyond that point, we need to see the implementation of the virtual university as a universitywide adaptive and cultural challenge as well as comprehensive digital transformation. ▶ Chapter 2, “Aligning the Vision for Technology-Enhanced Learning with a Master Plan, Policies, and Procedures,” explores the importance of setting out a vision for digital transformation that clarifies the concept of the virtual university at an institutional level. In this chapter, Ashford-Rowe et al. explain their “digital at

6

M. D. Sankey et al.

heart” vision for a virtual university and consider the critical steps required to engage stakeholders and to implement new strategic approaches to learning and teaching. The implementation of a vision and goals requires good governance to oversee the delivery of a consistent virtual learner experience. The creation of a tailored, personalized, increasingly self-paced, capability-based, and authentically taught and assessed experience should span the entirety of the learning journey. However, to achieve this in the virtual space needs an understanding of some of the paradoxes that exist for what is perceived as a virtual organization, but in practice is very tangible, particularly around the practice of governance. Smallman and Ryan in ▶ Chap. 3, “Transparency in Governing Technology Enhanced Learning,” explore some of the implications of virtual organization theory, which essentially calls for transparency and flexibility, enabled by technology. The virtual university, it is suggested, needs controlled transparency, well-defined governance frameworks, and clear delegations, coupled with good policy and productive processes, in a formal structure that simultaneously operates informally. In ▶ Chap. 4, “Laying and Maintaining the Foundations for Quality,” Marshall extends this thinking with a conceptual model and a range of “quality” dimensions, purposes, and systems that are required to ensure the ongoing success and improvement of our bureaucratic realities. It is one thing to carefully manage our political, systemic, and symbolic dimensions, but it is quite another to add “value” to the organization and see it improve. To do so, the virtual university needs quality measures and improvement activities to rise above facile representations of brand and marketing hype. It needs to provide opportunities for sustaining a practice of continual improvement through a sensemaking approach driven by communication with stakeholders. For cultural transformation to succeed, sensemaking and buy-in from stakeholders are critical. This theme reemerges time and time again, for example, in ▶ Chap. 6, “Academic Engagement in Pedagogic Transformation,” where Armellini and Maxwell share a case study that explores how senior decision-making, with regard to pedagogic transformation, impacts on staff engagement. They offer their informed insights into the impact of cultural change for the virtual university and share a quality enhancement model for pedagogic transformation, musing on how the academic community might be encouraged to participate in, shape, and even own the creation of a virtual university. Of course, there are other considerations that have real implications for the virtual university, such as the digital divide. For many reasons, students worldwide were unable to access online learning during the COVID-19 lockdown (Garcia et al. 2020; Packham 2020). This is a core consideration for the goal setting and governance of the virtual university. In ▶ Chap. 5, “A Social Equity–Based Framework Toward the Development of the Virtual University,” Tay considers ways we can overcome equity issues and shares his take on how a social equity–based framework, which recognizes barriers, considers access and equitable opportunities. Overall, this opening section provides us with success measures in the development of the virtual university.

1

The Virtual University: Moving from Fiction to Fact

7

The Virtual Learning Environment The virtual university embraces digital learning and teaching paradigms, by delivering digitally enhanced, high-quality learning and teaching experiences. It is clear that when well-designed frameworks become transparent, relevant policies, products, services, processes, systems, and tools that drive learning and teaching hum along quietly in the background. That said, it is still critically important that we consider how the technologies we use for learning and teaching engage our virtual learners to learn at a deep cognitive level. This is explored by Lai and Markauskaite in their discussion of the critical role of digital technologies in developing learning and deeper participation, in ▶ Chap. 7, “Innovation and the Role of Emerging Technologies.” They evaluate how technology-enhanced learning can be designed for the virtual university, underpinned by learning theory and evidence-based practice. They conclude that digital technologies are invaluable in their ability to enable learning across time and space but need targeted and deliberate application to engage learners successfully. Sim and Huijser, in ▶ Chap. 8, “Models of Professional Development for Technology-Enhanced Learning in the Virtual University,” then explore how targeted support can be used to enable academic staff to understand and leverage the affordances of digital technologies for learning and teaching, sharing case studies from practice in New Zealand and a professional development model that supports the enhancement of academic digital literacy. We have already acknowledged that the introduction of a virtual university represents an adaptive challenge to traditional higher education and a transformative model to change culture will be required. This is further explored by Wheaton and Young in ▶ Chap. 10, “Transition Techniques When Introducing Change: A Sociomaterial Approach to the Virtual University,” who examine the relationship between organizational strategy, technology, and teaching to understand and analyze the risks that emerge from new processes such as technological adoption and sustained engagement with digital technologies.

Supporting Staff and Students It is evident that models for engaging, training, and supporting the use of technology-enhanced learning, and the creation of transformation models to introduce change, are important elements of cultural change. While we have explored some top-down approaches to engaging stakeholders with change, a novel approach is proposed by Crehan et al. (▶ Chap. 9, “Peer Observation of Teaching in the Virtual University: Factors for Success”), which builds on peer observation of teaching for a virtual university. They share factors of success for developing such an approach, based on their own experience of offering support, guidance, and encouragement through online peer observation in a way that creates an ongoing learning conversation. Other technical transformations that will enhance the virtual university are likely to come from learning analytics and the ability to personalize learning and

8

M. D. Sankey et al.

understand the learner. The role of analytics when supporting staff and students in the virtual university are explored initially in ▶ Chap. 11, “The Role of Analytics When Supporting Staff and Students in the Virtual Learning Environment.” Here, Jones and Fitzgerald explore different ways in which data can be used to inform and enhance learning and teaching through evidence-based approaches. This chapter begins with considering a range of learning analytics frameworks used to inform widespread collaboration and adoption, before highlighting the need for sociocultural and pedagogical insights that sit alongside different technological aspects. They suggest that having a framework in place frees us up to consider the design principles to be applied and the support and professional learning necessary to empower institutional adoption. Learning analytics, once embedded into practice, offers the virtual university the lens it needs to understand learner and staff activity, as well as the opportunity to lead further research and development of its applications, guided by a coherent data strategy built on an institution’s framework. The framework provides the common ground to develop a shared understanding, consensus, and buy-in for all stakeholders concerned with improving student learning outcomes and the overall student learning experience.

Learning Theories and the Application of TEL For the virtual university, we need to consider a range of pedagogies, or major learning theories, that help us conceive how the teacher and learner should approach online learning: cognitivism, connectivism, and constructivism. That is not to preclude other theories and approaches, but essentially many of the newer theories arguably have their roots in these three fundamental conceptualizations. These three learning theories have underpinned the development and application of technology-enhanced learning now for more than 20 years, and have allowed us to embrace the fact that the contemporary online environment is much more than using an LMS. In fact, the virtual university will look at using a wide variety of tools that align with the LMS, tools to allow lecture capture and the recording of the teacher’s voice in both long- and short-form presentations. It will include tools that allow students to have a voice and a conversation (literally) with their peers and their teachers. It is anticipated that the virtual university would facilitate students to have the ability to represent themselves virtually. This would allow them to align what they have learned with a demonstration of their achievement of various skills or learning outcomes, such that they are able to can demonstrate this to potential employees, if they choose to so. Typically, this would be done though some form of ePortfolio tool, or digital profiling tool that would provide students with both private and public pages. Interestingly, a technology that has emerged more recently is a productivity tool, that is, a collection of communication and collaboration tools that are used in the workforce to help teams be more focused and productive. Increasingly, as universities are expected to produce more job-ready graduates, it is assumed that those

1

The Virtual University: Moving from Fiction to Fact

9

graduates will be proficient with the technologies that are used in the workforce. Consequently, many institutions are including these tools to support their LMS. Similarly, the rise of virtual classroom tools allows for meetings and live interactions which, during the pandemic, saw many universities upskill quickly. An implication of this increase in different technologies is that learning theories may be required to be bent, combined, modified, and/or directly applied in the virtual university context, and Campbell and Tran, in ▶ Chap. 12, “The 3C Merry-Go-Round: Constructivism, Cognitivism, Connectivism, Etc.,” provide some concrete examples of how one university has developed a range of well-developed practices in the online teaching space. Such concrete examples provide a glimpse into how students might engage in the virtual university. Regardless however, they all share an underlying premise. Nathaniel Ostashewski goes on to explore this premise in ▶ Chap. 14, “Adapting and Creating New Theories Through the Ongoing Research of Technology-Enhanced Learning,” when he unpacks three approaches to contemporary higher education that support quality practices in a virtual setting. While these approaches vary considerably, they all share some common elements. However, the key common thread that runs through them all is the clear focus on a learner-centered approach that contains scaffolded supports for learner-to-learner interactions. These forms of interactions exist in all elements of human learning activities. He contends that learners tend to choose what to learn and they find the resources and people to help them enable that learning. In the virtual environment, the ability to network around the key topics to be studied is important, and this is done within a community that supports learner diversity through meaningful engagement, which is simply not possible in traditional educational delivery approaches, but make the prospect of a virtual university incredibly appealing. The theoretical foundation for this idea, in the form of connectivism, had already been laid by Czaplinski and Huijser in ▶ Chap. 13, “The Role and Application of Learning Theories in the Virtual University,” who explores the importance of theory to inform learning design, with a particular focus on active learning. The ultimate goal, they contend, is to develop self-directed and self-regulated learners, who can leverage the affordances of the network.

Using Social Media in the Virtual University and the Future of the LMS Playing a distinct role in the virtual university is the prospect of using social media for “good” to engage different communities of practice and provide synergies with the world of work. However, to say “use for good” requires us to first make sense of two opposing views of social media: one that purports them to be full of opportunities for global connection, discourse, personal learning networks, and individual learning communities, and thus for society as a whole, and the opposing view, which suggests that social media can be addictive, distracting, with the potential to poison societal cohesion and threaten democracy. Each position holds some elements of truth. Rather than following this binary though, Panke in ▶ Chap. 15, “Social Media: Friend and Foe,” outlines both the

10

M. D. Sankey et al.

problems and the potential of using social media, which can present unprecedented opportunities and challenges for the virtual university. Although it might be appealing to err on the side of caution and simply avoid its use, she contends that for the virtual university, social media can hold important social affordances for both students and staff, as well as contribute to the development of job-ready graduates. The widespread, daily use of social media is a reality in student lives, and given many readers of this chapter also engage in various forms of social media use, it should not be too much of a stretch to conceptualize how we might engage with this in the virtual university to stimulate intellectual curiosity. Social media tools offer opportunities to teach principles of academic discourse in a space that transcends the digital walls of the institution. This aligns then with the changing conceptions of the LMS; that traditional place for online learning at university, and the need to find a narrative for how this can still (if at all) support the virtual university. This narrative, as argued in ▶ Chap. 16, “The Future of the Learning Management System in the Virtual University,” by Marshall and Sankey, includes first developing a vision of how and which technologies become essential in forming the university’s learning ecosystem. This of course needs to be underpinned by a technical architecture aligned to the processes and services to be offered, which has the buy-in of its partners: academics, students, and administrators. That all sounds like business as usual, until we start to think about the ongoing evolution of these systems within the virtual university context. At its core, all universities need academics, and the adoption and use of an LMS is strongly related to their roles and their professional identity (Liu and Geertshuis 2021). The virtual university is no different, and so an organization’s commitment to the provision of capability development for its teachers in the virtual space is fundamental to facilitating contemporary teaching (Liu and Geertshuis). As seen in the chapters prior, the chosen platforms need to enable academics to be active, collaborative partners, to enact a collegial university model of distributed leadership. The chosen systems therefore need to reflect engagement with diverse learners and a focus on expanding the reach and impact of the university into new learning contexts, which in turn should provide continuous educational experiences, responding in real time to changing needs for the learner, and ultimately employers and society. Presumably, this environment in the future will still include the management of content and learner information, but this will operate in support of a wider range of learning models informed by different work practices and within a rapidly evolving web of commercial relationships and business models (Newfield 2019). Not least of these is how these systems evolve to embrace the openness of knowledge and the advances of AI-driven large language models.

The Role Openness Plays in the Virtual University A particular focus is paid to the role open educational practice (OEP) can play in the virtual university in ▶ Chaps. 21, “Open Educational Practice as an Enabler for Virtual Universities,” and ▶ 22, “The Affordances of Openness for the Virtual University,” which is often and rightly proposed as an enabler and catalyst for student growth. In

1

The Virtual University: Moving from Fiction to Fact

11

▶ Chap. 21, “Open Educational Practice as an Enabler for Virtual Universities,” Bossu and Ellis contend that OEP, which includes the use of open educational resources (OER), can increase access to education to those who are often excluded from mainstream education, such as minority groups, older learners, or single mothers. But more so, they challenge us to think that this can go much further in the context of the virtual university, in that it has the possibility to be an enabler of “innovation” and thereby enhance the overall student learning experiences. It is worth considering their perspective that OEP is as an “opportunity” to provide more flexible learning opportunities to those unable to participate in traditional education (in a physical classroom), and that the virtual university can reach learners that tend to be otherwise excluded from higher education. OEP has already impacted education at all levels, as it tends to reignited debates around equity and access, including how wealthier countries can assist less privileged ones to increase access to free and open education (Willems and Bossu 2012). Although OEP has the potential to positively affect higher education, this has not yet been realized in mainstream practice (Weller 2014, p. 2), as many institutions appear to demonstrate little awareness of the affordances of OEP, primarily due to ingrained misperceptions or bias, and underlying fears that OEP may be of lower quality and therefore less reliable. Bossu and Ellis’ chapter looks to break down these myths by exploring some of the research-based evidence supporting OEP as an enabler and catalyst of innovation and change. This includes the enablers of open pedagogy, capacity building, policy development, and social justice and inclusion. Extending these thoughts further, Sanjaya Misha, in ▶ Chap. 22, “The Affordances of Openness for the Virtual University,” discusses how openness can provide a robust framework through which to rethink education and training in tertiary education settings. He provides ten dimensions of practice to be applied as enablers for the virtual university. These dimensions are designed to build a resilient and future-proof system for sustainable academic and management practices. Mishra’s contention is that openness fosters fairness, flexibility, and freedom, which ultimately makes learning accessible to all. Like Bossu and Ellis in the chapter before, Misha suggests that the virtual university should purposefully adopt an OEP policy that clearly situates OEP as a positive and proactive enabler, thereby opening up the possibility to realize the global promise of aligning to the United Nations Sustainable Development Goals, in particular Goal 4 (quality education) and Goal 10 (reduced inequalities). These UN goals consider the need to ensure inclusive and equitable quality education and the promotion of lifelong learning opportunities for all by 2030.

New and Emerging Forms of Online Assessment and Alternate Forms of Credentialing Not surprisingly, the virtual university also has the opportunity to redefine assessment thanks to technologies specifically designed to address the acquisition of twenty-first-century skills. This aligns with more recent forms of authentic learning that clearly pitch assessment toward the attainment of higher-order capabilities.

12

M. D. Sankey et al.

Matthew Hillier in ▶ Chap. 17, “Making Online Assessment Active and Authentic,” goes into some depth to position educational approaches that take advantage of existing but relatively new technologies across a range of disciplines. He provides an indication of how each of the featured methods can address the attainment of twentyfirst-century skills and how this might be done in an online environment. These approaches are supported by an evaluation which demonstrates the need for a balance between the perennial issues of authenticity, integrity, and scalability. It is incumbent on the virtual university to manage these demands by using a diversity of assessment approaches and technologies across their programs. The chapter by Gunning et al. (▶ Chap. 18, “Peer and Collaborative Assessment”) picks up on the idea of diversity in assessment approaches by offering an in-depth discussion about peer and collaborative assessment, and some of the associated tools. They note that a thoughtfully designed peer and collaborative assessment process supports student development in a broad range of personal, interpersonal, and technical skills that are transferrable across contexts. They argue that peer and collaborative assessment should therefore play a key role in the assessment approaches of the virtual university. Of course, collaboration in assessment is not free of risks associated with academic integrity, and this theme is further explored by Thomson et al., who note that concerns about integrity of assessment in online learning have arguably been exacerbated by the perceived experience of student cheating during remote teaching in response to COVID-19, and particularly in the translation of in-person invigilated examinations to online, “take-home” exams. Most recently, such concerns have led to more profound anxiety across the higher education sector with the emergence of ChatGPT and the potential impact of artificial intelligence (AI) more broadly. More important than ever, with the advent of AI-based large language models, authenticity and originality ranks high in the virtual university, not least because the absence of a face-to-face presence can raise issues in the minds of those who waver. Understanding this means one must also understand the data that accompany a learner and leaves digital traces and footprints. Leveraging that data requires the adoption of diverse approaches to learning analytics (LA), particularly those related to the virtual environment to inform and enhance learning and teaching through evidence-based decisions. Typically, we see this being understood through a series of frameworks that can be adopted to inform widespread collaborative adoption of LA. These frameworks generally consider a range of sociocultural and pedagogical factors aligned with the technology being adopted. To help us understand this, ▶ Chap. 11, “The Role of Analytics When Supporting Staff and Students in the Virtual Learning Environment,” by Jones and Fitzgerald, examines a concept of the “behavior change wheel” when looking to design an LA plan for the virtual university. It examines the benefits and challenges of LA adoption when applied to a series of design principles that can be actioned to support staff professional learning, and ultimately improve student learning outcomes. The next suite of affordances for the virtual university we move onto is that of attaining more transportable forms of credentials that can be used across the sector

1

The Virtual University: Moving from Fiction to Fact

13

and sectors. Typically, these take the form of microcredentials that are linked to the profile of a learner rather than to one institution. These are explored in ▶ Chaps. 23, “Micro-credentialing Models and Practice,” and ▶ 24, “The Opportunities and Challenges in the Portability and Authentication of Micro-credentials and Short Courses in a Post-COVID Landscape.” Central to this theme is the notion of individual student success and how this may align to institutional policy, provision, and consumption. Unlike normal long-form credentials, the agency of the student is much more central to the success of any microcredentialing effort, as generally the institutions have a more hands-off approach to these forms of credentials. This is partly due to the affordances offered by a rapidly evolving technological landscape, which facilitates a more agile approach to skilling. These newer approaches render more traditional models somewhat obsolete. However, it creates strong opportunities for the virtual university, as learner success, linked to a learner’s profile, can reach across borders and be highly transparent and hence transportable. The value of these forms of credentials is further heightened when employers form partnerships with providers, or even become the providers themselves. National and state governments are also stepping into this arena, looking to align national employability metrics with the notion of skill acquisition, based on national frameworks. Fitzgerald and Huijser investigate this and propose a simple approach to measure the success of microcredentials in ▶ Chap. 24, “The Opportunities and Challenges in the Portability and Authentication of Micro-credentials and Short Courses in a Post-COVID Landscape.” Their proposed metrics could be used by the virtual university to help plan their microcredentials offerings as part of a continuous improvement cycle. The promise of microcredentials has been around for many years, but more recently we have witnessed the rise of other forms of credentialling from mainstream universities, other than the more traditional degree, diploma, or certificate, which have been their mainstay. This has largely been in response to the consumer demand to augment these fuller qualifications and to allow people to upskill and stay current. The practice of this is especially appropriate for the virtual university which, by its very nature, looks to establish a strong and diverse learning environment that can honor a variety of learning from multiple sources, including short courses and microcredentials. While this may not suit every institution, the virtual university, whose boundaries stretch way beyond national borders, is ideally placed to normalize this practice in a global context. Furthermore, it provides opportunities to develop ongoing relationships with learners throughout their working lives, and perhaps even beyond in that broader context. Selvaratnam, in ▶ Chap. 23, “Micro-credentialing Models and Practice,” and Fitzgerald and Huijser, in ▶ Chap. 24, “The Opportunities and Challenges in the Portability and Authentication of Micro-credentials and Short Courses in a PostCOVID Landscape,” note the relevance of microcredentialing for the virtual university model, particularly one linked to a learner profile which transverses traditional educational borders. This includes employer groups and industry partnerships with educational providers, or as being providers themselves, to ensure that the success of the learner is aligned with the most relevant employment skills. Governments have

14

M. D. Sankey et al.

also increasingly stepped into this space, with the global rise of national employability metrics. These have been linked to the notion of skills (that microcredentials are so good at facilitating) and other educational offerings associated with re-skilling opportunities designed to create positive employment outcomes and uplift national productivity. This has been seen in the emergence of a range of national incentive schemes offered across different jurisdictions, which seek to encourage providers to speed up the diversification of their offerings. The value to the virtual university is when they can link this diversification of offerings to success metrics when planning to offer microcredentials as part of their continuous improvement cycle.

Gamification, Adaptive and Conditional Learning The focus of the book then shifts to the theme of gamification and related adaptive and conditional learning. Bell in ▶ Chap. 25, “Developing and Quantifying Intrinsically Motivating Instruction: Models and Principles of Gameful Design, Adaptive and Online Experiential Learning,” firstly discusses the important links between educational game design, pedagogy, and intrinsic motivators for learners. He presents four case studies through which he discusses exemplars for gamified course content that is likely to intrinsically motivate and engage students. He bases those on his SIMPLE matrix, which stands for Student Intrinsic Motivation in Personal Learning Environments, which was originally developed as a means of reviewing and quantifying the likelihood that a developed course or resource will engage a student group through implementation of evidence-based intrinsic motivators. This is important in a context where learner engagement is a key challenge, especially in online environments, and thus also in the virtual university. This is even more the case now with the advent of newer forms of artificial intelligence (AI) that are driven by machine learning with the capacity for natural language processing (NLP). As Bell contends, these will drive new means of accentuating motivators, and the examples provided demonstrate how ChatBots have been implemented in a number of courses where their feedback (on basic elements of course/subject comprehension) has been rated as the best advisor by participants unaware that they had been interacting with an algorithm, and generative AI applications, such as ChatGPT, have taken this to a whole new level. What this chapter does contest is the notion that an individual academic, can be the sole source of content delivery, support, advice, counseling, and of a thousand other moving parts, is now, at best, questionable. Rather, working with skilled instructional designers, (some) automated feedback and intrinsically motivating materials, designed to encourage engagement, and possibly even fun in online sessions seem now to be an attainable goal. Thompson et al., in ▶ Chap. 26, “The Role of Adaptive Learning Technologies and Conditional Learning,” extend on Bell’s discussion about adaptive learning and allow us to glimpse into the future in their discussion of the role of adaptive learning technologies and conditional learning. They provide two conceptual scenarios (undergraduate and postgraduate) to demonstrate how an adaptive learning system

1

The Virtual University: Moving from Fiction to Fact

15

could support students in the virtual university. They argue that adaptive learning systems can provide a supportive model for learners, teaching staff, and administrators to address the challenges associated with virtual learning. In the process, they can create a highly personalized learning experience, which has a range of potential benefits. However, they do warn of significant implications for learning and teaching in higher education from proposed adaptive technologies that should be carefully considered. Yet, the risk of not considering evidence-informed ways to approach the use of adaptive learning technologies in higher education is that adaptive learning models will only be driven by objectives related to competitive advantage, or financial performance, rather than opportunities to better support students and gain greater understanding of learning in the virtual university.

The Rise and Rise of AI, VR, AR, MR, and XR The next section builds on the theme of adaptive and personalized learning with a focus on the affordances of AI and virtual, augmented, mixed, and extended reality technologies. Interestingly, Marshall notes in ▶ Chap. 27, “Emerging, Emergent, and Emerged Approaches to Mixed Reality in Learning and Teaching,” that these various forms of mixed realities have been around for almost 60 years. However, he also identifies significant limitations in how these technologies are currently used in higher education, which does not necessarily take advantage of their potential for learning. In his chapter, Marshall identifies three themes: the value that these technologies play in bringing information into the environment of the learner; the ability to change the learner’s perceptions; and the implications for the virtual university as an evolving organization that can apply mixed reality technologies in meaningful ways to support learning. Again, this is important in addressing the earlier mentioned challenge of learner engagement. These technologies offer the promise of personalized learning that can intrinsically motivate learners by linking the learning experience to their prior knowledge, skills, and experiences. In addition, they hold considerable potential to be used in the design of authentic, work-based experiential learning, which is a theme taken up by Dean et al. in their chapter about preparing learners for the future of work and explores how virtual models of workintegrated learning (WIL) can provide a bridge between the virtual university and the reality of the workplace. Mason et al., in ▶ Chap. 28, “Artificial Intelligence and Evolution of the Virtual University,” extends the thinking of both Bell and Marshall taking a closer look at the place AI can now play in the virtual university as it continues to evolve. This chapter explores the potential for empowering human learning with the extended application of technology that had previously been only seen in the realm of science fiction. They argue that a distinguishing feature of the virtual university will be its “permeability” of knowledge and that, just as the move to cloud-based hosting services has transformed the notion of enterprise IT architecture, AI similarly can create the platform for the unbundling and repackaging of services that will are likely

16

M. D. Sankey et al.

to become a distinguishing feature of the virtual university. By extension, the emergence of an AI-based metropole, or convergence of information that is dynamically created for an individual, that is then extended by human interacting, exponentially extends the way in which the “academy” can function, as not just a convenor of knowledge, but as the engine for extending knowledge community. This dynamically created ubiquitous learning community facilitated by the virtual university, yet based on an extension of the individual, potentially could evolve to the point of generating new pedagogical constructs to be used by both individuals and other artificial entities.

Quality, Benchmarking, Learning, and Educational Analytics In the final section of the book, we revisit the question of quality assurance, benchmarking, and the use of institutional data for continuous improvement. In other words, many of the chapters have explored the potential of the virtual university, and the potential of a wide range of current and emerging technologies to facilitate learning. The logical next question is how we ensure that such potential is realized on a continual basis, or how we assure ourselves that the aims and objectives are being met. To that end, Dart and Cunningham, in ▶ Chap. 29, “Using Institutional Data to Drive Quality, Improvement, and Innovation,” explore how institutional data can be leveraged to drive quality, improvement, and innovation. Digital environments produce “rivers” of data, in particular student trace data. They note that if those data can be linked to more traditional university data, opportunities open up for evidence-based improvement across the board for the virtual university, but in particular create the context for improvement of student learning and learning experiences. They use a series of examples to illustrate how institutional data can be translated into action to successfully drive quality and innovation in a range of virtual learning and teaching contexts. It is all too easy to see established standards and benchmarking tools as guides to successful leadership, particularly when they are presented and promoted by government agencies, accreditors, and respected professional bodies. Marshall and Sankey, in ▶ Chap. 30, “The Role of Standards and Benchmarking in TechnologyEnhanced Learning,” demonstrate that the reality is not quite that simple. Standards and benchmarking tools must themselves change and evolve to respond to organizational capabilities and the rapidly shifting contexts defining the pathway toward the virtual university. It is important to consider the minimum that needs to be considered both by those maintaining and developing frameworks, and by those using the frameworks, to enact any change mechanisms. This includes the agencies, regulators, and accreditors working to enhance the qualities and outcomes needed from higher education in all societies. COVID-19 has emphasized the need to focus strongly on the diversity of needs throughout the student population and the range of contexts that they must learn from, as much as it has shown the importance of modern communication and collaboration tools for all forms of information work. These technological aspects demand a

1

The Virtual University: Moving from Fiction to Fact

17

pedagogical response and a reengagement with the impact that such changes are having on learners, educators, the workplace, and society at large. Frameworks need to be able to respond to rapidly changing technologies and pedagogies while also enabling and driving the collection of evidence to inform and shape that change. They need to be explicitly designed to change themselves, even though enabling change is a major challenge to those responsible for quality frameworks. The epistemology of the virtual university is built on creativity and imagination, both pedagogically and organizationally. Frameworks must contain enough flexibility in themselves to provide the opportunity for leaders to demonstrate new ideas that harness that creativity and imagination in powerful and often unexpected ways. Leaders need to also be willing to anchor their own ambitions with evidence and to make decisions that reflect comprehensive and accurate assessments of the strengths and weaknesses of the institution (see ▶ Chap. 29, “Using Institutional Data to Drive Quality, Improvement, and Innovation,” in this volume for an extended discussion of this important aspect). They further need to consider the ways that change can be connected and capability developed in networks of practice that operate across entire sectors of education and societies. Quality frameworks should provide common points of reference and a language for engaging in genuinely collaborative initiatives, responding to the shared challenges facing all universities. This latter point is fundamental to normative change and essential if universities are to lead their own destiny as a virtual university.

Concluding Thoughts This chapter has provided an oversight as to the wealth of information to be found in the chapters that follow, which will make it possible for the virtual university to move from a notion of fiction to a matter of fact. That is not to say that forms of the virtual universities do not already exist, as they do, but when the principles in this book are applied the environment is created for the opportunity for the virtual university to become a mainstream option. Like any good building project, this process starts by first laying down some solid foundations. These foundations are seen to be consistent policy and a coherent governance model. When established, this helps to align the vision for technologyenhanced learning (TEL) to the master plan, which extends into everyday procedures. Consistent with this, and present in kindred organizations, the virtual university thrives in the transparency of its governance of TEL, which has its roots firmly laid in the deep earth for quality. This is particularly important in the virtual environment. Any social equity–based framework that underpins the virtual university is based on the notion of academic engagement that embraces pedagogic transformation. That is, the role of innovation and the embracing of emerging technologies can provide support and empowerment to both staff and students. However, this does not just happen; it invokes models of professional development that are specifically designed to enhance the application TEL in the virtual university. Because we are

18

M. D. Sankey et al.

largely working in the virtual space the notion of the peer (working with your virtual colleagues) becomes important, both in a social and professional sense. One of the manifestations of this is peer observation of teaching in the virtual space, and the opportunity this provides to stimulate sociomaterial change for the virtual university, which blends well with the application of the social and material aspects of technology in learning and teaching. As the virtual university does so much of its work in the online space, understanding how people are responding and engaging in this space brings front and center the role of data analytics in supporting staff and students. However, this by itself is meaningless unless there is a purposeful design to what is occurring in this online space. For the virtual university, this flags the deliberate application of learning theories that relate to TEL. These have their roots in the 3Cs of constructivism, cognitivism, and connectivism, but manifest in many adaptions of these basic tenets. One thing is very clear, however: consistency, not sameness, in the application of these learning theories is key. Once this is present, we can make sense of the adaption and creation of new theories through our ongoing research of TEL. One of these adaptions that is particularly relevant for the virtual university is the role social media play in the life of our students and now our staff. This plays out further as we look into the future of more traditional online tools such as the learning management system, which are strongly linked to newer and emerging forms of assessment practice. These practices look to make online assessment more active and authentic through a greater emphasis on peer and collaborative assessment. Why? Because we are trying to prepare our students for the future of work and much of this will be online and interactive. This of course brings with it the challenges of ensuring this assessment is authentic and original, which in turn requires measures to detect those who may take advantage of this greater flexibility to consider cheating. The affordance that openness provides and more broadly open educational practice, as an enabler for the virtual universities, is not to be understated here, nor is the important role that new and alternate forms of credentials may play. Developing coherent new models to follow the development of microcredentialing practice, and understanding the opportunities and challenges to portability and authentication of online short courses offerings, is central to quality in the virtual university. In many ways, this makes us look to newer forms of learning that embrace approaches like gamification, adaptive and conditional learning, thus developing and quantifying intrinsically motivated instruction in the process. This demands the development of clear models and principles of gameful design and adaptive online experiential learning that embraces newer technologies that promote conditional learning and the rise of alternate reality spaces (AI, VR, AR, MR, and XR). Not least of these in recent times is the rise of artificial intelligence, and the large language models and opportunities this provides means that the virtual university may not have to reinvent the wheel. When aligned with an understanding of how others are dealing with these opportunities, through quality benchmarking and the application of learning and educational analytics, we can start to use out institutional data to drive further quality improvements and innovations.

1

The Virtual University: Moving from Fiction to Fact

19

As we work our way through the concepts contained within this volume, it is hoped that a picture will start to form that points to not just the validity of the virtual university, but also the limitless opportunities it may provide.

Cross-References ▶ The Virtual University in Practice

References Anderson, B., and M. Simpson. 2012. History and heritage in open, flexible, and distance education. Journal of Open, Flexible and Distance Learning 16 (2): 1–10. https://files.eric.ed.gov/ fulltext/EJ1080085.pdf. Broom, D. 2021. Home or office? Survey shows opinions about work after COVID-19. Future of Work. World Economic Forum. https://www.weforum.org/agenda/2021/07/back-to-office-orwork-from-home-survey/. Champagne, E., and A.D. Granja. 2021. How the COVID-19 pandemic may have changed university teaching and testing for good. The Conversation. https://theconversation.com/howthe-covid-19-pandemic-may-have-changed-university-teaching-and-testing-for-good-158342 Garcia, E., E. Weiss, and L. Engdahl. 2020. Access to online learning amid coronavirus is far from universal, and children who are poor suffer from a digital divide. Economic Policy Institute. https://www.epi.org/blog/access-to-online-learning-amid-coronavirus-and-digital-divide/. Keegan, D.A., and S.L. Bannister. 2021. More than moving online: Implications of the COVID-19 pandemic on curriculum development. Medical Education 55 (1): 101–103. https://doi.org/10. 1111/medu.14389. Liu, Q., and S. Geertshuis. 2021. Professional identity and the adoption of learning management systems. Studies in Higher Education 46 (3): 624–637. https://doi.org/10.1080/03075079.2019. 1647413. Newfield, C. 2019. Unbundling the knowledge economy. Globalisation, Societies and Education 17 (1): 92–100. https://doi.org/10.1080/14767724.2019.1602353. Packham, A. 2020. One in four students unable to access online learning during lockdown. The Guardian. https://www.theguardian.com/education/2020/sep/08/third-of-students-unable-toaccess-online-learning-during-lockdown-survey Paul, J., and F. Jefferson. 2019. A comparative analysis of student performance in an online vs. faceto-face environmental science course from 2009 to 2016. Frontiers of Computer Science 1 (7). https://doi.org/10.3389/fcomp.2019.00007. Pratiwi, S.S., I.H. Al Siddiq, P.P. Anzari, M.N. Fatanti, and D.F.V. Silvallana. 2021. Educators’ professional ability to manage online learning during the COVID-19 pandemic. In Social change and environmental sustainability, ed. I. Sumarmi, N.H.P. Meiji, J.H.G. Purwasih, A. Kodir, E.H.S. Andriesse, D.C. Ilies, and K. Miichi, 66–69. CRC Press, Taylor & Francis Group. https://doi.org/10.1201/9781003178163. Twigg, C., and D. Oblinger. 1996. The virtual university. Washington, DC: A Report from a Joint Educom/IBM Roundtable. https://www.educause.edu/ir/library/html/nli0003.html Weller, M. 2014. Battle for open: How openness won and why it doesn’t feel like victory. London: Ubiquity Press. Willems, J., and C. Bossu. 2012. Equity considerations for open educational resources in the glocalization of education. Distance Education 33 (2): 185–199. https://doi.org/10.1080/ 01587919.2012.692051.

Part II Laying a Solid Foundation: Policy and Governance Models

2

Aligning the Vision for Technology-Enhanced Learning with a Master Plan, Policies, and Procedures Kevin Ashford-Rowe, Holly Russell, Nona Press, and Judith Smith

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conceptual and Theoretical Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digital Transformation of Higher Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Drivers of Digital Transformation: Why Should They Matter? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Emergence of Fit-for-Purpose Digital Campuses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . On Being Digital at Heart: A Shared Vision for a Digital Campus (*A version of this excerpt was published in Campus Morning Mail (3 May 2020)) . . . . . . . . . . . . . . . . . . . . . . . . . . . Case Example: The University for the Real World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Implementation Strategies and Considerations for Fulfilling the Digital at Heart Vision . . . . . . Digital Campus Master Plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . RIPPLES as a Model for Supporting the Transformative Implementation of Innovations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reflections and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

24 26 26 26 27 29 30 32 32 33 37 38 39 39

Abstract

The higher education landscape is evolving as it strives to meet societal expectations and students’ growing needs. Universities have responded to this in a number of ways, including reimagining curriculum, reshaping course delivery, and reinvigorating student support mechanisms. Alongside this impetus for change are cycles of technological innovation, moving at ever faster rates, and where students have higher levels of expectation as they seek to access their learning experience. This chapter suggests an increasingly technology-enhanced learning K. Ashford-Rowe (*) · H. Russell · N. Press · J. Smith Queensland University of Technology, Brisbane, QLD, Australia e-mail: [email protected]; [email protected]; [email protected]; [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_2

23

24

K. Ashford-Rowe et al.

and working experience grounded in a “real-world” orientation to educational provision, considered within the context of a virtual university. To that end, this chapter seeks to 1) establish the importance of a vision for digital transformation in learning and teaching that clarifies understanding of the concept of virtual university at the institutional level; 2) explain that transformation can only properly occur when the institution embraces such digital transformation, i.e., an approach we’ve termed as being “digital at heart” with a focus upon how such an approach might best be harnessed to engage stakeholders; and 3) describe some of the critical steps for implementing the digital transformation of the learning and teaching experience. For our purposes, this was largely framed as a Digital Campus Master Plan, a vision and planning instrument embodying policies and procedures. This would provide a roadmap in the form of a clear and focused approach to considering the critical elements vital to the achievement of digital transformation. The chapter concludes with reflections and recommendations drawn from the experiences of the authors in leading and implementing such an approach. Keywords

Technology-enhanced learning · Digital transformation · Higher education · Virtual university · Digital learning · Digital literacy · Digital fluency · Digital campus · Digital at heart · Digital Campus Master Plan

Introduction The proliferation of digital technologies has precipitated far-reaching changes in twenty-first-century society, including education. Technology-enhanced and/or technology-enabled processes are transforming the way we live, work, learn, and play; they are reshaping business transactions, personal communication, and entertainment and many other aspects of our lives. It is increasingly clear that we are undergoing a digital revolution and, in the same way, that agrarian and industrial revolutions have disrupted economies and societies, so the digital revolution is impacting significantly upon many aspects of our lives (Doucet et al. 2018), including upon both the need and opportunity to learn (OECD 2019). One example of this trend has been the increased attention given to the notion of digital literacy (Press et al. 2019), which might be considered as symptomatic of an underlying understanding of the importance of being literate as a means of enabling success in any given age. This perspective furthers the case that the world is both facing and embracing a digital revolution. As noted, an agenda for digital transformation exists within the current higher educational setting. A process that would see the transformation of student learning experiences through the employment of information and communication (i.e., digital) technologies (Coldwell-Neilson 2017). If a function of higher education is to shape technological, cognitive, and attitudinal dimensions of student development then it transpires that this will best be mediated within technology-enabled learning environments, and by means of technology-enhanced teaching and learning.

2

Aligning the Vision for Technology-Enhanced Learning with a Master Plan,. . .

25

It is intended that such approaches will build students’ capabilities and capacities and best prepare them to navigate evolving workforce demands, encapsulating notions of graduate readiness and employability. The twentieth century saw this perspective as having a critical focus, as Brown and Campione (1994) contended: . . . there have been major changes in the aims and goals of education. Whereas the early goal was to produce graduates who possessed basic literacy skills, more recently the stakes have been increased to emphasize higher levels of literacy, greater understanding of traditional subject matters and technology, and the capacity to learn and adapt to changing workforce demands. (p. 289)

In many respects, this twentieth-century thinking paved the way for the development and greater examination of theories of the ways in which people learn, from behaviorist perspective to the cognitive orientation, and to learning, including notions of learning communities (Bjarnason 2001; Bransford et al. 2002; Brown et al. 1989; Wenger-Trayner and Wenger-Trayner 2015). The response to changes in educational provision in the 1990s has included the increasing adoption of information and communication (computer) technologies to facilitate the increasingly flexible delivery of, and access to, contemporary higher education with the aim of remaining competitive in a global knowledge economy. Likewise, the twenty-first century saw higher education adopting numerous ubiquitous technologies, in some instances providing students’ choice in personalizing their learning experiences and taking control over their online education. Online education has grown and evolved exponentially over the last few decades, producing a number of alternative and related terms with distinctive practices, possibilities, and prospects. One such term is “virtual university” – higher education institutions that provide flexible online education, which serves to increase equitable access to quality education. Given that what students experience is the physical campus and/or online environments within the university rather than the university itself, we have conceptualized the online environment of a university as a digital campus, where the digital transformation builds upon and extends the affordances of technology-enhanced learning. From this perspective, the ecosystem of the virtual university encapsulates the whole-of-student experience of the higher education provision. It acknowledges that the educational offerings within virtual universities have been advanced as a key means for reimagining and reconfiguring higher education to meet and indeed create new kinds of demand. The idea of digital learning, encompassing online learning, e-learning, mobile learning, remote learning, distance learning, and other technology-enhanced learning options, is now commonplace in all universities in Australia and indeed across the globe. Thus, it is worth pondering upon the extent to which such technologyenhanced learning can truly transform the higher education experience. In our specific context, the definition of the virtual university encompasses a vision that situates the virtual and physical as an integrated institution. In this educational context, learners move seamlessly between digital and physical environments and

26

K. Ashford-Rowe et al.

functions, where students access their learning both synchronously and asynchronously. It is intended that this chapter will then assist educators to establish a clearer understanding of digital transformation and related constructs, such as digital learning, digital at heart, and Digital Campus Master Plan. For the purposes of unpacking these constructs, the authors have sought to engage the RIPPLES Model (Benson and Palaskas 2006; Ensminger and Surry 2008; Muldoon et al. 2010) as the framework against which such transformation is considered. The chapter presents the development and planned implementation of a Digital Campus Master Plan for university learners and learning. The focus is then placed upon highlighting the challenges and opportunities that make up our ongoing research in this field of inquiry, to continue the narratives in higher education transformation.

Conceptual and Theoretical Bases Digital Transformation of Higher Education Digital transformation focuses on leveraging technology to reimagine and evolve organizational strategy, people, and processes to deliver improved customer and business outcomes. Thus, digital transformation does not only refer to a shift to technology but an all-encompassing societal function. As Stolterman and Fors (2004) contended, digital transformation can be understood as the “changes that digital technology causes or influences in all aspects of human life” (p. 689). It stands to reason that there is an imperative for institutes of higher learning, particularly those with an aim to develop future-ready graduates, to stay in step with the wider digital transformation (Castano-Munoz et al. 2016), that is occurring in industry, government, and community organizations. As higher education evolves and transforms, a shift to learner-centric foci is becoming increasingly important for the success of universities (Brown et al. 2015). In unpacking what it means to be learner centered in a world that is being shaped by digital transformation, understanding the expectations and needs of the diverse learners who are engaging with higher education is a critical undertaking. With expanding options for remote, flexible, and modularized learning, we are entering an increasingly competitive higher education market (Colbert et al. 2016; EY 2019). Learners no longer see digital learning as a differentiator, but as ubiquitous and embedded in everyday life at university (Dalton & Davis 2015; Kim 2019). There is a basic expectation that learners will have constant access to platforms at the time and place of their choosing; and there is increasing expectation for flexible access to services and support (Alexander et al. 2019).

Drivers of Digital Transformation: Why Should They Matter? The drivers of digital transformation of higher education in many ways mimic the drivers of digital transformation more broadly. In conceptualizing higher education

2

Aligning the Vision for Technology-Enhanced Learning with a Master Plan,. . .

27

as a positional “good” or commodity in which people invest for personal gain, most industrialized modern societies aim to provide mass higher education through a market system. As such the imperative to remain in step with a society’s pace of digital transformation is high (Press et al. 2019). The key drivers include better user experience, better (data-driven) decision-making, affordances for agility and innovation, meeting evolving expectations, including for flexible and personalized interactions, and bridging the gap between offline and online, and remaining competitive in rapidly evolving markets. Universities continue to transform and rethink how they operate to meet the diverse needs of stakeholders and harness the promise of technology to rise to further challenges in the sector. Thus, a clear outlook for a fit-for-purpose virtual university emerges (Lacy et al. 2017). It is therefore worth reflecting upon the rise of the idea of virtual universities as the key delivery agent to support digital learning experiences in various ways (Coates et al. 2017). Moreover, understanding the broader digital landscape in which learners study, work, live, and play provides an emerging vision for the role of the university in a digital society where the future is unknown (Barnett 2015), and where digital transformation influences organizational actions to challenge the status quo, as shown in the current pandemic crisis (Coates et al. 2021).

The Emergence of Fit-for-Purpose Digital Campuses Various terms are used to describe how universities position their physical and digital presence. For the purpose of forming a vision that can spark the imagination of stakeholders and decision-makers, framing the virtual or online university in a context that resonates with existing conceptualizations of university infrastructure is a useful exercise. The very tangible notion of a campus as a physical locale of teaching and learning conjures an image of connected buildings, green spaces, and walkways. To that end, the term “digital campus” has been used to describe a digital layer that sits over the physical campus experience, and more commonly has been used to communicate the online student experience as one of equal value to the physical campus experience (Garrison 2017; Goodfellow 2011). This has been suggested as a response to the higher expectations of more digitally savvy staff and students (ACODE 2014), to create better first impressions and student onboarding experiences, and to strategically shift away from inflexible and inefficient legacy systems. As universities rethink how they operate to meet the growing diverse needs of students and shift in societal expectations, the requirement to harness the promise of technology is evident (Dalton & Davis 2015; Dell Boomi 2019; Alexander et al. 2019). By way of illustrating an example, many universities in Australia have responded swiftly to the growing demands for digital transformation, particularly with the use of educational technologies (Palaskas and Muldoon 2003) that enhance student learning and provide greater access to higher education. As shown in Fig. 1, universities in Australia have distinctly labeled the technology-enabled layer of their campus program offerings, using the term “online” and significantly altering their

28

K. Ashford-Rowe et al.

Fig. 1 Examples of online universities in Australia

respective business models. These universities have a significant presence in social media, which signals the digital revolution engulfing higher education and the race to the rapidly advancing digital movement and its many affordances. In light of the fervor for establishing an online presence, a vision of a fit-forpurpose digital campus must emerge, encapsulating a range of needs that are evolving for the future of digital learning, including expectations around flexibility, authenticity, connection to networks and a sense of belonging to learning communities and. beyond (Peacock and Cowan 2019). With the continuing trend toward lifelong and lifewide learning (Jackson 2011), and ongoing and significant disruption to the job market, universities must consider their integration with industry and innovate their offerings to ensure currency and relevance (EY 2019; Hasso Plattner Institute of Design 2019; OECD 2019). To respond to this shift, digital learning will need to place greater emphasis on transdisciplinarit, disaggregation of degrees (EY 2019), and shifts to capabilities development (JISC 2014). Universities also need to better develop enterprise and interpersonal skills, and skills for dealing with change and disruptions (OECD 2019). Further, there is a need for digital learning to not just enable better digital collaboration (Brown et al. 2015) but also an emerging need to build relationships and communities (Ahn and Davis 2020; Garrison 2017; Colbert et al. 2016) and to contribute socially and civically (OECD 2019). Clearly, the idea of a digital campus embodies a learner-centered ecosystem that is a “dynamic, interconnected, ever-evolving community of learners, instructors, tools and content” (Brown et al. 2015, p. 3). Indeed, a vision of a digital campus conjures situations where students prioritize experiences along with feedback and

2

Aligning the Vision for Technology-Enhanced Learning with a Master Plan,. . .

29

assessment and operate in an ecosystem that supports capability, knowledge, confidence, and identity, and provides powerful learning experiences, sustained reflection, and discourse through a community of inquiry (Meehan and Howells 2019; Garrison 2017). Thus, a digital campus must first be a real, authentic campus enacting its purpose and providing a network for learning (Davies 1998; Doucet et al. 2018; Pangrazio 2019). In this context, the digital campus attempts to address the perennial problem of equitable access to quality education by providing an education that is sourced locally and globally, enhanced through, and enabled by, technology and available at the time and place that meets the needs of diverse students (K. Ashford-Rowe, personal communication, March 7, 2019). For individual universities, the approach to digitally enhanced learning reflects the strategic vision and positioning in localized contexts. By first identifying and owning institutional identity and purpose, a vision for a fit-for-purpose digital campus that embodies the core purpose and function of the institution is possible. Once that vision is established, the embedding of a whole of institution framework provides strategic oversight for the “building” of the digital campus, which includes the investment in underlying system infrastructure, the adoption of learning technologies, and the implementation of digital support for learning (Ahn and Davis 2020).

On Being Digital at Heart: A Shared Vision for a Digital Campus (*A version of this excerpt was published in Campus Morning Mail (3 May 2020)) As alluded to earlier, long before the rapid move to online that has resulted from the COVID-19 pandemic crisis, many universities were deeply engaged in digital transformation, with regard to the use of the educational technologies. Stakeholder roles were tested, reimagined, and mobilized. Though the approaches varied, such a move was often predicated upon using technology to improve existing means and mechanisms of student access and engagement (Lockwood and Papke 2018). This is an approach that Ashford-Rowe (2020) describes as digital in part. Here, information and communications technology are the focal point of transformation to mimic existing analogue processes, rather than fully embracing the affordances provided by such technology. If such practices are digital in part, what then is digital at heart? It is increasingly acknowledged that the student learning experience (both award and nonaward, formal or informal) can no longer be predicated upon the physical campus alone (Coates et al. 2021), even when augmented by technology. It needs to become a true blending of the physical and the virtual and everything in between, where the opportunities afforded by digital technologies are not simply leveraged to existing ways of “doing business” but are transformed by them. This is a perspective coined by Ashford-Rowe as digital at heart, where the affordances of digital technologies are harnessed to evolve new and different processes and means of operating. Such perspective acknowledges that today’s students are already accessing their learning

30

K. Ashford-Rowe et al.

pragmatically, to meet other life demands on their time, and that they are increasingly learning at the time and place that best suits them. More often, learning with the devices that they already own connects them to groups of learners and resources for intentional learning, encapsulating the notions of “always digital,” “always online.” The digital at heart perspective draws on digital transformation that places the students at the heart of the learning experience, where technologies are used as meaning-making devices and support and facilitate personalized, intentional learning in social learning communities (Felton and Lambert 2020; Loftus 2010; Tinto 2017; Wenger-Trayner and Wenger-Trayner 2015; Wenger-Trayner and WengerTrayner 2015). A departure from behaviorist approaches of transmitting or conveying knowledge to learners, this intentionality characterizes a constructivist perspective where the learning experience is enhanced with groups of learners committed to shared learning goals, co-constructing knowledge through collaborative engagement, and using technological tools with which to develop knowledge. To this end, a university needs to provide enhanced and engaged learning, teaching, and research, which capture “experiences that promote agility of its staff and students to be confident in their engagement with technological innovations” (Press et al. 2019, p. 259), because being digitally literate “is a prerequisite in our open and global educational environment” (ACODE 2014, p. 1). What this then calls for is digitally literate organizations. An organization must demonstrate to employees why digital matters and what digital changes benefit individual and organizational performance, and work productivity. The success of organizations relies upon ensuring that their digital workplace is agile and strong and embedding digital skills and capabilities throughout organizational culture. As JISC (2014, n.d.) explained: “A digitally literate organisation is better equipped to address a range of challenges so building capacity for strategic thinking and leadership around digital literacy and digital at all levels is critical for organisational change in this area.” This perspective echoes the intent and purpose of being digital at heart in that the focus is one of empowerment for the purpose of remedying digital inequities across digital ecosystems.

Case Example: The University for the Real World Queensland University of Technology (QUT) is a metropolitan university in Australia, known for its focus as a university for the real world. QUT is undertaking a digital transformation to enhance the ways in which many of the important functions of the university are facilitated. Importantly, this includes the mediation of the academic teaching and student learning experience. As QUT embarks upon digital transformation with the intention of seeking to be more deliberate and effective in the use of educational technology, the aim is to enhance the student learning experience that harnesses affordances of the digital transformation. To enable this to occur, such digital transformation in learning and teaching will need to materialize across the full range of functions that students need to interact with on their learning journeys. In order to progress this work, it is useful

2

Aligning the Vision for Technology-Enhanced Learning with a Master Plan,. . .

31

to envision these journeys as ones that occur across both a physical as well as a virtual campus, noting that increasingly for many of those students studying online and by distance, their journey will occur on the digital campus only. Such transformation will require enhancement of the digital learning ecosystem as well as an increase in the uptake and usage of educational technology by educators. It will also require that consideration is given to the preparedness of both educators and students to be able to access and engage with that learning and teaching experience. Against this backdrop, the university’s digital transformation is embedded in the institutional strategic plan as espoused in the QUT Blueprint 6 (2020). As has been observed throughout various institutional course renewals, the Blueprint stands at the center of curriculum planning processes. Such processes are underpinned by the QUT Quality Framework, based on a continuous cycle of plan/implement/review/improve. The QUT Blueprint 6 informs the design and renewal of courses to provide excellent outcomes for graduates, enabling them to work in a diverse and complex world characterized by increasing change, challenges, and an unknown future (Barnett 2015). One of the aims of Blueprint 6 is to further strengthen QUT’s reputation for producing graduates who can thrive in challenging and highly competitive environments over the long term. This is done by strengthening and extending the strategic partnerships with professional and broader communities to reflect both QUT’s academic ambitions and civic responsibility. Thus, partnerships embody QUT’s priorities in the strategic agenda and a plan of action for collaboration and engagement through a wide variety of relationships as shown in Fig. 2. Such priorities are activated across all spheres of activity, designed to infuse practices with a deliberate strategy of collaboration and engagement through a wide variety of partnerships – including with alumni, industry, government, universities, the research sector, employers, Indigenous communities, local and special interest community groups, and international partners (QUT Blueprint 6, 2020).

Fig. 2 Horizons focused on the evolution of digital learning practice (Ashford-Rowe and Smith, 2020)

32

K. Ashford-Rowe et al.

Drawing on the digital transformation as an enabler of institutional processes, it forms part of the strategic agenda and plays a critical role in conceiving the educational experience, research, and service. The priorities are pursued across broad spheres of activity: the student lifecycle; innovative, practical, and engaging learning and teaching; high-quality, relevant research; codesigned Indigenous Australian teaching, research, and learning; and organizational practices and culture (QUT Blueprint 6).

Implementation Strategies and Considerations for Fulfilling the Digital at Heart Vision As demonstrated, today’s higher education institutions consider change and innovation as essential to their success and therefore warrant a well-considered or welldesigned implementation plan. As Ensminger and Surrey (2008, p. 611) noted, a “well designed implementation plan is essential to the success of any innovation or organisational change” (p. 611), and it is a “major concern for educational organisations” (p. 612). Worthy to note is that, up until recently, the process of adoption and implementation at QUT has not been conceptualized based on a theoretical framework but rather it has been influenced by evolutionary approaches in response to institutional agenda and priorities, emanating practical contingencies along the way. Reflecting upon recent practices of technological adoption and implementation at an institutional level, QUT has focused on curricular and pedagogical requirements in concert with the university’s goal as a leader of the university for the real world, whose physical campuses integrate digital campus features. For QUT, its commitment to real-world learning hallmarks its real-world brand and reputation, and is crucial to its institutional competitiveness and future sustainability.

Digital Campus Master Plan Against this backdrop, with the strategic priorities and institutional appetite for digital transformation in place, the vision of a digital campus that enables a thriving learning ecosystem of seamlessly integrated physical and virtual spaces is conceptualized through a Digital Campus Master Plan. Campus Master Plans as defined by Dalton and Davis (2015, p. 1) intend to guide the physical development needed to support the mission and strategic plan of an institution of higher education. They direct how various aspects of the physical environment, such as academic facilities, open spaces, housing, and circulation come together to meet the needs of the college or university. Most importantly, the campus master plan establishes the setting in which higher education transforms students’ lives.

QUT’s Digital Campus Master Plan aligns with diverse stakeholder needs and contemporary ways of learning and working. Its aim is to address the challenges to transition to a digitally enhanced future and progressively leverage digital

2

Aligning the Vision for Technology-Enhanced Learning with a Master Plan,. . .

33

capabilities for learning and teaching. This includes three streams of digital transformation initiatives: 1) Digital Campus Foundations; 2) Digitally Enriched RealWorld Learning; and 3) Learning Innovation. These are supported by core underpinning technology initiatives. The Digital Campus Master Plan is not a technology strategy per se. It is a planning instrument for learning and teaching with people and underpinning transformative technological solutions at its core. It provides the basis for understanding the digital at heart vision and supports the design and delivery of that vision. The QUT Digital Campus Master Plan: • Brings together the strategic priorities of the University to create a vision of a digital campus • Defines the digital campus objectives and vision, outlining the key features, capabilities, and desired outcomes – the future state digital learning ecosystem • Identifies key benefits, challenges, and opportunities to be realized through the integration of the digital ecosystem • Outlines a digital learning framework that defines digital learning, sets digital design principles, and provides guidelines for quality digital design • Provides a high-level, indicative implementation plan by way of a roadmap with sequential steps that guide the transition to the future state. The roadmap sits at the center of the Digital Campus Master Plan. It sets out a series of high-level strategic steps or “horizons,” with an aim of supporting the detailed design and delivery of the digital campus. The idea of horizons, then, relates to categories of activities or actionable steps, and are strategically ordered as short-, medium-, and long-term actions within the plan. This sets out the roadmap, designed as pathways to achieving the digital at heart vision. The fast-paced evolution of digital learning practice and emergence of new technologies requires QUT to be agile in the focus for each horizon. The initiatives will not only be centered on delivering new digital solutions and improved processes, they will also enable and empower learners and staff with digital literacy to leverage these solutions, and create opportunities for digital innovation and transformation. Just how these initiatives are effectively deployed are worthy of consideration and action. What follows is the unpacking of the RIPPLES model (Surry et al. 2005) that provides practical considerations and guidance for QUT and other universities attempting to embark on a similar journey of digital transformation and toward becoming digital at heart.

RIPPLES as a Model for Supporting the Transformative Implementation of Innovations RIPPLES is an approach that universities can put in place to facilitate the adoption and effective use of new technologies in their campuses. The RIPPLES model

34

K. Ashford-Rowe et al.

explains a framework for supporting the implementation of innovations, a key aspect of which draws from prior theories of diffusion and implementation such as Rogers (2010), Ely (1999), and Stockdill and Morehouse (1992). The RIPPLES model embodies seven components: resources, infrastructure, people, policies, learning, evaluation, and support. Benson and Palaskas (2006, p. 551) asserted that this model comprehensively covers a range of factors for consideration including: • The fiscal resources associated with innovation adoption • The institution’s infrastructure, namely the hardware, software, facilities, and network capabilities in support of teaching resources, production resources, communication resources, student resources, and administrative resources • The needs, hopes, values, skills, and experiences of the people involved • Institutional policies and procedures • The relationship between the technology and learning outcomes • Evaluation and review (both summative and ongoing), including the • Impact of the technology on learning goals • The support systems and scaffolding required to ensure successful implementation In the context of QUT digital transformation initiatives, the RIPPLES model provides an instrumentalist perspective for focusing on specific aspects of the change process within the complex operation of the university for the real world. The QUT acknowledges that the success of innovations is directly associated with its successful implementation. As Ensminger and Surry (2008, p. 612) suggest, “organisations must not only be aware of variables that facilitate implementation, but need a means for determining which variables are most important to their organisation, given a specific innovation.” The factors that make up the RIPPLES model referred to in Muldoon et al. (2010) and Surry et al. (2005) are considered below, in accordance with the aims and purposes of the QUT Digital Campus Master Plan.

Resources: Financial, Materials, Personnel, and Support Structures “Resources” is an important component of implementation and refers to the availability and accessibility of resources needed to implement the innovation. Resources include the existing infrastructure as well as an organization’s finances, hardware, software, materials, personnel, and support structures (Ely 1999 cited in Ensminger and Surry 2008). The Digital Campus Master Plan for QUT acknowledges the importance of supporting the implementation of any new educational initiatives that require locating the necessary finances, equipment, materials, and facilities to facilitate the implementation. Here, change agents are crucial for soliciting the required resources for implementation, the most critical of which is financial resources. This is a core component of the RIPPLES model, alongside the “infrastructure” to support the technology integration and the need for personnel to support such integration efforts as part of the “support” component (Surry et al. 2005). QUT is anticipating digital transformation initiatives and associated investments will be monitored and adjusted to reflect this commitment to the business.

2

Aligning the Vision for Technology-Enhanced Learning with a Master Plan,. . .

35

Infrastructure: Hardware, Software, and Supporting Technologies “Infrastructure” refers to the critical role of hardware, software, and supporting technologies to the digital transformation and innovation process. It also refers to network capabilities to support the implementation, including support for teaching resources, production resources, communication resources, and administrative resources (Surry et al. 2005; Surry and Ensminger 2003). Within the Digital Campus Master Plan, the delivery infrastructure is designed to ensure that technology is used to both maximize students’ access to their learning and better enable student engagement and real-world connections as key determinants of their future success. It not only focuses on delivering new digital solutions and improved processes, it also empowers learners and staff with digital literacy to leverage these solutions. It transforms QUT from a digital in part focus to the application of technology in learning and teaching, where digital tools are used alongside existing analogue processes, to a truly digital at heart focus that ensures that the full range of benefits offered by contemporary technology platforms and tools are fully utilized to continually enhance the student’s learning experience and learner success. People: The Essential Role that the People Play Within the Organization in the Technology-Enabled Transformation and Innovation “People” refers to the effect, impact, or influence an innovation has on the personal, social, and cultural aspects of an organization. Changes of any scale often have a profound impact on people’s productivity, motivation, career plans, and even such personal things as one’s physical health or sense of self-worth. It is envisaged that QUT leaders will try to identify and account for the impact the digital transformation will have on staff, students, and other stakeholders. Shared vision, decision-making, communication, feedback, and other means of involvement are critical to understanding the human impact of the digital transformation at QUT. The three streams within the Digital Campus Master Plan are prioritized in partnership with faculties, divisions, and learners to ensure the digital transformation is people centered and business led, not technology driven. Policies: Institutional Policies and Procedures to Adapt to New Technology “Policies” refers to the impact an innovation has on organizational culture, encompassing the rules, regulations, traditions, and practices of an organization. The effective implementation of complex transformation and innovation such as the Digital Campus Master Plan requires organizational policies to be substantially updated, revised, or even totally rewritten. As Ensminger and Surry (2008, p. 217) suggest, any innovation “requires at least some accompanying changes to policies and practice to be successfully implemented.” This will be enabled by means of a Digital Campus Master Plan and a digital learning framework ensuring that an appropriate range of policies, products, procedures, processes services, systems, and tools are endorsed to enable a successful digital transformation.

36

K. Ashford-Rowe et al.

Learning: The Need of the Technology to Enhance the Educational Goals of the University “Learning” refers to the need to focus on curricular and pedagogical considerations during the transformation and innovation processes and remain focused on the intent and purpose of such transformation and innovation. Educational transformation and innovation elicit genuine attempts to enhance student learning and educational outcomes. Issues related to technological, organizational, and administrative problems may result in a loss of focus on such outcomes and may divert time, resources, and attention away from the learners and their success (Benson and Palaskas 2006; Surry et al. 2005; Ensminger and Surry 2008). Ongoing organizational commitment to maintain the highest learning and teaching standard is imperative to avoid serious damage to both the productivity and reputation of QUT. The Digital Campus Master Plan with its digital at heart focus will ensure that the full range of benefits offered by digital tools and technologies are fully utilized to continually enhance the student’s learning experience. Evaluation: Ongoing as well as Summative Evaluation of Technologies, Including the Impact on Learning Goals “Evaluation” refers to an ongoing assessment of the transformation and innovation initiatives and on their impact to individuals, departments, and the organization as a whole. Areas for evaluation, adapted from Surry et al. (2005), include: 1) seeking out data about the learning outcomes of the Digital Campus Master Plan; 2) evaluate the technical aspects of the learning systems; 3) evaluate the implementation of the Digital Campus Master Plan; and 4) undertake cost-benefit analysis to determine if the transformation and innovation is of value to the university. Moreover, strategies that help propel transformation and innovation enable the QUT community and its internal and external partners to research, prototype, pilot, evaluate, and scale meaningful and effective initiatives. The evaluation of learning innovation pilots informs whether the innovations are appropriate for adoption into the QUT digital learning ecosystem. Pilots are targeted small-scale experimentations that help QUT investigate and evaluate the value of new pedagogies and technologies which are relevant to user needs and QUT’s ambitions for an enhanced digital campus. Support: The Need to Have a Support System to Ensure Successful Adoption and Diffusion of Technologies “Support” refers to the need to continually provide assistance, guidance, encouragement, and direction to those affected by the transformation and innovation. There are four essential forms of support: administrative, pedagogical, technical, and training (Benson and Palaskas 2006; Muldoon et al. 2010; Surrey 2005). Learning experiences and support are data driven, with academic and professional staff empowered with a robust understanding of how learners engage with their learning. The aim is to enable better outcomes for all forms of support, underpinned by data about how they interact with learning (Horizon 1). Moreover, pathways and support are in place for academic staff to engage with new technology for learning (Horizon 2). Academic staff will have increased opportunities to leverage emergent digital technologies to support their learners (Horizon 3).

2

Aligning the Vision for Technology-Enhanced Learning with a Master Plan,. . .

37

The fast-paced evolution of digital learning practice and emergence of new technologies require QUT to continue being agile in the focus for each horizon. The idea of horizons are threaded through the narrative in the support component to illustrate the manner in which the implementation of digital transformation and innovation will proceed. The horizons apply equally to all components of the RIPPLES model but due to space limitations, this has not been illustrated in all components.

Reflections and Recommendations The digital revolution is upon us, and the ubiquity of digital technologies provides significant opportunities but also presents risks. Examining and understanding this phenomenon is crucial for twenty-first-century universities to remain current and relevant. It is imperative for universities to harness such technologies and to evolve new and different processes and means of operating in order to ensure that they are being truly digital at heart and thus maximizing the capacity for technologies to profoundly and richly transform and improve the academics’ teaching and students’ learning. They are not simply harnessed to existing ways of doing business (digital in part) but are transformed by them. In progressing a digital transformation of the learning and teaching experience a few key factors need consideration, particularly where the lack of them will likely negatively impact upon the ability of the organization to transform digitally. First and foremost is the requirement to ensure that digital transformation is recognized as a strategic priority of the organization itself, and its senior leadership. Without a clear mandate and the imprimatur of senior leadership then any such transformation can only be localized at best. This might be considered as “transformative” within that local context, but given that true transformation must occur at the whole of organization level then it will be, at best, only digital in part. Once this mandate is provided, and preferably accompanied with a preparedness to make significant investment in funding and time, then the next step is to work to develop a vision of how a digitally transformed academic teaching and student learning might look. To this end, it is important to think less about how the technologies will be engaged and more about what a transformed experience will achieve. For the purposes of the transformation that the authors have led, the drivers have not been to “implement technology” but to work closely with the academic and student communities to understand what a compelling student learning experience should provide in a contemporary university. The key phrases that have driven this consultation were “access” and “engagement.” This means the recognition and understanding that students increasingly expect to be able to access their learning experience and education provision in ways that are consistent with other aspects of their digital life. Thus, while they continue to value the on-campus aspects of their learning experience, a range of factors mean that they have increasingly to be able to access and engage with their learning at times and in places that are convenient to them, often away from the campus. Over and above this is the recognition of the importance of students feeling engaged with their university, most often in terms of

38

K. Ashford-Rowe et al.

engagement with the academics facilitating their learning. In considering both accessing the learning and the sense of engagement engendered, increasingly distributed and personalized digital approaches can further enhance the student learning experience. While the discussion around a contemporary and compelling learning and teaching experience is occurring, the “fitness for purpose” of the existing digital learning ecosystem needs to be confirmed. The “digital learning ecosystem” is defined as the range of the technologies that facilitate the student lifecycle where it relates to the students’ learning experience. Most usually, this will be the systems that students access and use to learn, and in terms of the student lifecycle, they will engage with them from the point of their “enrolment” with the university across the several years of their undergraduate or postgraduate learning and until they “graduate.” The systems and technologies that determine this ecosystem, their currency, configuration, and integration both to each other and seamlessly with the systems that support other aspects of the student experience are critical determinants of the student’s capacity to succeed. In order to determine this fitness for purposes, this ecosystem needs to be reviewed and mapped in terms of both the systems used and, more importantly, the educational and pedagogical affordances that they provide. Once a vision for a transformed learning experience has been communicated, preferably agnostic of any existing limitations, then is the time when that vision can be mapped, or overlayed, upon the existing ecosystems. It is at this point that it will become clearer where the strengths and opportunities arise as well as the weaknesses and threats to the delivery of that vision. It is also at this point that the development of frameworks can occur that can be used to drive the delivery of the pedagogical vision. At the same time, technology investment plans can be developed to ensure that the underlying technological infrastructure will be fit for the purpose of delivering the vision of a digital transformation. Simultaneous to the review of underlying technological infrastructure is the importance of reviewing the existing policy framework, for the most part to ensure that it is capable of enabling, enhancing, and encouraging the community to not just engage with the vision but to deliver it to its fullest extent. There is little point in developing visions, building up information and communication capabilities if a community is hampered in its ability to engage by unaligned policy and process.

Conclusion Phases of this work embody the truly digital at heart, digital campus with a clear vision, and outlined targets, in the short, medium, and longer term as to how it will be achieved. It will preface digital solutions and allow technology to challenge existing paradigms of supporting and facilitating learning and teaching. It will have identified academic champions and created networks that link these champions in collegial forums (virtual and physical) in which they will engage, challenge, and be challenged (synchronously and asynchronously), a university learning and teaching community that will lead the digital transformation.

2

Aligning the Vision for Technology-Enhanced Learning with a Master Plan,. . .

39

It will drive different means and methods of delivering the student experience across the student lifecycle in and out of the classroom and across the organization and it will provide a student learning and teaching experience that is: tailored, personalized, and increasingly self-paced and capability based, and authentically taught and assessed – in the classroom and in the workplace (physical & virtual). As a digital campus it will have embraced a digital learning and teaching paradigm; be delivering a digitally enhanced and high-quality learning and teaching experience; have devised and developed a digital policy framework; as well as the relevant policies, products, services, processes, systems, and tools that drive excellence in digital learning.

Cross-References ▶ Laying and Maintaining the Foundations for Quality ▶ Models of Professional Development for Technology-Enhanced Learning in the Virtual University ▶ The Role of Standards and Benchmarking in Technology-Enhanced Learning ▶ Transparency in Governing Technology Enhanced Learning ▶ Using Institutional Data to Drive Quality, Improvement, and Innovation

References Ahn, M.Y., and H.H. Davis. 2020. Four domains of students’ sense of belonging to university. Studies in Higher Education 45 (3): 622–634. https://doi.org/10.1080/03075079.2018.1564902. Alexander, B., K. Ashford-Rowe, N. Barajas-Murphy, G. Dobbin, J. Knoe, M. McCormack, . . . and N. Weber. 2019. EDUCAUSE Horizon Report: 2019 Higher Education Edition. Educause. Australian Council on Open, Distance and e-learning. 2014. Digital literacy – What is it and how is it achieved? Retrieved 1 July 2021 from https://www.acode.edu.au/mod/resource/view.php? id¼209 Barnett, R. 2015. Thinking and rethinking the university in the selected works of Ronald Barnett. Milton Park, UK: Routledge. Benson, R., and T. Palaskas. 2006. Introducing a new management learning system: An institutional case study. Australasian Journal of Educational Technology 22 (4): 548–567. Bjarnason, S. 2001. Managing the changing nature of teaching and learning. Minerva 39 (1): 85–98. http://www.jstor.org/stable/41821177. Bransford, J.D., A.L. Brown, and R.R. Cocking, eds. 2002. How people learn: Brain, mind, experience, and school. Washington, DC: National Academy Press. Brown, A.L., and J.C. Campione. 1994. Guided discovery in a community of learners. The MIT Press. Cambridge, Massachusetts. Brown, J.S., A. Collins, and P. Duguid. 1989. Situated cognition and the culture of learning. Educational Researcher 18 (1): 32–42. Brown, M., J. Dehoney, and N. Millichap. 2015. The next generation digital learning environment: A report on research. Retrieved from https://library.educause.edu/~/media/files/library/2015/4/ eli3035-pdf.pdf Castano-Munoz, M. Carnoy, and J.M. Duart. 2016. Estimating the economic payoff to virtual university education: A case study of the Open University of Catalonia. Higher Education 72 (1): 1–24. https://doi.org/10.1007/s10734-015-9935-1.

40

K. Ashford-Rowe et al.

Coates, H., P. Kelly, and R. Naylor. 2017. Leading online education for student success. International Journal of Chinese Education: 105–126. https://doi.org/10.1163/22125868-12340076. Coates, H., J. Shi, J. Zhang, and J. Yang. 2021. Diamond open global research on Chinese education. International Journal of Chinese Education 10 (3): 22125868211069273. Colbert, A., N. Yee, and G. George. 2016. The digital workforce and the workplace of the future. Academy of Management Journal 59 (3). https://doi.org/10.5465/amj.2016.4003. Coldwell-Neilson, J. 2017. Digital Literacy – A driver for curriculum transformation. In Research and development in higher education: Curriculum transformation, ed. R.G. Walker and S.B. Bedford, vol. 40, 84–94. Sydney, 27–30 June 2017. Higher Education Research and Development Society of Australasia, Inc. Dalton, L., and J. Davis. 2015. Campus master planning. In APPA leadership in educational facilities body of knowledge. https://www.appa.org/bok/campus-master-planning-2/. Dell Boomi. 2019. Building the connected campus in the era of digital transformation. Retrieved 28 June 2020 from https://resources.boomi.com/executive-briefs/building-connected-campusdigital-transformation Doucet, A., J. Evers, E. Guerra, N. Lopez, M. Soskil, and K. Timmers. 2018. Teaching in the fourth industrial revolution: Standing at the precipice. Oxon/New York: Routledge. https://doi.org/10. 4324/9781351035866 Ely, D.P. 1999. Conditions that facilitate the implementation of educational technology innovations. Educational Technology 39: 23–27. Ensminger, D.C., and D.W. Surry. 2008. Relative ranking of conditions that facilitate innovation implementation in the USA. Australasian Journal of Educational Technology 24 (5): 611–626. http://www.ascilite.org.au/ajet/ajet24/ensminger.html EY. 2019. Can the universities of today lead learning for tomorrow? The University of the Future. Retrieved from https://www.ey.com/Publication/vwLUAssets/ey-university-of-the-future/$File/ ey-university-of-the-future.pdf Farrell. 1999. The development of virtual education: A global perspective. A study of current trends in the virtual delivery of education. Commonwealth of Learning, Vancouver, BC. Felton, P., and L. Lambert. 2020. Relationship-rich education: How human connections drive success in college. Baltimore: Johns Hopkins University Press. Garrison, D. 2017. E-learning in the 21st century: A Community of Inquiry framework for research and practice. 3rd ed. New York: Routledge. Goodfellow, R. 2011. Literacy, literacies and the digital in higher education. Teaching in Higher Education 16 (1): 131–144. https://doi.org/10.1080/13562517.2011.54425. Hasso Plattner Institute of Design. 2019. Uncharted territory: A guide to reimagining Higher Education. Retrieved from https://dschool.stanford.edu/unchartedterritory Jackson, N. 2011. The lifelong and lifewide dimensions of living, learning and developing. In Learning for a complex world: A lifewide concept of learning, education and personal development, ed. N. Jackson, 1–21. Bloomington: Authorhouse. JISC. 2014. Developing digital literacies. Retrieved 1 July 2019 from https://www.jisc.ac.uk/fullguide/developing-digital-literacies Kim, J. 2019. 2020 trends to watch: Higher education technology. Retrieved from https://ovum. informa.com/resources/product-content/2020-trends-to-watch-higher-education-technologyenv006-000075 Lacy, W.B., G. Croucher, A. Brett, and R. Mueller. 2017. Australian Universities at a Crossroads: Insights from their leaders and implications for the Future. Retrieved from https://melbournecshe.unimelb.edu.au/__data/assets/pdf_file/0005/2368895/Australian-Universities-at-aCrossroads.pdf Lockwood, T., and E. Papke. 2018. Innovation by design. Wayne: The Career Press Inc. Loftus, S. 2010. Exploring communities of practice: Landscapes, boundaries and identities. In ed. J. Higgs, I. Goulter, S. Loftus, J. Reid, and F. Trede. Meehan, C., and K. Howells. 2019. In search of the feeling of ‘belonging’ in higher education: undergraduate students transition into higher education. Journal of Further and Higher Education 43 (10): 1376–1390.

2

Aligning the Vision for Technology-Enhanced Learning with a Master Plan,. . .

41

Muldoon, N., B. Tennent, and K. Tickle. 2010, June. Moodle implementation and the RIPPLES model: Reflections on a sustainable approach to technology integration and renewal of educational practice. In EdMedia+ Innovate learning, 345–354. Association for the Advancement of Computing in Education (AACE). OECD. 2019. Trends shaping education 2019. Paris: OECD Publishing. Palaskas, T., and N. Muldoon. 2003. Moving towards increased flexibility in learning and teaching. Excellence: Making the Connections 83–109. Pangrazio, L. 2019. Young people’s literacies in the digital age: Continuities, conflicts and contradictions. Abingdon: Routledge. Peacock, S., and J. Cowan. 2019. Promoting sense of belonging in online learning communities of inquiry in accredited courses. Online Learning Journal 23 (2): 67–81. https://olj. onlinelearningconsortium.org/index.php/olj/article/view/1488. Press, N., P.P. Arumugam, and K. Ashford-Rowe. 2019. Defining digital literacy: A case study of Australian Universities. In ed. Y.W. Chew, K.M. Chan, and A. Alphonso, Personalised learning. Diverse goals. One Heart. ASCILITE 2019 Singapore, 255–263. Rajasingham, L., and J. Tiffin. 2003. The Global Virtual University. 1st ed. Routledge. https://doi. org/10.4324/9780203464670. Rogers, E. M. 2010. Diffusion of innovations. Simon and Schuster. Ryan, S. 2012. The virtual university: The internet and resource-based learning. Abingdon: Routledge. Stolterman, E., and A.C. Fors. 2004. Information technology and the good life. Information Systems Research: Relevant Theory and Informed Practice 687–692. Surry, D. W. and D.C. Ensminger. 2003. Perceived importance of conditions that facilitate implementation. E-Journal of Instructional Science and Technology 6 (1). Surry, D.W., D.C. Ensminger, and M. Haab. 2005. A model for integrating instructional technology into higher education. British Journal of Educational Technology 36 (2): 327–329. Tinto, V. 2017. Through the eyes of students. Journal of College Student Retention: Research, Theory & Practice 19 (3): 254–269. Wenger-Trayner, E., and B. Wenger-Trayner. 2015. Learning in landscapes of practice. In ed. E. Wenger-Trayner, M. Fenton-O’Creevy, S. Hutchinson, C. Kubiak, and B. Wenger-Trayner.

3

Transparency in Governing Technology Enhanced Learning Clive Smallman and Peter Ryan

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Technology Enhanced Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Great Acceleration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Virtual Organization Theories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Theory in Practice: Our Lived Experience of a Virtual HEI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transparency: The Key to Supportive Governance of Technology Enhanced Learning and Virtual Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transparency Defined . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transparency As a Problem and a Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transparency and Institutional Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transparency and Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transparency and a Little Structure Go a Long Way . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Supporting Technology Enhanced Learning in Higher Education in a COVID Normal . . . . . . . Where Are We? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Road Ahead . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion: Governing the Virtual Higher Education Institution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

44 44 46 47 48 49 49 50 51 52 53 53 53 54 55 55

Abstract

In this chapter, we first explore the growth of information and communication technologies in education, promoting technology enhanced learning (TEL). We appraise the impact of COVID-19 on higher education before exploring the concept of a virtual organization and virtual higher education. We discuss our C. Smallman (*) Kingsford Institute of Higher Education, Sydney, NSW, Australia e-mail: [email protected] P. Ryan Consult Ed, Sydney, NSW, Australia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_3

43

44

C. Smallman and P. Ryan

collective lived experience in developing and working in a virtual higher education institution (Virtual University). We explore a crucial concept in the governance of any institution, transparency, before turning to a discussion of higher education in a post-COVID world. We conclude with a clear statement of the central roles of transparency, delegations, policy and process, and formal and informal structures in good governance of virtual and conventional HEIs. Keywords

Technology enhanced learning · Corporate governance · Academic governance · Transparency

Introduction The development of high-quality higher education curricula usually follows the principle of backward design, moving from learning outcomes, through assessment, on to learning activities and then content. Developing supporting governance structures to sustain technology enhanced learning (TEL) follows similar principles, which are familiar to strategists: vision, mission, objectives, strategy, and execution. Governance, the foundation of all effective higher education institutions (HEIs), unifies the two processes. Governance is complicated in higher education, not least since many academics resent oversight by boards of management with directors or trustees who are not necessarily academically qualified. Notwithstanding this, governance in higher education is complicated because HEIs, even small ones, are relatively complicated because of the many “moving parts” comprising them. Perversely, complicated institutions are rendered still more complex by regulators’ requirements to ensure adequate institutional governance. From early 2020 on, the very foundations of conventional higher education came under challenge. The advent of the prolonged COVID-19 pandemic turned on its head a model that primarily relied on delivering education face-to-face. The need to move education online suddenly became essential, requiring the exploitation of TEL, for which many institutions were ill prepared. Governance processes designed for slow and steady monitoring and quality assurance were suddenly required to move quickly to approve a myriad of changes, specifically around the pivot to learning and assessment online. At the time of writing, it is not yet clear what impact this will have on education quality. However, it seems to us that slow and deliberative higher education governance may be constraining innovation in higher education learning and teaching and is therefore under pressure to adapt.

Technology Enhanced Learning The introduction of technology enhanced learning (TEL) began in earnest in the early 2000s. An example of this, Teachers for Teachers for Tertiary (T4T4T), was a pilot web-supported professional development program designed specifically for the

3

Transparency in Governing Technology Enhanced Learning

45

use and benefit of New Zealand tertiary education teachers. One of the present authors was involved in developing this virtual community of inquiry (Wenmoth et al. 2004), where experienced tertiary teachers mentored less experienced colleagues in a virtual environment. This project explored the intersection between emerging pedagogies (Wenger 1998) and cutting-edge technology (the project was an early adopter of what became Moodle). The project developed understanding of the impact of online pedagogies and technology on learning. Among a series of findings from the project, the most pertinent in the context of this chapter is The T4T4T programme was allowed to develop according to input from participants. This democratic philosophy encouraged mentors in particular to be involved in and committed to the project. (Wenmoth et al. 2004, p. 4)

In other words, program participants were consulted on the structure and conduct of the project, contributing directly to the governance of this early virtual higher education teaching community. Further, more experienced and educated participants were able to assure quality in passing on their knowledge of higher education learning and teaching to less experienced colleagues. They were also not above questioning the approach of the project leader and grant holder, effectively holding them accountable, a central tenet of governance. The intervening years saw experiments with Internet technologies and electronic media in learning and teaching processes, student management, and teaching itself. The results of these projects are now standard: web-based lectures and tutorials (online TEL), learning management systems (LMS) supporting on-campus and online TEL, virtual learning environments (VLE), and from 2008 on, massive open online courses (MOOCs) (Kaplan and Haenlein 2016; Köhler et al. 2021, p. 12). Social media, too, has found its way into higher education (Rowan-Kenyon et al. 2016). Digital media in learning and teaching and integrating information and communication technologies (ICT) are now well-established as TEL. However, in on-campus higher education, technology generally remains an adjunct to, or a mediator of, traditional approaches to teaching, that is, the “sage” very definitely remains on the “stage,” a dominant paradigm stretching back to the founding of modern universities. That noted, up to early 2020, not all academic colleagues had enthusiastically adopted these innovations. For some, this was a matter of advancing professional competence. For others, technology was a means of managing the organization of courses in the wake of the dramatic scaling of student numbers in the last decade. However, acceptance of technology in higher education learning and teaching was, in some ways, limited. There was concern that students would lose communication and personal contact with one another and with faculty, as well as a diminution of skills that are commonly associated with on-campus experiences (Köhler et al. 2021, pp. 13–14). There may be some credence in this given the well-documented potential for social impoverishment of “life on the screen” compared with “life in person” (Turkle 1997, 2005, 2015, 2017). Accordingly, before 2020, the pivotal questions were

46

C. Smallman and P. Ryan What will the campus of 2025 look like? Which organisational models of e-learning . . . will prevail? (Köhler et al. 2021, p. 14)

Further, technological advancement in TEL was progressing. Developments in artificial intelligence, machine learning, and data science were transforming learning and teaching. Moreover, colleagues from different fields were “flirting” with technology too. In short, the technology was not “standing still.” Technology was changing higher education, and not just learning and teaching; administration, too, was shifting gears. Then in March 2020, the World Health Organization (WHO) declared a worldwide pandemic.

The Great Acceleration There are decades where nothing happens; and there are weeks where decades happen. –Vladimir Ilyich Lenin

The weeks leading up to and immediately following 11 March 2020, the day on which the World Health Organization declared COVID-19 a pandemic, imposed decades-worth of changes in our lives, business, and education (Galloway 2020, p. xvi). Many of us moved our lives online in weeks, and most business and education went virtual (Galloway 2020, p. xviii). With some worldwide exceptions, many HEIs were passively “tinkering” with virtual offerings. The coronavirus pandemic forced global experimentation with remote teaching. Sadly, however, undergraduate enrollments in the USA dropped from September to November 2020, although online institutions saw an increase (Hanson 2021). In Australia, HEIs suffered hugely from revenue lost from international students prevented from entering the country with little opportunity to make up the revenue gap from the domestic market. At the time of writing, international students are still allowed to study online more than is usually the case and governments worldwide are opening their borders to students and workers desperately needed in key economic sectors. However, enrollments have done little to offset the financial crisis faced by many HEIs and especially public universities with their considerable fixed costs. In this context, Köhler et al.’s (2021, p. 14) questions are reflected in the need to consider the following issues in the context of virtual universities: 1. Technology enhanced learning and teaching quality. 2. The quality and capacity of information and communications technology infrastructure to support TEL. 3. Changing the mindsets and behaviours (in fully accepting TEL) of significant proportions of faculty and students. (Govindarajan and Srivastava 2020)

These fall inarguably under the purview of academic governance. How can we support and assure the quality of TEL through governance, especially in the context of virtual higher education?

3

Transparency in Governing Technology Enhanced Learning

47

Virtual Organization Theories If we take the definition of the Virtual University (and by extension other virtual HEIs) to reflect the following, A higher-education institution, or networks of higher-education institutions, responsible for designing, developing and offering courses and programmes in a flexible online environment. It follows much the same organisational structure as a regular university, except that a sophisticated ICT infrastructure replaces the physical campus (Richards 2015)

Further, it is common for conventional HEIs to have a virtual division or be part of a consortium that delivers TEL online. This definition sits well with the modern organization contingency theory view that organizations, including virtual organizations (VOs), are dynamic, open systems that adapt to changes in their environment (Burns and Stalker 1961; Lawrence and Lorsch 1967; Pennings 1975; Thompson 1967). This is relevant to governance, since regulators require that governing bodies monitor the consequences of adapting to change on the quality of educational programs and the student experience. Morgan (2006) summarizes the principles of contingency theory thus: • Organizations are open systems, needing careful management to satisfy and balance internal needs and to best adapt to environmental circumstances. • There is no one best way of organizing. The appropriate organizational form depends on the kinds of tasks or environments the organization faces. • Management must be concerned, above all else, with achieving strategic alignments and good fits with the tasks or environments. • Different types or specifics of organizations are needed for different kinds of environments. In short, contingency theory promotes organizations that are decentralized and “flat.” How they react to their environment is essential. Burns and Stalker (1961) argue that firms with organic structures are more effective in dynamic economic sectors than those with more mechanistic structures. However, Sine et al. (2006) demonstrate that this does not hold for new ventures in turbulent, emergent, economic sectors. They show that new ventures with higher founding team formalization, specialization, and administrative intensity outperform those with more organic organizational structures. Our experience of VOs certainly accords with that. Postmodern (process) organization theory analyzes organizations and organizational science as processes performed in linguistic and other practices. Process organization theory views organization as a continuous process of articulating and establishing a stable set of relations and meaning structures (Hernes 2008, 2014; Langley 2007; Langley et al. 2013). Process organization theory builds out of the work of: • Whitehead (1929/1978) on process metaphysics • Latour (1987) on relativizing the social and the becoming of networks

48

C. Smallman and P. Ryan

• Luhmann (1978/2018, 1995) on autopoiesis (self-organization) and recursiveness in social systems • March (1988, 1994; March and Simon 1958, 1993) on decision processes and organization • Weick (1993, 1995, 2001) on organizing and sensemaking In process studies of organizing, taking a distinctly temporal view of organizational life shows how actors operate in an ongoing present in which they draw upon their past and project their history as ambitions for the future. Understanding this organizational and individual “becoming” (Tsoukas and Chia 2002) in which technologies, concepts, and social actors take part is crucial for making any type of organizational formation. Thus, a fundamental construct in process studies of organizing is events, which provide force, movement, and continuity to organizational life (Hernes 2014). The process-orientated dynamism of process organization theory is an excellent match with our experience of VOs, especially the Virtual University.

Theory in Practice: Our Lived Experience of a Virtual HEI The authors were fundamental in founding the first 100% virtual HEI in Australia. At the time of writing this chapter, the Higher Education Leadership Institute (HELI) remains as founded, a virtual HEI. However, new ownership will see HELI move to on-campus delivery now that international students are permitted to enter Australia once again. That noted, we predict that HELI will retain its strong commitment to its virtual foundations, not least given that HELI delivers a fully online Master of eLearning. For students who choose to study face-to-face, four units are offered only online. HELI’s governance structure is lean, with a five-person corporate governing body and a seven-person academic board. In both, independent members form the majority. In the latter, a HELI graduate represents the student view, and a senior academic represents the perspective of faculty. The academic board is a subcommittee of the corporate governing body and has delegated responsibility for all matters academic. In addition, course advisory and learning and teaching committees support the academic board. Finally, an executive management committee led by a chief executive officer manages day-to-day operations, with academic leadership provided by an executive dean. Decision-making is overwhelmingly by consensus. That noted, there is always space for mutual, rational, restrained, mildly conflictual debate among colleagues. At HELI, colleagues are geographically distributed across Australia (and sometimes internationally), linked via email, Zoom teleconferencing, and a shared document repository. HELI relies on lateral dynamic relationships to coordinate working from any place. High-quality and frequent communications are essential to operations, and unambiguous delegations and a policy set developed and refined over many years by HELI’s founder (one of the present authors) and colleagues. HELI’s

3

Transparency in Governing Technology Enhanced Learning

49

organization design is simple. HELI places value on a balance of experience and skill in its academics and professional staff. HELI’s online students are geographically dispersed (e.g., one graduate studied from Dubai, UAE). Looking back at the discussion of organization theory, we see reflections in HELI’s story thus far. The pandemic has proven the need for contingency theory in organizations. From a postmodern or process organization theory perspective, HELI’s day-to-day operations are certainly informed by collective experiences of the past. This enables an essentially smooth organizational life, which is not to say that HELI does not have its collective moments of truth, as is the case for individual staff. What is readily evident is a constant interrogation of process and policy by colleagues across the years, seeking incremental and stepwise improvement in policy and procedure to secure a more polished future. Like so many HEIs, HELI is event-driven by meetings of governance bodies, by academic timetables, by regulatory schedules, and not least by international, national, and local events. HELI’s familiarity and comfort in working virtually paid dividends as most of Australia lurched into lockdown caused by a surge in COVID-19 cases coupled with poor federal management of the pandemic response. In summary, over the few years of HELI’s existence, meetings of each governance body could be held physically, virtually, and mixed-mode (some participants around a board table, the rest on teleconference). The physical company of colleagues is always enjoyable, even when discussions tend to be challenging; however, purely virtual meetings are equally functional (HELI’s course advisory and academic quality committees have never met in person). Based on our experience, transparency, coupled with a well-specified governance framework, clearly defined delegations, good yet continually improving policy and procedures, and effective meeting chairs are the key to governance in general and the supportive governance of TEL in the context of virtual higher education in particular.

Transparency: The Key to Supportive Governance of Technology Enhanced Learning and Virtual Institutions Transparency Defined Transparency is one of the core principles of good corporate governance. In 1997, the United Nations Development Program (UNDP 1997) set out the principles of good governance that, with slight variations, appear in much of the literature and have a universal recognition (Graham et al. 2003). One of these central principles is transparency built on the free flow of information so that stakeholders have the necessary information to understand and monitor the organizations that they are concerned with (UNDP 1997). Transparency is demonstrated as a willingness by an organization to provide clear information to its stakeholders (AICD 2019); this includes owners (shareholders, trustees, or governments) and other stakeholders, such as students and staff in HEIs.

50

C. Smallman and P. Ryan

Transparency has many facets (Fitriani and Muljono 2019), including: • • • • •

A willingness to disclose financial performance data that are truthful and accurate Openness about the management processes of an organization Clarity around decision-making processes What an organization plans to do in the future The possible risks it may face

Transparency ensures that enough information is provided in easily understandable forms through various media so that stakeholders can have confidence in the decision-making and management processes of an organization.

Transparency As a Problem and a Solution Transparency applies throughout an organization; however, the culture of transparency is championed, established, and nurtured from the top by the corporate governing body (or statutory governing body) of the organization to filter down through every part of the organization. Therefore, decisions taken from the top-down follow explicit rather than implicit rules and procedures. As a result, information is freely available and directly accessible to those affected by such decisions. While transparency must be consistently demonstrated and reinforced by leadership, there is the need to balance what is shared with stakeholders. Transparency must not be mistaken for sharing every detail about an organization. Therefore, it is essential to recognize that some information simply will not add value to stakeholders. In an HEI, rules-based decision-making is apparent when policies affecting students, such as admission, granting of credit, progression, and exclusion, are publicly available, and the rules that govern these processes are clearly set down. Failure to follow those rules when making a decision may result in a student, or potential student, challenging that decision. So that transparency works for both the institution and the student, the rules must be written unambiguously so that the rules are fair to all, and consistent decisions are made based on the evidence provided. If not, appeals to review those decisions are likely to proliferate. However, regulations-based decision-making leaves little room for discretion in individual cases. As the rules are designed for universal application, they do not always translate well to an individual’s particular circumstances. As the rules must be applied consistently and reasonably, there may be little or no opportunity to make a decision that accounts for an individual’s circumstances if those circumstances do not fit neatly into the universal paradigm. This, in turn, may entrench socioeconomic disadvantage; however, this can be alleviated with specific programs to address disadvantage, but again, the rules of access must be clear and applied consistently. As education is seen as one of the key catalysts to break the cycle of disadvantage, special consideration for first nations people and scholarships specifically designed for students from low socioeconomic backgrounds are often provided by HEIs.

3

Transparency in Governing Technology Enhanced Learning

51

Transparency of financial performance data to a broad range of stakeholders, including governments, owners, students, and staff, is typical in a public institution or listed company but not common in private enterprises. Published financial data may reassure stakeholders that an institution is financially strong and viable for the longer term. For example, staff may feel more secure in their employment, students may be reassured that their course will be delivered in full, and owners will be relieved that they will not be required to provide further capital. Alternatively, the financial data may show that an institution is in poor financial health, which may negatively impact staff considering leaving, students deciding not to enroll or transfer to other institutions, negative regulatory consequences, or even public humiliation. Transparency can be a double-edged sword, balancing the need for what stakeholders would like to see against the information they are prepared to disclose, especially in individual data collection (Hood 2007). For example, an institution’s staff would expect that their academic profiles, qualifications, and scholarship would be publicly available, but not their performance appraisals, pay rate, or disciplinary matters. Likewise, students would not be comfortable with having their results or personal details made available publicly. For example, displaying personally identifiable information publicly (for example, on a website) may provide a combination of what might otherwise appear as innocuous data to provide a basis for identity theft. Therefore, the right balance must be struck between the conflicting principles of organizational transparency and an individual’s right to privacy.

Transparency and Institutional Behavior According to Jeremy Bentham (1748–1832), the more closely we are watched, the better we behave. However, transparency is not a panacea for wrong-doing or harmful behavior. On the contrary, it may create behavioral problems in an organization, such as creating a blaming culture, increasing distrust, increasing cheating, and sparking resistance (De Cremer 2016). Blame avoidance is often said to underlie much institutional behavior in practice (Hood 2007) as the principle of transparency as part of good governance collides with the propensity of human nature for blame avoidance and negativity bias. This may manifest itself in an institution-wide imperative to avoid risk, which may, in turn, stifle innovation and creativity as many people are most creative when they are not being observed or monitored. Organizations may be prepared to trade off good outcomes to limit negative ones. Individuals may tend to avoid blame for adverse effects rather than claim the credit for good ones. The concept that too much transparency can increase distrust or breed suspicion at first appears paradoxical (De Cremer 2016). However, the constant monitoring of individuals and processes in the name of transparency can sometimes create a feeling of mistrust. The open sharing of information on individual performance and pay levels often invoked to promote trust and collective responsibility can backfire as staff earnings is a highly controversial dimension of transparency (Birkinshaw and

52

C. Smallman and P. Ryan

Cable 2017). According to Hofmann and Strobel (2020), transparency of administrative processes and performance in teaching and research may not only increase staff satisfaction with their working environment, but conversely it may also increase their intent to leave. While the availability of information through transparency may facilitate holacracy through decentralized management and decision-making, it also dilutes the power of leadership. Rather than enhancing the decision-making process, it may lead to endless meetings and a vague decision-making authority where important decisions become hamstrung and delayed.

Transparency and Technology The ongoing digital transformation of organizations allows digital technologies to provide the means to track organizational processes and performance in real time so that there is increasing availability and visibility of performance data to both internal and external stakeholders (Hofmann and Strobel 2020). One of the first uses of technology to provide information to the public was through websites published on the worldwide web. HEIs offered a conduit for the instant sharing of crucial information with students and prospective students, notably the policies and course information relevant to their admission and their ongoing participation in the study. From the point of view of immediacy, this was a significant improvement in the provision of preenrollment information through the postal service. Constantly improving and complex technology has allowed virtually all interactions with students and prospective students to occur electronically and in real time. However, the use of technology is no guarantee that electronic communications have been received, with the term “lost in the mail” being replaced with “lost in the ether or the cloud” or “my internet connection is not so good.” Increased technology may be a catalyst for increased negative bias as the ramifications of adverse events and stories travel wide and fast in a digital world. Social media provides a powerful platform for people to raise concerns about an organization that might otherwise be hidden. However, because of the limited curation of social media platforms, the truth of some of the issues raised and claims made may remain unchallenged. This has led to organizations carefully “listening” to social media to determine what is being said about a brand, individual, or product through different social and online channels. Negative comments are analyzed, and, where found valid, corrective action can be taken. Alternatively, where statements are believed to be false or unfounded, organizations can put the other side of the case to balance the communication and ameliorate any negative impact. As described earlier, transparency does not mean that all information is available to all stakeholders. Certain information is limited to specific groups as dissemination to a broader population is not deemed to be in either the public or the organization’s interest. However, the increased use of online technologies provides a conduit for intrusion into institutional systems by hackers exposing private information to the

3

Transparency in Governing Technology Enhanced Learning

53

public. Not only are there regulatory and legal reasons to protect an institution’s data proactively, but an institution’s reputation also depends on it.

Transparency and a Little Structure Go a Long Way Transparency (allowing for our previously noted reservations) and a little structure go a long way to assuring quality; this is no more so than in the spatially dispersed and isolated world of virtual higher education.

Supporting Technology Enhanced Learning in Higher Education in a COVID Normal Where Are We? Based on our observations of the sector drawn from conversations we have had in international webinars on TEL before and during the pandemic, it seems to us that many institutions are at the “end of the beginning.” They have made a start but are unsure of where to go, and investment seems limited. This is where supportive governance (institutional and academic) must step in, providing leadership based on an understanding of what TEL, both online and on-campus, requires. Crucially, we must be better able to demonstrate the return on investment in TEL in business cases that match the quality of those that support massive investment in buildings. Furthermore, the principle of accountability in governance means that academic boards must demand that the learning analytics TEL renders more available are utilized as a means of assuring quality of learning. Then there is the question of changing mindsets and behaviors. It is fair to say that many HEIs of all types have a poor record of investing in the professional development of academics (e.g., a conference here, a training course on data analysis there). Most academics seem happy. Yet, it seems to us that HEIs worldwide continue to dodge an inconvenient truth: that most academic staff are not trained teachers, and certainly not trained to teach utilizing TEL. The qualification for an academic role in most institutions is a doctorate in the broad discipline that an academic teaches. To our knowledge, no institution demands their academics be trained teachers (except for education faculties). Most seem satisfied with a short course on fundamentals of university teaching. Instead of educating academics to teach using TEL, institutions instead employ instructional design teams, most of which are inadequately staffed for the volume of work they face (especially now). The challenge we see is that institutions need both: academics who are properly trained TEL teachers supported by instructional design teams. It seems that some institutions willingly pour billions into buildings and regulators demand bureaucratic quality inspection. Both seem to be missing the point: The future of higher education is aligned to TEL, and few academics are properly equipped

54

C. Smallman and P. Ryan

for this. Moreover, if they were so equipped, quality would be better assured, mindsets would change, and so would behaviors. Where academics go, so too do students. Where might this start from? Institutional and academic governance systems should insist on and support these requirements. Comparing the return on capital investment against investment in people and TEL systems is the starting point. The regulator has a role to: insist that academics be properly trained in teaching and TEL. So, are we all doomed? Thomas Frey, an eminent futurist at the Da Vinci Institute, has been predicting the demise of many lesser conventional universities for years now. He has consistently forecast a massive move online for all but the most elite (Weller 2016). Galloway (2020, p. 141) agrees, predicting a “death march” of HEIs: A semester of online education and reduced attendance will kill hundreds of schools. A year [or more] without the in-person experience, and the pricing power it brings, could drive 10 to 30% of universities out of existence

We do not share their grim forecasts but only on the proviso that HEIs, regulators, and members of institutional and academic governance bodies push for change to advance the adoption of TEL and properly educate and train the academics using it.

The Road Ahead A profound change in higher education has long been predicted (Christensen et al. 2011a, b). COVID-19 has accelerated the initiation of change. Technology sits at the heart of the developing transformation. Until recently, faculty and management have resisted this. The truth is that only a few in the sector are learning rapidly and are positioned well to accelerate the adoption and diffusion of learning technologies (Galloway 2020, pp. 142–143). Most try to replicate the in-class experience, failing to engage with students and losing them in the process. HELI’s experience is that engagement both synchronous and asynchronous is key, built on carefully considered design and development that exploits TEL delivered by first-class teachers, supported by transparent academic governance. Current learning management systems are crude with reference to other technologies. They are little more than indexed repositories for information of different types. They are brought to life by the careful curation of educators. A massive technology change is not far off as venture capital deployed into higher education takes root and flourishes (Galloway 2020, pp. 142–143). It is not simply the educational functionality and flexibility of technology that will accelerate its uptake. When properly implemented, it delivers to HEIs an opportunity to scale that those outside of the elite crave, and Tech creates scale, and scale increases both access (social good) and revenue (necessary fuel). (Galloway 2020, p. 145)

The best time to start thinking about the move to virtual higher education was 20 years ago. The next best time is now. Now is the time to think about how the

3

Transparency in Governing Technology Enhanced Learning

55

campus is utilized, how we can best integrate TEL into the virtual higher education toolbox, and how we can use it to expand the notion of the higher education experience. Academic governance must assuredly support technology enhanced learning and teaching quality and the quality and capacity of information and communications technology infrastructure to support TEL. Further, academic leaders must consciously lead a collective movement toward changing the mindsets and behaviors (in fully accepting TEL) of significant proportions of faculty and students. The starting point for that is requiring that faculty properly train as teachers in the context of TEL.

Conclusion: Governing the Virtual Higher Education Institution We find ourselves looking at a paradox between virtual organization theory and the practice of governance. Virtual organization theory calls for transparency and flexibility enabled by technology. The practice of governance cries “caution,” especially around the release of information. As we scale, challenges will arise. As we have indicated previously, our answer to these challenges remains controlled transparency, well-defined governance frameworks, and clear delegations, coupled with good policy and productive processes in a formal structure that simultaneously operates informally. The quality of people leading governance also matter enormously.

References AICD. 2019. Not-for-profit governance principles. Australian Institute of Company Directors. Birkinshaw, J., and D. Cable. 2017. The dark side of transparency. McKinsey Quarterly 1–7. Burns, T., and G.M. Stalker. 1961. The management of innovation. Tavistock. Christensen, C.M., M.B. Horn, L. Caldera, and L. Soares. 2011a. Disrupting college. How disruptive innovation can deliver quality and affordability to postsecondary education. Centre for American Progress. Christensen, C.M., M.B. Horn, and W. Johnson. 2011b. Disrupting class: How disruptive innovation will change the way the world learns. 2nd (Kindle) ed. McGraw Hill. De Cremer, D. 2016. When transparency backfires, and how to prevent it. Harvard Business Review, Digital article. https://hbr.org/2016/07/when-transparency-backfires-and-how-toprevent-it Fitriani, S., and H. Muljono. 2019. Beyond good governance: An ultimate key success for higher education quality. Journal of Education, Teaching, and Learning 4 (1): 210–216. https://doi.org/ 10.26737/jetl.v4i1.321. Galloway, S. 2020. Post corona: From crisis to opportunity. Bantam Press. Govindarajan, V., and A. Srivastava. 2020. What the shift to virtual learning could mean for the future of higher ed. Harvard Business Review. Govindarajan and Srivastava is an electronic article from the HBD log dated March 31 2020. Graham, J., A. Bruce, and T. Plumptre. 2003. Principles for good governance in the 21st century. Vol. 15. Institute On Governance. Hanson, M. 2021. Online education statistics. www.education.org, accessed 19 July 2021.

56

C. Smallman and P. Ryan

Hernes, T. 2008. Understanding organisation as process: Theory for a tangled world. Routledge. ———. 2014. A process theory of organisation. Oxford University Press. Hofmann, Y.E., and M. Strobel. 2020. Transparency goes a long way: Information transparency and its effect on job satisfaction and turnover intentions of the professoriate. Journal of Business Economics 90 (5–6): 713–732. https://doi.org/10.1007/s11573-020-00984-0. Hood, C. 2007. What happens when transparency meets blame-avoidance? Public Management Review 9 (2): 191–210. https://doi.org/10.1080/14719030701340275. Kaplan, A.M., and M. Haenlein. 2016. Higher education and the digital revolution: About MOOCs, SPOCs, social media, and the Cookie Monster. Business Horizons 59 (4): 441–450. Köhler, T., C. Lattemann, and J. Neumann. 2021. Organising academia online. In E-science: Open, social and virtual technology for research collaboration, ed. C. Koschtial, T. Köhler, and C. Felden, 11–28. Springer International Publishing. https://doi.org/10.1007/978-3-03066262-2_2. Langley, A. 2007. Process thinking in strategic organisation. Strategic Organisation 5 (3): 271–282. Langley, A., A.H. Van de Ven, C. Smallman, and H. Tsoukas. 2013. Process studies of change in organisation and management: Unveiling temporality, activity and flow. Academy of Management Journal 56 (2): 1–13. Latour, B. 1987. Science in action. Harvard University Press. Lawrence, P.R., and J.W. Lorsch. 1967. Organisation and environment: Managing differentiation and integration. Harvard University Press. Luhmann, N. 1978/2018. Organisation and decision. Cambridge University Press. ———. 1995. Social systems. Stanford University Press. March, J.G. 1988. Decisions and organisations. Basil Blackwell. ———. 1994. A primer on decision making: How decisions happen. Free Press. March, J.G., and H.A. Simon. 1958. Organisations. Wiley. ———. 1993. Organisations. 2nd ed. Blackwell. Morgan, G. 2006. Images of organisation. 2nd ed. Sage Publications. Pennings, J.M. 1975. The relevance of the structural-contingency model for organisational effectiveness. Administrative Science Quarterly 20 (3): 393–410. Richards, G. 2015. A guide to virtual universities for policy-makers. The Commonwealth of Learning. Available from: http://oasis.col.org/handle/11599/1723 Rowan-Kenyon, H.T., A.M. Martínez Alemán, K. Gin, B. Blakeley, A. Gismondi, J. Lewis, A. McCready, D. Zepp, and S. Knight. 2016. Social media in higher education. ASHE Higher Education Report 42 (5): 7–128. https://doi.org/10.1002/aehe.20103. Sine, W.D., M. Hitoshi, and D. Kirsch. 2006. Revisiting Burns and Stalker: Formal structure and new venture performance in emerging economic sectors. Academy of Management Journal 49: 121–132. Thompson, J.D. 1967. Organisations in action: Social science bases of administrative theory. McGraw-Hill. Tsoukas, H., and R. Chia. 2002. On organisational becoming – Rethinking organisational change. Organisation Science 13: 567–582. Turkle, S. 1997. Life on the screen: Identity in the age of the internet. Simon & Shuster. ———. 2005. The second self: Computers and the human Spirit. 20th Anniversary ed. MIT Press. ———. 2015. Reclaiming conversation: The power of talk in a digital age. Penguin. ———. 2017. Alone together: Why we expect more from technology and less from each other. 3rd ed. Basic Books. UNDP. 1997. Governance for sustainable human development–A UNDP policy document. United Nations Development Programme. Weick, K.E. 1993. Sensemaking in organisations: Small structures with large consequences. In Social psychology in organisations: Advances in theory and research, ed. J.K. Murnigham. Prentice-Hall. ———. 1995. Sensemaking in organisations. Sage Publications. ———. 2001. Making sense of the organisation. Blackwell.

3

Transparency in Governing Technology Enhanced Learning

57

Weller, C. 2016. A top futurist predicts the largest internet company of 2030 will be an online school. Business Insider Australia. Wenger, E. 1998. Communities of practice: Learning, meaning, and identity. Cambridge University Press. Wenmoth, D., H. Gilmore, and A. Trewern 2004. Towards a professional community of inquiry. A report to the Ministry of Education on the T4T4T pilot. Core Education Limited. Whitehead, A.N. 1929/1978. Process and reality. Cambridge University Press.

4

Laying and Maintaining the Foundations for Quality Stephen Marshall

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Framing Conceptions of Quality for Virtual Universities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sense-Making Quality for Virtual Universities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

59 62 65 71 72 73 73

Abstract

The virtual university is a social construct that is placed very firmly within a very real place in political, legal and economic ways that need to be carefully and thoughtfully incorporated into the quality systems used to improve the university and as importantly, communicate key qualities to external audiences and stakeholders. This chapter provides a conceptual model and a high-level reference point for leaders reflecting on the quality framework of the virtual university and the establishment of strong organizational foundations for improvement activities through a sense-making approach driven by powerful conversations with stakeholders. Keywords

Quality · Sense-making · Improvement · Stakeholders

S. Marshall (*) Centre for Academic Development, Victoria University of Wellington, Wellington, New Zealand e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_4

59

60

S. Marshall

Introduction Quality systems are powerful tools for shaping organizations and influencing priorities, processes and systems. They do this explicitly and implicitly by defining and influencing perceptions of success used by key participants and stakeholders in the organizations’ activities. The challenge for a virtual university is using quality conceptions and activities in ways that are responsive to the university’s needs, its dynamic environment and the rapid changes wrought by technology and human expectations (Marshall 2018). Quality in various forms has increasingly dominated thinking about universities since the late twentieth century. As will be seen later in ▶ Chap. 30, “The Role of Standards and Benchmarking in Technology-Enhanced Learning,” of this book, a halo of quality concepts and affordances such as performance indicators, analytics, standards and benchmarks are now normal features of any university. The widely repeated management truism “what gets measured, gets managed” is apparent in many uses of this data which ascribe organizational significance and meaning without always considering the underlying intent or assumptions that constrain or shape the messages that data convey. Within the technology-enhanced learning sphere, quality is often enacted defensively or in response to political and social factors that frame technology as a negative influence on the university experience (Noble 2002; Selwyn 2014). For many years, the “no significant difference” phenomenon was a focus of research and evaluation (Ramage 2002), with many practitioners and academics working to prove that the use of various technologies was not harming outcomes. Here the difference being considered was whether outcomes were worse when technological modes were introduced. Quality in this historical space was very much defined in instrumental terms focused on assessment activities which remained unchanged despite the opportunities offered by increasingly sophisticated digital tools. Similarly, there is a long history of academic concern with the impact that technology might have on the relationship between the academic and their students, and on academic freedoms, autonomy and authority in teaching. Historian David Noble was a noted early critic of the disruption technology threatened to cause the contemporary university (Noble 2002), but there have been many critics over the last decades who have focused on a perceived loss of the qualities of university education as described more than 150 years ago by Cardinal Newman (1853/1976). Rather than seeing an opportunity for the university to enact itself positively in new ways that respond to the complex and evolving needs of modern society, these critics see a destructive change impacting upon the élite character of the university that they conflate with its identity, “ruining” it and leaving the university “embattled,” “corrupt” and “adrift” (Bailey and Freedman 2011; Collini 2017; Hayes 2017; Holmwood 2011; Newfield 2016; Readings 1996). Examination of quality is often used as a proxy for political and economic interests in the place universities occupy in society. This is particularly apparent when considering public funding models and the growing importance placed on qualifications as enablers for economic growth and development, sometimes

4

Laying and Maintaining the Foundations for Quality

61

erroneously described as innovation. Universities are an important economic and social enabler of global commerce. The degree is widely regarded as facilitating and legitimizing a global market for talent. The European Bologna activities explicitly recognize the value of agreed system qualities and features that enable movement of people between countries and that ensure consistent expectations of quality are met when comparing qualifications from different national systems (Adelman 2009). Within national systems the history of large-scale virtual provision has not been one of success (Marshall 2018). Many initiatives have failed as a result of mistaking traditional reputational qualities as indicators of capacity to engage in new modes of delivery, particularly when seeing technology solely as a utilitarian means of extending provision geographically (Gilbert 2001; House of Commons 2005). The collapse or disruption of historically successful models such as that of the Open University (UK) and the University of Phoenix Online (Apollo Education Group 2016) points to the challenge that qualities are politically, socially and economically contextualized in complex and often unacknowledged ways that can lead to failure when translated into new markets or as a result of the changes wrought through success over time. A detailed analysis of the failures of the previous incarnation of the virtual university concept in the late twentieth and early twenty-first centuries, contrasted with the experience of the Massive Open Online Course (MOOC) over the last decade, has identified a number of features that contribute to failures (Marshall 2018, p. 184): • the importance of timing and the need to balance a disposition to urgency with strong systems and clear goals; • the importance of context and the need to recognize the complexity of education in different cultures; • the role qualifications systems play in influencing student perceptions and expectations; • the place technology plays as an enabler but not a driver of strategy; • the challenge of sustaining ongoing investment needed for significant change; • the complexity of collaborations and the need to recognize and effectively manage different agendas; • the value and limitations of reputation and branding; and • the overwhelming importance of leadership, vision and effective strategy as tools for sustaining any change in the face of inevitable challenges. Quality improvement systems in virtual universities need to respond to these features of the university environment as they enable change and development. Unlike the traditionally established university, a virtual university cannot by definition define itself by its history and physical presence, as a newly emerging model it must shape and influence its environment by addressing these potential failure points explicitly. A strategy driven by the desire to the first mover in a space is one that accepts the proposition that higher education is subject to network effects that privilege early

62

S. Marshall

success and the resources of large businesses in adjacent markets (such as Google and Microsoft). This disregards the importance that the social context plays in education while also assuming that technological developments are defining and driving educational change, rather than the qualification systems and regulatory bureaucracy that is for many stakeholders the definitive feature of higher education. The scale of investment and the enormous cost of creating a virtual university and sustaining its initial years of operation are perhaps the biggest problem to be solved. Shortcuts such as working in collaborative arrangements with other universities or commercial partners have a poor history of success and often introduce further complexity and costs. Similarly, the value of an established brand is often overestimated with reputation often failing to translate effectively in other global contexts. These can be overcome, but doing so requires a strong leadership able to weather short-term disruption and sustain a strategy even as necessary adaptations are made to a dynamic context. An important tool for such leaders is how the quality conversation is framed.

Framing Conceptions of Quality for Virtual Universities Inherent in the concept of the virtual university is the necessity to be shaped by and to shape perceptions of what a successful university is for a wider range of stakeholders than traditionally conceived universities. For a quality system to impact positively on the virtual university it needs to shape aspirations, guide and support rapidly changing activities and systems, and regenerate evidence of outcomes that both meet and stimulate expectations for what such an organization can achieve for individuals and societies. Beyond the aging concepts of continuous improvement, quality for a virtual university needs to lay a foundation that establishes, communicates and enhances its evolving identity. Success in so doing gives a virtual organization authority, confidence, coherence and social consequence commensurate with the place of the university in society. Blanco-Ramírez and Berger (2014) provide a useful framework for an analysis of the challenges facing a holistically conceived virtual university focusing on the bureaucratic, political, symbolic, systemic and collegial dimensions of the context it operates within (Table 1). Bureaucratic aspects reflect the structural and formal elements of the virtual university and the relationships it has with other organizations in the contexts it operates within. An obvious feature is the regulatory environment imposed by the governments of the countries that the virtual university operates within (James et al. 2011). These include legal restrictions on providers of education targeting nationals, requirements and limitations for course content, licensing or regulation of educators, and requirements for accreditation that affect the value of the resulting credentials or other outcomes for learners. As is increasingly apparent with the creation of China-specific MOOC providers (Rocheleau 2013; Shah 2016) and limitations on the operations of other internet businesses, virtuality does not transcend the legal requirements of different countries

4

Laying and Maintaining the Foundations for Quality

63

Table 1 Questions testing holistic quality dimensions reframed for a virtual university. (Modified from Blanco-Ramírez and Berger 2014, p. 97) Bureaucratic Political

Symbolic

Systemic Collegial

What are the formal structures and regulations that guide the pursuit of quality for a virtual university? What interests are served by varying approaches to and definitions of quality by the virtual university? What power bases are being used to further which agendas for the virtual university and its activities? What meanings and values are associated with different approaches to quality and how are those transmitted through symbolic norms and representations in the activities, processes, systems and values enacted by the virtual university? What broader forces beyond the virtual university influence the construction, delivery and assessment of quality within and about its activities? Who is involved and what voices are invited to engage in the important peerreview processes used in determining quality at the ground level of virtual university activity?

(Lessig 2006). Even within a legally harmonious environment such as the European Union, bureaucratic systems present challenges for educational activities across multiple countries. Allied to these legal constraints are the factors that are reflected by the political dimension. This recognizes the implications of stakeholder contention and the consequences these have on access to resources and influence that affect the impact of the quality system (Chalmers 2007). Transnational provision is increasingly subject to political scrutiny as concerns about the social consequences of learner mobility are raised by some (Fischer 2014; State Council 2017). Similarly, others perceive a neo-colonialist attitude in the activities of global educators (Altbach 2013; Barlow 2014; Sharma 2013). The interaction of bureaucratic regulatory elements with the political dimension of quality is readily apparent in the European Bologna process where highly subjective and politicized concepts such as trust and integrity play an important role: The principal reason [quality assurance] assumed a large profile in Bologna was to establish full trust across borders. It is assumed that if you and I, from different countries, use roughly the same public procedures and criteria to officially warranty that our institutions of higher education do what they are supposed to do and have the organization and means to continue doing it, then we trust that the credentials awarded by those institutions have integrity. And when we focus on academic programs within institutions, we offer the same warranties. With trust and integrity comes recognition. (Adelman 2009, p. 105)

The symbolic dimension of the virtual university is perhaps the most complex and potentially the most useful when considering what the foundations of quality for an as-yet unrealized new organizational entity. Organizational culture, symbols, rituals and metaphors are tools that help balance technocratic rationality with a humanistic framework that can resolve ambiguity and reduce conflict in organizational activities. The symbolic dimension reflects what Collins and Porras (1996, p. 68) call the organizational purpose. They state an “effective purpose reflects people’s idealistic motivations . . . it captures the soul of the organization.”

64

S. Marshall

At its most simple level, the purpose of the virtual university is to educate people and to recognize their achievement through qualifications. The symbolic nature of qualifications as social signals of worth is a major factor influencing the perception of quality. A major use of qualifications is as signals to others of the relative desirability of a potential employee (Spence 1973; Hussey 2012). This signaling arises from the “positional” nature of educational qualifications (Hirsch 1976; Brown 2003; Leney 2009), which itself reflects an inherent tension in the purposes universities serve within societies. Operating without the crutch of a physical campus or the historical legacy of shared experiences means that a virtual university must articulate for itself a clear and coherent set of purposes and values if it is to act effectively across a diverse range of contexts. The conception of quality and the way that the virtual university leadership uses this to strengthen the shared sense of purpose and values underpinning the virtual university are critical to its success over time (Clark 2004). Failure to manage the symbolic qualities of the virtual university can create what Alvesson and Berg call “symbolic pollution” (Alvesson and Berg 1992), where the social capital and societal trust in the institution of the university are betrayed by a replacement of deeper meaning and organizational integrity by a superficial shell of grandiosity and marketing, a university defined by a brand, rather than by the scholarly community and impact it has on society (Alvesson 2013; Bok 2013). A virtual university providing a holistic experience in multiple contexts will inevitably encounter diverse and conflicting stakeholder interests that have very little to do with their educational objectives but everything to do with the value education represents to societies (Harvey and Green 1993; Newton 2010). This wider context of higher education drives the systemic dimension of quality and the recognition of the importance of context. Systemic elements of a quality framework respond to the placement of the university within an interconnected web of societal elements including other educational providers, employers, regulators and communities that impose a physical reality upon learners that a virtual university cannot ignore. The systemic placement of the virtual university can also be seen as an opportunity to use virtuality as a mechanism that enables the university to be embedded within other parts of a society. This can be seen conceptually as the inverse of Clark Kerr’s Multiversity (Kerr 1963), with the virtual university enacting itself in a multifaceted way that enables a dispersed but coherent engagement in multiple contexts, rather than re-creating elements of those contexts within itself as disconnected shadows of the real world. The lack of a physical campus removes one of the historical legacies of the university as a guild or college where the academic faculty could seek a place of common interests and assert their rights to challenge orthodoxy and power using the protections of academic freedom and tenure safely within the protections of the campus. The collegial dimension reflects those elements of quality that speak to the collective scholarly ownership of the university. If virtuality refers to the physical embodiments of the university, then the collegial dimension speaks to the elements that sustain an identity that is truly that of a university, and not merely an educational factory.

4

Laying and Maintaining the Foundations for Quality

65

The tensions and contested nature of collegial qualities are at the heart of the academic concerns noted above with Noble, Readings, Collini and others identifying aspects of the university that have defined that identity, and which need to be embodied in new ways through the systems and activities of the virtual university. The first generation of virtual universities mistakenly adopted a technocratic view of the university that saw it conceived primarily as a form of educational media platform delivering content packed in qualification products for a mass market (Cunningham et al. 2000). More recently the MOOC has acted as form of educational strip-mining extracting material from universities without investing in sustaining that resource. The collegial dimension reflects the need for the virtual university to enact structures and systems that sustain and strengthen scholarship in ways that avoid the mistakes apparent in previous incarnations of the virtual university.

Sense-Making Quality for Virtual Universities The contextual challenges noted above combine with the traditional elements of educational quality, and the need to use quality as a tool for future-oriented change, to create a wicked problem that resists traditional quality conceptions. Established quality approaches (Harvey and Green 1993) have as a common assumption the operation of an organization in a space where success is not ambiguous and where a deterministic relationship between systems and outcomes operates. The future orientation of the virtual university and the need to discover new mode of operation and to redefine the scope and ambition of success argue against the application of quality tools that depend on pre-defined measures of performance, such as Six Sigma (Antony et al. 2012), stability of organizational process, such as Total Quality Management (Quinn et al. 2009; Rosa and Amaral 2007), or defined educational outcomes, such as graduate attributes or competencies (Arum et al. 2016; Bond et al. 2017). Instead, the dynamic nature of the virtual university and the need for responsiveness in its systems and outcomes argue for a quality model closer to that of a conversation (Bergquist 1995). Organizational conversations are central to the concept of sense-making (Weick 1995; Weick 2000) where engagement with new ideas by individuals generates understanding and meaning for the organization as a whole. Sense-making is a “process by which complex situations are explored, tested, understood and meaning developed” (Marshall 2018, p. 9) and is defined by Weick (1995, p. 17) as: • • • • • • •

social in nature; grounded in identity construction; retrospective; enactive of sensible environments; ongoing; focused on and by extracted cues; and driven by plausibility rather than accuracy.

66

S. Marshall

Using this approach, leaders can engage with an organization through a social process that encourages stakeholders to actively explore their identity and goals within the shared experience of the organization’s activities. This process actively encourages ongoing experimentation and analysis to identify plausible options for change and to generate the cues, or triggers, that stimulate and validate the change agenda. Quality as sense-making is a model that is designed to address wicked organization problems that resist structured quality frameworks (Marshall 2016). In this conception, change is supported by the recognition that education is too complex and too important to be defined by a small number of qualities relevant to a privileged group of stakeholders or by limited performance indicators such as financial efficiency; instead, it is experienced through an ongoing process that challenges complacency and the status quo. The focus of quality thus shifts away from specific products or artifacts such as strategies, plans or scenarios to questions that enable an ongoing collective organizational engagement with change and improvement. A key concept within sensemaking is that of the cue, “simple, familiar structure that are seeds from which people develop a larger sense of what may be occurring” (Weick 1995, p. 50). Cues are generated through engagement with qualities that can be seen in new ways either as a result of new technologies, different perspectives or a compelling vision of change. The challenge is using cues to act in new ways, resisting the natural tendency to retain existing conceptions, even in the face of substantial evidence, that these older approaches are failing to cope with the new environment. A common misconception in the application of technology and associated models, such as the virtual university, to an organizational space is that the model will result in changed practice in a deterministic and predictable way (Pegrum 2009). This sense of technocratic predestination is not consistent with the reality of change despite tools such as Gartner hype cycle implying otherwise (Gartner n.d.). This perception is reinforced by the reality that in many cases a new technology is reframed in ways that reinforce established patterns of behavior and work, losing the features or use that disrupt existing behaviors. An illustration of this can be seen in the evolution of audio-visual technologies, which in practice have resulted in very little change in the experience of lectures other than in the quality and attractiveness of the media used. Table 2 illustrates how the concept of sense-making can be translated into a quality framework for virtual universities that is based on a series of questions intended to generate cues for reflection and improvement which act against this technocratic complacency (Marshall 2016). This framework uses a matrix of perspectives (individual learner, virtual university, other universities, stakeholders, national system) to examine quality as measured by context, inputs, processes, outputs, and feedback (Van Damme 2004). Table 2 was developed by first framing the analysis from the perspectives of the virtual university, the learner and the national system of higher education they operate within. These perspectives and the interfaces between them are the space where sense-making can occur as activities generate cues. The process of sense-

Input What is the learner bringing to the educational process that is enabling their successful engagement? What is the virtual university doing to guide learners into appropriate programs of study? What defines the contribution the virtual university makes to the learner population being served? What resources are being invested by the virtual university into educational activities?

What is the virtual university doing to understand the context of the individual learner?

What is the virtual university doing to understand its strengths, weaknesses, and values?

Virtual university – individual learner

Virtual university

Individual learner

Context What is the personal situation of the learner and what is motivating their desire to learn?

What is the virtual university doing to maximize the effective and efficient use of the resources invested in educational activities?

Process What activities sustain the learner, motivate their learning, and generate outcomes they value? What is the virtual university doing to efficiently and effectively enable learning? What is the virtual university doing to enable learner autonomy?

What evidence is the virtual university using to show that resources were used effectively and efficiently?

What evidence is the virtual university using to demonstrate the impact of their activities on the learner?

Output What evidence is the learner using to demonstrate the value of their experience?

(continued)

Feedback What helps the learner understand the impact of their experience and motivates them to continue learning? What is the virtual university doing to help the learner choose the next steps for their education? How do learners influence the priorities for change and continuous improvement by the virtual university? How is the virtual university continuously re-evaluating their activities? What is the virtual university doing to improve its capability to educate learners now and in the future?

Table 2 Quality as sense-making by focus and interface between context, input, Process, outcomes, and feedback foci reframed for a virtual university. (Modified from Marshall 2016, 2018, pp. 343–344)

4 Laying and Maintaining the Foundations for Quality 67

Input What differentiates the virtual university from other providers? What synergies with other providers or stakeholders contribute to learner success?

What capabilities, systems and resources are contributing to the role played by the virtual university?

What capability and resources are contributing to the range of societal needs made of the system?

What role is the virtual university playing in the system and how does that role relate to those of other stakeholders at a system level?

What does society need from its educational system and which stakeholder’s interests are being met?

Virtual university – national system

National system

Virtual university – other universities and stakeholders

Context How do other providers and stakeholder groups contribute to the targeted learner population and educational context?

Table 2 (continued)

What is enabling learners and providers to operate effectively and efficiently within the system? What is being done to enable experimentation with new or different approaches?

What is the virtual university doing to effectively operate within the system?

Process What is the virtual university doing to maximize their own and other provider or stakeholder contributions to the success of the targeted learner population?

Output How are a range of stakeholders experiencing the outcomes of educational activities? What evidence is there of a collaborative contribution to learner success with the virtual university drawing on the strengths of other providers and stakeholders? What evidence is there of the nature of the impact the virtual university has had on the system and the value of its role within that system? What evidence demonstrates the impact of the system on individual learners and the value contributed by specific providers?

What helps learners identify effective learning pathways enabled by the system? How are provider roles sustained while encouraging diversity and change?

What helps the virtual university continuously re-evaluate their role in the system and respond with systematic improvements?

Feedback How are a range of stakeholder’s views on the activities engaged with? What are providers and stakeholders doing to strengthen their collaboration and address gaps in the services provided to the targeted learner population?

68 S. Marshall

4

Laying and Maintaining the Foundations for Quality

69

making is then further supported by considering five quality categories: Context; Input; Process; Outcomes and Feedback (Van Damme 2004), which are used to promote an awareness of the need to continuously improve activities rather than simply measure inputs or outputs. At the intersection of these two dimensions, questions are posed as illustrations to start organizational narratives. An important point to make is that these questions are not driven by the nature of the virtual university; rather, the answers must themselves define what that nature is. From the perspective of the individual learner the questions are intended to stimulate the virtual university to consider what it knows regarding where the learner is coming from and what motivates them to learn with the virtual university, what strengths they bring that can built upon, how the process of learning can be enabled and sustained, how the learner constructs and communicates success, and what is influencing their future learning choices. Reflection on these questions needs to be informed by awareness of the bureaucratic environment that affects learner conceptions of education and its role in their lives, the political influences that motivate them, and the symbolic aspects that contribute to the perceptions of learners as to the nature and value of the education they experience. Importantly, this needs to be responsive to evolution of the learners’ understanding as they select educational experiences, undertake them, and then reflect on the outcomes over a period of time. Addressing these presents greater challenges for virtual universities as they cannot simply use historical norms to avoid actively engaging with the new reality being constructed by and with learners. The intersection of the learner focus with that of the virtual university speaks to how the systems and processes of the organization engage effectively with the learners, both individually and collectively, to provide ongoing cues for sensemaking and improvement. Superficially, this is dominated by the technocratic nature of the systems and processes, but there is also a strongly symbolic and political dimension to quality in this context as it speaks to the nature of evolving relationship between the virtual university and learners. Here the virtual university must consider how it engages with the learners’ contexts and ensures that its systems align to these effectively, rather than passively, assuming learners will seek them out as a result of their reputation and place in society. The focus on the virtual university itself reflects the need for continuous selfassessment, reflection and improvement. Unlike externally framed views of quality, this engages particularly with the collegial dimension, with bureaucratic and symbolic aspects speaking to the shared sense of values and common purpose enacted by the formal structures, processes and policies of the virtual university. This also directly speaks to the challenge of identity construction that faces the virtual university if it is to be more than a technocratically enhanced traditional university. Answers to the questions in this row of Table 2 need to consider what voices are invited to be part of the response and how they are influencing activities implemented in response. The relationship between the virtual university and other stakeholders in education, including other universities and educational providers, perhaps raises the most complex issues. At the heart of any response to these questions is the need to engage deeply with the political and systemic implications of a virtual organization.

70

S. Marshall

Virtuality is not merely the absence of a physical classroom, but it speaks to the larger organizational space that challenges traditional structures, hierarchies and control systems. Virtual organizations have permeable boundaries that blend into those of other organizations mixing goals and norms of a dynamic mix of external and internal stakeholders. In a globalized world this inevitably includes cultural, legal and national factors as well as the opportunity to unbundle aspects of the traditional university through commercial relationships with the vast array of vendors operating in the online program management (OPM) and services industries (Czerniewicz and Walji 2019; Marshall 2018). The placement of virtual universities within national systems of education is dominated by the political dimension. As noted above there are clearly regulatory and funding aspects that are important influences and constraints, but this operates practically in service of political elements arising from current and historical drivers operating through society. Unless the virtual university operates as a purely commercial organization funded without recourse to public sources (either directly or indirectly), it must engage effectively with the political realities of the societies it operates in and must necessarily respond to the risk that a virtual organization will have less visibility and social ownership than a physical university with its links to place and visible contribution the life of cities. Failure to engage effectively with this political space is evident in the history of the University of Phoenix Online. In a little over a decade, this university exploded from a small operation with 4000 students to well over 200,000 (United States Senate 2012). Poor management of the political consequences of this growth generated a response that saw changes to US funding policies (United States Department of Education 2014) and ultimately a financial collapse (Apollo Education Group 2016; Blumenstyk 2015). The role of the virtual university as a disruptor of established models and norms for the university means that it must carefully articulate its place within the system in a way that generates a positive political response. Ideally, the virtual university can convey the ways that it provides solutions to complex social challenges that are aligned to political, cultural and social values. The narrative of disruptive innovation (Christensen and Eyring 2011), while technocratically attractive, carries political risks, particularly in societies that see education as building stability and progress by solving important problems, not creating new problems by unnecessarily breaking other parts of the system. The Western Governors University (WGU) illustrates a positive approach a virtual university can take to position itself within national systems by offering solutions to problems in that system using mechanisms that complement the operations of other providers in the same contexts. Here, the focus of the university has been maintained on providing a competency-based online education experience aimed at building learner skills for specific workplace needs (Blumenstyk 1995). WGU’s ongoing success (Western Governors University 2016) appears to reflect a careful and ongoing management of the political and systemic dimensions of its operations and is apparent in the creation of state-affiliated (and publically funded) colleges and an ongoing focus on employment (Gallup 2014).

4

Laying and Maintaining the Foundations for Quality

71

This last example illustrates the need to respond to the last focus area in Table 2, that of the national system(s) of education. The virtual university needs to have a deep understanding of the systemic dimension of its activities if it is to operate at the scale and level of impact needed for success. Here, the questions speak to the virtual universities’ ability to understand how others conceive the educational landscape at scale, in order to identify opportunities to operate within that landscape. An example of a failed attempt to operate as a virtual university that was unable to successfully navigate the national system focus can be seen in the United Kingdom e-University (UKeU). Here the conception of a virtual university was as an aggregator and promotor of educational activities developed by individual universities, creating a national virtual university as a digital mediator for the physical institutions (Blunkett 2000) with a utopian and ambitiously technocratic vision of education (Thompson et al. 2000). The UKeU ultimately failed for a variety of contested reasons that suggest the questions listed in Table 2 were not thoroughly explored and resolved to the satisfaction of the various stakeholders, most notably the individual UK universities (House of Commons 2005; Wilcox et al. 2005).

Discussion The metatechnology of the university is ‘half invented’. (Taleb 2012, p. 189)

The questions outlined in Table 2 and analyzed above are sense-giving stimuli designed to expose individuals to the different cognitive frames (Kaplan 2008) or perspectives used by others to interpret the value of the virtual university, its activities and systems. The CIPOF structure used in that table provides a useful high-level reference point for leaders reflecting on the quality framework of the virtual university and the establishment of strong organizational foundations for improvement activities. The questions within the table are provided as a starting point for rich conversations that need to be held throughout the emerging and evolving virtual university. This implies a leadership committed to collective, distributed action and the operation of a high trust and shared accountability environment. The process of engaging with these prompts is the point of the framework as the answers discovered will often only represent plausible but temporary responses to a rapidly evolving context and organizational capability. Sense-making is inherently dynamic and driven within organizations by a process of continuous social interaction and engagement, framed and enabled by sensegiving activities of leadership and, potentially, by quality systems that generate cues and challenge complacency. The social nature of sense-making is one of the seven properties identified by Weick (1995, p. 17) and enables the generation of collective understanding and commitment that are fundamental to the good operation of quality systems in large organizations. Universities embody in many ways the aspirations of communities and individuals, and an effective quality system communicates how the organization is respecting those many-fold and competing demands.

72

S. Marshall

The context of the virtual university defines the scope of the organizational foundations and how the leadership of the university conceives its context will dominate quality improvement. The idea of a virtual university in the abstract implies an organization operating without reference to a physical location, but the necessary placement of people in physical spaces means that this form of virtual context is illusory. The university is a social construct that is placed very firmly within a very real place in political, legal and economic ways that need to be carefully and thoughtfully incorporated into the quality systems used to improve the university and as importantly, communicate key qualities to external audiences and stakeholders. The virtual university is also very real in its need for resources that provide the inputs for its activities. A common mistake is to regard technology as a key resource shaping the activities of the university and defining its strategy, rather than recognizing that it is how the technology is executed and enables strategies that are key (Carr 2003). This awareness drives both the recognition of the need for significant resources for technology and the necessity to ensure that the technology is not limited or defined by the strategic interests of other stakeholders such as publishers or the vendors of specific systems such as Learning Management Systems or MOOC platforms. The problem, as illustrated by the failure of the UKeU, is that the investment needed to sustain a differentiated and capable platform is substantial. The rapid evolution of the MOOC in only a decade from its early conception as either a cMOOC for collaboration or xMOOC for operation at scale (Daniel 2012) into the complex ecology of providers, platforms and products that we now experience (Oliver 2019) illustrates the need for virtual university quality systems that are not only able to change but capable of proactively enabling quality improvement of processes and systems. This driver argues against the orderly model of the traditional university sector, with its monocultural model and bureaucratic systems, and instead for the type of robust chaos that typifies healthy ecosystems. Systems which use chaos to enable robustness and resilience, gaining strength from change, are antifragile (Taleb 2012). Technology platforms that enable this are likely to be enabled by a rich mix of focused tools integrated by open standards. Management of information flows is key to the successful operation of such systems and also contribute to the sense-making and quality improvement activities of the university, so organizational oversight of these tools and control of the information is a critical enabler of success.

Conclusion Demonstrating and sustaining successful outcomes for a virtual university in a dynamic environment is a significant challenge for leaders, requiring careful governance ▶ Chap. 3, “Transparency in Governing Technology Enhanced Learning,” and a sympathetically enabling policy environment ▶ Chap. 2, “Aligning the Vision for Technology-Enhanced Learning with a Master Plan, Policies, and Procedures.” Many quality systems fail as a result of misalignment of strategic goals with

4

Laying and Maintaining the Foundations for Quality

73

operational measures. As described above, measures speak to a range of quality dimensions and purposes, and any quality system needs to answer to the requirements of the bureaucratic realities of its context, while also carefully managing the political, systemic and symbolic dimensions. Outcomes need to be valued by the organization in reality, and not simply reflect a subset of external accountably. Leaders need quality measures and improvement activities that avoid the trap of facile representations of brands and marketing, or face the real risk of “dramaturgical compliance,” a quality theatre weakening the university’s ability to sustain the pace of improvement it needs, misdirecting activities in unproductive and irrational ways (van Kemenade and Hardjono 2010). Coordinating the different and often conflicting agendas of stakeholders in the diverse contexts of virtual university activities requires a careful awareness of timing, the necessity for urgent and decisive action, and the complex reality of collaboration. The quality foundations of the virtual university need to reflect these features and rather than merely cope with change, actively use them in anti-fragile ways to strengthen and provoke improvements that positively engage with the mission and values of a real university.

Cross-References ▶ The Role of Analytics When Supporting Staff and Students in the Virtual Learning Environment ▶ The Role of Standards and Benchmarking in Technology-Enhanced Learning ▶ Transparency in Governing Technology Enhanced Learning

References Adelman, C. 2009. The Bologna process for U.S. eyes: Re-learning higher education in the age of convergence. Washington, DC: Institute for Higher Education Policy. Retrieved from http:// www.ihep.org/Research/GlobalPerformance.cfm Altbach, P.G. 2013. MOOCs as neocolonialism: Who controls knowledge? [Blog post]. http:// chronicle.com/blogs/worldwise/moocs-as-neocolonialism-who-controls-knowledge/ Alvesson, M. 2013. The triumph of emptiness: Consumption, higher education, & work organization. Oxford: Oxford University Press. Alvesson, M., and P.O. Berg. 1992. Corporate culture and organizational symbolism. New York: de Gruyter. Antony, J., N. Krishan, D. Cullen, and M. Kumar. 2012. Lean Six Sigma for higher education institutions (HEIs). International Journal of Productivity and Performance Management 61 (8): 940–948. https://doi.org/10.1108/17410401211277165. Apollo Education Group. 2016. Apollo Education Group, Inc. reports third quarter fiscal year 2016 Results. http://www.businesswire.com/news/home/20160707006472/en/Apollo-EducationGroup-Reports-Quarter-Fiscal-Year Arum, R., J. Roksa, and A. Cook. 2016. Improving quality in American higher education. San Francisco: Jossey-Bass. Bailey, M., and D. Freedman. 2011. The assault on universities: A manifesto for resistance. London: Pluto Press.

74

S. Marshall

Barlow, A. 2014. Another colonialist tool? In Invasion of the MOOCs: The promise and perils of massive open online courses, ed. S.D. Krause and C. Lowe, 73–85. Anderson: Parlor Press. Bergquist, W.H. 1995. Quality through access, access through quality. San Francisco: Jossey-Bass. Blanco-Ramírez, G., and J.B. Berger. 2014. Rankings, accreditation, and the international quest for quality. Quality Assurance in Education 22 (1): 88–104. https://doi.org/10.1108/QAE-072013-0031. Blumenstyk, G. 2015, June 30. U. of Phoenix looks to shrink itself with new admissions requirements and deep cuts. The Chronicle of Higher Education. http://chronicle.com/article/U-ofPhoenix-Looks-to-Shrink/231247/ Bok, D. 2013. Higher education in America. Princeton: Princeton University Press. Bond, C.H., R. Spronken-Smith, R. McLean, N. Smith, S. Frielick, M. Jenkins, and S. Marshall. 2017. A framework for enabling graduate outcomes in undergraduate programmes. Higher Education Research & Development 36 (1): 43–58. https://doi.org/10.1080/07294360.2016.1170767. Brown, P. 2003. The opportunity trap: Education and employment in a global economy. European Educational Research Journal 2 (1): 141–179. Carr, N. 2003. IT doesn’t matter. Harvard Business Review 81 (5): 41–49. Chalmers, D. 2007. A review of Australian and international quality systems and indicators of learning and teaching. Chippendale: Carrick Institute for Learning and Teaching in Higher Education Ltd. Christensen, C.M., and H.J. Eyring. 2011. The innovation university: Changing the DNA of higher education from the inside out. San Francisco: Jossey-Bass. Clark, B.R. 2004. Sustaining change in universities. Milton Keynes: The Society for Research into Higher Education and Open University Press. Collini, S. 2017. Speaking of universities. London: Verso. Collins, J., and J.I. Porras. 1996. Building your company’s vision. Harvard Business Review 75 (5): 65–77. Cunningham, S., Y. Ryan, L. Stedman, S. Tapsall, K. Bagdon, T. Flew, and P. Coaldrake. 2000. The business of borderless education. Canberra: DETYA. Czerniewicz, L., and S. Walji. 2019. Issues for universities using private companies for online education. Cape Town: University of Cape Town. https://open.uct.ac.za/handle/11427/29813 Daniel, Sir J.S. 2012. Making sense of MOOCs: Musings in a maze of myth, paradox and possibility. Journal of Interactive Media in Education 2012 (3). http://jime.open.ac.uk/article/ view/2012-18/466 Fischer, K. 2014, May 19. Education trends in East Asia could disrupt flow of students to U.S. The Chronicle of Higher Education. http://chronicle.com/article/Education-Trends-in-East-Asia/ 146661/ Gartner. n.d. Gartner hype cycle. http://www.gartner.com/technology/research/methodologies/ hype-cycle.jsp Gilbert, A.D. 2001, April 6. The plans of Universitas 21. The Chronicle of Higher Education. http:// chronicle.com/article/The-Plans-of-Universitas-21/28430 Harvey, L., and D. Green. 1993. Defining quality. Assessment & Evaluation in Higher Education 18 (1): 9–34. Hayes, D. 2017. Beyond McDonaldization: Visions of higher education. Abingdon: Routledge. Hirsch, F. 1976. Social limits to growth. Cambridge, MA: Harvard University Press. Holmwood, J.A. 2011. A manifesto for the public university. New York: Bloomsbury Academic. House of Commons. 2005. UK e–University. (Third report of session 2004–05 report, together with formal minutes, oral and written evidence). London: House of Commons, Education and Skills Committee. Hussey, A. 2012. Human capital augmentation versus the signaling value of MBA education. Economics of Education Review 31 (4): 442–451. James, R., B. Tynan, S. Marshall, L. Webster, G. Suddaby, and R. Lewis. 2011. Regulatory frameworks for distance education: A pilot study in the Southwest Pacific/South East Asia region. Oslo: International Council for Open and Distance Education.

4

Laying and Maintaining the Foundations for Quality

75

Kaplan, S. 2008. Framing contests: Strategy making under uncertainty. Organization Science 19 (5): 729–752. Kerr, C. 1963. The uses of the university. Cambridge, MA: Harvard University Press. Leney, T. 2009. Qualifications that count: Strengthening the recognition of qualifications in the Mediterranean region. (Euro-Mediterranean Partnerships Education and Training for Employment project thematic report). Viale Settimio Severo: European Training Foundation. Lessig, L. 2006. Code. (Version 2.0). New York: Basic Books. Marshall, S. 2016. Quality as sense-making. Quality in Higher Education. https://doi.org/10.1080/ 13538322.2016.1263924. ———. 2018. Shaping the university of the future: Using technology to catalyse change in university learning and teaching. Sydney: Springer. https://doi.org/10.1007/978-981-10-7620-6. Newfield, C. 2016. The great mistake: How we wrecked public universities and how we can fix them. Baltimore: John Hopkins University Press. Newman, J.H. 1853/1976. In The idea of a university, ed. I.T. Kerr. Oxford: Oxford University Press. Newton, J. 2010. A tale of two ‘qualities’: Reflections on the quality revolution in higher education. Quality in Higher Education 16 (1): 51–53. Noble, D. 2002. Digital diploma mills: The automation of higher education. Toronto: Between the Lines. Oliver, B. 2019. Making micro-credentials work for learners, employers and providers. Melbourne: Deakin University. https://www.assuringgraduatecapabilities.com/uploads/4/5/0/5/45053363/ making-micro-credentials-work-oliver-deakin-2019.pdf Pegrum, M. 2009. From blogs to bombs: The future of digital technologies in education. Crawley: UWA Publishing. Quinn, A., G. Lemay, P. Larsen, and D.M. Johnson. 2009. Service quality in higher education. Total Quality Management & Business Excellence 20 (2): 139–152. https://doi.org/10.1080/ 14783360802622805. Ramage, T.R. 2002. The ‘no significant difference’ phenomenon: A literature review. Journal of Instructional Science and Technology 5 (1). http://www.ascilite.org.au/ajet/e-jist/docs/Vol5_ No1/ramage.html Readings, B. 1996. The university in ruins. Cambridge, MA: Harvard University Press. Rocheleau, M. 2013, October 10. China announces edX platform to power its new online learning portal XuetangX. Boston Globe. http://archive.boston.com/yourcampus/news/harvard/2013/10/ china_announces_edx_platform_to_power_its_new_online_learning_portal_xuetangx.html Rosa, M.J., and A. Amaral. 2007. A self-assessment of higher education institutions from the perspective of the EFQM excellence model. In Quality assurance in higher education: Trends in regulation, translation and transformation, ed. D.F. Westerheijden, B. Stensaker, and M.J. Rosa, 181–207. Amsterdam: Springer. Selwyn, N. 2014. Digital technology and the contemporary university. Abingdon: Routledge and the Society for Research into Higher Education. Shah, D. 2016. XuetangX: A look at China’s first and biggest MOOC platform. https://www.classcentral.com/report/xuetangx/ Sharma, G. 2013, July 15. A MOOC delusion: Why visions to educate the world are absurd. The Chronicle of Higher Education. http://chronicle.com/blogs/worldwise/a-mooc-delusion-whyvisions-to-educate-the-world-are-absurd/ Spence, M. 1973. Job market signaling. Quarterly Journal of Economics 87 (3): 355–374. State Council. 2017. China to improve education during 13th five-year plan period. Beijing: The State Council: People’s Republic of China. http://english.gov.cn/policies/latest_releases/2017/ 01/19/content_281475546145620.htm Taleb, N.N. 2012. Antifragile: Things that gain from disorder. London: Penguin Books. United States Department of Education. 2014. Obama administration takes action to protect Americans from predatory, poor-performing career colleges. http://www.ed.gov/news/pressreleases/obama-administration-takes-action-protect-americans-predatory-poor-performingcareer-colleges

76

S. Marshall

United States Senate. 2012. For profit higher education: The failure to safeguard the federal investment and ensure student success. Washington, DC: United States Senate Health, Education, Labor and Pensions Committee. Van Damme, D. 2004. Standards and indicators in institutional and programme accreditation in higher education: A conceptual framework and a proposal. In Indicators for institutional and programme accreditation in higher/tertiary education, ed. L. Vlasceanu and L.C. Barrows, 127–160. Bucharest: UNESCO. van Kemenade, E., and T.W. Hardjono. 2010. A critique of the use of self-evaluation in a compulsory accreditation system. Quality in Higher Education 16 (3): 257–268. https://doi. org/10.1080/13538322.2010.506715. Weick, K.E. 1995. Sensemaking in organizations. Thousand Oaks: Sage. ———. 2000. Quality improvement: A sensemaking perspective. In The quality movement & organization theory, ed. R.E. Cole and W.R. Scott, 155–174. Thousand Oaks: Sage. Wilcox, P., J. Petch, and H. Dexter. 2005. Towards an understanding of UKeU business processes within an e-learning lifecycle model. The Electronic Journal of e-Learning 3 (1): 77–86.

Part III The Virtual Environment

5

A Social Equity–Based Framework Toward the Development of the Virtual University Zhiqiang Amos Tay

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digital Equity Through Virtual University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Social Equity Framework for the Virtual University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Recognizing Barriers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Economic Barriers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Technological Barriers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cultural and Linguistic Barriers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Equitable Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Targeted Widening Participation Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Equitable Distribution of Financial Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Informational Aspects of the Distribution of Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Equitable Access to Technologies, the Internet, and Digital Literacy . . . . . . . . . . . . . . . . . . . . . . . Equitable Opportunities and Experiences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inclusive Pedagogical Practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social Networks Toward Community of Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Equitable Success and Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Identifying At-Risk Students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Improving Employability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

80 81 82 83 84 84 84 85 85 85 86 86 87 87 88 88 88 88 89 89 90

Abstract

Globalization and technological innovations are the major driving forces of the virtual university. The virtual university is largely focused on exploring how the use of technologies influences the learning and teaching processes and outcomes. In the context of an (often uncritical) adoption of technology for learning in Z. A. Tay (*) Queensland University of Technology, Brisbane, QLD, Australia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_5

79

80

Z. A. Tay

higher education, concerns over digital (in)equity are growing. While the adoption of technologies is not a panacea to ameliorating social inequity, the virtual university in a good position promotes equitable access, opportunities, and outcomes for student equity. This chapter discusses how a social equity–based framework can be applied in the development of the virtual university. A framework is presented that comprises four dimensions: (1) recognizing barriers, (2) equitable access, (3) equitable opportunities and experiences, and (4) equitable success and outcomes. This framework will inform the chapter and serve as a social equity–based approach to exploring how the virtual university could deliver a more equitable model of higher education. Keywords

Social equity · Equitable access · Equitable opportunities · Equitable outcomes · Virtual university

Introduction Universities have historically been shaped by political, economic, social, religious, and scientific forces. As contemporary societies become more digitally connected, universities have followed a similar path. The affordances of technologies have opened up a plethora of learning opportunities that are mobile, flexible, and ubiquitous for students (Huang et al. 2020). However, the discourse of virtual universities promoting enhanced learning and greater access is not without critique. While the adoption of technologies has led to positive learning experiences, whether that translates to improved outcomes and opportunities and more equitable access, in particular for students from disadvantaged backgrounds, is an ongoing debate (Drok 2020). The reality is that while using digital online technologies in universities might provide higher levels of accessibility, personalized learning experiences, and flexibility, there is also an exacerbation of social inequities induced by the adoption of digital technologies (Czerniewicz 2018). Discrepancy understanding the relationship between technology adoption and social justice demonstrates the importance of emphasizing digital equity in higher education (Stone 2017). Students face socioeconomic, sociocultural barriers that could lead to relatively higher attrition rates in universities. The virtual university represents an invaluable opportunity to advance the notion of online learning not just from a pedagogical perspective, but also from a social justice perspective. Drawing from lessons learnt in traditional as well as earlier online learning models, the virtual university could advance the notion of online learning by positioning social equity deliberately as a central theme rather than assuming this will “naturally” happen. This chapter seeks to reframe the discourse of the virtual university into one that accentuates social equity, which in turn could be seen as integral to good pedagogical practice. We begin by discussing the need for digital equity and how it could potentially be realized by the concept of the virtual university and propose a social

5

A Social Equity–Based Framework Toward the Development of the. . .

81

equity–driven framework for equitable access, opportunities, and outcomes in the virtual university.

Digital Equity Through Virtual University Persisting social inequity remains a pressing concern for governments and institutions of higher education (OECD 2019). The 2020 Horizon Report highlighted that issues of equity remain imperative in policies and practices within higher education (Brown et al. 2020). Equity can be referred to as the condition that governs the way resources and rewards are distributed based on the principles of fairness and ethical rights (Leventhal 1976). Conceptualizations of equity are largely focused on the differentiated treatment of individuals according to their needs, implemented through the enactment of policies and practices that promote equitable access, opportunities, and outcomes. Technological integration into the learning and teaching process could thus be perceived as supporting the widening participation agenda. The adoption of technologies in higher education could also potentially provide a differentiated and individualized learning experience for students based on their needs (McMahon and Walker 2019) and barriers such as distance from campus experienced by remote students could therefore potentially be resolved (Pollard 2018). Though the number of students gaining access to higher education has increased, the pervasive use of technologies in universities has inadvertently led to further entrenchment of social inequity in which individuals who are well equipped financially and culturally benefit the most. Research into the social justice implications that arise from the use of technologies have continued to be of interest in higher education due to its potential to provide some level of parity in terms of access to learning. According to the National Digital Inclusion Alliance (2019), digital equity refers to the “condition in which all individuals and communities have the information technology capacity needed for full participation in our society, democracy and economy.” Five dimensions of digital equity include access to: (1) hardware, software, and Internet connectivity; (2) meaningful, high-quality, and culturally relevant content in localized languages; (3) creating, sharing, and exchanging digital content; (4) educators who know how to use digital tools and resources; and (5) high-quality research on the application of digital technologies to enhance learning (Resta and Laferrière 2008). While the adoption and use of technologies could widen participation of students from diverse backgrounds to participate in higher education, the evidence on how such an approach could resolve complex social equity issues is inconclusive (Hengtao and Carr-Chellman 2016). Nevertheless, the potentiality of using technologies to improve student access, opportunities, and outcomes has continued to drive technological adoption, which is often regarded as a key strategy toward the provision of more equitable access, opportunities, and success for these students (Lambert 2020). However, it is crucial that the adoption and use of technologies do not fixate only on notions of initial accessibility to higher education, but also focus

82

Z. A. Tay

on supporting students’ opportunities and outcomes in higher education. Strategies of widening access can be implemented to address barriers faced by students to improve student access, participation, and outcomes in higher education (Drok 2020; Stone 2017). Elements that facilitate and hinder access and participation need to be illuminated to allow for appropriate interventions to be implemented. A substantial body of research explores how promoting equitable access, participation, success, and inclusion in higher education, in particular for disadvantaged students, is integral to promoting social justice in higher education (Pitman 2017). Disadvantaged students in higher education, such as low SES (socioeconomic status), regional students, remote students, and Indigenous students, are found to possess characteristics that are closely associated with lower completion rates (Edwards and McMillan 2015). Being preoccupied with the notion of labeling not only risks ignoring other individuals in equally vulnerable positions, but also perpetually positions them as financially and culturally deficient. For example, mature learners and single parents, or abuse victims, are often not widely discussed within the disadvantaged student narrative in higher education. Furthermore, an individual with a disability might also be low SES and thus face multiple barriers. It would be more constructive to identify different forms of barriers that could be associated with one or more equity groups. The focus is then on the recognition of barriers and not on the disadvantaged group. With a focus on the barriers, policies and practices can then be implemented to identify and overcome these barriers that any individual might face.

A Social Equity Framework for the Virtual University Many of the existing models of online learning are focused on pedagogical aspects with little or no consideration of socioeconomic and sociocultural barriers to online learning. A more philosophical approach would be to view education as not only preparation for life, but life itself (Dewey 1916). It is difficult to disentangle the learning process from the external factors that together constitute the experience of living. While current online learning models have contributed to a better understanding of how technologies can be integrated into learning, there is a need for a more social justice–motivated approach toward the development of not only online learning, but also improving access, opportunities, and success in a virtual university. Stone (2017) has provided ten guidelines to improve student access, participation, and success in higher education (Table 1). Informed by these guidelines, the framework toward developing an equitable virtual university consists of four dimensions: (1) recognizing barriers, (2) equitable access, (3) equitable opportunities and experiences, and (4) equitable success and outcomes (Fig. 1). Rather than policies and pedagogical practices that drive social equity, this framework prioritizes social equity as the core element that is enacted through policy reforms, and pedagogical transformations afforded by learning technologies, that, when combined, reinforce social equity.

5

A Social Equity–Based Framework Toward the Development of the. . .

83

Table 1 Ten guidelines to improve access, participation, and success in higher education (Stone 2017) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Know who your students are Develop, implement, and regularly review institution-wide quality standards for delivery of online education Intervene early to address student expectations, build skills and engagement Explicitly value and support the vital role of “teacher presence” Design for online Engage and support through content and delivery Build collaboration across campus to offer holistic, integrated, and embedded student support Contact and communicate throughout the student journey Use learning analytics to target and personalize student interventions Invest in online education to ensure access and opportunity

Identifying at-risk students

Economic barriers Technological barriers

Improving employability

Cultural and linguistic barriers

Inclusive pedagogical practices

Equitable success and outcomes

Recognising barriers

Equitable opportunities and experiences

Equitable access

Social networks towards community of support

Targeted widening participation policy

Equitable distribution of financial resources Informational aspects of the distribution of resources Equitable access to technologies, the Internet and digital literacy

Fig. 1 A social equity–based framework of a virtual university

Recognizing Barriers The initial stage of this framework involves a continuous process of identifying barriers experienced by students in ways that could hinder their ability to access and participate in higher education. Students often experience multiple barriers, for example, regional and remote students face multiple structural barriers that could be similar to low SES students: distance barriers, financial barriers, emotional

84

Z. A. Tay

barriers, and sociocultural incongruity (Nelson et al. 2017). Barriers can be organized into three dimensions: (1) economic, (2) technological, and (3) cultural and linguistic. Being intricately intertwined with one another, the categorization of these barriers does not imply they operate independently.

Economic Barriers Economic constraints have long been considered to restrict students’ access to participate in higher education (Li and Carroll 2020). In the context of a virtual university, financial hardship stemming from low SES could impede students’ ability to have access to technologies, which in turn adversely impacts their ability to participate in learning processes. Institutionalized financial aid in the form of scholarships and student loans has been instrumental in alleviating financial hardship for students and potentially leading to better student experiences and outcomes. Regional and remote students with higher levels of economic constraints are less likely than metropolitan students to consider aspiring to participate in higher education (Cooper et al. 2017). First-in-family or first-generation students with low SES are also more at risk of withdrawing in the first year of higher education (Li and Carroll 2020).

Technological Barriers While it is acknowledged that financial aid could improve student access to higher education, support through financial means might not be able to address the complexities involved in learning and teaching processes that are integrated with technologies in a virtual university setting. Barriers related to technologies can be categorized into three aspects: (1) access to the technological hardware; (2) access to reliable Internet connectivity; and (3) digital literacy skills. Accessibility to laptops, appropriate technical specifications and software, and Internet connectivity (home based and mobile) are crucial factors that influence students’ access to a virtual university (Ryan et al. 2000). Therefore, scholarships should encompass both the waiver of tuition fees as well as the issuance of laptops, Internet connectivity, and other necessary software (Murray and Gray 2021). Another aspect of technological barriers is the growing divide related to digital literacy. Students with lower digital literacy might face challenges in using suitable technologies as well as the ability to use them meaningfully and efficiently (Prayaga et al. 2017).

Cultural and Linguistic Barriers Culturally and linguistically diverse (CALD) students whose native language is not the dominant language might face linguistic challenges which in turn might affect the frequency and quality of social interaction with peers and teaching staff. The inability to articulate themselves in the dominant language across digital learning

5

A Social Equity–Based Framework Toward the Development of the. . .

85

spaces could negatively impact on the student’s learning experience and outcomes, which is further exacerbated in virtual universities in which much of the learning takes place through (a)synchronous technologies. Given the potential increase in the proportion of CALD students in the virtual university, there is a need for the provision of readily accessible learning resources to support learning for these students. Providing facilitator support, increasing social interactions among peers, and developing readily accessible resources are factors that could lower cultural and linguistic barriers for CALD students (Lin et al. 2021). A strategical approach can be undertaken by integrating intercultural education models into the virtual university. Interculturalism promotes deep understanding of different cultures. Promoting intercultural education in a virtual university could be envisaged through collaborative projects composed of CALD students to foster social interactions. The use of digital technologies such as videoconferencing, instant messaging services, and online threaded discussions could promote social interactions that would in turn enhance intercultural understandings of diverse cultures (Veytia-Bucheli et al. 2020).

Equitable Access Targeted Widening Participation Policy To increase access to higher education, universities are mostly preoccupied with adopting the widening participation agenda. While widening participation often involves developing an all-age policy that attracts learners from young to mature age groups, there is a disconnect between the policymaking process and the implementation of policies (Evans et al. 2019). For example, while outreach policies could improve enrolments, it still requires active counseling and a streamlined university application process for greater impact on students enrolling in higher education (Herbaut and Geven 2019). Drawing from lessons from widening participation policies, the virtual university needs to be driven by equity-based policies that are targeted specifically to address the barriers experienced by students. Barriers to accessing technologies often stem from economic barriers. Identification processes need to be in place to lower economic barriers for students to gain access to required technology. Within the context of the virtual university, the ability for students to have readily available and constant access to technologies is crucial to maintaining the continuity of learning. The identification of economic barriers supported by data analytics could allow academic staff to provide the appropriate interventions in the form of financial support to students.

Equitable Distribution of Financial Resources Tuition fees is a critical aspect of higher education faced by students, leading to the accumulation of large student debts (Clark et al. 2019). The provision of financial subsidies is required to ease the financial stress experienced by students. Efforts to

86

Z. A. Tay

widen access to higher education aims to resolve financial difficulties through discounted course fees, tuition loan programs, scholarships, and other forms of financial support (Pitman 2017). The virtual university may need to implement innovative ways to provide a high-quality learning experience at a discounted price for students, for example, where students pay most of the tuition fees through loan initiatives. Additionally, universities have also pursued online delivery of learning as a strategy to lower course fees (Yuan and Powell 2013). With a lowered economic barrier, students would have greater access to participating in higher education.

Informational Aspects of the Distribution of Resources Information about the financial support programs needs to be easily accessible and could have potential to increase enrolment rates if distributed early (Lambrechts 2020). The virtual university may need to collaborate with the preuniversity institutions to be able to identify students with low SES. Having prior knowledge about students could enable the virtual university to take preemptive actions to provide information about the suitable scholarships and financial support programs available. Profiles of students collected through such data analytics could then inform administrators to make the necessary interventions in providing equitable support. While the widening participation agenda through the provision of financial support could be a useful approach to improve equitable access to higher education, existing informational and procedural issues continue to undermine its effectiveness and ability to promote equitable access. From a social justice perspective, the distribution of financial resources needs to be complemented by informational and procedural justice. The timeliness of the information about the financial aid being provided to students is an important factor. The process for scholarship applications should be streamlined and assisted by online technologies. Multiple modes of communication such as outreach initiatives to regional and remote schools, emailing prospective and current students, or having student groups actively follow-up with students are strategies to ensure that students are well supported. Students who already have financial and/or geographical difficulties accessing the Internet may find it challenging to complete a submission, even with a smartphone. Other modes of communication such as through phone calls could be a means to providing greater support to students. It is posited that early commitment of financial aid in preuniversity could lead to improved enrolment rates (Herbaut and Geven 2019).

Equitable Access to Technologies, the Internet, and Digital Literacy Financial support is often used to enable students to purchase technological devices and software. As university libraries are mostly digitized, students are highly dependent on devices to access learning resources. The development of the virtual university should not assume that all students have ready access to technology, as

5

A Social Equity–Based Framework Toward the Development of the. . .

87

well as the knowledge to use technologies. Equitable and inclusive access to technology entails having access to technological devices and digital literacy (Warschauer 2004). The virtual university also needs to put in place assistive technologies for students, especially students with disabilities (Goldrick et al. 2014). Speech recognition transcription of live-streamed lectures and lecture recordings could support students with hearing impairments (Kent et al. 2018). The functionality of captioning of lesson recordings and any information disseminated in audio and visual format should be prioritized in the virtual university. The quality of synchronous online learning is often based on the premise of reliable Internet access, there could, however, also be varying degrees of Internet connectivity experienced by students. The virtual university could, as part of its equity-based policy, provide financial support for Internet plans of download/upload speeds that are compatible with the technical requirements. While the virtual university could provide support for students to have mobile Internet plans, ensuring its reliability might be challenging. The virtual university therefore needs to provide alternative options for students to review the lessons after the live-streaming session for greater inclusion for students who have poor access to Internet. Another aspect of equitable access would be to allocate more resources to develop digital literacy competency among students. Digital literacy involves acquiring and developing knowledge about the ability to access information online from reliable and accurate sources. Equipping students with the literacy skills to use technologies efficiently would be an important aspect of widening access to higher education. Rather than assuming that students have the required digital literacy, the virtual university could provide online courses that develop digital literacy.

Equitable Opportunities and Experiences Inclusive Pedagogical Practices Apart from national and institutional policy reforms, inclusive pedagogical strategies are required to improve learning experiences for students (Seale 2019). Principles of universal design for learning (UDL) are useful in addressing a higher education climate that has a higher proportion of students from diverse backgrounds, which in turn influences their learning and the way they learn (Olaussen et al. 2019). Multiple means of representation, expression, and engagement within text choice, syllabus format, assessment values and descriptions, assessment methods, and communication with students are important elements of the UDL (Rogers-Shaw et al. 2018). For example, the incorporation of closed captions into recorded and live lectures could lead to a more engaging learning experience for hearing-impaired students as well as linguistically diverse students (Alsalamah 2020). UDL is extended from the notion of universal design into education and conceptualized into a framework that allows for flexibility and accessibility to learners with different needs (Meyer et al. 2014). Technologies used to support UDL could promote greater levels of inclusion of students in higher education (McMahon and Walker 2019).

88

Z. A. Tay

Social Networks Toward Community of Support Social networks are crucial to promoting equitable access to students to participate in higher education (Mishra 2020). Institutional and personal engagements with university networks could construct a community of practice through which best practices and issues could be shared and resolved collaboratively between these universities and the virtual university. Having a community of support involving instructors, fellow students, and family is identified as a key component toward students’ participation and success in higher education (Barney 2018). More one-toone consultations with mentors could act as a reference to improve participation and success among students (Blackburn 2017). Within the context of a virtual university, AI chatbots that could perform automated support and/or direct the student to the relevant staff who then provide human assistance could be a practical approach to provide student-support services, and could promote a sense of community and connectedness to the university that could in turn improve completion rates (Barrett et al. 2019). The virtual university could use computer-supported collaborative strategies to foster online learning community, such as blog-based teaching portfolios that allow students and academic teaching staff to negotiate and co-construct online learning communities (Tang and Lam 2014). In the same vein, the integration of social media platforms and videoconferencing as part of the course structure could serve as a motivating experience to interact with peers within a learning community (Haar 2018). It is therefore the development and maintenance of social networks marked by regular interactions and supported by the use of various forms of technologies that may afford collaboration and interactions, and that can help build a sense of community among the online students.

Equitable Success and Outcomes Identifying At-Risk Students Disadvantaged students have been found to be at a higher risk of dropping out of higher education (Li and Carroll 2020). An influential dropout model by Tinto (1975) suggests that while a student’s commitment to enroll in higher education could be related to the family background, academic and social integration with the higher education institution is key to influencing their commitment to complete higher education. With the affordance of identifying at-risk students, data collected through learning analytics could be used to self-regulate learning as well as to provide timely interventions that could potentially improve academic performance of students (Foster and Siddle 2020).

Improving Employability While it might seem instrumental, employability is still an important source of motivation when learning in higher education (Andrewartha and Harvey 2017).

5

A Social Equity–Based Framework Toward the Development of the. . .

89

The virtual university could incorporate work-integrated learning (WIL) and work placement as part of the study program. Such relationship between the virtual university and industry should not be superficial, a shared understanding of the purpose and rationale of the WIL is needed. The incorporation of paid work placements in the student’s final year has been argued to be a significant factor in predicting postgraduate employability (Pitman et al. 2019), however, the inclusion of WIL and work placements as part of the students’ study could be challenging for the virtual university. This issue prioritizes the significance of the provision of career services to students in the virtual university. It is important for the virtual university to provide career services to equip students postgraduation (Andrewartha and Harvey 2017). Career services in traditional universities include development of students’ curriculum vitae, students’ job interview skills, and providing students with general career information (Harvey et al. 2017). Understanding barriers faced by students in relation to employability, monitoring of career service uptake by disadvantaged students, and provision of customized career services to all students are important factors that the virtual university needs to consider when formulating policies and practices to improve the employability of graduating students.

Conclusion and Future Directions While education is and can be an instrument toward social justice, it also potentially reinforces and reproduces existing social inequity in society. This potentiality is an impetus for the virtual university to be driven by the need to promote social equity. The discourse of recognizing barriers is fundamental to the development of the virtual university. Equitable access, equitable opportunities and experiences, and equitable success and outcomes drive policies and practices that shape the virtual university. Equitable access could be attained if individuals, regardless of their SES, racial, social, and cultural identities, are able to have physical, cultural, and social access to the social, cultural, and economic affordances of technologies. Supporting students to access higher education is a means of providing equitable opportunities and experiences as well as success and outcomes for students. This chapter has argued for the potentiality for the virtual university, driven by the need for social equity, to be continuously engaged in an ongoing process of recognizing barriers and developing innovative strategies with the use of technologies to address these barriers toward equity of societal attainment.

Cross-References ▶ Academic Engagement in Pedagogic Transformation ▶ Innovation and the Role of Emerging Technologies ▶ Preparing Students for the Future of Work and the Role of the Virtual ▶ Transparency in Governing Technology Enhanced Learning

90

Z. A. Tay

References Alsalamah, A. 2020. Using captioning services with deaf and hard of hearing students in higher education: A systematic review. American Annals of the Deaf 165 (1): 114–127. https://doi.org/ 10.1353/aad.2020.0012. Andrewartha, L., and A. Harvey. 2017. Employability and student equity in higher education: The role of university careers services. Australian Journal of Career Development 26 (2): 71–80. https://doi.org/10.1177/1038416217718365. Barney, K. 2018. Community gets you through: Success factors contributing to the retention of Aboriginal and Torres strait islander higher degree by research (hdr) students. Student Success 9(4): 13–24. https://link.gale.com/apps/doc/A581622986/AONE?u¼anon~309c8bf2& sid¼bookmark-AONE&xid¼fb0be091 Barrett, M., L. Branson, S. Carter, F. DeLeon, J. Ellis, C. Gundlach, and D. Lee. 2019. Using artificial intelligence to enhance educational opportunities and student services in higher education. Inquiry: The Journal of the Virginia Community Colleges 22(1): 11. https:// commons.vccs.edu/inquiry/vol22/iss1/11 Blackburn, H. 2017. The status of women in STEM in higher education: A review of the literature 2007–2017. Science & Technology Libraries 36 (3): 235–273. https://doi.org/10.1080/ 0194262X.2017.1371658. Brown, M., M. McCormack, J. Reeves, C. Brooks, S. Grajek, B. Alexander, M. Bali, S. Bulger, S. Dark, N. Engelbert, K. Gannon, A. Gauthier, D. Gibson, R. Gibson, B. Lundin, G. Veletsianos, and N. Weber. 2020. 2020 Educause horizon report teaching and learning edition. Louisville: EDUCAUSE. https://www.learntechlib.org/p/215670/ Clark, T., R. Hordósy, and D. Vickers. 2019. ‘We will never escape these debts’: Undergraduate experiences of indebtedness, income-contingent loans and the tuition fee rises. Journal of Further and Higher Education 43 (5): 708–721. Cooper, G., J. Baglin, and R. Strathdee 2017. Access to higher education: Does distance impact students’ intentions to attend university? Perth: National Centre for Student Equity in Higher Education, Curtin University. https://www.ncsehe.edu.au/wp-content/uploads/2017/02/RMIT_ Cooper-Baglin-Strathdee.pdf Czerniewicz, L. 2018. Inequality as higher education goes online. In Networked learning reflections and challenges research in networked learning, ed. N. Bonderup Dohn, S. Cranmer, J.-A. Sime, M. de Laat, and T. Ryberg, 1st ed., 95–106. Springer International Publishing. https://doi.org/10. 1007/978-3-319-74857-3. Dewey, J. 1916. Democracy and education: An introduction to the philosophy of education. The Floating Press. Drok, K. 2020. Improving student outcomes in online learning: Tracing the impact of opportunity through online learning. Perth: National Centre for Student Equity in Higher Education, Curtin University. http://www.transmogrify.com.au/wp-content/uploads/2020/11/ImpactReport_ CathyStone_FINAL.pdf Edwards, D., and J. McMillan. 2015. Completing university in Australia: A cohort analysis exploring equity group outcomes. Joining the Dots Research Briefings 3(3): 12. https:// research.acer.edu.au/joining_the_dots/29 Evans, C., G. Rees, C. Taylor, and C. Wright. 2019. ‘Widening access’ to higher education: The reproduction of university hierarchies through policy enactment. Journal of Education Policy 34 (1): 101–116. https://doi.org/10.1080/02680939.2017.1390165. Foster, E., and R. Siddle. 2020. The effectiveness of learning analytics for identifying at-risk students in higher education. Assessment & Evaluation in Higher Education 45 (6): 842–854. https://doi.org/10.1080/02602938.2019.1682118. Goldrick, M., T. Stevns, and L.B. Christensen. 2014. The use of assistive technologies as learning technologies to facilitate flexible learning in higher education. In Computers helping people with special needs. ICCHP 2014, Lecture notes in computer science, ed. K. Miesenberger, D. Fels, D. Archambault, P. Peňáz, and W. Zagler, vol. 8548, 342–349. Cham: Springer. https://doi.org/ 10.1007/978-3-319-08599-9_52.

5

A Social Equity–Based Framework Toward the Development of the. . .

91

Haar, M. 2018. Increasing sense of community in higher education nutrition courses using technology. Journal of Nutrition Education and Behavior 50 (1): 96–99. https://doi.org/10.1016/j. jneb.2017.04.015. Harvey, A., L. Andrewartha, D. Edwards, J. Clarke, and K. Reyes. 2017. Student equity and employability in higher education. Report for the Australian Government Department of Education and Training. Melbourne: Centre for Higher Education Equity and Diversity Research, La Trobe University. Hengtao, T., and A. Carr-Chellman. 2016. Massive open online courses and educational equality in China: A qualitative inquiry. Journal of Educational Technology Development & Exchange 9 (1): 49–66. https://doi.org/10.18785/jetde.0901.04. Herbaut, E., and K.M. Geven. 2019. What works to reduce inequalities in higher education? A systematic review of the (quasi-) experimental literature on outreach and financial aid. A Systematic Review of the (Quasi-) Experimental Literature on Outreach and Financial Aid (April 2, 2019). World Bank Policy Research Working Paper(8802). https://ssrn.com/ abstract¼3364697 Huang, R.H., D.J. Liu, A. Tlili, J.F. Yang, and H.H. Wang. 2020. Handbook on facilitating flexible learning during educational disruption: The Chinese experience in maintaining undisrupted learning in COVID-19 outbreak, 1–54. Beijing: Smart Learning Institute of Beijing Normal University. Kent, M., K. Ellis, N. Latter, and G. Peaty. 2018. The case for captioned lectures in Australian higher education. TechTrends 62 (2): 158–165. https://doi.org/10.1007/s11528-017-0225-x. Lambert, S.R. 2020. Do moocs contribute to student equity and social inclusion? A systematic review 2014–18. Computers & Education 145: 103693. https://doi.org/10.1016/j.compedu. 2019.103693. Lambrechts, A.A. 2020. The super-disadvantaged in higher education: Barriers to access for refugee background students in England [Article]. Higher Education 80 (5): 803–822. https:// doi.org/10.1007/s10734-020-00515-4. Leventhal, G.S. 1976. The distribution of rewards and resources in groups and organisations. Advances in Experimental Social Psychology: 91–131. https://doi.org/10.1016/S00652601(08)60059-3. Li, I.W., and D.R. Carroll. 2020. Factors influencing dropout and academic performance: An Australian higher education equity perspective. Journal of Higher Education Policy and Management 42 (1): 14–30. https://doi.org/10.1080/1360080X.2019.1649993. Lin, F.F., L. Del Fabbro, J. Needham, D. Sidwell, and J. Shaw. 2021. Supporting culturally and linguistically diverse (CALD) undergraduate nursing students undertaking clinical placements in Australia: An exploratory qualitative study of clinical facilitator and CALD student perceptions. Nurse Education Today 97: 104712. https://doi.org/10.1016/j.nedt.2020.104712. McMahon, D., and Z. Walker. 2019. Leveraging emerging technology to design an inclusive future with universal design for learning [Article]. Izkoriščanje porajajoče se tehnologije za oblikovanje vključujoče prihodnosti z univerzalnim modelom za učenje. 9 (3): 75–93. https:// doi.org/10.26529/cepsj.639. Meyer, A., D.H. Rose, and D.T. Gordon. 2014. Universal design for learning: Theory and practice. CAST Professional Publishing. Mishra, S. 2020. Social networks, social capital, social support and academic success in higher education: A systematic review with a special focus on ‘underrepresented’ students. Educational Research Review 29: 100307. https://doi.org/10.1016/j.edurev.2019.100307. Murray, R., and H. Gray. 2021. Forced migrants in higher education: ‘Sanctuary scholarships’ in a hostile environment. Journal of Ethnic and Migration Studies: 1–18. https://doi.org/10.1080/ 1369183X.2020.1816814. National Digital Inclusion Alliance. 2019. https://www.digitalinclusion.org/definitions/ Nelson, K. J., C. Picton, J. McMillan, D. Edwards, M. Devlin, and K. Martin. 2017. Understanding completion patterns of equity students in regional universities. National Centre for Student Equity in Higher Education. https://research.usc.edu.au/discovery/fulldisplay/ alma99450667302621/61USC_INST:ResearchRepository OECD. 2019. Society at a glance 2019. https://doi.org/10.1787/soc_glance-2019-en.

92

Z. A. Tay

Olaussen, E.J., A. Heelan, and K.A. Knarlag. 2019. Universal Design for Learning–license to learn: A process for mapping a Universal Design for Learning process on to campus learning. In Transforming higher education through Universal Design for Learning, ed. S. Bracken and K. Novak, 11–32. London: Routledge. Pitman, T. 2017. Widening participation in higher education: A play in five acts. Australian Universities’ Review 59 (1): 37–46. Pitman, T., L. Roberts, D. Bennett, and S. Richardson. 2019. An Australian study of graduate outcomes for disadvantaged students. Journal of Further and Higher Education 43 (1): 45–57. https://doi.org/10.1080/0309877X.2017.1349895. Pollard, L. 2018. Remote student university success: An analysis of policy and practice. National Centre for Student Equity in Higher Education. https://www.ncsehe.edu.au/publications/ remote-student-university-success-analysis-policy-practice/ Prayaga, P., E. Rennie, E. Pechenkina, and A. Hunter. 2017. Digital literacy and other factors influencing the success of online courses in remote indigenous communities. In Indigenous pathways, transitions and participation in higher education, ed. J. Frawley, S. Larkin, and J.A. Smith, 189–210. Singapore: Springer. https://doi.org/10.1007/978-981-10-4062-7_12. Resta, P., and T. Laferrière. 2008. Issues and challenges related to digital equity. In International handbook of information technology in primary and secondary education, ed. J. Voogt and G. Knezek, 765–778. Springer. https://doi.org/10.1007/978-0-387-73315-9_44. Rogers-Shaw, C., D.J. Carr-Chellman, and J. Choi. 2018. Universal design for learning: Guidelines for accessible online instruction. Adult Learning 29 (1): 20–31. https://doi.org/10.1177/ 1045159517735530. Ryan, S., H. Freeman, B. Scott, and D. Patel. 2000. The virtual university: The internet and resource-based learning. Routledge. https://doi.org/10.4324/9781315042022. Seale, J. 2019. E-learning and disability in higher education: Accessibility research and practice. Routledge. https://doi.org/10.4324/9780203095942. Stone, C. 2017. Opportunity through online learning: Improving student access, participation and success in higher education. Perth: The National Centre for Student Equity in Higher Education (NCSEHE), Curtin University. https://www.ncsehe.edu.au/publications/opportunity-onlinelearning-improving-student-access-participation-success-higher-education/ Tang, E., and C. Lam. 2014. Building an effective online learning community (OLC) in blog-based teaching portfolios. The Internet and Higher Education 20: 79–85. https://doi.org/10.1016/j. iheduc.2012.12.002. Tinto, V. 1975. Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research 45 (1): 89–125. https://doi.org/10.3102/00346543045001089. Veytia-Bucheli, M.G., J. Gómez-Galán, and D. Vergara. 2020. Presence of new forms of intercultural communication in higher education: Emojis and social interactions through WhatsApp among graduate students. Education Sciences 10 (11): 295. https://doi.org/10. 3390/educsci10110295. Warschauer, M. 2004. Technology and social inclusion: Rethinking the digital divide. MIT Press. Yuan, L., and S.J. Powell. 2013. MOOCs and open education: Implications for higher education. https://e-space.mmu.ac.uk/id/eprint/619735

6

Academic Engagement in Pedagogic Transformation Rachel Maxwell and Alejandro Armellini

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Large-Scale Pedagogic Transformation at the University of Northampton . . . . . . . . . . . . . . . . . . . . Lessons from the Implementation of ABL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . From Pre-pandemic ABL to Emergency Remote Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ERT Versus “proper” Online Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Implications for the Virtual University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

94 96 98 100 102 102 104 106

Abstract

Between 2014 and 2018, the University of Northampton underwent an intense 5-year period of institutional transformation that impacted its approach to learning and teaching, in readiness for its move to a brand new, digitally rich campus in September 2018. This chapter considers both the direct and indirect lessons learned from our experiences in engaging the academic community at Northampton in this large-scale pedagogic transformation and how these lessons might inform the debate about virtual universities. Key decisions and their impact on staff engagement with the project are explored through the lens of a quality enhancement model for pedagogic transformation. Ultimately, transformation on an institutional scale required strong strategic leadership and a rationale to set an unequivocal direction of travel. We reflect on how this evidence-support-agency approach to pedagogic change R. Maxwell (*) Community Manager, Solutionpath Ltd, Leeds, UK e-mail: [email protected] A. Armellini University of Portsmouth, Portsmouth, UK e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_6

93

94

R. Maxwell and A. Armellini

unknowingly prepared the university for the COVID-19 pandemic. We consider how such changes and catalysts such as a global pandemic could inform the future development of an entirely virtual university. Our experiences suggest that the creation of a virtual university will require similar lessons and activities to those used to transform learning and teaching and Northampton: Substitution and replication will not suffice. Rather, there needs to be an intentional set of interrelated processes that turn disruptions into positive and lasting change. We propose an adapted version of Northampton’s quality enhancement model for pedagogic transformation that demonstrates how the academic community might be encouraged to participate in, shape, and even own a radical and innovative development such as the creation of a virtual university and thereby mainstream large-scale pedagogic transformation. Keywords

ABL · Active Blended Learning · Emergency remote teaching · Online learning · Pedagogic transformation · Quality enhancement

Introduction This chapter considers how the lessons learned from the introduction of wholesale pedagogic transformation through Active Blended Learning (ABL) at the University of Northampton in the United Kingdom can inform the development of the virtual university. It focuses particularly on how the academic community can be a genuine stakeholder in the change process. We use the University’s quality enhancement model for pedagogic transformation to reflect on how the whole academic community was engaged in radical and innovative shifts in learning and teaching that mainstreamed niche practices and experiences. The chapter also considers how the COVID-19 pandemic of 2020–2021 acted as an eye opener to reconceptualizing HE provision and introducing more online learning. Our reflections on the changes at Northampton and the impact of COVID-19 inform an emerging conceptual framework that can inform future discussion on how to effectively engage the academic community in the creation of a virtual university. Over a 5-year period (2014–2018), Active Blended Learning became the standard approach to learning and teaching at the University of Northampton (Armellini and Padilla Rodriguez 2021; Lomer and Palmer 2021; Maxwell 2020). In the process, the University shifted itself conceptually and practically, defining and refining ABL as its unique selling point: Active Blended Learning is built on years of experience that combines face-to-face teaching with carefully crafted digital experiences, allowing you to . . . take control of your own learning. . . . Welcome to a bespoke, customised experience, more in tune with the world of work than traditional learning and teaching methods. (University of Northampton n.d.)

Each constituent part of ABL matters:

6

Academic Engagement in Pedagogic Transformation

95

• Being “active” requires students to intentionally engage in higher-order thinking processes that enable them to understand their own thoughts (Veenman et al. 2006): “The general narrative suggests that active learning rejects traditional lectures, often of a unidirectional or ‘content delivery’ nature, in favour of a student-centred, interactive approach” (Armellini and Padilla Rodriguez 2021, p. 7). • “Blended” is much more extensive and complex than an arguably simplistic combination of face-to-face and online. It includes multiple other dimensions that add value to the educational experience (Armellini and Padilla Rodriguez 2021). • The focus on “learning” signals a shift away from the tutor, to what students are learning (by doing and by thinking about what they do), which must be addressed in both course design and teaching practice. There are many ways in which ABL can be implemented. For example, learners could be given a set of presession sense-making activities with embedded content to address asynchronously, individually, or in groups. During the real-time session, students analyze, critique, and discuss those activities and set their own learning goals for the following week. After the session, students review the work done in preparation for and during the session, consolidate that knowledge, and reflect on its application for the next part of the course. The difference between ABL and flipped learning is that ABL focuses on the active engagement with course materials, critiques, and application of learning in the preparatory phase as opposed to the mere exposure to content (see further Armellini and Padilla Rodriguez 2021). TeamBased Learning ® is another example of ABL in practice (Maxwell and Khatri 2021). The three principles that guided and informed the creation of the university’s vision for this “unique learning and teaching model” (University of Northampton 2015) were those of evidence, support, and agency: • Evidence of the pedagogical benefits of active and blended learning was obtained from the literature and, over time, from colleagues at the University (Armellini et al. 2021; Institute of Learning and Teaching in Higher Education 2020; Usher 2015; University of Northampton Learning Technology Team n.d.; Khatri and Siddons 2015). • Support was provided via considerable and ongoing investment in central learning design and academic staff development teams and ranged from formal workshops centered around identified staff needs, to more personalized support provided on a small group or 1:1 basis. • Agency ensured that academic staff were supported to learn new digital skills and implement changes to practice within the virtual learning environment as well as in the classroom. Given that ABL was designed to become the University’s standard approach to learning and teaching, it was necessary to actively, explicitly, and strategically engage the entire academic community in this shift. “Selling” ABL was therefore

96

R. Maxwell and A. Armellini

a multistranded piece of work that occurred over several years and, in many respects, remains ongoing today.

Large-Scale Pedagogic Transformation at the University of Northampton The University’s 2015–2020 strategic plan (University of Northampton 2015) articulated the thinking behind the pedagogic shift: “For things to stay the same, things will have to change” (di Lampedusa 1958). The message from senior leadership was clear: For the University to continue (i.e., to stay the same), changes to our learning and teaching approaches across the board were required. It should be noted that a significant number of other changes were also being implemented, affecting ways of working for all staff, among other aspects (Maxwell and Howe 2022). For the purposes of this chapter, the following model illustrates the approaches employed to bring about the quality enhancements to learning and teaching practice and inform the shift to ABL (Fig. 1): The literature on quality assurance in higher education (HE) is broad and covers multiple aspects across geographical areas and educational settings. However, pedagogic quality enhancement frameworks and models that operate at an institutional level are much harder to find. The Standards and Guidelines for Quality Assurance in the European Higher Education Area (ESG), for example, are a set of standards and guidelines for internal and external quality assurance in HE. The ESG provide guidance in vital areas for successful quality provision and learning environments in HE, including research and innovation (European Association for Quality Assurance in Higher Education [ENQA] et al. 2015). At the heart of all quality assurance activities are the twin purposes of accountability and enhancement. [. . .] A successfully implemented quality assurance system will provide information to assure the higher education institution and the public of the quality of the higher education institution’s activities (accountability) as well as provide advice and recommendations on how it might improve what it is doing (enhancement). Quality assurance and quality enhancement are thus inter-related. They can support the development of a quality culture that is embraced by all: from the students and academic staff to the institutional leadership and management. (ENQA et al. 2015)

The focus of the university’s large-scale transformation process was firmly on quality enhancement. The impetus for this change originated from the very top of the University. Realizing this bold and ambitious vision for a unique learning and teaching model, located in the “radical-innovative” section of Fig. 1, necessitated strong central leadership and a healthy appetite for risk. The University invested substantially in the change processes: first, through an annual Learning and Teaching Innovation Fund to incentivize staff to trial, evaluate, and share different pedagogic innovations; and second, via mechanisms of practical support, in the form of academic staff development workshops and associated staff resource (Padilla Rodriguez and Armellini 2021). At all times, robust quality assurance and

6

Academic Engagement in Pedagogic Transformation

97

Fig. 1 The University of Northampton’s quality enhancement model for pedagogic transformation

accountability mechanisms remained in place in terms of meeting professional body requirements and national subject benchmark statements. Finding a way into an ABL-focused conversation with each academic and a way to help them explore the principles of ABL in their own contexts was critical to engagement. The rate of change varied depending on the ability of staff to realign their views of what it meant to teach in a way that aligned with the principles of ABL. Momentum increased with the growth of local empirical evidence. Based on the principles of appreciative inquiry and informed by our strategic “research to practice to impact evaluation to policy” approach, this UON-specific evidence base reaped benefits for many. Accompanying research on student perceptions of ABL and the consequent lessons for staff was conducted, published, and shared (Lomer et al. 2017), primarily to ensure that classroom practice upheld the theoretical ABL-design. The emerging “developmental-incremental” changes (Fig. 1) consisted of staff actively exploring and implementing new classroom practices or technologies that

98

R. Maxwell and A. Armellini

would not simply replicate but would innovate or transform learning and teaching within and across subject areas. Staff who readily understood and engaged early with the principles of ABL took advantage of the opportunity provided by the Innovation Fund to explore and iteratively build on incremental changes to their teaching practice, while harnessing the power of existing and emerging educational technologies (Institute of Learning and Teaching in Higher Education 2020; Usher 2015; University of Northampton Learning Technology Team n.d. and see also Khatri and Siddons 2015). In turn, other colleagues were prompted to try something new to them (Lomer et al. 2017). The third area of the model shown in Fig. 1, “responsive-reactive” change, primarily occurred through responding to student feedback captured through annual module evaluation surveys prompting iterative enhancements to teaching practices. Empirical data, including student satisfaction insights obtained via the National Student Survey to internal data submitted annually to the Higher Education Statistics Agency (HESA), was also used to focus time and effort on aspects of course design and delivery that were either not proving effective for students or pointed to structural issues (e.g., high withdrawal or low progression rates). Niche enhancement activities deployed by individual colleagues or within subject areas were adopted and adapted by others within a team or by different subject areas. Over time, the ambitious vision to develop a unique learning and teaching model became increasingly tangible as niche enhancements became mainstream pedagogic practice originating from each of the three types of quality enhancement activity (Fig. 1). Active monitoring and oversight of progress toward being “ABL-ready” enabled the transformation to stay on track and meet the fixed deadline (September 2018) that the move to the new Waterside campus provided. Some academic colleagues were sufficiently uncomfortable with the University’s direction of travel that they sought new roles elsewhere. Senior management publicly recognized this as a possible outcome of the strategic decisions that were being implemented and communicated to staff at regular Vice Chancellor’s Roadshows. Recruitment of staff to a virtual university would be different to what happened initially at Northampton, where academics were asked to redefine their understanding of what it meant to teach there (i.e., to consider how they had to change, in order to “stay the same”). In the context of a new virtual university, academics would know what they were signing up to in advance. This is the position Northampton now finds itself in: Recruitment processes are clear that ABL is the standard approach to learning and teaching. It is not an add-on to their job. It is their job. Candidates apply and are recruited for positions on that basis.

Lessons from the Implementation of ABL As an institutional change project, the introduction of ABL was not without its difficulties. The introduction of ABL followed a pragmatic approach to the introduction of practical change with an expectation that shifts in attitudes would follow

6

Academic Engagement in Pedagogic Transformation

99

(Guskey 1986, 2002, 2020). Staff responses were grouped into four broad categories: 1. “Active innovators” – colleagues who embrace ABL in theory and apply it to their pedagogic practice 2. “Lagging innovators” – colleagues who are happy with ABL in principle, but are unable to fully implement it 3. “Sceptical but obliging” – colleagues who hold negative beliefs about ABL but are prepared to comply 4. “Sceptical and resistant” – colleagues who believe ABL will not work and therefore will not consider implementing it (Teixeira Antunes et al. 2021) Our experiences of introducing and supporting ABL at the University of Northampton provide reflective insights into how highlighting the benefits of pedagogic change and the intended improvements to outcomes can engender support from the academic community and realize the stated vision. Part of “selling” major institutional change to the academic community involves engaging their imagination in something that does not yet exist. Storytelling has a meaningful part to play here if the desired transformation is to be “accessible,” within the conceptual grasp of academics not currently working in this way. To help colleagues imagine, envision, and make sense of ABL and the associated new ways of working that would be realized at the new campus, the then Head of Library and Learning Services created a narrative “day in the life” of a student at Waterside. As a piece of “transformative fiction” (Ricoeur n.d., as cited in Taylor 2006 p. 98), this narrative had sufficient familiarity and connection for many academic colleagues to see themselves as part of the conceptual new reality. Enabling academic staff and students to autonomously acquire, develop, and refine their digital skills is essential if online teaching and learning is to prove effective and, critically, scalable. Multiple digital skills’ frameworks exist within the UK, both nationally (e.g., Advance HE 2020; Jisc n.d.; Gov.UK 2019) and at institutional level (see University of Edinburgh Information Services 2019). As an essential part of the teaching toolkit (whether teaching online or in-class using technology), staff need not only the knowledge about digital skills and the affordances of technology, but also the wisdom to know when to deploy each to maximize their effectiveness, considering the multiple factors influencing academics’ agency over curriculum design and teaching practice. Emphasizing the importance of the individual tutor as a success factor in the student learning experience can help tutors to feel confident of their place within the learning environment. We posit that large-scale pedagogic transformations such as the shift to ABL at Northampton or the creation of a virtual university signify an acceleration in the redefinition of the academic role away from the academic as the “fount of all knowledge” to a “facilitator of learning.” This does not imply a dumbing down of the academic role – subject knowledge and understanding alongside appropriate

100

R. Maxwell and A. Armellini

qualifications and experience remain imperative in recruitment and promotion contexts. As with any good learning experience, the provision of and appropriate responses to feedback from colleagues engaging in transformational pedagogic change are essential to deeper and consolidated learning. This includes swiftly and openly addressing misconceptions that can arise, particularly in the early days of defining a new concept. For example, without a clear articulation of what learning and teaching at a virtual university will entail, the potential for comparisons with distance learning experiences remains. At Northampton, it was helpful to shift the conversation from face-to-face or online learning to whether the learning opportunity is synchronous or asynchronous. In this way, the conversation focused less on the technology per se, and more on the nature and immediacy of the learning instance. There is, of course, a major assumption underpinning this discussion of how to engage the academic community in technology-enhanced learning for the purposes of establishing a virtual university: the need for confidence in the capabilities, stability, and reliability of the digital infrastructure that will support daily operations. From synchronous webinars to online access to HR systems and structures, staff need to be confident that they can do their job without recourse to a physical location and access to colleagues in the flesh. Maxwell and Howe (2022) provide an overview of the technological and operational changes to the way in which the University of Northampton functioned that were implemented alongside the introduction of ABL. These changes included the provision of IT equipment, the operational introduction of activity-based working (Candido et al. 2016; Engelen et al. 2018), and supportive “work anywhere” policies. Innovation in the academic community is not new. However, achieving universal institutional change requires strategic leadership and a well-articulated value proposition that all stakeholders can engage with. Without this direction, pedagogic change at Northampton would have continued as piecemeal, parochial, and largely individual pockets of good practice, driven over short periods of time by institutional objectives, e.g., the need for professional recognition. Accountability through standard structures of academic governance (reporting to committees) helped to keep the project on track.

From Pre-pandemic ABL to Emergency Remote Teaching The reactive shift to emergency remote teaching (ERT) witnessed in the early months of COVID-19 fundamentally and perhaps permanently disrupted the provision of higher education. As a result, the pandemic would sit firmly within the Responsive-Reactive section of Fig. 1. The speed of the shift certainly is atypical in a sector that is usually slow to innovate and change (Mcmullan and Long 1987; Caruth and Caruth 2013; Sylvestre et al. 2013). The rapid decision-making, and pivot to ERT, triggered a proliferation of ideas, lessons, examples, and case studies of what makes good online learning and teaching or how to support students starting at

6

Academic Engagement in Pedagogic Transformation

101

university during a pandemic (see Brown 2020; Haslam 2021; Jisc 2020; Parkin and Brown 2020). ERT is defined as “provid[ing] temporary access to instruction and instructional supports in a manner that is quick to set up and is reliably available during an emergency or crisis” (Hodges et al. 2020). It is unclear which aspects of learning and teaching introduced as part of ERT should be retained or in which contexts, even if in an adapted form (see The British Academy 2020; Johnson 2020; Mills 2021; Zaretsky 2021). Moreover, as Lea (2021) argues, it is probably too soon at the time of writing to fully understand and assess the data on student experiences during COVID-19 to make meaningful longer-term decisions. A study of UK-based academics (n ¼ 1148) on the impact of ERT in response to the closure of universities (Watermeyer et al. 2021) reported a belief expressed by many of their participants: Staff and students already operating in the digital sphere were more prepared for ERT at the start of the pandemic. “Respondents as a whole recognised that institutions already invested in digital education and with a preexisting and developed infrastructure and capacity to deliver LTA [learning, teaching and assessment] online would be advantageously positioned to attract greater numbers of students” (p. 629). The earlier work on ABL at Northampton left the university in a favorable position to deploy ERT in March 2020. In line with earlier research findings (Watermeyer et al. 2021), the university’s transition to ERT was arguably less painful than at many other institutions. The benefits of iterative enhancement, experiential learning, and appreciative inquiry within a specified time frame were evident. The lessons learned from both the earlier transition to ABL and the pivot to ERT are relevant when considering the best ways to engage the academic community with the notion of a virtual university. Findings from research conducted by Sir Michael Barber, Chair of the sector regulator for HE in the UK, the Office for Students, indicates both the scale of the shift to ERT in the UK and a sense of what teaching staff and students would like to preserve. Having engaged in ERT, 70% of teaching staff subsequently agreed that digital teaching and learning (DTL) (strategically planned, designed, and proactively delivered) provided opportunities to “teach students in new and exciting ways” (p. 36) with 51% indicating a desire to engage in DTL for the long term (Barber 2021). University leaders echoed the sentiments of their staff, reporting active reconsideration of ways to adapt courses for digital and blended formats. Similar findings were reported by Jisc (2020). Eringfield (2021) argues that a blended approach combining virtual and in-person education is required to “ensure that HE remains an embodied and communal experience” and contributes to improved retention and attainment metrics (Thomas et al. 2017). Proactive ownership of changes seems necessary. As Watermeyer et al. (2021) argue, mapping the terrain of digital transformation is “vital” and waiting to see what happens next would amount to a “neglect of responsibility.” They argue that academics must not merely guide the forthcoming change process but actively claim ownership and agency over it.

102

R. Maxwell and A. Armellini

ERT Versus “proper” Online Learning Watermeyer et al. point out that COVID-19 has “not only forced change but revealed quite how much such change is overdue” (2021, p. 624). They go on to argue that the time for “endless pages” about the future of the university is now “inextricably linked” to the digital transformation of higher education. This failure to engage in debate around the use and disruptive potential of digital transformation has “dogged many universities and caused their arrest in rethinking their role and relevance” (p. 625). It is appropriate and relevant to highlight the stark differences between ERT and effective online teaching. The most obvious and significant of these differences is to recognize that effective online education (a key expectation in a virtual university) results from a planned and deliberate approach to learning design with careful consideration of the learner experience. Effective online education includes the creation of active and intentional online interactions between students, and with their tutors and the content (Bernard et al. 2009; Miyazoe and Anderson 2010). Effective online education also includes the recognition that learning is both a “social and a cognitive process, not merely a matter of information transmission” (Hodges et al. 2020). Hodges et al. (2020) usefully highlighted the differences between ERT and adequately planned online education: Online learning carries a stigma of being lower quality than face-to-face learning, despite research showing otherwise. These hurried moves online by so many institutions at once could seal the perception of online learning as a weak option, when in truth nobody making the transition to online teaching under these circumstances will truly be designing to take full advantage of the affordances and possibilities of the online format. (Hodges et al. 2020)

This difference between ERT and effective online education can also be viewed from the perspective of ERT as a reactive response to external factors whereas effective online learning is proactive and intentional, designed and scaffolded over time to support student learning – as was ABL. Recognition of the difference between ERT and effective online learning can help members of the academic community to review their expectations on the quality of provision beyond the pandemic and across modes of study. Adjustments to teaching practice are likely to be required.

Implications for the Virtual University As we consider how best to engage the academic community in the vision of a virtual university, the lessons from the introduction of ABL and the wider experiences across the sector from the COVID-19 pandemic enable us to posit a revised version of the quality enhancement model for pedagogic transformation shown in Fig. 1. In this revised model, work to improve the quality of learning, teaching, and assessment can be conceptualized as being more linear than the original model. Notions of

6

Academic Engagement in Pedagogic Transformation

103

a virtual university are not new (see Calvani 2003). While they may remain, for many, firmly in the radical-innovative area of our original model (Fig. 1), ERT and the shift to fully online learning have challenged, if not permanently disrupted, the international higher education sector. What is apparent from the ABL experience at the institutional level is that for radical innovative ideas to be realized and owned by the academic community, they also need to be part of a developmental-incremental process where changes occur within the culture, over time. ERT, on the other hand, by definition, was an emergency measure in response to a crisis. For many, it was radical and innovative too, although it would not, per se, move to the mainstream (Fig. 1). At the level of personal pedagogic practices, an iterative and symbiotic cycle of developmental-incremental and responsive-reactive enhancements is needed to enable proactive experimentation, evaluation, and reflection (Fig. 2). In this way, the community can envision themselves teaching in new ways and in new contexts, participating in what has, for many until recently, been a pipe dream or a conceptual experiment. Figure 2 presents a simplified version of a hugely complex process. The linear nature of Fig. 2 starts with a radical or innovative idea, which is driven typically by senior management and shaped by the academic community into a viable and preferred reality. The application of this process to the virtual university results in what is described in Fig. 3. A vision of the new concept is shared with the academic community at which point it enters an iterative process where members of that academic community implement changes to pedagogy in line with the vision. This process of conceptualization, trialing, evaluation, and refinement results, in due course, in a tangible manifestation of the virtual university. In helping members of the academic community to make the conceptual shift from their current teaching practices to those required in a virtual university, it is important not to underestimate the impact of the COVID-19 pandemic. In a preCOVID-19 world, the shift from a fully face-to-face teaching environment to a fully virtual one would have seemed daunting for many, if not impossible to achieve. Due to the introduction of ABL, academics at the University of Northampton, similar to

Fig. 2 Revised model of quality enhancement for pedagogic transformation

104

R. Maxwell and A. Armellini

Fig. 3 Revised model of quality enhancement for pedagogic transformation in the context of the virtual university

their counterparts at specialist distance learning institutions like the Open University in the UK, had already taken steps along the continuum away from fully face-to-face toward online learning, making use of the affordances of learning technologies in the process. COVID-19 forced all members of the academic community away from face-toface learning and teaching. As a result, the gap between their practice and full online teaching has been shortened. Some may have gone further and shifted away from a reactive response – ERT – to more proactive and intentionally designed online learning and teaching experiences. Those academics should therefore find it easier to conceive of themselves working for a fully online institution such as the one proposed in this book. Therefore, COVID-19 can be seen as a catalyst that has helped to consolidate understanding and the critical review of possibilities that were previously dismissed. This unexpected development has brought the affordances of online learning into focus. Early signs that senior academic leaders are considering new ways to utilize their physical estate are indicative that higher education is unlikely to return to the way it was prepandemic.

Conclusion and Future Directions Conceptualizing learning and teaching as a continuum from fully face-to-face to fully online would see technology-enhanced learning positioned somewhere between the two extremes, depending on the pedagogic objectives, the learning environment, and the appropriateness of the chosen technologies in achieving those objectives. The digital capabilities of both tutors and students will also impact on their effectiveness, influenced in part by the extent to which those technologies are effectively blended into practice. But whereas earlier notions of online practices would imply distance learning or a massive open online course (MOOC), the pivot to emergency remote teaching has raised awareness of the affordances of digital technologies and how they can enable active, synchronous, and asynchronous online teaching. Interactive learning activities such as group discussions, the cocreation of educational artifacts, and student-generated content help to foster that much-desired

6

Academic Engagement in Pedagogic Transformation

105

sense of community and belonging. Emphasizing the importance of teaching in the virtual university is therefore an important facet of selling the concept to members of the academic community and mainstreaming effective online teaching as an option for prospective students. Used appropriately, educational technologies can assist pedagogic enhancement and innovation. Used inappropriately, the technologies can function as barriers to both learning and innovation. The shift to ABL at Northampton was a planned, intentional, and time-bound developmental-incremental response to a radical-innovative idea (Fig. 1). On the other hand, the imposition of ERT across the global HE sector in response to the COVID-19 crisis was, by definition, an unplanned, responsive reaction to a crisis of unknown duration. The introduction of ABL resulted in a much smoother shift to ERT, as much of the pedagogical paradigm shift had been completed and staff were already implementing features of effective online teaching practice. These staff, like others already engaging effectively in digital teaching practices, will be keen to explore the concept of a virtual university and more likely to imagine their future careers there. ERT provided this group with a valuable opportunity to engage with pedagogic change, consolidate their practice, and align it to what teaching in a virtual university is likely to require. In a post-ERT context, academics could move back to traditional campus-based provision. Alternatively, they could take advantage of the newly acquired skills and opportunities as they transition to a truly digital virtual university. It is likely that there will be a reasonable proportion who are eager to return to prepandemic style face-to-face learning and teaching. For many of those academics, ERT does not represent a preferred scenario for shaping future learning and teaching practice. Without any external influence encouraging or requiring pedagogic change, many may choose to return to the pre-COVID status quo, having chosen not to harness the potential for pedagogic enhancement resulting from the pandemic. Strong, visionary leadership and effective, timely communications are fundamental prerequisites to the success of engaging the academic community in radical projects like the creation of the virtual university. Together, these two factors should galvanize members of the staff body into contributions (Fig. 3) that help to shape the output and meet the expectation articulated by Watermeyer et al. (2021), namely, that the academic community need to actively claim ownership and agency of the terrain of future digital enhancement to learning and teaching. To succeed in the introduction of a radical idea like a virtual university, it is important that academics can, early on in the process, see themselves in the new environment. They need to be convinced that radical and innovative proposals are fully underpinned by empirical evidence that is applicable to their student demographic and that any changes to pedagogy will enhance the learning experience, the teaching experience, and student outcomes. Colleagues also need to have the appropriate support structures in place. These range from pedagogical and technical support, as well as supportive HR policies that permit flexible and collaborative working. Finally, they need to feel a sense of ownership and agency, not only of their own teaching materials and practices, but also within the wider learning community.

106

R. Maxwell and A. Armellini

References Advance HE. 2020. Digital literacies. Advance HE. Retrieved 9 June 2021 from https://www. advance-he.ac.uk/knowledge-hub/digital-literacies Armellini, A., and B.C. Padilla Rodriguez. 2021. Active blended learning: Definition, literature review, and a framework for implementation. In Cases on active blended learning in higher education. IGI Global, ed. B.C. Padilla Rodriguez and A. Armellini. https://doi.org/10.4018/ 978-1-7998-7856-8.ch001. Armellini, A., V. Teixeira Antunes, and R. Howe. 2021. Student perspectives on learning experiences in a higher education active blended learning context. TechTrends 65: 433–443. https:// doi.org/10.1007/s11528-021-00593-w. Barber, M. 2021. Gravity assist: Propelling higher education towards a brighter future – Report of the digital learning and teaching review. Office for Students. Retrieved 3 May 2021 from https:// www.officeforstudents.org.uk/publications/gravity-assist-propelling-higher-education-towardsa-brighter-future/recommendations/ Bernard, R.M., P.C. Abrami, E. Borokhovski, C.A. Wade, R.M. Tamim, M.A. Surkes, and E.C. Bethel. 2009. A meta-analysis of three types of interaction treatments in distance education. Review of Educational Research 79 (3): 1243–1289. Brown, S. 2020. The first weeks may be critical for the 2020 cohort. WonkHE. Retrieved 28 April 2021 from https://wonkhe.com/blogs/the-first-weeks-may-be-critical-for-the-2020-cohort/ Calvani, A., ed. 2003. Technological innovation and changes in the university: Moving towards the virtual university [online]. Firenze: Firenze University Press. Retrieved 22 April 2021 from http://digital.casalini.it/fulltext/is.asp?isbn¼8884530849. Candido, C., J. Zhang, J. Kim, R. de Dear, L. Thomas, P. Strapasson, and C. Joko. 2016, April. Impact of workspace layout on occupant satisfaction, perceived health and productivity. Proceedings of 9th Windsor Conference: Making Comfort Relevant. Windsor, UK. https://opus.lib. uts.edu.au/bitstream/10453/122457/1/WC16_Candido.pdf Caruth, G.D., and D.L. Caruth. 2013. The impact of distance education on higher education: A case study of the United States. Turkish Online Journal of Distance Education 14 (4): 121–131. Di Lampedusa, G.T. 1958. The Leopard. Milan: Feltrinelli. Engelen, L., J. Chau, S. Young, M. Mackey, D. Jeyapalan, and A. Bauman. 2018. Is activity-based working impacting health, work performance and perceptions? A systematic review. Building Research & Information 47 (4): 468–479. https://www.tandfonline.com/doi/abs/10.1080/ 09613218.2018.1440958. Eringfield, S. 2021. Higher education and its post-coronial future: Utopian hopes and dystopian fears at Cambridge University during COVID-19. Studies in Higher Education 46 (1): 146–157. European Association for Quality Assurance in Higher Education (ENQA), European Students’ Union (ESU), European University Association (EUA) & European Association of Institutions in Higher Education (EURASHE). 2015. Standards and guidelines for quality assurance in the European Higher Education Area (ESG). Brussels, Belgium. Retrieved 16 October 2021 from https://www.enqa.eu/wp-content/uploads/2015/11/ESG_2015.pdf Gov.UK. 2019. Essential digital skills framework. Department for Education, UK Government. Retrieved 9 June 2021 from https://www.gov.uk/government/publications/essential-digitalskills-framework/essential-digital-skills-framework Guskey, T.R. 1986. Staff development and the process of teacher change. Educational Researcher 15: 5–12. https://doi.org/10.3102/0013189X0150050054. ———. 2002. Professional development and teacher change. Teachers and Teaching 8: 381–391. https://doi.org/10.1080/135406002100000512. ———. 2020. Flip the script on change: Experience shapes teachers’ attitudes and beliefs. The Learning Professional 41 (2): 18–22. https://learningforward.org/wp-content/uploads/2020/04/ flip-the-script-on-change.pdf.

6

Academic Engagement in Pedagogic Transformation

107

Haslam, M.B. 2021. What might COVID-19 have taught us about the delivery of nurse education, in a post-COVID-19 World? Nurse Education Today 97 (February). https://doi.org/10.1016/j. nedt.2020.104707. Hodges, C., S. Moore, B. Lockee, T. Trust, and A. Bond. 2020. The difference between emergency remote teaching and online learning. Retrieved 5 May 2021 from https://er.educause.edu/ articles/2020/3/the-difference-between-emergency-remote-teaching-and-online-learning Institute of Learning and Teaching in Higher Education. 2020. Learning enhancement and innovation fund – Previous projects [online]. Retrieved 26 April 2021 from https://www. northampton.ac.uk/ilt/research-and-funding/learning-enhancement-and-innovation-fund-previ ous-projects/ Jisc. 2020. Learning and teaching reimagined: Change and challenge for students, staff and leaders. Jisc. https://www.jisc.ac.uk/reports/learning-and-teaching-reimagined-change-and-challenge ———. n.d. What is digital capability? Jisc. https://www.digitalcapability.jisc.ac.uk/what-isdigital-capability/ Johnson, J. 2020. Covid’s forced review of teaching practice has taught me a lesson. https://www. timeshighereducation.com/blog/covids-forced-review-teaching-practice-has-taught-me-lesson. Khatri, R., and L. Siddons. 2015. Team-based learning in occupational therapy: A student-centred approach [Paper presentation]. In University of Northampton learning and teaching conference 2015, Northampton, UK. Lea, J. 2021. Online learning and its discontents. Retrieved 28 April 2021 from https://thesedablog. wordpress.com/2021/02/17/online-learning-and-its-discontents/ Lomer, S., and E. Palmer. 2021. ‘I didn’t know this was actually stuff that could help us, with actually learning’: Student perceptions of active blended learning. Teaching in Higher Education. https://doi.org/10.1080/13562517.2020.1852202. Lomer, S., E. Palmer, and I. Bashliyska. 2017. Overcoming barriers to student engagement with active blended learning. Northampton: University of Northampton. https://www.northampton. ac.uk/ilt/research-and-funding/publications/. Maxwell, R. (2020). Pedagogic transformation at the University of Northampton. In Designing study structures flexibly – A challenge for universities and quality assurance: Contributions to the 7th AQ Austria annual meeting 2019, ed. Agency for Quality Assurance and Accreditation Austria, 126–139. https://www.aq.ac.at/de/ueber-uns/publikationen/sonstige-publikationen.php Maxwell, R., and R. Howe. 2022. To record or not to record? That was the question. In Digital learning – COVID and beyond, ed. M. Smith and J. Traxler. London: Edward Elgar Publishing. Maxwell, R., and R. Khatri. 2021. Exploring active blended learning through the lens of team-based learning™. In Cases on active blended learning in higher education, ed. B.C. Padilla Rodriguez and A. Armellini. Hershey, PA: IGI Global. Mcmullan, W.E., and W.A. Long. 1987. Entrepreneurship education in the nineties. Journal of Business Venturing 2 (3): 261–275. Mills, G. 2021. Post-Covid, mental well-being should be built into every course. Retrieved 3 May 2021 from: https://www.timeshighereducation.com/blog/post-covid-mental-well-being-shouldbe-built-every-course Miyazoe, M., and T. Anderson. 2010. The interaction equivalency theorem. Journal of Interactive Online Learning 9 (2): 94–104. http://www.ncolr.org/jiol/issues/pdf/9.2.1.pdf. Padilla Rodriguez, B.C., and A. Armellini, eds. 2021. Cases on active blended learning in higher education. IGI Global. https://doi.org/10.4018/978-1-7998-7856-8. Parkin, D., and G. Brown. 2020. Creating socially distanced campuses and education project: Final Capstone Report. Advance HE. Retrieved 29 April 2021 from https://www.advance-he.ac.uk/ knowledge-hub/creating-socially-distanced-campuses-and-education-project-final-capstonereport Sylvestre, P., R. McNeil, and T. Wright. 2013. From Talloires to Turin: A critical discourse analysis of declarations for sustainability in higher education. Sustainability 5 (4): 1356–1371. Taylor, G.H. 2006. Ricoeur’s philosophy of imagination. Journal of French Philosophy 16 (1): 93–104. Available from: https://papers.ssrn.com/abstract¼1352664.

108

R. Maxwell and A. Armellini

Teixeira Antunes, V., A. Armellini, and R. Howe. 2021. Beliefs and engagement in an institutionwide pedagogic shift. Teaching in Higher Education. https://doi.org/10.1080/13562517.2021. 1881773. The British Academy. 2020. COVID-19: What’s next for the future of universities? The British Academy. Retrieved 23 April 2021 from https://www.youtube.com/watch?v¼zT5_u8OLlXc Thomas, L., M. Hill, J. O’Mahony, and M. Yorke. 2017. Supporting student success: Strategies for institutional change. What works? Student Retention and Success programme. Paul Hamlyn Foundation, Higher Education Academy and Action on Access. https://www.heacademy.ac.uk/ system/files/hub/download/what_works_2_-_full_report.pdf. University of Edinburgh Information Services. 2019. Digital skills framework. Retrieved 9 June 2021 from https://www.ed.ac.uk/information-services/help-consultancy/is-skills/digital-skillsframework University of Northampton. 2015. Transforming lives + inspiring change: Strategic plan 2015–2020. Northampton: University of Northampton. ———. n.d. How we teach. Retrieved 26 April 2021 from https://www.northampton.ac.uk/ undergraduate/how-we-teach/ University of Northampton Learning Technology Team. n.d. Learning technology Blog: News and updates from the Learning Technology Team. Retrieved 26 April 2021 from https://blogs. northampton.ac.uk/learntech/ Usher, J. M. 2015. Programme design steps towards an appreciative inquiry approach. Retrieved 22 April 2021 from https://blogs.northampton.ac.uk/learntech/2015/05/29/programme-designsteps-towards-an-appreciative-inquiry-approach/ Veenman, M.V.J., B.H.A.M. Van Hout-Wolters, and P. Afflerbach. 2006. Metacognition and learning: Conceptual and methodological considerations. Metacognition and Learning 1 (1): 3–14. https://doi.org/10.1007/s11409-006-6893-0. Watermeyer, R., T. Crick, C. Knight, and J. Goodall. 2021. COVID-19 and digital disruption in UK Universities: Afflictions and affordances of emergency online migration. Higher Education 81 (3): 623–641. Zaretsky, R. 2021, April 6. Insisting on in-person classes post-Covid is not a line we can hold. Times Higher Education. Retrieved 3 May 2021 from https://www.timeshighereducation.com/ blog/insisting-person-classes-post-covid-not-line-we-can-hold

7

Innovation and the Role of Emerging Technologies Polly K. Lai and Lina Markauskaite

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Innovative Online Study Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digital Technologies in Learning Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Providing Students with an Opportunity to Visualize Invisible Phenomena . . . . . . . . . . . . . . . Supporting Students to Generate Ideas and Construct Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . Enabling Students to Gain Embodied Experiences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Empowering Students and Teachers to Exchange Ideas and Co-construct Authentic Understanding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion: A Way Forward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

110 111 113 114 115 116 119 121 122 122

Abstract

This chapter discusses the role of digital technologies from the perspectives of how students learn and how to support their deep engagement with learning. We do this at two levels: course or unit level and learning activity level. We first overview some recently emerged course models of virtual technology-enabled learning in Australian universities. Then, we suggest that innovations at the course level should be complemented with innovations at the learning activity level. These innovations should be informed by theories and research on how people learn, and educational design should focus on learning processes that result in deep learning. Deep learning reflects on students thinking about or P. K. Lai (*) Centre for Teaching and Learning, Southern Cross University, Gold Coast, Australia e-mail: [email protected] L. Markauskaite Sydney School of Education and Social Work, The University of Sydney, Sydney, Australia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_7

109

110

P. K. Lai and L. Markauskaite

working through the materials at a deep level of cognitive engagement. We discuss four critical roles of digital technologies in students’ learning and give examples from current research in STEM education. These four roles are to (1) provide students with an opportunity to visualize invisible phenomena, (2) support students to generate ideas and construct knowledge, (3) enable students to gain new embodied experiences, and (4) empower students and teachers to co-construct authentic understanding. We conclude the chapter with an invitation to move toward more participatory forms of virtual universities. Keywords

Technology-enhanced learning · How students learn · Digital technologies · Learning design

Introduction Today’s higher education institutions face challenges from rapidly rising pressures for flexible learning offerings. Some of these pressures are due to the COVID-19 pandemic, but some new, more flexible models of teaching and learning emerged as a result of longer, more planful educational reforms. For example, some universities started to offer joint cross-institutional courses (Holbert et al. 2020), and other institutions introduced micro-credentials and intensive block courses (Loton et al. 2020; Mccluskey et al. 2020; Samarawickrema and Cleary 2021; Goode et al. 2022, 2023). The use of digital technologies to support more flexible forms of teaching and learning has been seen as a must-do item in many Australian universities. However, what is the role of digital technologies in these new study models? Since the nineteenth century, the distance learning model has been utilized to provide higher education in Australian regional areas (White 1982). This model initially delivered information through tape recordings and printed materials and recently through online learning environments packed with digital resources and video lectures. Research has shown that this kind of lecture-based learning needs careful design (Mayer et al. 2020). However, one-way teaching limits students’ opportunities for immersion, interaction, and engagement in deep critical thinking (Chi 2021; Chi and Wylie 2014). In contrast, some more recently introduced technology-enhanced models of online teaching and learning focus not only on information delivery but also on students’ learning processes (Badia et al. 2017; Lai et al. 2018; Lai et al. 2017; Sejzi et al. 2012). This shift from a teacher-centered to a student-centered model requires educators and institutions to understand how people learn and how to support students’ learning in online learning environments. Digital technology has the potential to enhance students’ learning if educators use it appropriately. However, technology alone cannot transform how students learn. Pedagogy, the nature of subject knowledge, teaching and learning context, and the

7

Innovation and the Role of Emerging Technologies

111

nature of emerging learning processes all need to be taken into account in the design and teaching phases (Archambault and Barnett 2010; Mishra et al. 2011). In other words, what kind of technology is used, how it is used, and when it is used all play crucial roles in enabling successful students’ learning (Wekerle et al. 2020). To this end, the technological pedagogical content knowledge (TPACK) framework proposed by Mishra and Koehler (2006) emphasizes that teachers’ technology knowledge and skills alone are never enough; good teaching requires integrating technological, pedagogical, and content knowledge. Further, the effectiveness of technology-assisted learning depends on the degree of students’ engagement in learning activities (Chi and Wylie 2014). Online courses should provide a constructive and interactive learning environment where teachers are facilitators, and technology provides scenarios and scaffolding, helping students access information, solve problems, and co-construct various knowledge products collaboratively. This learning process should engage students in authentic meaning-making and promote their higher-order thinking (Chi 2021; Chi and Wylie 2014; Menekse and Chi 2019). In the rest of the chapter, we first discuss how digital technologies could be used at the course design level and introduce some examples of intensive, flexible, and wholly online study models that have been implemented in Australian universities. Then, we discuss the roles of digital technologies in learning activities and illustrate how technologies could be used in combination with different pedagogical strategies.

Innovative Online Study Models Several innovative wholly online study models have been implemented in Australian universities to meet the learning needs of a culturally diverse student population nationally and internationally and improve learning outcomes. These examples include PLuS Alliance’s online programs (Holbert et al. 2020), Victoria University’s (VU) Block Model (Loton et al. 2020), and Southern Cross University’s (SCU) 6  6 Model (Goode et al. 2023). The PLuS Alliance includes collaboration among three universities: Arizona State University (ASU), King’s College London (KCL), and the University of New South Wales (UNSW). These three universities offer their postgraduate courses for students across the universities. It means that a student enrolled in any of three universities can select online courses from the other two and study online with teachers and peers. One example is the online course of Nuclear Safety, Security, and Safeguards that UNSW offers in the Master of Nuclear Engineering program (Holbert et al. 2020). In this course, students are first asked to analyze and evaluate cases of safety hazards, then create solutions, and present their justification for the cases. After presentations, they are asked to provide constructive feedback to their peers. As students learn across two time zones (Sydney and Phoenix), several technologies are used to overcome the time and distance barriers in the course, such as recording videos, virtual classrooms (e.g., Collaborate Ultra and Zoom), and asynchronous

112

P. K. Lai and L. Markauskaite

discussions. The choice of technologies is well aligned with physical and time constraints and the pedagogical course design that aims to engage students in participatory learning. For example, the asynchronous discussion board is used to run a student-moderated forum activity. Every week, one group of students is given a contentious topic to introduce to the class, and all other students participate in a discussion. To promote an open debate, students’ responses are not marked; instead, the marks are given for the level of engagement and ability to moderate peers’ discussion. Additionally, a live video platform, Collaborate Ultra, is used to provide a venue for students to meet, discuss, and present their safety cases and solutions. Various intensive online study models are also increasingly used in Australian universities, for instance, the VU Block Model and SCU 6  6 Model. These models aim to meet the learning needs of students with work–life commitments (Ambler et al. 2021; Goode et al. 2023). Such courses typically last 4–6 weeks and combine virtual video classroom and self-directed learning components. Students take one or two courses at a time and complete a maximum of four courses in a 12-week semester. Moreover, these models typically promote small-size virtual video classrooms (no more than 35 students); active, inquiry-based, and problem-based learning (no lectures); as well as authentic assessment tasks (Ambler et al. 2021; Samarawickrema and Cleary 2021; Goode et al. 2023). Further, the design focuses on what students do and purposefully combines the virtual video classroom with active, self-directed learning. For instance, in the course on Criminal Law, students are presented with a branching scenario and asked to generate argumentation reports through an online interactive tool, H5P, in the selfdirected learning space. H5P (h5p.org) is an open-source content collaboration framework, which could be integrated in various learning management systems. When students go through the scenarios, they are asked to write down their points of view and rationales against the relevant acts through their decisions. After, the students share and discuss their reports with peers in the virtual video classroom (via Collaborate Ultra, Zoom, or Webex). Likewise, the Botanical Medicine course uses similar design principles but different technologies. For example, in one activity, students use Padlet. Padlet (padlet.com) is an online platform that supports students’ virtual learning experiences through enabling knowledge sharing, brainstorming, and collaboration both synchronously and asynchronously, without the need to be in the same physical space. In this activity, students are asked to take photos of medical plants in fields; write plant descriptions, therapeutic actions, and clinical indications; and post them in a shared Padlet. During the virtual synchronous class, the teacher selects some medicinal plants from the Padlet and asks students to share and discuss the information with the class. Finally, the teacher concludes students’ discussions by synthesizing and reinforcing the targeted concepts. In the examples above, digital technologies play a pivotal role in creating a virtual learning environment for students’ active, deep engagement with knowledge by enabling interactions between students, learning content, and teachers. That is, digital technologies are harnessed to support learning by combining them with

7

Innovation and the Role of Emerging Technologies

113

different pedagogical strategies. However, how can we ensure that these combinations of pedagogy and technology drive deep learning? How can we use technologies appropriately? In the next section, we discuss how different kinds and uses of digital technologies relate to students’ cognitive engagement and, consequentially, shape learning processes and outcomes.

Digital Technologies in Learning Activities To understand the roles of digital technologies in learning activities, we need to understand the relationship between what students do and their engagement in deep learning. The interactive, constructive, active and passive (ICAP) framework, proposed by Chi and Wylie (2014), identifies four modes of learning with progressively increasing cognitive engagement: passive, active, constructive, and interactive. These modes are associated with knowledge-change processes and expected cognitive and learning outcomes. Passive learning mode refers to learning activities when students sequentially go through the study materials without interacting with them (e.g., watching an online video or listening to an online lecture without doing anything else). Such activities do less to engage students in thinking about the connections between pieces of information or concept (Vermunt and Donche 2017). However, it is important not to undermine the role and value of direct instruction. Research shows that welldesigned direct teaching activities could be highly engaging and effective (Kirschner et al. 2006; Schwartz and Bransford 1998). Active learning mode refers to activities involving student physical interactions with the learning materials (e.g., pausing or rewinding an online video or writing notes). This interaction creates more possibilities for students to activate their prior knowledge by the selected information; however, they are less likely to process the information deeply and make any connections. Constructive learning mode involves inferring processes such as connecting, comparing, and contrasting information to infer new knowledge (e.g., using computer simulations to generate and test hypotheses). During constructive activities, students may discover relations between separate facts or views and, by doing this, understand the meaning of the phenomenon about which they learn deeper (Vermunt and Donche 2017). Finally, interactive learning mode involves two or more students mutually creating new learning artifacts or knowledge (e.g., writing an assistive technology report of a virtual clinic interview in a small group, a diagnostic report during role-playing in a virtual clinic, or working on a concept map in pairs in a virtual environment). Students may try to connect what they study with practical examples from their previous experiences or think about how they would apply what they learn in future practice (Vermunt and Donche 2017). Once we understand how students learn, we are then able to help them learn by combining high-impact pedagogical strategies with the most appropriate digital technologies in virtual learning environments (Fig. 1).

114

P. K. Lai and L. Markauskaite

Fig. 1 The relationships of how students learn, high-impact pedagogical strategies and appropriate digital technologies in virtual learning environments

When we do this, it is important not to privilege pedagogy over technology or technology over pedagogy (Bayne et al. 2020). Technology and pedagogy co-constitute each other (Bayne 2015), and it is the fusion of technological affordances and pedagogical strategies that shape students’ learning activity. Hence, the roles of digital technologies could range from well-designed videos for explaining new concepts (Mayer et al. 2020) to collaborative learning environments in which students interact and co-create authentic knowledge artifacts (Trede et al. 2019). We discuss and illustrate four such roles: 1. 2. 3. 4.

To provide students with an opportunity to visualize invisible phenomena To support students to generate ideas and construct knowledge To enable students to gain new embodied experiences To empower students and teachers to exchange ideas and co-construct authentic understanding

Providing Students with an Opportunity to Visualize Invisible Phenomena Videos play an important role in science education. According to cognitive theories, videos help students form mental representations of scientific phenomena (de Koning and Tabbers 2011; Rapp 2005). Further, videos combine different modalities, and, according to Mayer’s (2005) cognitive theory of multimedia learning, learning is more effective when complementary information is presented in the visual/pictorial and auditory/verbal modalities simultaneously. An additional advantage of videos is the control of playback available to students (Fiorella and Mayer 2018; Noetel et al. 2021). Videos allow students to manage their learning pace by pausing to take notes, rewinding key points or difficult sections, or accelerating the information they already learned.

7

Innovation and the Role of Emerging Technologies

115

These video technology features were used in a study conducted by Lai and her colleagues (2016, 2018) to investigate how students learn complex nanoscience concepts. It used two animated videos (approximately 10 min each) – Nano Gold and Nano Magnetics – to describe the concept of size-dependent properties at the nanoscale. For example, the Nano Gold video showed that when light interacts with gold nanoparticles of a different size, the color of the nanoparticles changes. Therefore, students had an opportunity to see the interactions and causal relationships between light and gold nanoparticles from the video without physically using an expensive scanning tunneling microscope or an atomic force microscope. Students could start, stop, and rewind the videos as many times as needed. The post-tests showed that students recalled the explanations from the videos and successfully answered questions about the introduced concepts, such as the surface-area-to-volume ratio. Similarly, Shabani et al. (2011) developed a video to assist undergraduate students in learning nanoscience concepts related to superhydrophobic surfaces. The video provided explanations and realistic detailed visualizations of the difference between a microscale rough hydrophobic surface with and without nanoparticles, which allowed students to see the nanoscale objects and the impact of nanoparticles on material properties. Students were asked to watch the video at their own pace and reflect on what they learned from it. The comparison of pre- and post-survey results showed that students felt that the videos helped them gain needed knowledge. The educational designs used in these studies provided students with an opportunity to visualize the nanoscale phenomena and control the pace of video lessons. While watching the videos, students tried to remember and understand the meanings of the targeted concepts; thus, the videos helped students learn factual knowledge. The findings of the above studies are in line with the expected cognitive outcomes in the ICAP framework (Chi and Wylie 2014), which suggests that the passive and active learning modes help learn knowledge that can be recalled verbatim and applied in similar contexts. However, research from a perspective of the Cognitive Load Theory warns that realistic details in visualizations could be a source of extraneous load (Skulmowski and Rey 2020). A large number of details in realistic visualizations may lead to lowered learning performance (Skulmowski and Rey 2020), particularly for students with low spatial abilities (Berney et al. 2015; Brucker et al. 2014). However, other explanations suggest a higher interest generated through realistic visualizations could lead to increased retention performance (Goldstone and Son 2005; Skulmowski and Rey 2021), which is associated with the findings of the studies above and the ICAP framework.

Supporting Students to Generate Ideas and Construct Knowledge Several software tools have been developed to support students’ learning by generating ideas and constructing knowledge, such as online simulations and branching scenarios. The first example is Molecular Workbench (mw.concord.org) online simulation, which has been used to facilitate learning physics at the molecular

116

P. K. Lai and L. Markauskaite

level (Xie and Lee 2012; Xie and Pallant 2011). Molecular Workbench simulates the movements of atoms and molecules by numerically solving Newton’s equation of motion according to their interactions. It allows students to control and interrogate simulated micro-level processes (e.g., self-assembly) shown on the computer screen in real time. Furthermore, Molecular Workbench has a unique assessment system embedded within the software, which supports multiple-choice and open-ended questions. Xie and his team (2012) reported that Molecular Workbench, used for simulation-based experimentation, helped undergraduate students develop an integrated understanding of complex concepts in nanotechnology. The second example is branching simulations used in public health and nursing education. They mimic real-life, rapidly changing patient conditions and are used to facilitate the development of real-time decision-making skills (Bouwer and Guerrero 2020; Pasklinsky et al. 2021). For instance, Pasklinsky et al. (2021) developed branching simulation scenarios and used them in the undergraduate Community Health course at New York University. The authors aimed to ensure that nursing students are provided with the opportunities to apply targeted knowledge, critical thinking, and communication skills to the provision of senior care. Therefore, these scenarios were aligned with the threshold learning outcomes in the International Nursing Association for Clinical Simulation and Learning Standards (INACSL Standards Committee 2016). The studies discussed above involved simulation technologies, inquiry-based and case-study learning approaches. These learning approaches are in line with the constructive learning mode from the ICAP framework, in which students try to connect, compare, and contrast information to infer new knowledge and to discover the relations between separate facts and views (Chi 2021; Chi and Wylie 2014). Such learning has the potential to result in a deep understanding and knowledge transfer. Furthermore, the Cognitive Load Theory suggests that a medium degree of interactivity, such as clicking on the elements or changing the variables presented on the screen, fosters learning performance (Skulmowski and Xu 2021).

Enabling Students to Gain Embodied Experiences Research suggests that agent-based models (ABMs) offer an effective learning environment for learning complex scientific concepts (Blikstein and Wilensky 2009; Goldstone and Wilensky 2008; Lai et al. 2018; Levy and Wilensky 2009; Sengupta and Wilensky 2009). Particularly, ABMs enable students to explore complex dynamic phenomena, for example, the spread of viral diseases, carbon cycle, and nanoscale properties, and observe their invisible and often counterintuitive properties, such as nonlinearity, emergence, feedback, and delay. Additionally, ABMs allow students to engage in hypothesis testing actively – to develop inferences, test those inferences, and, through this, deepen their understanding. For example, the study mentioned above by Lai et al. (2016, 2018) used videos in combination with ABMs. It used NetLogo (ccl.northwestern.edu), an agent-based

7

Innovation and the Role of Emerging Technologies

117

programmable modeling environment that allows creating and running different interactive computer models (Wilensky 1999; Wilensky and Rand 2015). Two ABMs were developed to assist students in learning the topics of Nano Gold and Nano Magnetics and the concepts of surface-area-to-volume ratio and sizedependent properties (Fig. 2). Students enrolled in the undergraduate Nanotechnology course engaged in activities that asked them to test hypotheses, observe the simulated representations of nanoscale phenomena, find out how it works, and formulate conclusions from their interactions with the Nano Gold and Nano Magnetics models. The results showed that when students experienced how nanoparticles behave through exploring them in the ABM environment, they gained a deep understanding of the targeted nanoscience concepts. Students’ post-test results were significantly higher than pre-test scores on explanatory knowledge and transfer tasks. These results suggested that ABMs could help students learn invisible and counterintuitive nanoscience concepts. Furthermore, the findings from the students’ think-aloud protocols and video recordings indicated that the ABMs helped students change their reasoning strategies. Before learning nanoscience concepts with the ABMs, students tended to recall their previous knowledge and then describe it only at a general system level of emerging properties. However, after learning from the ABMs, the students shifted their perspective and generated causal inferences related to the targeted concepts at a nanoparticle level. For instance, some students imagined that they were a gold nanoparticle and tried to visualize how this particle interacted with other gold nanoparticles and the coming lights at the nanoparticle level, resulting in the emergence of a particular color at the system level (e.g., purple or red color). Students had not had this perspective-shifting experience before learning from the ABMs.

Fig. 2 Screenshot of Nano Gold model. Students can change the size of the individual gold nanoparticle by using the slider to simulate the representation of the lights coming in and hitting individual gold nanoparticles that lead to the color change

118

P. K. Lai and L. Markauskaite

Additionally, Peng, Isaac, and Wilkins (2012, June) reported the effectiveness of a large-scale 3D virtual reality on students’ learning of nanoscience knowledge, such as nanoscale, and structures of nanomaterials. The 3D virtual reality projected on a wall allowed students to play the role of a nanoparticle immersed in the carbon nanotubes. They interacted with each other as if they were nanoparticles and saw the chemical reactions within carbon nanotubes. In this way, the students gained an embodied learning experience of how chemical reactions work from a nanoparticle’s perspective. The study showed that students’ learning engagement significantly increased, and the students also believed that this learning experience enhanced their knowledge of the targeted nanoscience concepts. Similarly, an empirical study conducted by Parong and Mayer (2018) showed that learning activities in an immersive virtual reality combined with appropriate instructional strategies improved students’ factual and conceptual knowledge of biology. An interactive biology simulation, called The Body VR, consisted of narration and animations of the circulatory system and parts of cells. The Body VR provided students with a perspective of blood particles while traveling through an artery and into a cell. During their journey, a narrator explained the purpose and functions of the cells within the artery. The students also could physically touch, move, and rotate the bloodstream, cells, and mitochondrion. Students were allowed to pause and play The Body VR when needed and were asked to summarize and reflect on what they had just learned. The results showed that adding appropriate instructional strategies, such as summarizing and reflection, to the virtual reality simulation significantly improved students’ learning outcomes, motivation, and engagement. The findings from the above studies are consistent with Lindgren’s (2012) argument that when students take the first-person perspective of simulated events, they learn better because changing one’s perspective changes the understanding of the relationships and surrounding environment. Similarly, ABMs and virtual reality create possibilities for students to explore complex phenomena from a first-person perspective (e.g., a nanoparticle or blood particle) and gain new embodied learning experiences that help understand how these phenomena function deeper. Simultaneously, there is a current debate about whether this type of immersive embodied learning is always beneficial. Immersive learning environments are rich in details, and, according to the Cognitive Load Theory, this may create distractions and lead to a depletion of learners’ cognitive resources that otherwise would be used for learning core information (Frederiksen et al. 2020; Skulmowski and Xu 2021). However, other researchers have shown that even though the immersive embodied learning experiences do not result in better retention of information compared to watching videos and written instruction, they increase students’ enjoyment, motivation, and abilities to transfer what they have learned to new tasks (Jacobson et al. 2020; Lai et al. 2018; Lai et al. 2016; Makransky et al. 2019). That is, immersive embodied learning does not affect retention and has important additional benefits.

7

Innovation and the Role of Emerging Technologies

119

Empowering Students and Teachers to Exchange Ideas and Co-construct Authentic Understanding According to sociocultural learning theories, the learning process involves joint meaning-making and knowledge construction that is enabled by social interactions, such as interactions between students and between students and teachers (Vygotsky 1978). In remote learning, such knowledge co-construction is often mediated by digital technologies that enable the creation of joint digital objects (notes, sketches, diagrams, etc.) and discussion. A range of available technologies has been used in higher education to increase students’ engagement in knowledge co-construction in various disciplinary areas. For example, Google Jamboard has been used in biomedical science (Sweeney et al. 2021), CmapTools in education (Wang et al. 2017), a virtual safety tour in nanoscience (McWhorter and Lindhjem 2013), and an artificial peer collaborator in computer science (Howard et al. 2017). For example, Sweeney et al. (2021) used Google Jamboard in an applied radiology and surface anatomy online workshop in an undergraduate medical course at the University of Dundee. Google Jamboard (jamboard.google.com) is a digital interactive online whiteboard that can be used simultaneously by a group of people working remotely in virtual collaborations. In this workshop, students worked online in small groups. They were asked to discuss and apply the concepts they learned in relation to surface anatomy and radiographic imaging on Google Jamboard (Fig. 3) while interacting via the videoconferencing system Blackboard Collaborate. After, each group made a 5-min presentation to the whole class. A combination of Google Jamboard and Blackboard Collaborate allowed group members to discuss, post notes, and edit on the shared whiteboard simultaneously.

Fig. 3 Source: An exemplar presentation slide from the University of Dundee, created by students using provided images on Google Jamboard (Sweeney et al. 2021, p. 2)

120

P. K. Lai and L. Markauskaite

Similarly, web-based collaborative concept mapping tools have been used to support group learning and knowledge co-construction. For example, Wang et al. (2017) incorporated the concept mapping tool Cmap in a discussion forum and used it for group concept- and design-oriented tasks (Fig. 4). Cmap (cmap.ihmc.us) is a software for creating and sharing knowledge models represented as concept maps. Students did group tasks by co-creating and co-editing online concept maps, initiating and responding to messages, and sharing learning resources. The findings indicated that students had extensive group discussions in which they exchanged information and disputed ideas. The concept mapping tool allowed students to visually present concepts and their relationships, leading to further discussions (Wang et al. 2017). Some courses used collaborative virtual reality environments to enable students’ joint learning through knowledge co-construction (Fazarro et al. 2011; Merchant et al. 2012; Mikropoulos and Natsis 2011; Schönborn et al. 2016). For example, McWhorter and Lindhjem (2013) developed a virtual nanotechnology safety tour and used it in an undergraduate Nanoscience course. The virtual tour consisted of several stops at various learning stations, which introduced nanoparticles, nanotubes, nanotechnology products, and related safety procedures. Students attended a 45-min tour by choosing the avatars and walking through the virtual laboratory (e.g., a nanoparticle containment room) while discussing the safety procedures and protocols and appropriate protective gear. The post-experience survey showed that 92% of the students found this learning experience helpful. One of the fast-changing fields in educational technology is the use of artificial intelligence (AI) for teaching and learning (Popenici and Kerr 2017; Suoranta et al. 2021; Zawacki-Richter et al. 2019). It includes intelligent tutoring systems and software applications based on machine learning. Machine learning is “a subfield

Fig. 4 Source: Screenshot of Cmap and discussion forum (Wang et al. 2017, p. 30)

7

Innovation and the Role of Emerging Technologies

121

of artificial intelligence that includes software able to recognize patterns, make predictions, and apply newly discovered patterns to situations that were not included or covered by their initial design” (Popenici and Kerr 2017, p. 2). For instance, Howard et al. (2017) created an artificial peer collaborator that interacted with students during problem-solving activities aimed to improve their understanding of data structures in computer science. The artificial peer collaborator argued and discussed with students how to solve given problems. When students were stuck in a particular area, the peer collaborator took over and moved the problem-solving forward. When both the student and the AI were stuck, they would work together to overcome the issues. The findings indicated that students, who interacted with the artificial peer collaborator, had richer deliberations and negotiations while solving problems than those who interacted with an artificial tutor. The above studies used digital technologies to create virtual learning environments that enable joint exploration of problems and co-construction of shared objects. In addition, the pedagogical strategies encouraged online collaboration and, in some of these studies, guided students’ discussions. This kind of learning is consistent with the interactive learning mode from the ICAP framework and has shown significant benefits (Ambler et al. 2021; Chi 2021; Chi and Wylie 2014; Menekse and Chi 2019). According to the ICAP framework, students’ interactions when they co-create new products, interpretations, procedures, ideas, and other knowledge objects foster the most profound and authentic understanding (Chi 2021; Chi and Wylie 2014).

Conclusion: A Way Forward Learning is a complex cognitive, social, and embodied phenomenon that unfolds through time and across spaces (Jacobson et al. 2019). Digital technologies have become an inseparable part of higher education, resulting in new course models and learning activities. This chapter discussed digital technologies and new forms of learning primarily from the perspective of how students learn and how to support students’ higher-level cognitive engagement. It examined four important roles of digital technologies: (1) to provide students with an opportunity to visualize invisible phenomena, (2) to support students to generate ideas and construct knowledge, (3) to enable students to gain new embodied experiences, and (4) to empower students and teachers to exchange ideas and co-construct authentic understanding. These four roles are not the only ways in which digital technologies can be used in teaching and learning. Students’ higher-level cognitive engagement is also not the sole purpose of higher education. For example, preparing students for professions that do not yet exist requires a far broader range of graduate qualities, including collaboration, communication, creativity, and resilience (Kirschner and Stoyanov 2020). As we are moving toward more hybrid and diverse forms of learning, the role of digital technologies needs targeted and deliberate application. There is a lot of space for reimagining what, how, when, with whom, and where students learn (Bayne et al. 2020; Suoranta et al. 2021; Trede et al. 2019). For

122

P. K. Lai and L. Markauskaite

example, wholly online learning models such as virtual universities and short flexible forms of learning could help make higher education accessible to a broader range of people and contribute more rapidly to the emerging demands for new skills. However, the adoption of new educational models and course designs should draw on a robust and deep understanding of how people learn and be based on sound research. As we argued throughout the chapter, the use of digital technologies and innovation in higher education should always be considered in relation to our growing understanding of how people learn with these technologies across time and spaces.

Cross-References ▶ Emerging, Emergent, and Emerged Approaches to Mixed Reality in Learning and Teaching

References Ambler, T., Solomonides, I., and Smallridge., A. 2021. Students’ experiences of a first-year blockmodel curriculum in higher education. The Curriculum Journal 32: 533–558. https://doi. org/10.1002/curj.103. Archambault, L. M., and Barnett, J. H. 2010. Revisiting technological pedagogical content knowledge: Exploring the TPACK framework. Computers & Education 55 (4): 1656–1662. Badia, A., Garcia, C., and Meneses, L. 2017. Approaches to teaching online: Exploring factors influencing teachers in a fully online university. British Journal of Educational Technology 48 (6): 1193–1207. https://doi.org/10.1111/bjet.12475. Bayne, S. 2015. What’s the matter with ‘technology-enhanced learning’? Learning, Media and Technology 40 (1): 5–20. https://doi.org/10.1080/17439884.2014.915851. Bayne, S., Evans, P., Ewins, R., Knox, J., and Lamb, J. 2020. The manifesto for teaching online. Cambridge, MA: MIT Press. Berney, S., Bétrancourt, M., Molinari, G., and Hoyek, N. 2015. How spatial abilities and dynamic visualizations interplay when learning functional anatomy with 3D anatomical models. Anatomical Sciences Education 8 (5): 452–462. https://doi.org/10.1002/ase.1524. Blikstein, P., and Wilensky, U. 2009. An atom is known by the company it keeps: A constructionist learning environment for materials science using agent-based modeling. International Journal of Computers for Mathematical Learning 14 (2): 81–119. https://doi.org/10.1007/s10758-0099148-8. Bouwer, M., and Guerrero, J. 2020. The use of branching scenarios to improve pharmacology content application. Paper presented at the IMPRS. Brucker, B., Scheiter, K., and Gerjets, P. 2014. Learning with dynamic and static visualizations: Realistic details only benefit learners with high visuospatial abilities. Computers in Human Behavior 36: 330–339. https://doi.org/10.1016/j.chb.2014.03.077. Chi, M. T. H. 2021. Translating a theory of active learning: An attempt to close the research-practice gap in Education. Topics in Cognitive Science 13(3): 1–23. https://doi.org/10.1111/tops.12539 Chi, M. T. H., and Wylie, R. 2014. The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist 49 (4): 219–243. https://doi.org/10.1080/ 00461520.2014.965823. Committee, I. S. 2016. INACSL standards of best practice: SimulationSM simulation design. Clinical Simulation in Nursing 12: S5–S38.

7

Innovation and the Role of Emerging Technologies

123

de Koning, B. B., and Tabbers, H. K. 2011. Facilitating understanding of movements in dynamic visualizations: An embodied perspective. Educational Psychology Review 23 (4): 501–521. https://doi.org/10.1007/s10648-011-9173-8. Fazarro, D. E., Lawrence, H. R., and McWhorter, R. R. 2011. Going virtual: Delivering nanotechnology safety education on the web. Journal of STEM Tacher Ecation 48 (2): 38–62. Fiorella, L., and Mayer, R. E. 2018. What works and doesn’t work with instructional video. Computers in Human Behavior 89: 465–470. https://doi.org/10.1016/j.chb.2018.07.015. Frederiksen, J. G., Sørensen, S. M. D., Konge, L., Svendsen, M. B. C., Nobel-Jørgensen, M., Bjerrum, F., and Andersen, S. A. W. 2020. Cognitive load and performance in immersive virtual reality versus conventional virtual reality simulation training of laparoscopic surgery: A randomized trial. Surgical Endoscopy 34 (3): 1244–1252. https://doi.org/10.1007/s00464-01906887-8. Goldstone, R. L., and Son, J. Y. 2005. The transfer of scientific principles using concrete and idealized simulations. The Journal of the Learning Sciences 14 (1): 69–110. Goldstone, R. L., and Wilensky, U. 2008. Promoting transfer by grounding complex systems principles. Journal of the Learning Sciences 17 (4): 465–516. https://doi.org/10.1080/ 10508400802394898. Goode, E., Nieuwoudt, J., and Roche, T. 2022. Does online engagement matter? The impact of interactive learning modules and synchronous class attendance on student achievement in an immersive delivery model. Australasian Journal of Educational Technology 38 (4): 76–94. https://doi.org/10.14742/ajet.7929 Goode, E., Roche, T., Wilson, E., and McKenzie, J. W. 2023. Implications of immersive scheduling for student achievement and feedback. Studies in Higher Education 1–14. https://doi.org/10. 1080/03075079.2023.2184472 Holbert, K. E., Fletcher, J., Burr, P. A., and Obbard, E. G. 2020. PLuS Alliance nuclear engineering online course exchange program. Transactions of the American Nuclear Society 122: 43–45. Howard, C., Jordan, P., Di Eugenio, B., and Katz, S. 2017. Shifting the load: A peer dialogue agent that encourages its human collaborator to contribute more to problem solving. International Journal of Artificial Intelligence in Education 27 (1): 101–129. https://doi.org/10.1007/s40593015-0071-y. Jacobson, M. J., Levin, J. A., and Kapur, M. 2019. Education as a complex system: Conceptual and methodological implications. Educational Researcher 48 (2): 112–119. https://doi.org/10.3102/ 0013189X19826958. Jacobson, M. J., Goldwater, M., Markauskaite, L., Lai, P. K., Kapur, M., Roberts, G., and Hilton, C. 2020. Schema abstraction with productive failure and analogical comparison: Learning designs for far across domain transfer. Learning and Instruction 65. https://doi.org/10.1016/j. learninstruc.2019.101222. Kirschner, P. A., and Stoyanov, S. 2020. Educating youth for nonexistent/not yet existing professions. Educational Policy 34 (3): 477–517. https://doi.org/10.1177/0895904818802086. Kirschner, P. A., Sweller, J., and Clark, R. E. 2006. Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist 41 (2): 75–86. https://doi.org/10.1207/ s15326985ep4102_1. Lai, P. K., Jacobson, M. J., and Markauskaite, L. 2016. Agent-based models versus video-based visualisation to learn nanoscience concepts: An embodied cognition perspective. Paper presented at the 2016 annual meeting of the American Educational Research Association, Washington DC. Lai, P. K., Portolese, A., and Jacobson, M. J. 2017. Does sequence matter? Productive failure and designing online authentic learning for process engineering. British Journal of Educational Technology 48 (6): 1217–1227. https://doi.org/10.1111/bjet.12492.

124

P. K. Lai and L. Markauskaite

Lai, P. K., Jacobson, M. J., and Goldwater, M. 2018. Learning nanoscience concepts through a nanoscale experience. Paper presented at the 13th International Conference of the Learning Sciences, London, UK. Levy, S. T., and Wilensky, U. 2009. Crossing levels and representations: The connected chemistry (CC1) curriculum. Journal of Science Education and Technology 18 (3): 224–242. Lindgren, R. 2012. Generating a learning stance through perspective-taking in a virtual environment. Computers in Human Behavior 28 (4): 1130–1139. https://doi.org/10.1016/j.chb.2012. 01.021. Loton, D., Stein, C., Parker, P., and Weaven, M. 2020. Introducing block mode to first-year university students: A natural experiment on satisfaction and performance. Studies in Higher Education 47: 1–24. Makransky, G., Terkildsen, T. S. and Mayer, R. E. 2019. Adding immersive virtual reality to a science lab simulation causes more presence but less learning. Learning and Instruction 60: 225–236. https://doi.org/10.1016/j.learninstruc.2017.12.007. Mayer, R. E., ed. 2005. The Cambridge handbook of multimedia learning. New York: Cambridge University Press. Mayer, R. E., Fiorella, L., and Stull, A. 2020. Five ways to increase the effectiveness of instructional video. Educational Technology Research and Development 68 (3): 837–852. https://doi.org/10. 1007/s11423-020-09749-6. Mccluskey, T., Smallridge, A., Weldon, J., Loton, D., Samarawickrema, G., and Cleary, K. 2020. Building on the VU Block foundations: Results from the inaugural first year cohort. Paper presented at the 42nd HERDSA annual international conference, Auckland, New Zealand. McWhorter, R. R., and Lindhjem, K. A. 2013. Virtual learning environments: How they can benefit nanotechnology safety education. IEEE Nanotechnology Magazine 7 (2): 15–17. Menekse, M., and Chi, M. T. H. 2019. The role of collaborative interactions versus individual construction on students’ learning of engineering concepts. European Journal of Engineering Education 44 (5): 702–725. Merchant, Z., Goetz, E. T., Keeney-Kennicutt, W., Kwok, O. M., Cifuentes, L., and Davis, T. J. 2012. The learner characteristics, features of desktop 3D virtual reality environments, and college chemistry instruction: A structural equation modeling analysis. Computers & Education 59 (2): 551–568. Mikropoulos, T. A., and Natsis, A. 2011. Educational virtual environments: A ten-year review of empirical research (1999–2009). Computers & Education 56 (3): 769–780. Mishra, P., and Koehler. M. J. 2006. Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record 108 (6): 1017–1054. Mishra, P., Koehler, M. J., and Henriksen, D. 2011. The seven trans-disciplinary habits of mind: Extending the TPACK framework towards 21st century learning. Educational Technology 51(2): 22–28. Noetel, M., Griffith, S., Delaney, O., Sanders, T., Parker, P., del Pozo Cruz, B., and Lonsdale, C. 2021. Video improves learning in higher education: A systematic review. Review of Educational Research 91 (2): 204–236. https://doi.org/10.3102/0034654321990713. Parong, J., and Mayer, R. E. 2018. Learning science in immersive virtual reality. Journal of Educational Psychology 110 (6): 785–797. https://doi.org/10.1037/edu0000241. Pasklinsky, N., Graham-Perel, A., Villacarlos-Philip, P., Slaka-Vella, M., and Tilley, C. P. 2021. Real-time decision-making in chronic illness branching simulation. Mhealth 7: 14. Peng, X., Isaac, B., and Wilkins, R. T. 2012. Development of nanoscale virtual reality simulations for the teaching of nanotechnology. Paper presented at the 119th American Society for Engineering Education, San Antonio. Popenici, S. A., and Kerr, S. 2017. Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning 12 (1): 1–13. https://doi.org/10.1186/s41039-017-0062-8.

7

Innovation and the Role of Emerging Technologies

125

Rapp, D. N. 2005. Mental models: Theoretical issues for visualizations in science education. In Visualization in science education, ed. J. K. Gilbert, 43–60. Dordrecht, Netherlands: Springer. https://doi.org/10.1007/1-4020-3613-2_4. Samarawickrema, G., and Cleary, K. 2021. Block mode study: Opportunities and challenges for a new generation of learners in an Australian university. Student Success 12 (1): 13–23. Schönborn, K. J., Höst, G. E., and Palmerius, K. E. L. 2016. Nano education with interactive visualization. Nano Today 492: 1–4. https://doi.org/10.1016/j.nantod.2015.10.006. Schwartz, D. L., and Bransford, J. D. 1998. A time for telling. Cognition and Instruction 16 (4): 475–522. https://doi.org/10.2307/3233709. Sejzi, A. A., Aris, B., and Yahya, N. 2012. The phenomenon of virtual university in new age: Trends and changes. Procedia-Social and Behavioral Sciences 56: 565–572. Sengupta, P., and Wilensky, U. 2009. Learning electricity with NIELS: Thinking with electrons and thinking in levels. International Journal of Computers for Mathematical Learning 14 (1): 21–50. https://doi.org/10.1007/s10758-009-9144-z. Shabani, R., Massi, L., Zhai, L., Seal, S., and Cho. H. J. 2011. Classroom modules for nanotechnology undergraduate education: Development, implementation and evaluation. European Journal of Engineering Education 36 (2): 199–210. https://doi.org/10.1080/03043797.2011. 573536. Skulmowski, A., and Rey, G. D. 2020. The realism paradox: Realism can act as a form of signaling despite being associated with cognitive load. Human Behavior and Emerging Technologies 2 (3): 251–258. https://doi.org/10.1002/hbe2.190. Skulmowski, A., and Rey, G. D. 2021. Realism as a retrieval cue: Evidence for concretenessspecific effects of realistic, schematic, and verbal components of visualizations on learning and testing. Human Behavior and Emerging Technologies 3 (2). https://doi.org/10.1002/hbe2.209. Skulmowski, A., and Xu, K. M. 2021. Understanding cognitive load in digital and online learning: A new perspective on extraneous cognitive load. Educational Psychology Review 1–26. https:// doi.org/10.1007/s10648-021-09624-7. Suoranta, J., Teräs, M., Teräs, H., Jandrić, P., Ledger, S., Macgilchrist, F., and Prinsloo, P. 2021. Speculative social science fiction of digitalization in higher education: From what is to what could be. Postdigital Science and Education 1–13. https://doi.org/10.1007/s42438-02100260-6. Sweeney, E. M., Beger, A. W., and Reid, L. 2021. Google Jamboard for virtual anatomy education. The Clinical Teacher 1–7. https://doi.org/10.1111/tct.13389. Trede, F., Markauskaite, L., McEwen, C., and Macfarlane, S. 2019. Education for practice in a hybrid space: Enhancing professional learning with mobile technology. Singagore: Springer. Vermunt, J. D., and Donche, V. 2017. A learning patterns perspective on student learning in higher education: State of the art and moving forward. Educational Psychology Review 29 (2): 269–299. https://doi.org/10.1007/s10648-017-9414-6. Vygotsky, L. S. 1978. Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press. Wang, M., Cheng, B., Chen, J., Mercer, N., and Kirschner, P. A. 2017. The use of web-based collaborative concept mapping to support group learning and interaction in an online environment. The Internet and Higher Education 34: 28–40. https://doi.org/10.1016/j.iheduc.2017. 04.003. Wekerle, C., Daumiller, M., and Kollar, I. 2020. Using digital technology to promote higher education learning: The importance of different learning activities and their relations to learning outcomes. Journal of Research on Technology in Education 1–17. https://doi.org/10.1080/ 15391523.2020.1799455. White, M. 1982. Distance education in Australian higher education – A history. Distance Education 3 (2): 255–278. https://doi.org/10.1080/0158791820030207. Wilensky, U. 1999. NetLogo. Evanston: Center for Connected Learning and Computer-Based Modeling. Northwestern University.

126

P. K. Lai and L. Markauskaite

Wilensky, U., and Rand, W. 2015. An introduction to agent-based modeling: Modeling natural, social, and engineered complex systems with NetLogo. Cambridge, MA: MIT Press. Xie, C., and Lee, H. 2012. A visual approach to nanotechnology education. International Journal of Engineering Education 28 (5): 1006–1018. Xie, C., and Pallant. A. 2011. The molecular workbench software: An innovative dynamic modeling tool for nanoscience education. In Models and modeling, models and modeling in science education, ed. M. S. Khine and I. M. Saleh, vol. 6, 121–139. New York: Springer. Zawacki-Richter, O., Marín, V. I., Bond, M., and Gouverneur, F. 2019. Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education 16 (1): 1–27. https://doi. org/10.1186/s41239-019-0171-0.

Part IV Supporting Staff and Students

8

Models of Professional Development for Technology-Enhanced Learning in the Virtual University Kwong Nui Sim and Henk Huijser

Contents Introduction: Where Are We Now? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contexts/Rationales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . In the Virtual University, Teaching and Learning Is Accessible 24/7 (an Advantage of Being Virtual) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . From “Sage on the Stage” to “Steering on the Net” (Changes in Conceptions of Teaching and Learning) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . After COVID-19: Into a Virtual Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case Studies: What Is the Reality? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digital Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Demand for More “Hands-On” Support and “Examples” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The “Panic” Pre/During Lockdown . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digital Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Perspectives Around the Role of Digital Tools in Teaching and Learning Practices . . . . . . Questionable Use of Digital Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proposed Model: Developing Digital Capabilities and Digital Integration . . . . . . . . . . . . . . . . . . . Discussion: Professional Development for the Virtual University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion: Ensemble Efforts Can, and Should, Realize the Virtuality . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

130 131 131 131 132 132 133 133 134 135 135 136 138 140 143 144 144

K. N. Sim (*) Sydney International School of Technology and Commerce, Sydney, NSW, Australia e-mail: [email protected] H. Huijser Learning and Teaching Unit and Faculty of Engineering, Queensland University of Technology, Brisbane, QLD, Australia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_8

129

130

K. N. Sim and H. Huijser

Abstract

Due to the rapid progress of technologies used in academic practices, higher education today is dynamic and constantly changing, which makes it necessary for academics to apply and continuously reflect on new and refreshed teaching and learning strategies (e.g., virtual mode). This chapter draws on data from two case studies that evaluates academic digital capabilities. The case studies were based at a New Zealand institution which implemented “new” strategies related to educational technologies. The (un)seamless technology integration in teaching and learning practices that emerged from the cases demonstrates diverse perspectives on the role of educational technologies in teaching and learning, leading to questionable use of technologies in terms of both “efficiency” and “effectiveness.” Based on these case studies, we propose that explicit academic TEL (technology-enhanced learning) development support should be offered to academics to enable them to leverage the affordances of educational technology tools in a pedagogically sound manner in teaching and learning practices. In this chapter, we propose a model for engaging, training, and supporting academic use of TEL. This model considers the needs of academics, and suggests how academic TEL development could be designed for the virtual university. Keywords

Academic development · Digital literacy · Digital capability · Educational technologies · Technology-enhanced learning · Teaching and learning

Introduction: Where Are We Now? As academic developers, the applied nature of our research directly informs teaching practices as well as teaching development, the substance of which serves as the catalyst for this chapter. The model proposed in this chapter is aimed at engaging, training, and supporting teaching staff by targeting what their TEL (technologyenhanced learning) needs are, how academic TEL development can respond to those needs, and how TEL could be designed to be responsive to the needs of teaching and learning experiences in the virtual university. This is particularly relevant since the global COVID-19 pandemic has challenged academics to respond creatively and innovatively to novel and evolving conditions when physical spaces were disrupted overnight and TEL activities were the only solution. This chapter begins with an explanation of the context and rationale for the detailed case studies that follow, which in turn leads to a proposed model as well as a discussion, before offering some concluding thoughts. The model uses the concept of learning ecologies, and the role of communities of practice in reflecting teaching and learning as a dynamic and collaborative endeavor in the virtual university.

8

Models of Professional Development for Technology-Enhanced Learning in. . .

131

Contexts/Rationales As a result of the global COVID-19 pandemic, universities have been forced to move teaching and learning into the virtual world, which has created both opportunities and challenges. The reimagination of higher education (e.g., in the form of the virtual university) involves reflections on the fundamental need for a right balance between pedagogical and technological academic development, especially when it comes to pedagogical approaches to the pivot online. The complexity, however, is that for many academics teaching in a virtual context was quite a radical shift from what they had been accustomed to. Thus, while the response to COVID-19 necessitated an immediate response, there is a longer-term project in developing more effective and engaging ways of teaching and designing for learning in online environments, and in particular in the specific context of the virtual university.

In the Virtual University, Teaching and Learning Is Accessible 24/7 (an Advantage of Being Virtual) Sim and Cowling (2020) have highlighted that as long as you are digitally connected, everything on the World Wide Web is accessible 24/7. Educators should make use of this affordance to create different yet effective teaching and learning experiences. The centrality of the lecture theater with a synchronous audience of students is thus replaced by a range of alternative options in teaching and learning practices, such as preparing voice or video recordings of the course content, along with accompanying activities and assessments, which can be self-paced, synchronous, asynchronous, or a combination thereof. This has in recent years, for example, given rise to a range of flipped learning initiatives (e.g., Bredow et al. 2021), which leverage synchronous teacher-student interactions for discussion-based workshop activities that rely on students exploring the content prior to the workshops, by accessing digital content such as video-based lecturer materials and readings (Bredow et al. 2021; Brewer and Movahedazarhouligh 2018; Lee et al. 2017). In such formats, regardless of where the teacher or the students are located, the disciplinary content is still being taught, and there are a wide range of digital tools that could potentially be used to facilitate the teaching and learning process, and are accessible 24/7.

From “Sage on the Stage” to “Steering on the Net” (Changes in Conceptions of Teaching and Learning) The global COVID-19 pandemic has shaken up many industries, not least higher education, where physical distancing and the closure of physical spaces has driven the forced uptake of new TEL initiatives based on virtual teaching, learning, and collaboration. Our long-standing habitat of lecture theaters, chalk dusters, and whiteboards was suddenly replaced by Zoom calls and chat windows, with all the

132

K. N. Sim and H. Huijser

benefits and limitations that it brings. Echoing recent evidence (e.g., EDUCAUSE 2020) which suggests that this mode will become the “new” or even “continuous” normal in the third decade of the twenty-first century, the virtual university is ideally positioned to take a leading role in what this “normal” may look like, including how professional development occurs. This “new” context is the foundation of the virtual university, and this chapter offers possible ways to develop teaching and learning expertise in the virtual university.

After COVID-19: Into a Virtual Future Sim and Cowling (2021) note TEL could encourage a stronger sense of collaborative spirit in the teaching space (e.g., sharing question pools), where the workload of teaching and learning online could be shared, potentially resulting in enhanced pedagogical and scaffolding experiences for students. Where there is a stronger sense of collaborative spirit among teachers (e.g., Bolisani et al. 2021), such as sharing video lectures, difficulties caused by various levels of digital literacy could fall less on one person’s shoulders. The community of practice model provides much potential in this respect, especially in a virtual university context where teaching staff and learning designers can work together to design learning and to learn from each other (Fitzgerald et al. 2020; Huijser et al. 2016; Wenger 1999). And there has never been a better time to discuss this than right now, as universities worldwide settle into a long haul that includes the uncertainty of a global pandemic and other potential disruptions, including the potential impacts and development of artificial intelligence (Chen et al. 2020). However, despite the promise of the virtual university, our readiness for teaching and learning in virtual universities is still questionable, as levels of digital literacy vary greatly. This in turn may create considerable anxiety, especially during a time of profound instability and uncertainty. Again, the social aspect of communities of practice, if fostered well, may go some way in addressing this. Overall then, the virtual university needs to therefore establish an infrastructure around professional development that aims to develop both required digital literacy levels in staff as well as their currency in that respect, undergirded by a scaffolding support structure. This is a crucial element of ensuring engaging as well as effective teaching and learning environments in the virtual university, and the two case studies below show examples of how this need may be addressed.

Case Studies: What Is the Reality? An interpretivist research approach (Erickson 1998) framed two of the case studies, which analyzed participant responses to questions about areas for improvement in the application of educational technology as well as the use of learning management systems in their daily teaching and learning practices. For the former, the feedback data were analyzed shortly after they were gathered after every face-to-face and

8

Models of Professional Development for Technology-Enhanced Learning in. . .

133

online workshop, which had a pedagogical focus on educational technology use, between 2015 and 2019. For the latter, observation and discussion data were collected from six volunteering academic participants from various disciplines over one semester (13 weeks) in 2017. Analysis of the data contributed to the development of ideas about the perceptions held by the participants, and these were refined progressively across the changes made in the workshop delivery over the five-year period. The perceptions were thus checked and rechecked, and refined against each workshop series as well as with every participant as they were collected. Such an iterative and inductive approach (Thomas 2006) involved thematic analysis (Silverman 2001) and the capture of major and common ideas (Mayring 2000) or themes expressed by academic participants about their experiences after attending workshops or using the learning management system (LMS) for teaching and learning practices. By checking and rechecking, refining, and confirming, the lead author sought to articulate the academic participants’ perceptions that matched the recommended way(s) of running educational technology workshops, and based on that new workshops were developed for learning management system use at the institution. Of course, these workshops took place before COVID-19 and were partly in face-to-face mode. However, the findings, combined with reflections on the changed context since COVID-19, could provide ideas and lessons that could be taken into account when designing professional development in the virtual university. The findings could be seen as specific to the particular context with a specific focus (LMS use), as well as being particularly targeted at a particular cohort only, and they are therefore not generalizable to all academics. The results, however, offer new understandings and insights that can be leveraged for the virtual university.

Digital Capabilities The Demand for More “Hands-On” Support and “Examples” The “hands-on” term appeared repeatedly in the analysis of the workshop feedback data. Given that the workshops were centered on digital tools for teaching and learning, it is not surprising that the participants would prefer to have active operational participation. However, these workshops were repeatedly run on a semester basis and the objectives were spelt out in the descriptions of the workshops, i.e., they were pedagogically focused, while technological hands-on support was provided at the individuals’ convenience by learning technologists or the lead author after the workshops. For example, some of the frequently requested content for the workshops, which reflects the participants’ expectations, included: “How to setup [a discussion board]” “I was hoping for a more step by step how to upload slides or readings, but I can figure that out” “Technical maybe, not so much pedagogical”

134

K. N. Sim and H. Huijser

Therefore, it seems like “technological” issues were very much the primary concern among academics, rather than seeing TEL as a pedagogical shift in the teaching and learning process. Emergency remote teaching (Hodges et al. 2020), which was widely adopted across the sector in response to COVID-19, only reinforced this primary focus on the “how to” of educational technology in a practical sense, rather than the “why” and “how to best for learning.” Another theme identified through the analysis was the request to have more examples of use for each digital tool in the LMS, despite examples being provided for every digital tool introduced in the workshops. Similar to the specifically identified need for hands-on examples, the academic participants mentioned particular areas that they would want to know more about, such as: “More examples of how assessment tools have been used in courses” “More trialling [of] an example scenario for Grade Centre setup and setting options” “More practical stepping us through e.g., how to use excel”

In other words, it appears as if the academics have limited ideas on how to apply TEL in their teaching and learning process. The requests for practical examples suggest a desire to copy exemplars, rather than applying the use of digital tools in deliberate ways for specific pedagogical reasons. In other words, these digital tools are literally seen as tools, rather than a means to pedagogical ends. Moreover, the theme highlights the limited readiness of academics’ digital capabilities for the virtual university pre-COVID-19. It is likely that the general proficiency with digital tools has significantly increased, but that does not necessarily translate to digital capability in relation to effective teaching and learning in a virtual university.

The “Panic” Pre/During Lockdown The pilot online workshops were run during the lead author’s research study leave in early 2019 and at the time they were not well received, compared to the face-to-face workshops. However, following the COVID-19 lockdown, the demand from academics became overwhelming. The lead author ran 15 online workshops in April and May 2020 with 143 participants, following a series of “regular” face-to-face workshops in March 2020, and similar numbers of workshops were conducted on a rolling basis at the second author’s institution. This encouraging online attendance excluded individual virtual consultations, weekly virtual drop-in sessions, and faculty-based online workshops during these two months. New online workshops were developed in response to the demands, namely: • Shifting Online and Sustaining Wellness – to explore ideas for teaching and learning in response to the lockdown and how these ideas could be applied in teaching and learning into the future; and to explore different tips and

8

Models of Professional Development for Technology-Enhanced Learning in. . .

135

mechanisms in order to develop and sustain effectiveness and efficiency of teaching and learning in a digital context. • Teaching and Learning: Beyond “Experiences” and Moving Forward – to discover how shifting online might have led to digital anxiety or even digital fatigue; and to explore different tips and mechanisms in order to sustain digital wellness, especially during these unusual times. These workshops were not only targeted at academics but also designed for professional staff who support teaching and learning. Teaching innovation and curriculum development are constantly in progress, especially when the targeted audience is university staff, and the use of digital tools has proven to be ubiquitous as well as the sole and inevitable solution to unexpected or sometimes unavoidable disruptions. The virtual university should be able to demonstrate high levels of digital capabilities among staff, but existing digital capabilities of university staff may be insufficient overall, and establishing effective teaching and learning practices virtually (e.g., García-Peñalvo 2021) may be an additional challenge. Furthermore, effective teaching and learning practices in a virtual context are dynamic and forever changing, so professional development therefore has to be dynamic and adaptable as well to ensure staff remain current and “ahead of the technology curve” in the virtual university. While it is not always clear what academics’ digital literacy levels should be in virtual universities, partly because this may to some extent differ for different disciplines, the importance of establishing strong, effective, and sustainable professional development, digital infrastructure, and an overall digital environment is clearly of fundamental importance.

Digital Integration Perspectives Around the Role of Digital Tools in Teaching and Learning Practices While daily conversations of academic developers and learning designers with academics or educational technology workshops could be seen as “one-offs,” they actually entail the “starting point of conversations” (Pleschová et al. 2021) or even “planting the seeds,” and as academic developers we establish relationships with individual academics after the workshops by guiding and supporting them through the course redesign process for the ultimate benefit of students’ digital teaching and learning experiences. Such relationships are not restricted to “content knowledge” only, as they also involve mentorship in the form of emotional support and confidence building, and indeed “care” (Iqbal and Vigna 2021), as was reflected in frequent email contact with both academic and professional staff during the lockdowns. Two of them read:

136

K. N. Sim and H. Huijser

Last year I had an opportunity to attend one of your courses on Advanced Features of Blackboard, and I was contacting you to see if you might be prepared to share some of your teaching notes? I took some information down, but not – apparently – as much as I had thought. The reason for the request is that we are moving to more digital [teaching and] learning environments to allow resilience in our teaching in the face of possible COVID-19 quarantines or University shutdown. I remember that Blackboard had some neat features that might allow some aspects of our tutorials to be run online, but my notes don’t give me enough information to set up and try these features. (Senior Lecturer) I’ve attended a couple of your Blackboard sessions which have been very helpful. I am part of the X Team and we are gearing up for Y for T2! It will predominately exist on BB. I am interested in creating personal folders for each of the students on our Blackboard site. I want these students to be able to access their folders only and upload JPEGs, word documents, screen shots, etc. to their folder so at the end of Y, I can go into their folder and review their work. (Professional staff member)

In other words, the perspectives on the role of digital tools led to changing practices among university staff. In this case, staff obviously had no choice due to the external circumstances, but gradual changes in practice are evident, and indeed will move center stage in a virtual university. Academic developers could be the “catalyzing agents” in developing a model of sustainable professional development (e.g., staff drawing on each other for mentorship and development as a community) in realizing these changing practices, particularly in the context of the virtual university.

Questionable Use of Digital Tools Although it was stated in various ways prior to and during each workshop that separate technological support was available upon request, another theme that emerged from the data of the two case studies was how participants could transfer what had been learnt in the workshops to their actual practice, which is about continuous and ongoing academic development in a sustainable manner. One of the academic participants expressed this need as follows: “Good to learn what features are available but is only useful if there is a follow-up workshop about putting such things into practice.” “Useful but I will need further training in-house to make the most of Blackboard.”

Even though the workshops were run in groups by default, there were academic participants who actually demanded individual assistance: More one on one attention

There was even one participant who signed up for the online workshop and said: Was not tailored to my specific needs; it did not work well in Zoom because most users were unfamiliar with video conferencing software.

8

Models of Professional Development for Technology-Enhanced Learning in. . .

137

In addition, most participants showed their use of the virtual learning environment as a repository, either for assignments or for slides, rather than an evolving teaching and learning environment underpinned by a coherent pedagogical foundation. As Sim (2021) has noted: It is legitimate to use Blackboard as a repository but this only restricts the interactivity between the teacher and the students as well as among students due to the one-way communication structure. [. . .] On the other hand, Blackboard provides various built-in tools for interactivity, such as Discussion Board, Journal, and Blog that appear to be underused, as seen in this study. [. . .] The commonly adopted tools are the announcement (a one-way communication channel), the folders with an abundance of information about the course (an overwhelming resource platform), and the grade centre (a strategy to keep the students’ interest to log into Blackboard). (p. 103)

In brief, these responses reflect individual academics’ lack of confidence and competency in using digital tools for teaching and learning processes. Regardless of how the delivery of workshops changed, with the goal of inserting the affordances of digital teaching and learning tools in the process of teaching and learning as well as the explicit technological support being carried out (e.g., individual appointments with the learning technologists or the lead author as an academic developer), after the workshops, the majority of the participants still primarily focused on the use of digital tools (rather than pedagogical design for online teaching and learning) in this type of academic development workshop. In other words, the idea of shifting pedagogical practices influenced by the affordances of digital tools was largely being overlooked by staff in this case. Again, this points to the fundamental difference between developing a community of learners or peers, on the one hand, and detached and isolated preconceived instances of professional development in the form of pre-scheduled workshops on the other. The tension between the two is often captured by discussions about resources and workload. However, while prescheduled workshops have their place, their prevalence is often more about ease of reporting than about ultimate impact. If the aim is to support pedagogical practices in a reflective and sustainable manner in the virtual university, then this is an important lesson to learn. Again, without dismissing the possibilities of improving the workshops, if digital integration for effective teaching and learning practices stays at the technological level in higher education institutions, it seems like there could be a shortage of expertise for the virtual university (e.g., Bowles and Sendall 2020). The goal therefore is to cultivate a professional development framework/model tailored to the development of virtual universities based on the anticipated challenges highlighted thus far in order to produce virtual academics. At the same time, for the virtual university we are looking for a type of educator who is a reflective practitioner and lifelong learner, and who has an interest in teaching and learning in dynamic virtual environments.

138

K. N. Sim and H. Huijser

Proposed Model: Developing Digital Capabilities and Digital Integration In order to address the two challenges in developing virtual universities based on the findings from the abovementioned case studies (digital capabilities and digital integration), a proposed model which takes the relationship between humans and digital teaching and learning environments into account could be beneficial to unpack the barriers as well as outline opportunities in academics’ professional development. A theoretical framework describing relationships between humans and technologies in doctoral education contexts (Sim and Stein 2019) is adopted and proposed here (see Fig. 1). The aim of this proposed model is for engaging, training, and supporting the use of digital tools by targeting what the digital literacy needs of academics are, how academic TEL development could respond to those

EXTERNAL FACTORS

INTERNAL FACTORS

ty ili

T

Li te ra

Personal Constructs

b

la ai Av

tu r

e

IC

Context

Possibilitics

T&L Requirements

IndivudalPractices

Goals

Pe e

rI

Rationale

nf

lu

al

en

ce

Individual Exposure

r tm pa e D

t en

Social Dynamics

Fig. 1 Humans and technologies relationships model (Adapted from Sim and Stein 2019)

Institution Culture

Individual Beliefs

Decisions/ Changes

Nature of T&L

Action

ICT Affordances

Senior Management

Awareness

8

Models of Professional Development for Technology-Enhanced Learning in. . .

139

needs, how teaching and learning are enhanced in a virtual context, and what we need to know in order to do so, as well as how the use of digital tools could be designed to be responsive to the needs of teaching and learning in the virtual university. The model draws from Bronfenbrenner’s (Bronfenbrenner 1979) concept of ecological systems, which frames interconnections between and among the individual and the systems and subsystems within which individuals exist and grow. Such interconnections draw on individuals’ existing skills and knowledge of TEL. Moreover, the main feature of the model concerns actions, decisions, and judgements about the use of digital teaching and learning tools. These actions, decisions, and judgements are usually the result of internal factors related to influencers and determinants of context (such as prior knowledge and experience related to thoughts, beliefs, and practices of TEL) situated within, and reflective of, external factors related to social dynamics, wider literature and trends, and norms and expectations of discipline and institutional context. Well-developed and supported communities of practice can build on prior knowledge in organic ways, thus influencing the context in constructive and sustainable ways. Again, pedagogical design of teaching and learning in the virtual university is a dynamic and collaborative endeavor, and isolated professional development would be ill-equipped to address the professional development needs in this respect. The purpose of the theoretical framework is to help with mapping the dynamics that feed into observable individual (staff) actions and decision-making via professional development in the virtual university. Referring to Fig. 1, the external factors, such as institutional culture and community of practices, set the scene for academics to engage with the use of digital tools in teaching and learning practices. Personal constructs, which consist of individual beliefs, practices, and exposures, are then evolved as factors that influence behaviors. This then forms part of the context for academics and influences their digital teaching and learning practices. It depends on academics’ awareness of what their digital literacy needs are and how their teaching and learning practices could be enhanced in order for them to determine to what extent they need to develop or change their practices, and this process is much more effective in a collaborative context (as per community of practice) rather than individuals reflecting on their own practice in isolation. Only then, actions could be taken by both individual academics and the broader academic community as part of being responsive to how digital teaching and learning environments could be designed to address the needs of students in the virtual university. For instance, if staff’s digital capabilities improve due to individual actions and decision-making, within a supportive community of practice, the support of digital technology integration for teaching and learning practices could be intensified to the “next” level (i.e., virtual pedagogies), which is precisely what is needed in the virtual university. It is worth noting here that a number of conceptual frameworks describing ICT acceptance and adoption have been in existence since the early 1970s. The concernsbased adoption model (Hall 1974) comprises three dimensions of innovation adoption, two of which describe and explain observable actions and behaviors (levels of use and stages of concern), and a third focuses on the diagnosis of those behaviors and actions (innovation configuration). The staged model has been found to be

140

K. N. Sim and H. Huijser

useful in educational institutional settings as it makes links between user systems and resources systems. The technology acceptance model (Davis 1989) highlights an individual’s acceptance of digital technologies. Perceived usefulness and perceived ease of use are the two variables that this model uses as a basis to describe and determine acceptance. The unified theory of acceptance and use of technology (UTAUT) model (Venkatesh et al. 2003) was developed as a result of an analysis of eight existing models. The UTAUT model is the outcome of that analysis. It focuses on intention and behavior related to the adoption and use of IT and takes cognizance of the dynamic influences of organizational context, user experience, and demographic characteristics. The proposed model here, however, focuses on persondigital-context connections, but pays specific attention to how people (i.e., academics in this context) adapt and enact their digital selves in the higher education context, specifically in the virtual context. There is potential for further empirical work to be done on examining how the proposed model may benefit from the other frameworks/models, in the process of developing a virtual university. However, this proposed model is able to be further adopted into both the academic and student contexts, where it highlights that individual academic participants as well as students hold assumptions about, and have expectations of, digital teaching and learning, and those expectations and assumptions influence and determine their judgements about digital teaching and learning environments as well as their use of digital tools in such environments. This is important as academics and students are both key players for the success of a virtual university. Moreover, more complex levels of perceiving and working with digital tools within a context (i.e., in the process of teaching and learning) gives some focus to interconnections, where people (i.e., academics and students) collaborate. As reflected in the findings of the two case studies above, this includes digital affordances that are seen as worthwhile when they support and enhance the work of teaching and learning in ways that make sense to the academics and students, and when an academic and/or a student alters and changes thinking or practices because of the influence and affordances of digital tools. Yet no evidence was found to support a possible argument that as well as digital affordances causing individuals to alter and modify their thinking and behaviors, those tools, in turn, are able to alter how they respond to the people who use those systems.

Discussion: Professional Development for the Virtual University With the proposed model set as the foundation of understanding the relationship between humans and digital tools for virtual universities, two implications emerge. It is evident that explicit academic TEL development support needs to be provided to academics in order for them to understand the affordances of digital tools and, thus, to use them pedagogically in the process of teaching and learning, based on their existing skills and knowledge.

8

Models of Professional Development for Technology-Enhanced Learning in. . .

141

Across Australasia, it is common for higher education institutions to use workshops to support in-service learning around digital literacies. Workshops as a format for higher education learning and teaching has been around for decades and are used to: (a) Develop a common understanding on campus concerning the nature of (upskilling) (b) Establish and maintain the academic integrity of (professional development) (c) Increase the confidence of faculty as they implement a new pedagogy (d) Increase the likelihood that service-learning is institutionalized in higher education (Bringle and Hatcher 1995, p. 112) If digital learning–related workshops for academic development are to “facilitate behavior change” (Green et al. 2003, p. 468) and “involve the opportunity to practice teaching using content from the [workshop] being developed” (Saroyan et al. 2004, p. 6), then participants need to approach the workshops with that mindset. The themes that emerged from the analysis of 5 years of ICT workshops in an academic development context suggest that the workshop participants concentrated primarily on “how to use an ICT tool” instead of “how to integrate an ICT tool” into their teaching and learning processes for a change of practice. Not only were the underpinning pedagogies not being mentioned, the academics’ digital capabilities were also questionable when the findings in both case studies revealed the prominence of technical support after attending the workshops. The requests to have a more handson session, an examples-based workshop and follow-up support to make their knowledge transferable to their day-to-day context, showed that most participants were relatively inexperienced users of available digital tools and applications, especially when the examples were already provided in the workshops, yet they requested hands-on or follow-up support after the workshops. It could be that academics’ levels of digital literacy were overshadowed or taken for granted as a consequence of their academic progression when the institution implemented “new” strategies, such as a “digital vision” for teaching and learning. Therefore, instead of “integrating” digital teaching and learning tools as a shift in teaching and learning practices, academics tend to “apply an icing layer” onto their existing practices as a way of “complying with” the newly employed institutional strategy. Hence, the pursuit of efficiency compromised effectiveness, and the effectiveness might have been pursued at the expense of the individuals’ efficiency when the academic’s focus is on “how” they can apply digital tools on top of their current teaching and learning process in their individual contexts. In this case, the notion of “digital integration” applies to the academics who act according to their constructs of “needs” and “outcomes” only. What the academics constructed as being efficient and effective use of digital tools in the process of teaching and learning led to their construct of the “best possible ways” in regard to digital teaching and learning integration. In the virtual university, the idea of “applying an icing layer” is no longer relevant, as digital teaching and learning has moved front and center of the whole teaching and learning process.

142

K. N. Sim and H. Huijser

When reflecting on our daily work in academic development, we have encountered both academics and students talking about their experiences of using digital tools. For many of them, especially academics, digital tools can bring either joy or challenge their well-versed academic practices, and either create barriers to their development or be the answer to their needs. While some grasp and pursue opportunities to make use of various digital tools for teaching and learning processes, others struggle. Despite documented and anecdotal positive and enthusiastic urges to adopt digital tools to reap benefits for increasing and improving efficiency and effectiveness of academic practices, academics and students who struggle experience digital teaching and learning as needless interruptions to their daily performance, and, as difficult to learn and use, will find this aspect even more challenging when they teach and learn in the virtual university. In short, resisting digital change in teaching and learning is simply not an option in this context. As revealed by the model above, academic participants do possess a vast array of digital literacy skills, knowledge, and abilities. They have a variety of digital teaching and learning experiences as well as varying reasons and levels of motivation for being involved in teaching and learning processes. Their skills and capacity to make use of digital tools to support their roles in the teaching and learning process vary as well. The findings that have emerged from continuing studies will inform the planning for support activities to enhance academics’ professional development, whatever their background and needs. This is significant for the virtual university (e.g., Anderson 2020). Regardless, however, the key element of setting up a professional development process is to establish and develop a community of learners and peers, who can draw on each other’s expertise and who develop and adapt their digital pedagogies in responsive and sustainable ways. Academic developers are key to facilitate this process, and they play a continuing role in keeping such communities functioning in constructive and effective ways. However, this is very different from academic developers being solely responsible for a professional development schedule that academic may or may not make use of. It is worthwhile to explore the effective and efficient process of digital teaching and learning as academics’ professional development. Echoing views such as those expressed by Castañeda and Selwyn (2018), digital tools are central to facilitating effective and efficient teaching and learning processes, especially in the virtual university. In the case studies outlined in this chapter, we have explored the experiences of those involved, academics in particular, of professional development workshops, via discussions about their practices and views, and with a specific focus on the use of digital tools in the process of teaching and learning. This raised question about the process of digital teaching and learning and how to achieve digital literacy within academic domains. As stated by Gourlay and Oliver (2012), the idea of “learning” has been widely theorized but not necessarily in relation to “educational technology” teaching and learning. It is therefore important to ascertain what the ongoing digital needs of academics are, and how academic development could respond to those needs, and what they need to know in order to do so, as well as how digital teaching and learning could be designed to be responsive to the needs of students’ experiences in the virtual university. Castañeda and Selwyn (2018)

8

Models of Professional Development for Technology-Enhanced Learning in. . .

143

further argued that it is important that we have an “active commitment to ‘thinking otherwise’ about how these technologies might be better implemented across higher education settings” (p. 8). In the context of the virtual university, digital tools play a central role. Therefore, seeing such tools in relationship to the person and to the setting is essential, and integrating professional development into everyday practices, in the form of a communities of learners facilitated by academic developers, seems most likely to succeed if the objective is holistic (Sutherland 2018), sustainable professional development. Academic development that takes this perspective into account may result in sustainable, integrated, and reflective academic and pedagogical practices, which align with the idea of a dynamic, ever-changing virtual university. As Kellermann (2021) has argued: Content can enable [teaching and] learning, but it cannot provide an education. [. . .] Education should be better than ever, as we are now able to point at myriad incredible resources, possibly on the web, perhaps in our library, where we act as content aggregator, not creator.

This realization shift the pedagogical focus as well, and draws attention to the dynamics of digital teaching and learning environments. Academics’ understanding and use of digital tools for teaching and learning cannot be considered independently of the work that they are involved in, which includes their relationship with teaching and learning, their research projects, and the tools they do (or could) engage with. Nevertheless, the recent COVID-19-related lockdowns might have brought a change of mindset, “if you stay ready, you won’t have to get ready” – in this case, acquiring prior knowledge about digital affordances. It is important to ensure the higher education sector is prepared to service academics going forward, not just to be prepared for a possible “next wave” of the pandemic, but also to reframe the “stage” of academic development going forward (e.g., Batara and Rapat 2020), particularly in the virtual university.

Conclusion: Ensemble Efforts Can, and Should, Realize the Virtuality As argued by Laurillard (2013), “teachers in the twenty-first century, in all educational sectors, have to cope with an ever-changing cultural and technological environment” (p. 1). In the virtual university, it is clear that academics need to embrace digital teaching and learning tools actively in their daily practices. When the use of digital tools is commonly agreed upon, they become a norm, and the aim is therefore to make the use of digital tools the norm in the virtual university, yet the norm should at the same time include a level of reflective practice that ensures ongoing questioning and exploring of the best ways to use digital tools and as part of pedagogical practice in sustainable ways in an overall teaching and learning ecology. Therefore, institutions should articulate a vision about the role of digital tools and

144

K. N. Sim and H. Huijser

ensure that the vision is communicated clearly and embedded in institutional practices. Strategically, a professional development model, such as the one that has been proposed in this chapter, reinforces this vision and part of such a vision would be to emphasize the need to focus on the process of teaching and learning, as well as the outcomes of the process. The proposed model serves as a framework for engaging, training, and supporting the use of digital tools by targeting what the ongoing digital literacy needs of academics are, how academic TEL development could respond to those needs, how teaching and learning could be enhanced virtually, and what we need to know in order to do so, as well as how digital teaching and learning could be designed to be as responsive as it needs to be. In short, if traditional teaching is the “sage on the stage,” pontificating to an audience, then maybe COVID-19 has presented us with an opportunity to reframe the “stage,” especially while the whole world is embarking on a challenging, unconventional journey. The virtual university is the ideal stage to explore the future of higher education in this unsettled and unsettling world, and in order to leverage the affordances of this stage, a reflective and collaborative approach to professional development is crucial.

Cross-References ▶ Academic Engagement in Pedagogic Transformation ▶ Aligning the Vision for Technology-Enhanced Learning with a Master Plan, Policies, and Procedures ▶ Laying and Maintaining the Foundations for Quality ▶ Peer Observation of Teaching in the Virtual University: Factors for Success ▶ Transparency in Governing Technology Enhanced Learning ▶ Using Institutional Data to Drive Quality, Improvement, and Innovation

References Anderson, V. 2020. A digital pedagogy pivot: Re-thinking higher education practice from an HRD perspective. Human Resource Development International 23 (4): 452–467. https://doi.org/10. 1080/13678868.2020.1778999. Batara, C., and C.I. Rapat. 2020. Design and implementation of virtual university based on ICT. International Journal of Research 8 (4). https://doi.org/10.29121/granthaalayah.v8.i4.2020.19. Bolisani, E., M. Fedeli, L. Bierema, and V. De Marchi. 2021. United we adapt: Communities of practice to face the CoronaVirus crisis in higher education. Knowledge Management Research and Practice 19 (4): 454–458. https://doi.org/10.1080/14778238.2020.1851615. Bowles, D.C., and M.C. Sendall. 2020. COVID-19: The elephant in the virtual classroom. Pedagogy in Health Promotion: The Scholarship of Teaching and Learning 6 (3): 156–158. https:// doi.org/10.1177/2F2373379920938419. Bredow, C.A., P.V. Roehling, A.J. Knorp, and A.M. Sweet. 2021. To flip or not to flip? A metaanalysis of the efficacy of flipped learning in higher education. Review of Educational Research 91 (6): 878–918. https://doi.org/10.3102/00346543211019122.

8

Models of Professional Development for Technology-Enhanced Learning in. . .

145

Brewer, R., and S. Movahedazarhouligh. 2018. Successful stories and conflicts: A literature review on the effectiveness of flipped learning in higher education. Journal of Computer Assisted Learning 34 (4): 409–416. https://doi.org/10.1111/jcal.12250. Bringle, R.G., and J.A. Hatcher. 1995. A service-learning curriculum for faculty. Michigan Journal of Community Service Learning 2 (1): 112–122. Bronfenbrenner, U. 1979. The ecology of human development: Experiments by nature and design. Harvard University Press. Castañeda, L., and N. Selwyn. 2018. More than tools? Making sense of the ongoing digitizations of higher education. International Journal of Educational Technology in Higher Education 15 (22): 1–10. https://doi.org/10.1186/s41239-018-0109-y. Chen, L., P. Chen, and Z. Lin. 2020. Artificial intelligence in education: A review. IEEE Access 8: 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510. Davis, F.D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13 (3): 319–340. https://www.proquest.com/scholarly-journals/ perceived-usefulness-ease-use-user-accep/docview/218114880/se-2?accountid¼14700 EDUCAUSE. 2020. 2020 EDUCAUSE horizon report: Teaching and learning edition. https:// library.educause.edu/-/media/files/library/2020/3/2020_horizon_report_pdf.pdf?la¼en& hash¼08A92C17998E8113BCB15DCA7BA1F467F303BA80 Erickson, F. 1998. Qualitative research methods for science education. In International handbook of science education, ed. B.J. Fraser and K.G. Tobin, vol. 2, 1155–1173. Kluwer Academic. Fitzgerald, R., H. Huijser, D. Meth, and K. Neilan. 2020. Student-staff partnerships in academic development: The course design studio as a model for sustainable course-wide impact. International Journal for Academic Development 25 (2): 134–146. https://doi.org/10.1080/ 1360144X.2019.1631170. García-Peñalvo, F.J. 2021. Avoiding the dark side of digital transformation in teaching. An institutional reference framework for eLearning in higher education. Sustainability 13 (4). https://doi.org/10.3390/su13042023. Gourlay, L., and M. Oliver. 2012. Curation, combat or coping? Student entanglements with technologies in HE (Paper presentation). Society for Research into Higher Education, Newport. Green, M.L., C.P. Gross, W.N. Kernan, J.G. Wong, and E.S. Holmboe. 2003. Integrating teaching skills and clinical content in a faculty development workshop. Journal of General Internal Medicine 18 (6): 468–474. Hall, G.E. 1974. The concerns-based adoption model: A developmental conceptualization of the adoption process within educational institutions (Paper presention). Annual Meeting of the American Educational Research Association (AERA), ERIC Document 111791. Hodges, C., S. Moore, B. Lockee, T. Trust, and A. Bond. 2020. The difference between emergency remote teaching and online learning. Educause Review. https://er.educause.edu/articles/2020/3/ the-difference-between-emergency-remote-teaching-and-online-learning Huijser, H., J. Wilson, D. Johnson, and J. Xie. 2016. Implementing a communities of practice model to enhance learning and teaching at a transnational university in China. Compass: Journal of Learning and Teaching 9 (13). https://doi.org/10.21100/compass.v9i13.339. Iqbal, I., and J. Vigna. 2021. Care in collaborations: Opening up conversations about teaching. International Journal for Academic Development 26 (3): 383–387. https://doi.org/10.1080/ 1360144X.2021.1937183. Kellermann, D. 2021. Academics aren’t content creators, and it’s regressive to make them so. Times Higher Education. https://www.timeshighereducation.com/opinion/academics-arent-contentcreators-and-its-regressive-make-them-so Laurillard, D. 2013. Teaching as a design science: Building pedagogical patterns for learning and technology. Routledge. Lee, J., C. Lim, and H. Kim. 2017. Development of an instructional design model for flipped learning in higher education. Educational Technology Research and Development 65 (2): 427–453. https://doi.org/10.1007/s11423-016-9502-1.

146

K. N. Sim and H. Huijser

Mayring, P. 2000. Qualitative content analysis [28 paragraphs]. Forum: Qualitative Social Research 1 (2): 20. http://nbn-resolving.de/urn:nbn:de:0114-fqs0002204 Pleschová, G., T. Roxå, K.E. Thomson, and P. Felten. 2021. Conversations that make meaningful change in teaching, teachers, and academic development. International Journal for Academic Development 26 (3): 201–209. https://doi.org/10.1080/1360144X.2021.1958446. Saroyan, A., C. Amundsen, C. Weston, L. McAlpine, L. Winer, S. Cowan, and T. Gandell. 2004. The course design and teaching workshop: Why and what? In Rethinking teaching in higher education, ed. A. Saroyan and C. Amundsen, 3–14. Stylus Publishing. Silverman, D. 2001. Interpreting qualitative data, 2nd ed. Sage. Sim, K.N. 2021. The use of learning management system (LMS): Are we “using” it right? Journal of Applied Learning and Teaching 4 (1): 100–106. Sim. K.N., and M. Cowling. 2020. Education tools for the 21 century. Newsroom. https://www. newsroom.co.nz/ideasroom/education-tools-of-the-21-century Sim. K.N., and M. Cowling. 2021. Ensemble efforts can, and should, replace the lone lecturer. Times Higher Education. https://www.timeshighereducation.com/opinion/ensemble-effortscan-and-should-replace-lone-lecturer Sim, K.N., and S. Stein. 2019. Re-constructing the roles of information & communication technologies in doctoral research processes. Ako Aotearoa. https://ako.ac.nz/knowledge-centre/ enhancing-the-role-of-ict-in-doctoral-research-processes/guidelines/ Sutherland, K.A. 2018. Holistic academic development: Is it time to think more broadly about the academic development project? International Journal for Academic Development 23 (4): 261–273. https://doi.org/10.1080/1360144X.2018.1524571. Thomas, D.R. 2006. A general inductive approach for analyzing qualitative evaluation data. American Journal of Evaluation 27 (2): 237–246. Venkatesh, V., M. Morris, G. Davis, and F. Davis. 2003. User acceptance of information technology: Toward a unified view. MIS Quarterly 27 (3): 425–478. https://www.proquest.com/ scholarly-journals/user-acceptance-information-technology-toward/docview/218137148/se-2? accountid¼14700 Wenger, E. 1999. Communities of practice: Learning, meaning, and identity. Cambridge University Press.

9

Peer Observation of Teaching in the Virtual University: Factors for Success Martina Crehan, Morag Munro, and Muireann O’Keeffe

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Peer Observation of Teaching (PoT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PoT and the Virtual University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Online PoT: Identifying Factors for Success . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sharing and Listening to Stories of Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Focus on Pedagogy in a Listening Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Honesty and Openness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Longitudinal Approach Enabled Deep Reflection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PoT and the Pandemic Online Pivot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Teaching Online Is Different . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Teaching Presence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Observing Teaching Online: What Matters? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Building Trust and Collegiality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Support and Guidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PoT in the Virtual University: Establishing Factors for Success . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Creating a Clear and Common Lens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Recognizing the Lens of the Observer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Relationship Building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Approach in Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Establishing Relationships of Trust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agreeing the Lens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

148 149 150 150 151 152 152 153 153 153 154 154 155 155 156 157 157 158 158 159 160

M. Crehan Health Professions Education Centre, Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Dublin, Ireland e-mail: [email protected] M. Munro Office of the Dean of Teaching and Learning, Maynooth University, Maynooth, Ireland e-mail: [email protected] M. O’Keeffe (*) College of Arts and Tourism, Technological University Dublin, Dublin, Ireland e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_9

147

148

M. Crehan et al.

Establishing the Boundaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phased Approach to Reflective Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

160 160 160 161

Abstract

Teaching online can feel isolating, with a lack of opportunity to share, discuss, and reflect on practices and pedagogy. Peer observation of teaching (PoT), a structured process whereby participants both offer and receive feedback on teaching practice, can be a way to overcome isolation, share practice and develop online teaching practices in the virtual university, and ultimately improve student learning. Building on the findings of an evaluation of PoT work with faculty in face-to-face contexts, along with feedback from faculty and educational developers on their experiences with, and perspectives on PoT in online contexts, in this chapter we identify factors for success when implementing PoT in the virtual university. Keywords

Online teaching · Peer observation of teaching

Introduction Teaching online can feel isolating, with a lack of opportunity to share, discuss, and reflect on practices and pedagogy. Peer observation of teaching (PoT) can be a way to overcome isolation, to share experiences, and to develop online teaching practices, and ultimately to improve student learning. From a faculty perspective, effective peer relations can help to create and sustain a collaborative and learningfocused institutional climate, which promotes faculty development, growth, and selfefficacy and, ultimately, student progress and learning. The development of faculty members’ confidence and their sense of self-efficacy has been shown to be positively related to student motivation and achievement (Zee and Koomen 2016). The establishment of effective peer and colleague relationships, coupled with opportunities for feedback and discussion, has been identified as a key feature of effective faculty development and support in many disciplinary areas (e.g., Steinert et al. 2016), and often at national level. For example, Ireland’s first framework to support the professional development of those who teach across the higher education sector specifically encourages staff to engage in peer dialogue and support (NFETL 2016). Peer observation of teaching (PoT) is a structured process whereby participants both offer and receive feedback on teaching practice, with a view to mutual development of their teaching skills. While PoT has heretofore mainly been implemented in face-toface teaching settings, there is potential to extend the approach to online teaching contexts. Building on the findings of the evaluation of a PoT scheme carried out with

9

Peer Observation of Teaching in the Virtual University: Factors for Success

149

faculty in face-to-face contexts, along with feedback from faculty and educational developers on their experiences with, and perspectives on, PoT in online contexts, in this chapter we identify factors for success when implementing PoT in the virtual university.

Peer Observation of Teaching (PoT) Gosling (2002) has identified three possible purposes for PoT: an evaluation model, a developmental model, and a peer review model. The developmental and peer review models foreground collegiality, trust, and mutual respect, aiming to foster reflection and critical discussion on what constitutes good teaching (Yiend et al. 2014), while the evaluation model is often equated with performance appraisal (McMahon et al. 2007). It is the peer review model of PoT that underpins the approach that we describe in the current chapter. This model of PoT has demonstrated potential benefits for both observers and observees. For observees, the benefits include learning from feedback provided by the observer (Hendry and Oliver 2012), and gaining reassurance and confidence in one’s abilities as an educator (Donnelly 2007; Whipp and Pengelley 2017). Observers report benefits derived from learning about new teaching and learning strategies, and being prompted to test these in their own practice (Hendry and Oliver 2012), as well as from comparing and contrasting the observees’ context with their own (Tenenberg 2016). Through observing others’ practice, observers also learn more about, and reflect on, their own practice (Sullivan et al. 2012). More generally, such approaches to PoT can contribute to the development of collegiality among colleagues, encouraging teaching to be seen as a topic for communal discourse (Whipp and Pengelley 2017). Integral to the peer review model of PoT is its role in encouraging critical selfreflection (Hammersley-Fletcher and Orsmond 2004; Peel 2005). As Gosling (2002, p. 38) puts it: The spirit of collaborative peer observation is not that the peer claims expertise in observation but rather he or she is a colleague who operates in good faith to assist the teacher being observed to reflect on and consider teaching problems as interesting professional issues about which all teachers should be curious.

Following the implementation of a peer review model of PoT, Kenny et al. (2014) report that the opportunity for collective reflection facilitated an appreciation of collegial professional development. The role of participants in peer observation as constructive, critical friends is therefore key to supporting both reflection and effective dialogue (Carroll and O’Loughlin 2014). However, effort needs to be expended in creating the structures and environments in which such reflection and dialogue can flourish. McCormack and Kennelly (2011) report that three factors – connection, engagement, and safety – facilitate the creation of such “conversation communities” (p. 528).

150

M. Crehan et al.

PoT and the Virtual University The emergence of the virtual university requires the conception of PoT to be reconsidered for the online context. A limited number of online PoT initiatives have heretofore been discussed in the literature. Reported benefits of online approaches to PoT include the ability to have access to a wider range of teaching artifacts and resources (West and Clauhs 2019), and opportunity for participants to gain insights particular to teaching in the online environment (Bennett and Santy 2009; Harper and Nicolson 2013). Challenges include difficulties in hearing or seeing parts of a lesson due to the limitations of technology (West and Clauhs 2019); differing perspectives on of what is, and what is not, observable online (Bennett and Barp 2008); and in the context of asynchronous online teaching, consideration for how best to select and isolate a “chunk” of online learning and teaching as the focus for the observation (Bennett and Barp 2008). In addition, while standards and frameworks relating to quality in online teaching exist (see, for example, ASCILITE (2020) and Sankey et al. (2014), there is no established consensus on what constitutes good online teaching (Swinglehurst et al. 2008). Furthermore, West and Claus (2019) report that initial interactions in online PoT were “awkward,” but do note that it is difficult to ascertain if this was due to the online format, or because the observers and observees had not had adequate time to build up a trust relationship prior to the observation. Indeed, Walker and Forbes (2018) has highlighted that building trust and rapport is crucial for successful online PoT. The opportunities presented by facilitating PoT in the virtual university also allow us to consider how we can extend the reach of the process. Tenenberg (2016) argues that PoT is best applied in the context of a single discipline, arguing that it is essential that the observee has an understanding of the disciplinary context, the material being taught, and the signature pedagogies of the discipline. However, for Torres et al. (2017, p. 824), “it can be precisely this disciplinary focus that sometimes hinders deep reflection about teaching practices.” On the other hand, cross-disciplinary PoT pairings can move participants away from a primary focus on the disciplinary context and on the material being taught, and toward a focus on the teaching and the method (Nicolson and Harper 2013). Furthermore, cross-disciplinary PoT can facilitate exposure to pedagogical approaches outside those traditionally employed within one’s home discipline, and can allow for a more collaborative and equitable relationship in the PoT pairing (Torres et al. 2017). Although much of the literature focuses on PoT in the context of a single institution, reports of cross-institutional approaches to PoT are beginning to emerge in the literature, particularly in online contexts. Advantages include the removal of issues of power, and the potential for unbundling teaching from institutional context (Walker and Forbes 2018; West and Clauhs 2019).

Online PoT: Identifying Factors for Success The authors are educational developers working in three different higher education institutions in Ireland. In the remainder of this chapter, we build on longitudinal work carried out with a group faculty engaged in PoT in face-to-face and online

9

Peer Observation of Teaching in the Virtual University: Factors for Success

151

contexts across the three institutions, and identify factors for success for PoT in the virtual university context. In 2017, we initiated a collaborative cross-disciplinary and cross-institutional scheme of face-to-face PoT across the three universities. The scheme was underpinned by Gosling’s (2002) peer review model, with the vision being to “open the doors” of cross-disciplinary classrooms, with a view to fostering dialogue, collaboration, and reflection about teaching and learning practices. Participants were from a range of disciplinary backgrounds, including education, business, computer science, pharmacy, geography, economics, management, and health informatics. Large-group teaching was a common teaching context for the group, and thus became a shared focus of both the observations and the professional conversations between peer dyads. Participants engaged in initial workshops on peer observation, reflective practice, and feedback approaches. Reflective templates were provided for participants to submit a written reflection on their experience and learning. A preliminary evaluation of the scheme gathered data on participants’ perspectives via a survey and follow-up focus group. Findings indicated that the benefits for participants included the feedback received by their peer; the opportunity to learn from observing their peer; and the opportunity to view their teaching practice through a different lens, particularly in the cross-institutional and interdisciplinary context (O’Keeffe et al. 2021). By late 2019, although no further formal work had progressed in this initial PoT scheme, we were aware that conversations had continued between some of the peer dyads. We thus sought to explore the longitudinal impact of the initial scheme of PoT, as well as the continuing conversations between participants. A structured format was utilized for this. Firstly, participants undertook to revisit their initial individual reflections, and considered how they had evolved and developed in the intervening period. A reflection template was provided to guide these reflections. Secondly, the original peer dyads met to discuss and document ongoing conversations using the reflections from the template. Finally, the whole group then participated in a facilitated reflective discussion. The educational developers then identified enabling factors for effective PoT arising from this follow-up evaluation; these included: sharing and listening to stories of practice; focus on pedagogy in a listening environment; honesty and openness; and a longitudinal approach enabling deep reflection. Underpinning these factors (O’Keeffe et al. 2021), the role of the educational developers as designers and co-reflectors in the process scaffolded these enablers. These factors for success are illustrated in Fig. 1.

Sharing and Listening to Stories of Practice Sharing and listening to stories of practice was integral to the participants’ reflective conversations. The presence of another and the task of reflecting together on a teaching experience supported the ability to construct these narratives and communicate them to each other. Analysis of the participants’ reflective discussions revealed a number of elements, which appeared to foster the best conditions for effective professional conversations to occur: Similar to the structured framework of PoT invoked by Nicolson and Harper (2013), our PoT process began with an

152

M. Crehan et al.

Fig. 1 PoT – Factors for success

induction phase giving specific time for the paired individuals to become comfortable with each other, and to have a number of meetings before the observation. Trusting relationships formed during the initial stage underpinned what participants described as authentic reflection and learning. In addition, the cross-institutional context appeared to create a safe environment which supported authenticity in reflective conversations, as well as a space in which to unpack disciplinary or institutionally based assumptions.

Focus on Pedagogy in a Listening Environment The focus of both the observations and the subsequent conversations was on pedagogy rather than the curriculum, and on observing one another’s teaching without bias or judgement and through the eyes of the student. Looking at practice through the lens of a practitioner from another discipline created an opportunity to step back and to question previously accepted approaches and assumptions in relation to structure and process. There was also a sense that participants were “authentically learners in each other’s classes,” and a sense of comfort in being open about all aspects of practice, “warts and all.”

Honesty and Openness Establishing a sense of trust enabled honest and open conversations, and further supported the ability to question assumptions. Participants were forced to explain

9

Peer Observation of Teaching in the Virtual University: Factors for Success

153

details of their teaching in a way that they were less likely to do in conversations with a colleague from their own discipline.

A Longitudinal Approach Enabled Deep Reflection A common theme of participants’ reflections was the impact of the duration of the process, and the opportunity to return to the initial reflections and reexamine these as dyads. This, for some, provided an insight into the potential for a different level of reflection as a consequence of an established longitudinal relationship with their observation partner. There was a sense that an established relationship and a trust in the process could enable a shift from an initial tactical focus on mechanical issues of practice to broader aspects of teaching impact. The explanation of that change was a source of critical reflection and is a noteworthy aspect of a structure which facilitated repeated opportunities to engage in reflective conversations. For some, this was expressed as a recognition of the need to further interrogate self-development through the process. For others, it was an affirmation and extension of existing views on the role of dialogue between practitioners.

PoT and the Pandemic Online Pivot In 2020, conversations between the authors and participants from the initial PoT scheme continued against the backdrop of a rapid shift to online teaching due to the global COVID-19 pandemic. Not surprisingly, some of the participants discussed how their peer observations and reflective dialogue experiences were now digitally mediated and supported. The role of PoT in the online environment was thus deemed to be worthy of further investigation. An online focus group was conducted in order to build on the factors for success in PoT already identified (O’Keeffe et al. 2021), and to seek insights into participants’ experiences and perceptions of PoT in online environments (Crehan et al. 2021). By this point, some of the participants had engaged in online PoT. Areas of exploration considered during the focus group included: challenges associated with online PoT; benefits of online PoT; how best to build trust and collegiality in the context of online PoT; how to make online PoT an authentic learning experience; and factors for success in online PoT. Factors emerging from the analysis of this data included an acknowledgment that teaching online is different; the importance of teaching presence; the need for consideration of what is most important when observing teaching online; the importance of building trust and collegiality; and a need for support and guidance for participants.

Teaching Online Is Different The focus group highlighted a need to clarify what constitutes teaching in the online environment, and also to consider what can, and should, be observed. Participants noted the possibility that observation of teaching could take place through a variety

154

M. Crehan et al.

of technologically mediated mechanisms, involving synchronous and asynchronous teaching activities. One of the participants also suggested that online PoT may offer unique opportunities for rapid observation and learning from others’ online teaching practice. Participants also highlighted that it may be difficult to conduct online PoT without a clear consensus on what “good” online teaching is. They drew comparisons between what is possible to observe online versus what can be observed face to face, both in terms of the actions of the teacher and their students: for example, it is more difficult to observe facial expressions and body language. Participants highlighted that in the online context, more planning and a clear learning design would be needed for any component of teaching; also, in seeking feedback, it was noted that peers would need to ensure clarity in what feedback was being sought on. While more planning might be involved, this ultimately would strengthen a peer reciprocal approach to observation, empowering participants by defining and planning teaching activities and seeking specific feedback.

Teaching Presence Many of the participants grieved a perceived loss of the affective aspects and physical social presence of teaching when teaching moved online. Focus group participants shared a sense of loss of the experience of “being” in the teaching space with their students. The retrospective observation of recorded lectures raised questions as to what it means to “be” in and experience the teaching space of another, and whether it is possible to experience this after the fact: for example, participants also noted that the silences and “dead space” that are common in online teaching (Bennett and Barp 2008; West and Clauhs 2019) may be experienced differently when listening to a recorded lecture or seminar, as opposed to when experienced in real time. Thus, guidance and frameworks should assist participants in recognizing this contrast.

Observing Teaching Online: What Matters? Bennett and Barp (2008, p. 568) argue that “many aspects of peer observation do not simply ‘translate’ directly online”: this raises questions in relation to the foci of the observation process in an online environment. For a number of our participants, this was a central theme of their perceptions, and was linked to their views on the authenticity of the experience and the necessary redefinition of what this means in an online PoT environment. There was a sense that the online context shifts the focus to technical and teacher performance aspects, rather than student reactions and interaction. This was expressed as a frustration with being unable to gauge student reactions in the online context. The online environment was also perceived as moving the focus of observation to one that foregrounds procedural aspects of teaching. This was linked to the inability to gauge the affective aspects and the consequent tendency to focus on more technical aspects. One of the participants

9

Peer Observation of Teaching in the Virtual University: Factors for Success

155

referred to perceived misalignment between the intended outcomes of a teaching session (which focused on complexity in decision-making) and the observation focus, as evidence in the feedback conversation with the PoT partner. Participants’ sense of and definitions of interaction in an online learning and teaching context were apparently intertwined with their views of what can be “observed” and what sense can be made of those observations. Aligned with the work of Gosling (2014) and Swinglehurst et al. (2008), this suggests a need to refocus and reframe the act of PoT in an online context, with a concomitant need for specific support and scaffolding structures.

Building Trust and Collegiality In the solely online environment, building relationships, mutual respect, and a sense of community among teaching colleagues becomes more nuanced and complex. Careful design over time of online community building (Whipp and Pengelley 2017) is necessary and important to scaffold participants into a constructive social space for reflective dialogue about teaching. In the initial face-to-face scheme of PoT, an induction event was held prior to partaking in the mutual observations of teaching. This was an opportunity for participants to meet their peer observer, and was key to supporting the development of dialogue between participants, and enabling them to become critical friends (Carroll and O’Loughlin 2014). Focus group participants noted that the immediacy of the online context could diminish the time and space necessary for dialogue and reflection. One participant remarked that she “would not have felt comfortable if new to this and didn’t know the observer” stressing that meeting beforehand and building trust within an observation partnership were key to the process. The same participant also drew attention to the “labor of getting to know somebody,” while another suggested that developing a relationships would be even more important in the online context, but more challenging in terms of establishing the necessary rapport and trust. Participants also noted that online PoT may be perceived to be a much more formal endeavor than when conducted face to face. The need to establish a sense of collegiality and trust, and the perceived difficulties in achieving this in an online context led participants to reflect on the supporting frameworks which might be necessary.

Support and Guidance Participants identified a need for guidance and support specifically targeted to the online context, incorporating all aspects of the online PoT process: from planning to implementation, and to communication and feedback. One participant reported a positive experience with an “experienced” observer who was able to focus and provide feedback on the substantive aspects of the teaching encounter and move beyond the purely technical focus. It was felt that modeling such an approach, as well as providing exemplars of best practice would be particularly useful in

156

M. Crehan et al.

acculturating peer observation partners to the specific parameters of the online context. The planning stage and the focus of observation was also perceived as requiring specific attention and support. The factors identified in our research with faculty are also supported by discussions with educational developers about the challenges and opportunities presented by online PoT. During the 2021 Staff and Educational Development Association (SEDA) Spring Conference, we workshopped our general findings and discussed the challenges of defining PoT in the online context; perceptions of giving feedback; and how we can best build trust and collegiality. Participants noted that PoT in online contexts should consider both synchronous and asynchronous teaching modes; highlighted the need for specific guidance for participants; and suggested a move away from an expert-novice model of PoT. We value these conversations in the context of the appetite for a wide and ongoing discussion on constructing approaches to online PoT, and the opportunity this presents to consider suggested steps.

PoT in the Virtual University: Establishing Factors for Success Overall, our findings have illustrated that PoT carried out online is experienced differently than when implemented face to face, highlighting a variety of teaching foci in the online environment. Teaching presence, building trust, and collegiality came to the fore, and the need for specific support and guidance for online PoT were also highlighted. It is clear that guidance and a support infrastructure are always important for those involved in PoT, but are even more relevant in the context of online observation (Nicolson and Harper 2013). Such guidance will also require specific tailoring to the online context, and should include a clear focus on strategies for building collegiality and trust between observation partners. Honest and authentic conversations about both the opportunities and the limitations of online PoT should be a key aspect of this guidance, and there is evidently a key role for educational developers in scaffolding and supporting these conversations. As discussed above, our participants clearly expressed their views on the differential experience of engaging in PoT online than when implemented face to face. This mirrors the perceived differentials in the experience of teaching online and the substantial research on the implications of this for professional development. Carril et al. (2013), for example, report that it is necessary to evaluate all of the changes that online teaching entails and define the role and competencies of the online educator before designing professional development opportunities and resources. We do not, in this chapter, focus on the steps which this full cycle might entail, but our research, combined with a review of the literature and other extant approaches, allows us to propose some key framing elements for PoT in the virtual university. As is the case with established models of face-to-face PoT, a structured approach should be maintained, consisting of planning, a preobservation meeting, conducting a teaching observation, a postobservation meeting, and submission of feedback/ reflections, and control of the process resides with the observation partners (Gosling

9

Peer Observation of Teaching in the Virtual University: Factors for Success

157

2002; McMahon et al. 2007). The general structure does not therefore need to differ from traditional face-to-face models, but there are key frames of reference within this structure which requires specific attention in the online context: creating a clear and common lens, recognizing the lens of the observer, and relationship building.

Creating a Clear and Common Lens If educators struggle with the boundaries of defining online teaching, and thus the focus of a related peer observation, the necessary first step is an agreed and shared understanding of the foci of the observation, as well as the elements which will be “ignored” or deemed unlikely to be adequately observed. To extend the focus beyond the purely technical, peer observation partners should agree the blend (as relevant and appropriate) of synchronous and asynchronous teaching observation, and clearly define the differing elements of teaching which might be reviewed. A 360 view should be discussed, incorporating a focus on the design of the learning environment, why and how engagement activities have been designed and incorporated (again both in the synchronous and asynchronous domains), and how to maintain a focus on student engagement in the absence of the visual cues which can be relied on in the face-to-face context. Actively discussing the lack of affective aspects and social presence of teaching, and the loss of experience of “being” in the teaching space of another, can help educators to identify what engagement means for them in the online space. Encouraging faculty to engage with and reflect on learning design standards and frameworks may be useful in considering the constituent elements of online teaching. The TELAS framework (ASCILITE 2020), for example, focuses on four key practice domains: online learning environment, learner support, learning and assessment tasks, and learning resources.

Recognizing the Lens of the Observer As previously noted, we recognize Gosling’s (2002) peer review model as the primary structuring approach to effective PoT. While the model therefore involves peers as observers (and avoids issues of hierarchy in observer partnerships), it is still important that the specific lenses of the peer partners are named and discussed. Experience, as in any form of teaching, undoubtedly shapes both expectations and experiences. Confidence in online teaching, particularly in relation to facilitation skills, is, not surprisingly, often reported by more experienced online educators (e.g., Chang et al. 2014) and thus may impact both the observation lens of those individuals, and the feedback they request in an observation session. Indeed, our research participants identified the specific challenges of online PoT for those new to teaching roles generally, and specifically to online teaching. The cross-disciplinary model of our original research study has direct relevance to the virtual university context, but also presents another set of both constructive and challenging lenses. The additional

158

M. Crehan et al.

importance of building the trust and collegiality necessary to successful PoT highlights the establishment of safe, reflective, and longitudinal relationships as key framing scaffolds.

Relationship Building The role of the educational developer in scaffolding and supporting relationship building and authentic, reflective conversations is central to PoT whether in the faceto-face or online context (Crehan et al. 2021). However, in order to ensure sustainability of these relationships for longitudinal professional development, specific models of establishing communities of practice may be considered. Two models which are already utilized in higher education are the community of inquiry and coaching models. The community of inquiry model (Garrison et al. 1999), considering as it does the intersection of cognitive, social, and teaching presence as the nexus of learning, provides a useful hook on which to hang both the shared reflection on online teaching and the creation of an ongoing community of practice. Coaching models, with a focus on establishing mutually supportive relationships aimed at improving practice (Lu 2010), provide a strong supportive frame for longitudinal reflective conversations. Ben-Peretz et al. (2018, p. 310) define the nonhierarchical peer coaching relationship as “a form of joint deliberation that provides experienced professionals with opportunities to raise important professional issues in their practice.” As academic coaching for students (Deiorio et al. 2016) becomes a more common feature of university settings, the linkages between student development and professional development for educators could conceivably create a nexus for holistic models of observation of teaching. Each university will need to evaluate the optimal model for the specific context of practice. Regardless of which model is chosen, the key elements of relationship building, establishing trust and collegiality, and scaffolding honest and authentic learning conversations must be built into the approach adopted to support observation partners, as exemplified in the adaptation of our original model (Fig. 2). Below we outline the application of these elements in the context of one of the author’s recent experiences of the implementation of PoT in an online context.

The Approach in Practice Technological University Dublin’s Postgraduate Certificate in University Learning and Teaching program pivoted online in March 2020. An ethos of the program is fostering a community of practice among participants. Dialogue and reflection on teaching practice are key, and PoT is a cornerstone activity in supporting conversations on teaching. At the point of the pandemic pivot, most participants began engaging in virtual synchronous teaching. Since all of these lecturers were entirely new to teaching online, developing strategies to engage students in the virtual synchronous environment was essential.

Peer Observation of Teaching in the Virtual University: Factors for Success

Se n sa se fet of y

9

y

ns

e fet of y

sa

Phased approcach to reflective conversations

Virtual University Context

Sense of safety

t fe sa

Se

Creating community

Establishing the boundaries of online teaching

159

ns e

of

Se Grounding in a model

An agreed and shared lens

Support and Scaffolding Fig. 2 PoT in the virtual university: factors for success

Establishing Relationships of Trust In supporting the development of an online community of learners, it is integral that trust is built among participants. Therefore, before participants engaged in the PoT process, trust through relationship building activities was incrementally built up over the weeks of the PGCert program. Also, modeling of online teaching practices by the lecturers was vital in providing examples of strategies that would help to build engagement. For example, chat activities, ice breaking activities, structured activities for breakout rooms, and other strategies were modeled so that participants could gain ideas on what to do in their online teaching practices. At the time of engagement in observation of teaching, participants had built up trust with one another, while also collecting a bank of pedagogical strategies that they could try out in an online PoT context.

160

M. Crehan et al.

Agreeing the Lens When the PoT commenced, a preobservation discussion was organized among peers; it was vital that the observer be aware of what the observee wanted feedback on, so as to provide appropriate feedback. A postobservation conversation followed the observation, again supporting dialogue and triggering reflection on the observation of teaching. Finally, the participants wrote reflective pieces on the observation of teaching, culminating in further thinking and future actions for their practice.

Establishing the Boundaries Two of the participants were not teaching in synchronous virtual classrooms, since their teaching involved facilitating group project work. The focus of observation of teaching was different in these cases, and, at first, we felt that aspects of observation did not simply translate online. However, through dialogue with the participants and returning to the core values of PoT – observation that enables dialogue, conversation, and reflection on practice – we realized that “observation,” while not live and synchronous, could still be performed in asynchronous means.

Phased Approach to Reflective Discussions In the above scenario, a teaching activity was planned and the observer and observee discussed the teaching activity, the students were given a period of time to engage and complete the activity. PoT participants then engaged in a further discussion on what worked and what might change in the activity plan into the future. In this sense, observation of teaching became about the learning activity, and the conversation happening around the class planning for that activity and the outcomes of the lesson. This demonstrates that PoT in the virtual university can take many guises, and can be successfully embedded in conversations and reflection about teaching; but at the heart of the process there must be a commitment to trust building, dialogue, and reflection while working toward the aim of supporting student-centered active learning. Therefore, the virtual university extends opportunities to explore teaching in many forms, opening more space and opportunity for dialogue and conversation on teaching practices.

Conclusion and Future Directions PoT online is experienced differently vis-à-vis the face-to-face experience, but requires the same, if not greater, emphasis on building trust and collegiality in order to facilitate the development of authentic partnerships and conversations within those partnerships. Online observation provides flexibility in terms of context and observation opportunities, particularly in relation to cross-disciplinary and

9

Peer Observation of Teaching in the Virtual University: Factors for Success

161

cross-institutional observation, but thus also presents specific challenges in terms of defining the foci of observation and reflective conversations. These challenges, in turn, provide opportunities for expanding the scope of PoT into a longitudinal peer development model, and the creation of ongoing learning conversations. The elements of the approach discussed in this chapter necessitate clear and focused dialogue on defining the parameters of online teaching in an observation context and the lens which will be adopted. These first steps may be key in developing ongoing learning conversations structured around shared challenges and foci of development (Allard et al. 2007).

References Allard, C.C., P.F. Goldblatt, J.I. Kemball, S.A. Kendrick, K.J. Millen, and D.M. Smith. 2007. Becoming a reflective community of practice. Reflective Practice 8 (3): 299–314. ASCILITE. 2020. TELAS: Technology Enhanced Learning Accreditation Standards. https://www. telas.edu.au/framework/ Bennett, S., and D. Barp. 2008. Peer observation – A case for doing it online. Teaching in Higher Education 13 (5): 559–570. https://doi.org/10.1080/13562510802334871. Bennett, S., and J. Santy. 2009. A window on our teaching practice: Enhancing individual online teaching quality though online peer observation and support. A UK case study. Nurse Education in Practice 9(6): 403–406. http://dx.doi.org.dproxy.library.dc-uoit.ca/10.1016/j.nepr.2009. 01.019 Ben-Peretz, Miriam, Eli Gottlieb, and Ido Gideon. 2018. Coaching between experts – Opportunities for teachers’ professional development. Teacher Development 22 (3): 303–313. https://doi.org/ 10.1080/13664530.2018.1438310. Carril, P.C.M., M. Gonzalez Sanmamed, and N. Hernandez Selles. 2013. Pedagogical roles and competencies of university teachers practicing in the e-learning environment. The International Review of Research in Open and Distance Learning 14 (3): 462–487. Carroll, C., and D. O’Loughlin. 2014. Peer observation of teaching: Enhancing academic engagement for new participants. Innovations in Education and Teaching International 51 (4): 446–456. https://doi.org/10.1080/14703297.2013.778067. Chang, C., H.-Y. Shen, and Z.-F. Liu. 2014. University faculty’s perspectives on the roles of e-instructors and their online instruction practice. The International Review of Research in Open and Distributed Learning 15 (3). https://doi.org/10.19173/irrodl.v15i3.1654. Crehan, et al. 2021. Opening virtual doors: Exploring faculty perspectives on online peer observation of teaching. In Emerging issues in higher education IIII, ed. M. Keane, C. McAvinia, and I. O’Sullivan. EDIN. Deiorio, N.M., P.A. Carney, L.E. Kahl, E.M. Bonura, and A.M. Juve. 2016. Coaching: A new model for academic and career achievement. Medical Education Online 21 (1). https://doi.org/ 10.3402/meo.v21.33480. Donnelly, R. 2007. Perceived impact of peer observation of teaching in higher education. International Journal of Teaching and Learning in Higher Education 19(2): 117–129. http://www.isetl. org/ijtlhe/pdf/IJTLHE167.pdf Garrison, D.R., T. Anderson, and W. Archer. 1999. Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education 2 (2–3): 87–105. https://doi.org/10.1016/S1096-7516(00)00016-6. Gosling, D. 2002. Models of peer observation of teaching. LTSN Generic Centre. ———. 2014. Collaborative peer-supported review of teaching. In Peer review of learning and teaching in higher education: International perspectives, ed. J. Sachs and M. Parsell. Springer.

162

M. Crehan et al.

Hammersley-Fletcher, L., and P. Orsmond. 2004. Evaluating our peers: Is peer observation a meaningful process? Studies in Higher Education 29: 489–503. https://doi.org/10.1080/ 0307507042000236380. Harper, F., and M. Nicolson. 2013. Online peer observation: Its value in teacher professional development, support and well-being. International Journal for Academic Development 18 (3): 264–275. https://doi.org/10.1080/1360144X.2012.682159. Hendry, G.D, and G.R. Oliver. 2012. Seeing is believing: The benefits of peer observation. Journal of University Teaching & Learning Practice 9(1). https://ro.uow.edu.au/jutlp/vol9/iss1/7 Kenny, A., E. Mitchell, D. Ní Chróinín, E. Vaughan, and E. Murtagh. 2014. ‘In their shoes’: Exploring a modified approach to peer observation of teaching in a university setting. Innovations in Education and Teaching International 51 (2): 218–229. https://doi.org/10.1080/ 14703297.2013.771971. Lu, H.L. 2010. Research on peer coaching in preservice teacher education – A review of literature. Teaching and Teacher Education 26 (4): 748–753. https://doi.org/10.1016/j.tate.2009.10.015. McCormack, C., and R. Kennelly. 2011. ‘We must get together and really talk . . .’. Connection, engagement and safety sustain learning and teaching conversation communities. Reflective Practice 12 (4): 515–531. https://doi.org/10.1080/14623943.2011.590342. McMahon, T., T. Barrett, and G. O’Neill. 2007. Using observation of teaching to improve quality: Finding your way through the muddle of competing conceptions, confusion of practice and mutually exclusive intentions. Teaching in Higher Education 12: 499–511. https://doi.org/10. 1080/13562510701415607. National Forum for the Enhancement of Teaching and Learning in Higher Education (NFETL). 2016. National professional development framework for all staff who teach in higher education. https://www.teachingandlearning.ie/our-priorities/#!/professional-development Nicolson, M., and F. Harper. 2013. Online peer observation: An exploration of a cross-discipline observation project. International Journal of Teaching and Learning in Higher Education 26 (2): 251–259. O’Keeffe, M., M. Crehan, M. Munro, A. Logan, A. Farrell, E. Clarke, M. Flood, M. Ward, T. Andreeva, C. Van Egeraat, F. Heaney, D. Curran, and E. Clinton. 2021. Exploring the role of peer observation of teaching in facilitating cross-institutional professional conversations about teaching and learning. International Journal for Academic Development 26 (3): 266–278. https://doi.org/10.1080/1360144X.2021.1954524. Peel, D. 2005. Peer observation as a transformatory tool? Teaching in Higher Education 10 (4): 489–504. https://doi.org/10.1080/13562510500239125. Sankey, M., H. Carter, S. Marshall, R. Obexer, C. Russell, and R. Lawson. 2014. Benchmarks for technology enhanced learning. Available from https://www.acode.edu.au/pluginfile.php/550/ mod_resource/content/8/TEL_Benchmarks.pdf Steinert, Y., K. Mann, B. Anderson, B.M. Barnett, A. Centeno, L. Naismith, D. Prideaux, J. Spencer, E. Tullo, T. Viggiano, H. Ward, and D. Dolmans. 2016. A systematic review of faculty development initiatives designed to enhance teaching effectiveness: A 10 year update: BEME guide no. 40. Medical Teacher 38 (8): 769–786. https://doi.org/10.1080/0142159X. 2016.1181851. Sullivan, P., A. Buckle, G. Nicky, and S. Atkinson. 2012. Peer observation of teaching as a faculty development tool. BMC Medical Education 12 (26). https://doi.org/10.1186/1472-6920-12-26. Swinglehurst, D., J. Russell, and T. Greenhalgh. 2008. Peer observation of teaching in the online environment: An action research approach. Journal of Computer Assisted Learning 24: 383–393. https://doi.org/10.1111/j.1365-2729.2007.00274. Tenenberg, J. 2016. Learning through observing peers in practice. Studies in Higher Education 41 (4): 756–773. https://doi.org/10.1080/03075079.2014.950954. Torres, A.C., A. Lopes, J.M.S. Valente, and A. Mouraz. 2017. What catches the eye in class observation? Observers’ perspectives in a multidisciplinary peer observation of teaching program. Teaching in Higher Education 22 (7): 822–838. https://doi.org/10.1080/13562517.2017. 1301907.

9

Peer Observation of Teaching in the Virtual University: Factors for Success

163

Walker, R., and D. Forbes. 2018. Cross-institutional peer observation by online tutors: Sharing practice ‘outside the family’. Innovations in Education and Teaching International 55 (3): 285–293. https://doi.org/10.1080/14703297.2017.1281751. West, C., and M. Clauhs. 2019. Peers observing peers: Exploring cross-institutional observations and dialogues facilitated by videoconferencing software. Journal of Music Teacher Education 28 (2): 43–55. https://doi.org/10.1177/1057083718803633. Whipp, P.R., and R. Pengelley. 2017. Confidence building through peer observation of teaching and peer coaching in university departments: A good investment for some and not others. International Journal of Mentoring and Coaching in Education 6 (2): 99–115. https://doi.org/10.1108/ IJMCE-07-2016-0059. Yiend, J., S. Weller, and I. Kinchin. 2014. Peer observation of teaching: The interaction between peer review and developmental models of practice. Journal of Further and Higher Education 38 (4): 465–484. https://doi.org/10.1080/0309877X.2012.726967. Zee, M., and H.M.Y. Koomen. 2016. Teacher self-efficacy and its effects on classroom processes, student academic adjustment, and teacher well-being: A synthesis of 40 years of research. Review of Educational Research 86 (4): 981–1015. https://doi.org/10.3102/ 0034654315626801.

Transition Techniques When Introducing Change: A Sociomaterial Approach to the Virtual University

10

Hilary Wheaton and Sherman Young

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Background and Context in Existing Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transformative Model of Teaching with Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Organizational and Individual Factors for Technology-Enhanced Learning (TEL) Adoption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case Studies from RMIT University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Project Rewire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . COVID Pivot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Analysis and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

166 167 167 170 174 174 177 179 183 183 184

Abstract

Large-scale institutional change raises many challenges. In higher education, this includes a failure to review transition processes and a risk that change can be rushed, imperfectly embedded, and lack positive impact on teaching and learning practice. Within this context, the implementation of virtual learning environments (VLE) and associated technologies, and the focus brought upon their use and influence considering the Coronavirus pandemic (COVID), offer identifiable stages of transition toward the virtual university. The VLE, and institutional technologies, connect the process of technological adoption required for learning and teaching at individual level to its institutionalization, and the “linkage between individual and organizational purposes within this need to be addressed” (Casanovas 2010, p. 73). The virtual university requires a characterization of what an effective transition means in the context of pedagogy as part of this H. Wheaton (*) · S. Young Education Portfolio, RMIT University, Melbourne, VIC, Australia e-mail: [email protected]; [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_10

165

166

H. Wheaton and S. Young

adoption, such as achievement of Graham’s (2012) transformative model of teaching with technology. In the context of various theoretical frameworks, this chapter reflects on two case studies of transition initiated at RMIT in Melbourne, Australia. Individual and organizational purposes are analyzed, bringing together both technical and pedagogical transition, to provide some practical and conceptual guidance that utilizes sociomaterial insights to align change, adoption, and engagement practices with transition toward a virtual university. Keywords

Institutional change · Transition · Adoption · Engagement · Technology · Online · Sociomaterial · COVID

Introduction Kemran Mestan, in a 2019 Australasian Journal of Educational Technology (AJET) feature article, writes, “Higher education institutions across the globe are increasing the extent to which they teach in a blended mode. However, in the rush to transition to blended teaching, institutions often fail to systematically review their transition process” (2019, p. 70). This neatly summarizes the reality of institutional challenge, embracing the (necessary) new can be rushed, and the scale and scope required often means that change is not deeply embedded. Emerging from these statements are several separate but interrelated challenges for the higher education context: • How do institutions undertake transitions in the context of different beliefs and aspirations for the university’s purpose? • Are there competing ideologies operating in universities that may not be addressed? • Is it possible to reconcile the relationship between pedagogy and technology in transition techniques? These three challenges are at the core of higher education transition, especially when virtual learning environments (VLE) such as learning management systems (LMS) are utilized as part of techniques to shift or change expectations around teaching and learning. This chapter examines two case studies of institutional change at RMIT, a Melbourne-based dual sector university with over 90,000 students and multiple campuses nationally and internationally. The first case study involves RMIT’s implementation of a new LMS, Canvas, and its simultaneous establishment as the digital backbone for learning and teaching accompanied by a quality assurance process for courses. The second case study is on the COVID-19 response, and how the LMS and supporting technologies were pivotal to online learning and teaching.

10

Transition Techniques When Introducing Change: A Sociomaterial Approach. . .

167

Background and Context in Existing Literature For an effective transition to a virtual university, it is necessary to first establish the pedagogical foundation of transition, namely, what Graham (2005) characterizes as the transformative model of teaching with technology. Furthermore, as Casanovas acknowledged, the process of technological adoption for online learning and “the linkage between individual and organizational purposes need to be addressed” (2010, p. 73). A review of current theoretical background explored through two themes will validate the premises and challenges for the virtual university and contextualize the RMIT case studies: first, an exploration of the relationship between technology and teaching to understand the “transformative model”; and second, an interrogation of the organizational and individual purposes and reasons for potential disconnects that can affect methods and processes for technological adoption and sustained engagement.

Transformative Model of Teaching with Technology Much emphasis has been historically placed on the impact of technology for enabling organizational and pedagogical change in education. Additionally, much time has also been spent on identifying “best practice” (Bates 2019; Richardson 2010; Salmon 2013); at the same time, reports and reviews have attempted to identify the trends in technologies and how they will “disrupt” education and promote new pedagogies (Kukulska-Hulme et al. 2021; McCormack et al. 2020). Thus, there is a continual shift between the pedagogy first versus technology first debate (Sankey et al. 2020). Here explicitly reside concerns around technology and the sociology of education, drawn from our higher education institutions (shaped by changing political funding structures) and the individual experiences of our students. In discussing technology and education, the general focus is on the digital technologies of the twentieth century onward: computing software, the Internet, web 2.0, data, and social media. Zawacki-Richter and Latchem state that “the field of instructional and educational technology is a relatively young academic discipline” (2018, p. 136). The progress to date for higher education transformation using technological adoption from the late 1990s is epitomized by the LMS and Library digitization. Despite the diversity and complexity of technology in use, the LMS is an expected feature of the educational landscape (Pinho et al. 2018; Selwyn and Facer 2014, p. 482). LMSs have usually been adopted at institutional levels (McGill and Klobas 2009), though this does not imply mandated or consistent engagement across faculty; nevertheless, this institutional level implementation is not simply a desire for informational infrastructure but also for change in pedagogical practice, such as online learning (Boelens et al. 2017; Graham et al. 2013; Panigrahi et al. 2018; Woods et al. 2004). However, the LMS, while successfully technically implemented by institutions (as evidenced by their prevalence), frequently fails in delivering aspirations for improved educational practice (Børte et al. 2020; Hannon 2013; Liu and Geertshuis 2021; Sinclair and Aho 2018).

168

H. Wheaton and S. Young

The LMS is seen as a precursor, a necessity, in enabling educators to engage with a shift in educational practice that has several attributes that signify an anticipated change in pedagogy. This could include a mix of face-to-face and online learning; the use of digital technologies as part of learning; increased active learning, student centered-learning, collaborative learning, and reduction in didactic knowledge transfer; and self-paced or personalized learning (Alammary et al. 2014; Boelens et al. 2017; Boelens et al. 2018; Graham 2005; von Konsky et al. 2014). It is interesting to note that aspects of online pedagogy incorporate technology-dependent characteristics (e.g., online, technologies, and personalized) while others are established pedagogical strategies that require no technological intervention (Auster and Wylie 2006; Biggs and Tang 2011). Thus, there is evidence of Selwyn’s (2020) observation that many challenges in education predate technology and consequently add to the complexity that emerges if technology is used to enable a transformation in teaching practice. Table 1 builds upon an earlier metataxonomy by Mestan (2019) and incorporates theoretical perspectives to form an overarching view of the relationships between technology and teaching. The original metataxonomy included the following theorists in blended: Graham’s (2005) enhancing, enabling, and transformative models of blended, the latter whose impact is a radical transformation of pedagogy that requires technology (p. 80); Twigg’s (2009) five-course redesign models of supplemental, replacement, emporium, buffet, and fully online, of which the emporium model was transformative due to student choice in when, what, and how quickly to engage with learning materials (Mestan 2019, p. 71); and finally, Alammary et al.’s (2014) low-, medium-, and high-impact blends focus on the level of change and challenge associated with the highest impact being a total redesign or radical change (p. 447). The updated taxonomy in Table 1 shifts focus to the organizational sociomaterial aspects of technology and teaching as part of a transformative transition; this assists in exploring the second premise around individual and organizational purposes that influence engagement. Six categories are mapped to the three stages of transition: extent of change, organizational intent, function of technology, effect on pedagogy, effective role of educator, and affective role of student. Organizational intent is included and articulated using the SAMR model (Puentedura 2006) as a means of explicitly linking technology and change. Taking heed of Hamilton et al.’s (2016) commentary on the SAMR model, here it is contextually applied and simply indicates the intent upon which technology has been introduced. The minimal viable product implementation of an LMS is characterized by the Early state of transition, but many institutions’ intent during the change process is to achieve the Mid or Late transition so that a cultural shift in teaching practice occurs alongside technological adoption. Technology becomes the nexus for pedagogy and complex sociomaterial alterations in the relationship between educator and student; therefore, the adoption of technology must also see engagement more broadly linked to practice. It is only at the Late transition where organizations redefine practice through technology, requiring educators to design based on affordances of technologies adopted, and similarly students construct their own personal experiences in response. Consequently, in a

Extent of change Low

Medium

High

Stage of transition Early

Mid

Late

Redefinition

Modification

Organizational intent (SAMR) Augmentation

Precursory

Deliverable

Function of technology Procedural

Transformative

Enhancing

Effect on pedagogy Enabling

Table 1 Updated metataxonomy of transformation to virtual university when teaching with technology

Designer-expert

Effective role of educator Administrativedidactic Curator-facilitator

Affective role on student Increase access and support Supplement and diversify Personalized and agent driven

10 Transition Techniques When Introducing Change: A Sociomaterial Approach. . . 169

170

H. Wheaton and S. Young

transformative model of teaching with technology there is a change in function and relationship across all elements: pedagogy, educator, and student. This change would be a foundational requirement in the establishment of the virtual university.

Organizational and Individual Factors for Technology-Enhanced Learning (TEL) Adoption The sociomaterial metataxonomy of transition defined by the relationship between technology, pedagogy, educator, and student allows for the ability to explore why these transitions may be hindered. The concept and research of adoption offers a means by which to understand both organizational and individual factors as it encompasses the reasons for use of technology. This consequently informs any engagement strategies that target the personal response to change that dictates sustained practice, requiring management processes in transitions. For the organization, namely, higher education institutions, Hannon eloquently summarizes the purpose for the adoption of learning technologies, including LMSs.: Learning technology systems offer a distinctive capacity to the organization; they provide a nexus for the convergence of different components of the university, assembling institutional e-learning strategies, procedures, technologies, discourses, teaching staff, and students. In particular, learning technologies bring practices from distinct domains of organizational work to the same space of activity. (2013, p. 170)

Higher education institutions are facing economic and competitive pressures, brought about by socioeconomic factors including globalization, ideological changes, regulatory reforms, and government-funding models. In Australia, recent changes have seen “. . .increasing global competitiveness, accountability, efficiency, quality- and standards-driven policy reforms, and higher education stratification” (Zajda 2020, p. 55), and it is therefore no surprise that organizations are looking to utilize technology to drive market innovations and business alignment. At the individual level, there are three main stakeholders that exist within the university that have distinct factors that influence adoption: professional staff, academic/educators, and students. In considering professional staff, they occupy various roles across the organization, most notably within administrative functions that drive quality and accountability of which technologies offer solutions in the form of scalability, consistency, and reporting functionalities. There is also the growing cohort of technologists and learning designers whose focus is on individual educational development work with the academic or are centralized and driven by the coordination of innovation and planning (Smith 2012). Academics or educators, involved in teaching, are observed to most likely be driven to adopt technologies when they know that pedagogy is a key driver (Parsons 2018, p. 42), or that it will improve student learning, increase student interest, be easy to use (Porter et al. 2016, p. 18), and improve communication, or if they experience innovative teaching practices, good student feedback, and enthusiastic colleagues (Sinclair and Aho

10

Transition Techniques When Introducing Change: A Sociomaterial Approach. . .

171

2018, pp. 161, 163). For students, anticipated drivers include access, flexibility, and equity offered by TEL, but additionally the millennial market’s familiarity with technology has led to the belief that they similarly require such platforms to entice them into academic discussion (Au-Yong-Oliveira et al. 2018, p. 954). Additionally, the “Learner Experience Project” conducted by RMITs CX+Digital team (2020) produced a report that identified eight learner personas using a human-centered design approach. Key insights from this report indicated that students are well prepared for and expect the inclusion of digital technologies and online practices, while also valuing the human element of face-to-face interactions (RMIT CX+Digital 2020, pp. 61–62). At an individual level, the technological transition for the virtual university could similarly be understood through models that address decisions for adoption. The Technology Acceptance Model (TAM) is a popular model (McGill and Klobas 2009; Pinho et al. 2018, p. 86). Scherer et al.’s (2019) meta-analysis confirmed that TAM is a successful means by which to predict user behavior, and that it is split into two categories (preimplementation and postimplementation phases) which are relevant for the implementation of technology in education (2019, p. 31). It is clear that TAM core variables, as with external variables that influence them, will provide indicators of either optimism or hesitancy in engaging with TEL, and therefore technology adoption “requires a multidimensional approach that goes beyond strengthening teachers’ competences and competence beliefs” (Scherer et al. 2019, p. 31). Engagement and change processes to support individuals must target their specific stakeholder values, mapping these not only to the functional affordances of systems but also to the practices, especially pedagogical, that are performed with them. This poses additional risks during the selection of technologies such as the LMS for the virtual university in that some may have functional affordances that go against the values of individuals, with examples such as data and monitoring in the case of students and staff expressing an invasion of privacy or perception of managerial oversight and “control.” The TAM model is limited in its applicability for the technology of the LMS as it treats usage as a binary, ignoring that the core issue is not usage per se, but utilization of maximum functionality (Sinclair and Aho 2018, p. 159). This issue is similarly present with other models such as unified theory of acceptance and use of technology (UTAUT), expectation confirmation theory, and the technology-to-performance chain (TPC) model explored to understand LMS usage (McGill and Klobas 2009). The broader sociomaterial aspects that impede adoption of technology, specifically the LMS, in the manner with which it is intended can help highlight this dilemma of functional affordance. When barriers to adoption are listed in research, they take on a conflated set of characteristics including technological, pedagogical, or organizational barriers across both LMS implementations and online/active learning. Porter et al. list barriers for academics at different levels of adoption that included quality concerns, time constraints, skill and ability, financial compensation, lack of equipment, and support for development of technology-driven pedagogy (2016). Børte et al. similarly list barriers to student active learning, a known aspirational trait of online pedagogy, that occurs across institutional levels including physical barriers,

172

H. Wheaton and S. Young

institutional barriers, pedagogical barriers, teacher-related barriers, student-related barriers, technological barriers, and teacher support (2020, p. 10). COVID-specific challenges in the move to online only learning were also identified by TEQSA through student feedback across multiple institutions, including IT-related issues, academic interaction, examinations, staff technical expertise, and specific discipline issues (Martin 2020). Thus, for the virtual university to succeed, any transition must address not just the adoption and successful engagement of the LMS but all those interdependent barriers and systems involved in the change. Broadening the scope of transition from technological adoption to these other sociomaterial considerations may assist in closing the gap between organizational and individual factors. Rogers’ “Diffusion of Innovation Theory” (1995) with its five-stage model of knowledge, persuasion, decision, implementation, and confirmation, was adapted and extended by West et al. (2007) to address “. . .how people implement an innovation that they have chosen to adopt, and what happens after” (p. 4); the adapted model identifies experiences during the implementation stage (experimentation, technical and integration challenges, comfort level, and adaptation) that influence to what extent use will continue (p. 12). Rogers’ (1995) diffusion theory also offers variables that help determine the rate of adoption: perceived attributes of innovation, type of innovation-decision, communication channels, nature of social system, and extent of change agent’s promotion efforts (p. 207). Diffusion theory therefore provides a means by which to address and communicate organizational change of the LMS in the context of its social system. This bridges the gap between adoption of technologies and functional affordances, and the engagement with social and pedagogical practices during this change by individuals. Graham et al.’s (2013) blended learning adoption framework takes on this pedagogical perspective, closing the gap even further between adoption and engagement with a comprehensive set of categories that include strategy, structure, and support with detailed subthemes that cross-reference against a differentiation of stages for adoption, namely, awareness/exploration, adoption/early implementation, and mature implementation/growth (p. 7). This framework is useful for understanding the scope of various institutional requirements in the context of higher education. While the gap may have been closed regarding the organizational and individual drivers for technological adoption for the virtual university, the missing piece from a sociomaterial perspective is the constitution informing the context of the social system in which transition must be supported. The social system of the university is distinct from other organizations where transitions utilize adoption and change processes supported by targeted engagement strategies. Dwivedi (2019) identifies seven logics operating in higher education: Academic, Community, Corporation, Market, State, Social, and Cybercultural (p. 147). The Academic logic is that of the individual derived identity of disciplinary research and teaching practice, and it consequently poses problems for organizational change as academics are individualized agents only tangentially aligned to university strategic interests. The Community logic is framed through the larger academic community within a university which connects disciplines and their associated departments with the values and priorities of the university they inhabit. This learning community is essentially the

10

Transition Techniques When Introducing Change: A Sociomaterial Approach. . .

173

support network which brings together the various aspects of an institution and may involve individuals and elements outside of formal learning opportunities. It is also apparent in the specific learning community of the classroom, manifested in student engagement and (sometimes) collaborative learning guided by the teacher. Irrespective of the actual teaching praxis (where it can emerge as group work or teamwork activities), this type of learning activity is a theoretical construct made apparent by physical presence – the reality of students and teachers in an environment together. The Corporation logic sees the university as an enterprise where questions of prioritization (and workload) and perception of increased managerialism and standardization are wrapped up in ideas of quality and professionalism. In this logic, the student is central but is treated as a customer. Market logic extends the Corporation logic to a commodification of education – requiring that every activity has a monetary price. Only then can the institution compete in the marketplace for students and must do so from an effective position of efficient delivery. State logic speaks to Governmental goals around higher education, which have predominantly focused on graduate employment and the economic project of the state. Occasionally the need for engaged citizens emerges in this discourse, but increasingly State logic is an extension of Market logic, albeit as driven by the market priorities of Government. The final, Cybercultural logic emerges out of the increased use of digital technologies which is expressed in technological determinism, situating progress in a discourse of digital inevitability. Understandably, in the learning and teaching space of higher education, all logics claim the student as their motivating factor. The more “commercially minded” see improving “customer sat” scores through student feedback as a key indicator of progress and cite those as measures of success. The more traditionally academically minded argue for a more nuanced understanding of the student experience that focuses more on the student as part of the community of scholars, but all are in thrall (one way or another) to the Cybercultural logic and the inevitability of digital disruption that precipitates the virtual university. In higher education, the framing of that Cybercultural logic sees the other university logics play out through the implementation of digital possibilities. The Corporate logic is constrained by the limited number of enterprise-grade “solutions” available, which can see teaching approaches driven by technology vendor roadmaps at the expense of creative pedagogical thinking. This is often in opposition to the Community logic, which has a long history of home-brew solutions driven by individual academic creativity; using digital technologies to support self-defined optimizations for student experience and learning. The Market logic privileges a consistency of experience and aligns itself (as it normally does) with the Corporate logic. As with Sinclair and Aho’s rejection of TAM for its binary application to technology use (2018), these logics are overlaid with a dominant, overly simplistic view, which casts the use of digital technologies as on campus as opposed to online for the virtual university. Arguably that bifurcation limits more creative and innovative approaches to learning and speaks to a technological determinism which puts the technology before the pedagogy. Thus, it is clear why respected and robust transition techniques for technology and transformation required for the virtual university encounter challenges. They

174

H. Wheaton and S. Young

need to address not only the complexities highlighted in technology and innovation adoption models (to first ensure use), but also the logics of higher education institutions in corresponding engagement strategies (to change the practices of individuals through use).

Case Studies from RMIT University Project Rewire RMITs previous Learning Management System (LMS), Blackboard, provided a poor experience for staff and students and was both modest in its adoption across the university and inconsistently used. Between 2015 and 2016, RMIT undertook business requirements gathering and a “Learning Environment Review” to inform its decision to move to a new LMS as part of the “Ready for Life and Work” (RFLW) 2015–2020 strategic plan. Staff and student interviews conducted as part of the review produced 340 individual requirements that were positioned across seven primary objectives aligned to the RFLW plan: a rebranded platform, improved student and teaching experience, support for innovation, systems and process across all RMIT group/partners, LMS support, and data insights from teaching delivery. RMITs RFLW strategy prioritized a transformative student experience, especially one that was supported by technology and innovation. Implementing systems that provided enterprise-wide consistent, simple, and cleaner process and resource management was the “digital backbone” to learning and teaching. Digital media and digital experiences underpinned both the staff and system considerations alongside a distinct and quality-focused approach to pedagogy. Thus, a business case initiated what became known as “Project Rewire” in late 2016, with formal commencement of Canvas implementation from the first-quarter of 2017 to the fourth-quarter of 2018. The project was a joint initiative between Information Technology Services (ITS) and the Education Portfolio, with what emerged over time as two distinct, yet complementary streams of work (Technical and Transformation) based on the priorities of each group’s ideologies and objectives. In addition to a funded project team of contract staff, including LMS Champions, there were also staff in permanent business units that focused their operational capabilities on supporting project goals; each College’s Academic Development Groups (ADGs), as well as the Education Portfolio Studios Team, prioritized the Canvas implementation and educational support. The focus of these two business units aligning with the project was a logical necessity; Studios would become the business owner of Canvas postimplementation and the ADGs provide the development of disciplines within their scope. The project therefore maintained and utilized a selection of technical, professional learning and teaching (L&T), multimedia, training, communication, and reporting staff in a mix of contract and permanent capacities. The initial outcomes identified for Project Rewire included an increase to both student and staff satisfaction levels, this incorporated the provision of an on-demand

10

Transition Techniques When Introducing Change: A Sociomaterial Approach. . .

175

LMS support model. For staff, two additional outcomes focused on productivity and compliance, as well as the ability to share digital content/learning objects which, from a technical perspective, the only outcome related to seamless integrations with core systems and approved external sources according to governance, specifically security and privacy. Regarding organizational buy-in and stakeholder engagement, the project had a visible and proactive sponsor in the DVCE, who championed the project in communications, town halls, and additional formal channels. Senior leadership, at the executive level, were active and engaged in decisions along with their professional staff within the academic development groups. Being an enterprise-wide project, a Project Control Group (PCG) operated on a monthly basis and had representation from Colleges, ITS, Student Union, Education Portfolio, and Project Leadership. This was the primary mechanism for decision-making and leadership buy-in to support activities and collaboration across the University. Additionally, the Project established College-specific working groups to consult and form decisions relating to their requirements at an operational level with staff. Each College had an assigned Academic Transformation Manager (ATM), who represented them within the Project and was a central touch point for information, feedback, and organizing activities. It was established early on that the technical implementation was a like-for-like approach, while from the pedagogical perspective, there was a desire to drive improvements in the use of technology informing a baseline for blended practice. The “Digital Learning and Teaching Framework” had been established in early 2017, designed by ADGs and Studios, and defined six guiding principles of connected, clear, aligned, inclusive, dynamic, and consistent (Wheaton and Young 2019). This framework incorporated 80+ standards that underpinned the process for design and quality review of online learning at RMIT. The framework informed the chosen early adopters and evaluation criteria in 2017. Initially, the intent was that staff would plan and design their courses, and build and then undertake review before publication. However, as the project progressed throughout 2017 workload implications required content to be lifted, and shifted, from Blackboard to Canvas by the Project team with the LMS Champions assisting teaching staff in design. Thus, in order to effectively operationalize the framework and its guiding principles, the “14 Elements for Canvas Success” were authored as criteria required for course publication. The Elements provided explicit instructions on what was required to pass quality assurance (QA), and resources and templates to assist, and formed the basis of staff training when learning how to use Canvas. QA was a manual process that was tracked and reported on, where Champions checked courses against the Elements and then published. Thus, in the early stages of Project Rewire, the Elements provided a means by which to consistently create training, and resources, and direct staff in the usage of Canvas functionality. However, by the end of the Project the Elements risked perception as a “check-box” item; Schools formed bootcamps to work with LMS Champions to ensure their courses went live, and while these offered opportunities to engage in design and exploration of Canvas, they also became singularly driven by the QA process.

176

H. Wheaton and S. Young

Measuring success was not a metric that was reserved for the summation of the project. Throughout Project Rewire, insights were sought to verify that the primary outcomes were being met (incrementally or otherwise), from both students and staff. Data was gathered from a variety of sources and included survey and user experience testing with students, to identify the impact of Canvas on the student experience. Course experience survey (CES) data in 2017 and 2018 included questions relating to Canvas and indicated that, overall, students preferred Canvas to Blackboard but that technical teething issues existed with use and consistent setup of their course. This is unsurprising given Canvas was only rolled out university wide in Semester 2 of 2017, prior to this being limited to early adopter pilots in Semester 1 to provide lessons and advocacy. The CES data obviously situated Canvas in the broader experience of the course, and thematically insights aligned with both technical aspects as well as how a course was taught in terms of content and staff, consequently what emerged prominently as best aspects also appeared as needing improvement, e.g., navigation, layout of resources, and notifications. Additionally, in December of 2017 students were recruited to undertake interviews and user testing by conducting a series of common tasks within both Blackboard and Canvas and provided qualitative responses. The testing concluded that across the areas of usability, usefulness, desirability, collaboration, and satisfaction there was a clear preference for Canvas over Blackboard. This student data served to validate the choice of Canvas to improve student experience and fed into the use of the “14 Elements” and staff professional development in implementing these into courses. For RMIT staff, data was primarily gathered in the form of engagement with training and QA performance to understand the influence on teaching practice using Canvas. Training was intimately linked to the application of the “14 Elements,” where every course owner was invited to either face-to-face or online workshops after being provided with an initial setup of their course in Canvas using a predesigned template produced by ADG, Studio, and project staff. While facilitated online training workshops were essential, it was noted that RMIT staff responded best to face-to-face training and support (Wheaton and Mastro 2018). Training included “Staff Essentials,” a general introduction to using Canvas for teaching, and “Course Coordinators” which explored the setup and design of a course (linked to QA requirements). Additionally, other forms of training were designed to simply offer ad hoc support such as “Drop-ins” and “Finish Line” sessions to seek answers to problems or pass QA. Over time, based on staff feedback and CES insights, sessions such as “Assignments,” “Grading and Rubrics,” and processes for rollover of content from semester to semester were included. The QA process was a key measure of success throughout the Project, signifying a clear moment at which courses were assessed as meeting the criteria for going live to students at the start of each term. The “14 Elements” include an introductory module structure with specified content, details of teaching team (contact, bio) and instructions for interacting with Canvas, course schedule with key activities, requirements for assessment information, menu structure for consistent navigation, and accessibility and style considerations (Wheaton and Young 2019). The QA process required assessment that these were present and involved a manual check by Project

10

Transition Techniques When Introducing Change: A Sociomaterial Approach. . .

177

staff; therefore, over time templates and a guide with increased levels of specificity were needed to ensure consistent assessment against the Elements. Project Rewire was understood to be an enterprise-wide transformative change project, with the intent being a shift in the perception of how technology and teaching were connected. Alongside this was demand for a cultural shift and introduction of new “ways of working” that increased visibility of practices across the organization which had well-established silos. As a large dual-sector institution, it was hard to drive change and deliver strategy without duplication, divergence, and inconsistency; Project Rewire was delivering a solution to this using technology and a QA process as its methodology to unify practice. A “new for old” mentality existed across technology and pedagogy which assisted this transformative shift, with the introduction of Canvas prompting increased focus on the use of multimedia, clear branding, and design, alongside the opportunity of “app based” access on mobile phones for students and staff. The mix of technology and teaching shift crystallized in the introduction of QA as something that persisted beyond the Project, necessitating a systems and data process that ensured courses were scheduled, assigned to staff, and created in Canvas for development with sufficient time prior to term starts. This was tracked and coordinated by the Project team and required close communications with administrative staff centrally and within Colleges/Schools. As a result, Project Rewire necessitated a shift in thinking around the role of technology for teaching; every course delivered at RMIT required a Canvas presence, with some exceptions, and thus it became a core tool that connected educators, students, and professional administration in a single process.

COVID Pivot The Coronavirus pandemic involved a necessary shift to entirely online delivery, effectively establishing RMIT as a virtual university (albeit temporarily). As with most Australian Universities, RMIT had an existing online baseline (previous case study) and had opt-out lecture recordings in Echo 360, with an implementation of Collaborate Ultra, among other LMS feature technology using Learning Tools Interoperability (aka LTIs). The initial COVID response had a targeted focus on international students from China who were unable to return to Australia and consisted of documenting “Online Learning Guidelines” (OLG, a short document which articulated the baseline standard for fully online delivery), a confirmation of technology availability in global geographies, and increasing professional development opportunities for teaching staff. The governance around the development and delivery of this activity involved a small fast team (SFT) consisting of the Associate Deputy Vice-Chancellor from the Education Portfolio centrally, and the senior academic leadership from each of our four colleges to account for discipline-specific considerations. Activity was predicated on the reality that there was no expectation for course coordinators to completely redesign their courses for online delivery, but rather to deliver what was already planned (as far as possible) in the online mode. Initially, it was felt that some key learning activities (laboratories, studios, etc.) that

178

H. Wheaton and S. Young

would need significant reconsideration would simply be delayed until a return-tocampus was possible. An enterprise-wide project was instituted, including a focus on technology, professional development, and quality assurance. For technology, the university’s IT system teams and learning technology teams prioritized the scaling of existing tools used by staff to ensure availability globally in locations such as China. However, given the diversity of teaching activities and preferences for types of tools, there was a need to consolidate to a limited set that could be effectively supported across locations. Professional development was key and required alignment to expectations to ensure engagement. The QA process baseline existed to support on-campus teaching and did not extend to its substitution; therefore, staff were given guidance on how to adapt their practice to a fully online environment, and while encouraged to explore new affordances, the OLG ensured that students could continue to engage with their course material, their educators, and their peers. A range of professional development resources (many of which already existed) were curated and promoted through engagement strategies that targeted disciplines or practices, with well-attended online workshops delivered to assist in everything from basic technology skills to thinking about online pedagogies. At the intersection of technology and pedagogy during the response, assessment emerged as a change concern for many staff. An (over)reliance on invigilated examinations, which were already communicated in the published course guides, required a rethink and redesign of assessment tasks for the online environment. RMIT’s online proctoring pilot had highlighted issues with existing proctoring tools, and together with our institutional preference toward authentic assessment, this was an opportunity to begin a discussion around designing authentic assessment tasks that could initially be delivered online. This engagement extended beyond the COVID pivot and includes competing academic and organizational opinions for academic integrity and assessment design solutions that go beyond invigilated exams. Finally, the quick pivot required a rethink to the existing QA approach. Initially, an “Online Learning Checklist” was prepared to ensure that course coordinators were aware of minimum requirements for delivering classes. For the initial cluster of courses delivered to stranded international students, meeting this checklist was a mandated requirement in addition to the 14 Elements. As the scale of the challenge continued to grow, that checklist morphed into a more comprehensive set of online guidance and a decentralized approach was taken to ensure compliance. In some cases, staff took advantage of the online affordances and reconsidered learning activities, while in others, they reused existing lecture recordings with little context. Therefore, the updated QA process opened up flexibility in terms of the depth and breadth of personal adoption and engagement, as the support and professional development available ensured ample opportunity to utilize online affordances. Given the challenging situation impacting emotional responses and mixed capabilities, the quality of online delivery was varied with some courses being more engaging than others despite the pre-COVID Canvas presences.

10

Transition Techniques When Introducing Change: A Sociomaterial Approach. . .

179

Analysis and Discussion The historical context and case studies have attempted to address the complex background required to adequately explore the transition toward a virtual university, and the intent behind technological transformation in the context of the LMS and dependent pedagogical practices. When supporting staff and students through transition and change, the case studies provided some practical insights and also illustrated examples of the broader challenges articulated in the introduction to this chapter. In terms of practical implementation insights, both Canvas and the COVID response validate well-documented and standard practices that have been identified as contributing to the success of organizational change projects that employ technology. Smith (2012) provided clear lessons from the analysis of literature on the diffusion of innovative learning and teaching practices in higher education that included the following: Senior staff need to support innovation for it to spread, that innovation is time consuming to implement and embed, staff and students must be adequately skilled to engage with innovative practice, innovations that sit well within a specific context spread better, supportive networks can facilitate diffusion of innovation, and institutional infrastructure needs to be in place to support innovation (pp. 174–177). A mixture of technical implementation, functionality checking, and fit for purpose analysis occurred in both case studies, while simultaneously attention was paid to establishing benchmarks for engagement and demonstrating practice, to support staff in both technical and pedagogical aspects of process. In this context, there are clear distinctions to be had between the case studies. Project Rewire had a strong period of established affective adoption to the LMS change, though the individual academic perspectives on the value of engaging would have been heavily shaped by the QA process. Indeed, in Project Rewire and beyond, the persistence of a QA process ensured that engagement with the LMS would not cease, with intent for baseline engagement fostered by College ADGs and Studios to further pedagogical discussions, training, and technologies for affective studentteacher interactions. With COVID, the climate for affective commitment and change was problematic; the emotional and psychological context both personally and professionally was disrupted by factors beyond control, resulting in the frequent phrase of “pivoting” that was dependent on resilience and agency to engage in professional or academic roles. The academic and educator’s commitment to teaching as part of their identity, especially those that were student-focused, are shown to have a positive relationship with the LMS and other innovative technologies for teaching (Liu and Geertshuis 2021, p. 633). In the COVID response, academics most passionate in their discipline and in teaching were perceived as still providing a great educational experience despite technical or social difficulties by students (CEID 2021). Professional identity also emerged during the RMIT COVID response in the context of broader Community and Institutional logic regarding assessment; College professional staff had different views or recommendations for managing assessment based on disciplines, and the value of invigilated exams as especially pivotal for some programs even with discussions of authentic assessment. Collectively, these

180

H. Wheaton and S. Young

various community perspectives were tasked during the COVID response to develop suggested solutions for assessments based on available tools, providing a level of consistency through the technologies available while catering to individual recommendations. What emerges from the details of the two case studies and techniques discussed is a clear dependency of the broader sociomaterial aspects of the context in which they occurred at RMIT. These sociomaterial aspects included the institution structure, its complexities and difficulties, as well as the technology utilization in each instance and the intent acted out through the people, attitudes, and benchmarks for performance (14 Elements, Online Learning Guidelines). The case studies speak to the competing logics and the framing of technology. Project Rewire imposed an enterprise view, the Corporate logic, in order to provide a consistent student experience, an end which can be at odds with Community logic. Its success, or otherwise, as a transition toward a more virtual university might be assessed differently depending on the logical framing. The COVID-pivot imposed a similar Corporate Logic, but the external threat of the global pandemic required a response from the Community logic that seemed to need a Cybercultural response. The convergence of these logics implies a commonality of purpose, but the tension between the logics continued to manifest themselves in more nuanced but not unexpected ways, as the various actants involved in implementation engaged with the process. With the demonstrated change practices, reflection also offers commentary on the nature of success achieved and to what extent the transition approach enabled transformation. For Project Rewire, the listed success criteria were specific and when measured with that frame of reference, the criteria were achieved. However, broadly speaking the less measurable criterion was a cultural shift in attitude and behaviors toward the LMS and institutional pedagogy, this was not technical or objectively measurable in the form of templates or QAs completed. This was conveyed in discussions, and from the DVCE’s leadership, that by establishing a technological platform and a benchmark of practice there would be a shift to datadriven decisions, further technologies explored through LTIs, collaborative practices, and student-centered teaching (online and on campus). This appeared to be a desire to align Community, Corporation, and Cybercultural logic to ensure embedded change. These aspirations were a significant driver for institutional change, and by the end of the Project there was indeed a shift in the overall steps toward achieving them. But this required more than the Project, and the continued improvement would be dependent on organizational practices as part of business as usual (BAU). Similarly, for COVID the immediate success criteria for providing solutions to international or isolated students, then evolving as the situation progressed to online delivery, were achieved. But this situational solution requires context in a broader university strategy, distributed across various logics at individual or organizational level based on values, regarding practices for teaching on campus or online and the pedagogical implications. For some, the COVID response was limited to its necessity, whereas for others there are acknowledgments of opportunities for long-

10

Transition Techniques When Introducing Change: A Sociomaterial Approach. . .

181

term change, to drive digital skills, and use the campus differently or shift selected teaching online; this is the “new COVID-normal.” An interesting dichotomy therefore emerges, in implementation and transition strategies that are frequently coupled with formal project methodologies for delivery, between the organizational “project commitment” versus BAU. This dichotomy may seem inevitable, but is it desirable when transformational change in addition to technical implementation is the actual goal for the virtual university? It impacts how support is established, operationalized, and maintained long term when mechanisms like the QA process ensure adoption but impact engagement in both a positive and negative light for educators with the Academic logic. Connecting the case studies to the transformative model of teaching with technology and organizational versus individual factors for adoption, there is a need to update the metataxonomy presented in Table 1. The case studies can be assigned to stages in Table 1 with Project Rewire demonstrating an early to midstage transition, while that of the COVID response demonstrates midstage transition yet has the potential, as an external force of change, to raise discussion on late-stage transition aspirations. An updated metataxonomy is shown in Table 2, integrates the logics, or ideologies, identified by Dwivedi (2019), and adjusts the context of the taxonomy to include basis of strategy and organizational impact on individual. While there are clear transition strategies utilized, what is missing is an encompassing strategy that helps bring together the sociomaterial interdependencies present in transitions within higher education. Table 2 shows organizational and attitudinal values mapped to functional use of technology for teaching practices as incorporated in transition strategies for achieving transformation. While Table 2 does not indicate stages of transition, fundamentally the function of technology as a precursor could not be achieved without already addressing it as procedural and deliverable. As with Table 1, some of the key elements still stand, but with the procedural function of technology the transition strategy is focused on offering a consistent identity and educational content approach, with improved interactions enabled by technology and removal of siloed activities. This does not change the dominating logics of the institution, though it might be challenging them in certain aspects for the benefits gained. With the deliverable function of technology, the transition strategy is articulated to take advantage of new affordances offered for learning and teaching, be that quality, data, and modalities for pedagogy. Here, Corporation, Market, and State logic begin to dominate in terms of benefits achieved. Finally, with the precursory function of technology, a transition strategy operates with the expectation that there is a significant shift by which the university operates. Here, innovation in technology sits alongside disruption in teaching practice, with the emergence of some pedagogies not yet in existence or understood, but adaptability and fluency of technology is integral to their existence. The Cybercultural logic and Social logic are intimately connected here as the needs of society are integrated with technological advances that have disrupted industry and culture, consequently requiring their reflection in the practices of higher educational institutions.

Precursory

Deliverable

Function of technology Procedural

Basis of transition strategy Organizational intent Effect on (SAMR) pedagogy Augmentation Enabling Development of a consistent university identity with associated content, focus on teacher/ student/ content interactions and improved social connection to remove silos Modification Enhancing A means by which to use new sources of data to drive improvements, increase quality, and explore new opportunities based on technology affordances Redefinition Transformative A disruption in what the university is and new means by which to engage with industry and influence society Technology becomes key to innovation and the means to adapt to changing needs of student and broader market Social and Cybercultural

Corporation, Market, and State

Academic and Community

Organizational logics (Dwivedi) Dominating logics

Designer-expert

Curator-facilitator

Personalized and agent driven

Supplement and diversify

Organizational impact on individual Effective role of Affective role on student educator AdministrativeIncrease access and didactic support

Table 2 Organizational and individual ideological account of metataxonomy of transformational levels of the virtual university when teaching with technology

182 H. Wheaton and S. Young

10

Transition Techniques When Introducing Change: A Sociomaterial Approach. . .

183

Conclusion and Future Directions As with many activities in higher education, transition and transformation in organizational technologies, and pedagogical practice, there are a complex coalescence of sociomaterial factors at play. Perhaps this explains why the use of validated and well-documented transition techniques, either for understanding innovation, implementing technology, or organizational change, feel insufficient and lead to the conclusion that change is not deeply embedded with mixed engagement. When it comes to transitions for the VLE and attempts to establish the virtual university, these factors are intimately integrated with the challenges listed at the beginning of this chapter. The case studies offer a point of critical observation in terms of transitions and the support strategies used. The distinction between driver and outcome is encompassed in technology and pedagogy, universities and the societal practice of education, organization and individual, and competing ideologies operating across transitions. There are two changes that need to occur, two transition strategies to be achieved and supported, the pedagogy and the technology. These have been problematically coupled according to the institutional logics identified, but at the same time technology has become the mechanism and reflection point by which a future transformation to higher education institutions is defined for the virtual university. In practice, the organizational strategy toward technology and pedagogy as summarized in Table 2 may require existing transition techniques to also include a more targeted focus on both academic and professional staff development and engagement to align ideologies. This may include thoughtful professional development and qualifications for higher education academics that inform ideologies on teaching (as well as ensuring desirable skills are present), and similar formalized learning for those staff that support them in professional roles such as learning designers, academic developers, and technologists. Reconciling the organizational logics of higher education toward a clear strategy for transition is vital, relying on each institution’s use of the perspectives and values of its various actants and historical context to define engagement and adoption with the right lens. Prioritizing or valuing one logic, or one stakeholder over another (such as academic vs professional staff), will not serve to support transition approaches. This alone is not a transition solution but can inform the driver for change, such as a teaching approach facilitated by technologies. However, it appears that, as shown in the case studies, many of these transitions are a step toward an outcome seeking to embed change. It is this outcome that is still to be defined and clearly articulated but is nevertheless part of our discussions on the virtual university and transformative teaching with technology.

Cross-References ▶ Aligning the Vision for Technology-Enhanced Learning with a Master Plan, Policies, and Procedures ▶ Laying and Maintaining the Foundations for Quality

184

H. Wheaton and S. Young

▶ Models of Professional Development for Technology-Enhanced Learning in the Virtual University ▶ The Future of the Learning Management System in the Virtual University ▶ The Role of Standards and Benchmarking in Technology-Enhanced Learning ▶ Transparency in Governing Technology Enhanced Learning

References Alammary, A., J. Sheard, and A. Carbone. 2014. Blended learning in higher education: Three different design approaches. Australasian Journal of Educational Technology 30 (4): 440–454. https://doi.org/10.14742/ajet.693. Auster, E.R., and K.K. Wylie. 2006. Creating active learning in the classroom: A systematic approach. Journal of Management Education 30 (2): 333–353. https://doi.org/10.1177/ 1052562905283346. Au-Yong-Oliveira, M., R. Gonçalves, J. Martins, and F. Branco. 2018. The social impact of technology on millennials and consequences for higher education and leadership. Telematics and Informatics 35 (4): 954–963. https://doi.org/10.1016/j.tele.2017.10.007. Bates, T. 2019. Teaching in a digital age: Guidelines for designing and learning. 2nd ed. Tony Bates Associates Ltd. Biggs, J., and C. Tang. 2011. Teaching for quality learning at University. McGraw-Hill Education. Boelens, R., B. De Wever, and M. Voet. 2017. Four key challenges to the design of blended learning: A systematic literature review. Educational Research Review 22: 1–18. https://doi.org/ 10.1016/j.edurev.2017.06.001. Boelens, R., M. Voet, and B. De Wever. 2018. The design of blended learning in response to student diversity in higher education: Instructors’ views and use of differentiated instruction in blended learning. Computers and Education 120: 197–212. https://doi.org/10.1016/j.compedu.2018. 02.009. Børte, K., K. Nesje, and S. Lillejord. 2020. Barriers to student active learning in higher education. Teaching in Higher Education: 1–19. https://doi.org/10.1080/13562517.2020.1839746. Casanovas, I. (2010). Exploring the current theoretical background about adoption until institutionalization of online education in universities: Needs for further research. Electronic Journal of e-Learning, 8 (2): 73–84. CEID. 2021. SES 2020 qualitative analysis. RMIT University. CX+Digital. 2020. Learner experience report. RMIT University. Dwivedi, N. 2019. Exploring institutional logics for technology-mediated higher education. Taylor & Francis Group. Graham, C.R. 2005. Blended learning systems: Definition, current trends, and future directions. In Handbook of blended learning: Global perspectives, local designs, ed. C.J. Bonk and C.R. Graham. Wiley. Graham, C.R., W. Woodfield, and J.B. Harrison. 2013. A framework for institutional adoption and implementation of blended learning in higher education. The Internet and Higher Education 18: 4–14. https://doi.org/10.1016/j.iheduc.2012.09.003. Hamilton, E.R., J.M. Rosenberg, and M. Akcaoglu. 2016. The Substitution Augmentation Modification Redefinition (SAMR) Model: A critical review and suggestions for its use. TechTrends 60 (5): 433–441. https://doi.org/10.1007/s11528-016-0091-y. Hannon, J. 2013. Incommensurate practices: Sociomaterial entanglements of learning technology implementation: Incommensurate practices. Journal of Computer Assisted Learning 29 (2): 168–178. https://doi.org/10.1111/j.1365-2729.2012.00480.x. Kukulska-Hulme, A., C. Bossu, T. Coughlan, R. Ferguson, E. FitzGerald, M. Gaved, . . . S. Zhang. 2021. Innovating pedagogy 2021: Open university innovation report 9. Milton Keynes: The Open University.

10

Transition Techniques When Introducing Change: A Sociomaterial Approach. . .

185

Liu, Q., and S. Geertshuis. 2021. Professional identity and the adoption of learning management systems. Studies in Higher Education 46 (3): 624–637. https://doi.org/10.1080/03075079.2019. 1647413. Martin, L. 2020. Foundations for good practice: The student experience of online learning in Australian higher education during the COVID-19 pandemic. https://www.teqsa.gov.au/latestnews/publications/foundations-good-practice-student-experience-online-learning-australian McCormack, M.D., C. Brooks, and B. Shulman. 2020. Higher education’s 2020 trend watch and top 10 strategic technologies. E. C. f. A. a. R. (ECAR). https://www.educause.edu/ecar/ research-publications/higher-education-trend-watch-and-top-10-strategic-technologies/2020/ introduction McGill, T.J., and J.E. Klobas. 2009. A task–technology fit view of learning management system impact. Computers and Education 52 (2): 496–508. https://doi.org/10.1016/j.compedu.2008. 10.002. Mestan, K. 2019. Create a fine blend: An examination of institutional transition to blended learning. Australasian Journal of Educational Technology 35 (1): 70–84. https://doi.org/10.14742/ajet. 3216. Panigrahi, R., P.R. Srivastava, and D. Sharma. 2018. Online learning: Adoption, continuance, and learning outcome – A review of literature. International Journal of Information Management 43: 1–14. https://doi.org/10.1016/j.ijinfomgt.2018.05.005. Parsons, F. 2018. Chapter 4 – Institutional transformation through collaboration. In Collaboration and the academic library (pp. 39–51). Elsevier Ltd. https://doi.org/10.1016/B978-0-08-1020845.00004-3. Pinho, C., M. Franco, and L. Mendes. 2018. Web portals as tools to support information management in higher education institutions: A systematic literature review. International Journal of Information Management 41: 80–92. https://doi.org/10.1016/j.ijinfomgt.2018.04.002. Porter, W.W., C.R. Graham, R.G. Bodily, and D.S. Sandberg. 2016. A qualitative analysis of institutional drivers and barriers to blended learning adoption in higher education. The Internet and Higher Education 28: 17–27. https://doi.org/10.1016/j.iheduc.2015.08.003. Puentedura, R. 2006. Transformation, technology, and education. Hippasus. http://hippasus.com/ resources/tte/ Richardson, W. 2010. Blogs, wikis, podcasts, and other powerful Web tools for classrooms. 3rd ed. Corwin. Rogers, E.M. 1995. Diffusion of innovations. 4th ed. Free Press. Salmon, G. 2013. E-tivities: The key to active online learning. Routledge. https://doi.org/10.4324/ 9780203074640. Sankey, M., Z. Yongsheng, and X. Junhong. 2020. Putting the pedagogic horse in front of the technology cart. Journal of Distance Education in China 5 (544). https://doi.org/10.13541/j. cnki.chinade.2020.05.006. Scherer, R., F. Siddiq, and J. Tondeur. 2019. The technology acceptance model (TAM): A metaanalytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers and Education 128: 13–35. https://doi.org/10.1016/j. compedu.2018.09.009. Selwyn, N., and K. Facer. 2014. The sociology of education and digital technology: Past, present and future. Oxford Review of Education 40 (4): 482–496. https://doi.org/10.1080/03054985. 2014.933005. Selwyn, N., T. Hillman, R. Eynon, G. Ferreira, J. Knox, F. Macgilchrist, and J.M. Sancho-Gil. 2020. What’s next for Ed-Tech? Critical hopes and concerns for the 2020s. Learning, Media and Technology 45 (1): 1–6. https://doi.org/10.1080/17439884.2020.1694945. Sinclair, J., and A.-M. Aho. 2018. Experts on super innovators: Understanding staff adoption of learning management systems. Higher Education Research and Development 37 (1): 158–172. https://doi.org/10.1080/07294360.2017.1342609. Smith, K. 2012. Lessons learnt from literature on the diffusion of innovative learning and teaching practices in higher education. Innovations in Education and Teaching International 49 (2): 173–182. https://doi.org/10.1080/14703297.2012.677599.

186

H. Wheaton and S. Young

Twigg, C.A. 2009. Improving learning and reducing costs for online learning. In Encyclopedia of distance learning, ed. P.L. Rogers, G.A. Berg, J.V. Boettcher, C. Howard, L. Justice, and K.D. Schenk. Hershey: Information Science Reference. von Konsky, B.R., R. Martin, S. Bolt, T. Broadley, and N. Ostashewski. 2014. Transforming higher education and student engagement through collaborative review to inform educational design. Australasian Journal of Educational Technology 30 (6): 619–633. https://doi.org/10.14742/ ajet.742. West, R.E., G. Waddoups, and C.R. Graham. 2007. Understanding the experiences of instructors as they adopt a course management system. Educational Technology Research and Development 55 (1): 1–26. https://doi.org/10.1007/s11423-006-9018-1. Wheaton, H., and A. Mastro. 2018. Juggling a Canvas Roll-out: The three balls of quality, consistency, and support. InstructureCon, Colorado. https://community.canvaslms.com/t5/ InstructureCon-2018/Juggling-a-Canvas-Roll-Out-The-3-Balls-of-Quality-Consistency/m-p/ 384162 Wheaton, H., and S. Young. 2019. It’s elemental: Technology Enhanced Learning (TEL) as scalable and sustainable student-centred practice in context. Personalised learning. Diverse goals. One Heart, Singapore. Woods, R., J.D. Baker, and D. Hopper. 2004. Hybrid structures: Faculty use and perception of web-based courseware as a supplement to face-to-face instruction. The Internet and Higher Education 7 (4): 281–297. https://doi.org/10.1016/j.iheduc.2004.09.002. Zawacki-Richter, O., and C. Latchem. 2018. Exploring four decades of research in Computers & Education. Computers and Education 122: 136–152. https://doi.org/10.1016/j.compedu.2018. 04.001. Zajda, J. 2020. Globalisation and neo-liberalism in higher education: Australia. In Globalisation, ideology and neo-liberal higher education reforms (vol. 21, pp. 47–57). The Netherlands: Springer Netherlands.

The Role of Analytics When Supporting Staff and Students in the Virtual Learning Environment

11

Hazel Jones and Rachel Fitzgerald

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . How Learning Analytics Supports Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Developing Deep Insights with Learning Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Enabling Cultural Change Using the Behavior Change Wheel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Application of the Behavior Change Wheel in Learning Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . Benefits and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data, Ethics, and Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Embedding Learning Analytics as a Virtual University: A Plan of Action . . . . . . . . . . . . . . . . . . . . Conclusion and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

188 188 190 191 191 194 195 196 196 198

Abstract

We are developing a maturity in our understanding of the potential and value of learning analytics. Learning analytics applications are moving from a reactive and retrospective use of data in education to modeling methods of proactive and purposeful data collection about learning and teaching: “learning analytics are about learning” (Gasevic et al., Techtrends 59:64–71, 2015). This chapter considers evidence-based approaches that purposefully collect and use data to inform and empower teaching and learning in meaningful ways. We explore the possibilities of providing teachers in the virtual university with access to relevant data and tools in everyday practice for a positive impact on the student experience. We explore what is needed to integrate learning analytics into mainstream processes H. Jones Griffith Business School, Griffith University, Brisbane, QLD, Australia e-mail: Hazel.Jones@griffith.edu.au R. Fitzgerald (*) The Faculty of Business, Economics and Law, University of Queensland, Brisbane, QLD, Australia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_11

187

188

H. Jones and R. Fitzgerald

and consider data ethics and privacy. We also build on the discussions in previous chapters and consider the implications of implementation and change management. We propose the behavior change wheel as a potential theoretical framework for more effective data-driven learning and teaching practice in the virtual university. Keywords

Learning analytics · Change management · Student experience · Behavior change wheel

Introduction In a virtual university, there is a vast array of data available regarding students and their interactions with various aspects of their learning that can provide a comprehensive insight into how learners are engaging. Learning analytics (LA) has been defined as the “measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs” (LAK11 2011, para. 5). In a virtual university, it may be possible that every “click” and interaction that a student has in their learning environment is recorded automatically. Data from these interactions can be collated into a range of reports that the university can access, analyze, and interpret to gain insight into student engagement. However, LA can be used to go beyond creating a log of interactions; it can offer insights into the learning that is occurring (Lodge and Lewis, 2012), giving us a more holistic understanding of students and the various ways in which they approach their learning. Virtual universities can gather such insights by ensuring that their approach to LA goes beyond just counting those clicks in an LMS and include data from a range of sources and contextualizing the information to consider how a student learns and interacts with the learning environments, rather than just how often they interact (Lodge et al. 2017).The learning opportunities provided to students, through course design and teacher interaction, may also be considered through the use of LA data obtained from learning management systems (Lockyer et al. 2013).

How Learning Analytics Supports Learning LA has its origins in educational data mining. Early frameworks and subsequent discussion focused on data science and processes, and the emphasis was around data collection, cleaning, and manipulation. In recent times, there has been a shift to a more holistic approach, with consideration of a range of socio-technical aspects. Table 1 provides a summary of frameworks that have been developed over the last decade that provide examples of institutional-wide LA implementation or adoption by individual staff that can be considered by virtual universities. One can identify a

11

The Role of Analytics When Supporting Staff and Students in the. . .

189

Table 1 Learning analytics frameworks LA framework Exploratory Learning Analytics Toolkit (eLAT) (Dyckhoff et al. 2011)

I Framework (Jones 2015)

Model of Strategic Capability (Colvin et al. 2016)

Learning Analytics-Learning Design (LA-LD) Framework (Gunn et al. 2017)

Key features Provides a set of software design goals that virtual universities can consider when developing LA tools, namely, usability, interoperability, extensibility, reusability, realtime operation, and data privacy. These point to the importance of including software designers if virtual universities choose to develop their own LA tools or when looking at purchasing such tools Provides a practical approach for individual academics to implement LA at a subject level through an iterative process involving five steps, all of which fit within the unique context of an individual institution The five iterative steps are based on questions that academics can ask to step through the LA process
• Impetus: What question am I wanting to investigate? • Input: What data is available to help me address this question? • Interrogation: How will I analyze and interpret the data in my context? • Intervention: What changes will I make to my subject and teaching approach as a result? • Impact: How do I know if my intervention was successful? This model is aimed at leadership and presents a dynamic systems model of LA implementation that includes “6 enablers of Conceptualisation, Leadership, Strategy, Stakeholders, Technology and Context.l” (p. 27). This type of approach can be adopted in virtual universities who conceptualize and value LA as a way to understand students’ learning and experiences, rather than just a measure of student interactions with the virtual learning spaces Provides a series of prompts for teachers to focus on questions and relevant actionable insights at three points in the regular teaching cycle: prepare and plan/design and develop, teach and assess/implement, and review and evaluate. The aim being to help teachers better understand how LA can both inform LD and measure the impact of LD on students’ learning. The framework also provides a guide for educators in the selection of LA data appropriate to the questions they are seeking to answer with an aim of promoting positive (continued)

190

H. Jones and R. Fitzgerald

Table 1 (continued) LA framework

Supporting Higher Education to Integrate Learning Analytics (SHEILA) framework (Tsai et al. 2018)

Key features changes to tertiary teaching and learning design practice Provides virtual universities with a framework for informing the LA policy and strategies that can “enhance systematic adoption of learning analytics on a wide scale” (p. 5). The framework consists of six components: map political context, identify key stakeholders, identify the desired behavior changes, development engagement strategy, analyze internal capacity to effect change, and establish monitoring and learning frameworks. The SHEILA website (https://sheilaproject.eu/) provides a tool that virtual universities can use to create their own policies as well as exemplars of LA policies from around the world which can be adopted

progression from a focus on the technical aspects of LA to more inclusion of the pedagogical components and developing staff capability with LA.

Developing Deep Insights with Learning Analytics To engage deeply with LA in their role as educators, virtual university teachers will require appropriate knowledge, skills, motivation, and time. None of these are easy to acquire given the complex work context of the modern academic (Saroyan and Trigwewll 2015). Lack of access to data in a usable format can further exacerbate the lack of use (Bichsel 2012; Klein et al. 2019). Understanding the reasons teachers choose to engage, or not engage, with LA or LA frameworks can be an important step in increasing engagement and insightful use. In the virtual university, ensuring that there is support and training provided to empower teachers to use LA will bring increased awareness and uptake. New teachers in a virtual environment should be offered opportunities to connect with colleagues, in which Crehan et al. (2022) identify as an important aspect of developing a support network and a community of practitioners. Peer support allows a sharing and deep understanding of practice (Gunn et al. 2017; Rehrey et al. 2019), so this needs to be structurally incorporated from the outset. Increasing staff engagement with LA to inform and measure learning will require a level of collaborative change management and cultural change processes, as teaching and learning design staff may not necessarily be used to using LA in their teaching. The use of the behavior change wheel (BCW) has proved effective in educational technology adoption (Buchanan et al. 2013) and is an approach that can be adopted for LA implementation.

11

The Role of Analytics When Supporting Staff and Students in the. . .

191

Enabling Cultural Change Using the Behavior Change Wheel It is important to understand the capabilities and motivations of teaching staff, as well as the opportunities afforded to them, to encourage a change of behavior. In the behavior change wheel (BCW) framework, these aspects are considered through the central hub of the wheel, the COM-B model, which is a model of the relationships between capability (physical and psychological), opportunity (physical and social), and motivation (reflective and automatic) (COM) and behavior (B). Working through the steps of a BCW process can lead to determination of an effective implementation plan. Nine intervention functions are included in the BCW, which are defined as “broad categories of means by which an intervention can change behaviour” (Michie et al. 2014, p. 109). These nine functions are education, persuasion, incentivization, coercion, training, restriction, environmental restructuring, modeling, and enablement. Behavior change techniques are specific strategies or actions that can be used as mechanisms of change for chosen intervention functions. The outer layer of the BCW involves consideration of policies that could support the delivery of the intervention.

Application of the Behavior Change Wheel in Learning Analytics The BCW offers an approach that virtual universities can adopt for LA implementation that will empower staff and support widespread engagement in LA processes. The first step is to determine the current levels of knowledge and skills of staff of the different aspects of LA and their motivations for wanting to engage with LA. Areas of knowledge and skills that are needed for the successful use of LA include: • What is meant by LA and how these can be used to enhance and inform teaching practice. • What tools are available in the LMS and how to use these. • What data is available and how to analyze and interpret data. • What actions could be put in place based on the analysis and interpretation. • How to evaluate success of the action. • Build confidence in the ability to perform these tasks. These are all considered as psychological capabilities in BCW terminology, and no physical capabilities are required for LA implementation. Reflective motivation refers to processes involving plans (self-conscious intentions) and evaluations (beliefs about what is good and bad) and in the LA context could include beliefs that adopting LA will be beneficial to themselves, students, and/or the institution. Automatic motivation involves emotional reactions, desires (wants and needs), impulses, inhibitions, drive states, and reflex responses and in the LA context includes gaining satisfaction and positive outcomes from adopting LA and developing routines to include LA in normal workload. These capabilities and motivations

192

H. Jones and R. Fitzgerald

can be determined through a combination of surveys, focused group discussions, and interviews. Virtual universities will also need to determine what opportunities will be provided to staff to support LA implementation. Physical opportunities, those afforded by the environment involving time, resources, locations, cues, and physical “affordance” could include provision of time, training, and support provided for all of the aspects of LA implementation noted above under psychological capability. Social opportunities, those afforded by interpersonal influences, social cues, and cultural norms that influence the way we think about things, could include opportunities for collaborating with colleagues across the university and access to good practice exemplars. It is likely that each virtual university will find that teachers have a unique combination of the COM components which will lead to inclusion of different combinations of support within their implementation plan. As an example, staff who are already highly motivated to engage with LA will not need to have the benefits of using LA explained to them. Once it has been determined which components are the most important, appropriate intervention functions can be chosen through matching COM components utilizing a matrix provided within the BCW processes. Table 2 provides examples of how each intervention function can be applied for the design of a LA implementation plan. The BCW suggests choosing several intervention functions and then a range of behavior change techniques (BCTs) as the practical strategies to enact the desired behavior change. Table 3 provides examples of BCTs that can be applied for LA implementation. The final step in the BCW is consideration of which of the seven policy categories could be considered by institutional management to enhance the widespread use of Table 2 Intervention functions applied to learning analytics implementation Intervention function Education Persuasion Incentivization Coercion Training Restriction Environmental restructuring Modeling Enablement

Example as relevant to LA implementation in virtual universities Online resources supporting LA development Discussing the benefits of using LA with participants Providing hours in workload Considered inappropriate for LA implementation One-on-one sessions with participants to show how to access and interpret LA reports in the LMS Considered inappropriate for LA implementation Developing institutional policies and guidelines Making access to data easier Providing case study examples of how LA has been successfully adopted Increasing capabilities to reduce barriers Facilitating networking opportunities to promote collaborative knowledge building

Adapted from Michie et al. (2014, pp. 111–112)

11

The Role of Analytics When Supporting Staff and Students in the. . .

193

Table 3 Exemplar BCTs for LA implementation Intervention function Education/ training

Individual BCTs Feedback on behavior

Feedback on outcomes of behavior Enablement

Social support (practical)

Goal setting (behavior)

Review behavior goal(s)

Modeling/ training

Demonstration of the behavior

Training

Instruction on how to perform the behavior

BCT definition Monitor and provide informative or evaluative feedback on performance of the behavior Monitor and provide feedback on the outcome of performance of the behavior Advise on, arrange, or provide practical help (e.g., from friends, relatives, colleagues, “buddies” or staff) for performance of the behavior Set or agree on a goal defined in terms of the behavior to be achieved

Review behavior goal (s) jointly with the person, and consider modifying goal(s) or behavior change strategy in light of achievement. This may lead to resetting the same goal, a small change in that goal, or setting a new goal instead of (or in addition to) the first or no change Provide an observable sample of the performance of the behavior, directly in person or indirectly, e.g., via film, pictures for the person to aspire or imitate Advise or agree on how to perform the behavior

Application in LA implementation Provide report of levels of engagements with LA reports and tools Discuss changes in student interactions as a result of actions resulting from using LA data Group discussions and support website with opportunities for discussion

Agree on one question to be addressed and follow through I Framework to address this in an agreed timeframe Discuss progress through stages of I Framework in individual consultations

Work through LA reports available in LMS and how to use in individual consultations Provide guides to reports and tools on support site Individual discussions on which reports and tools are the most beneficial depending on the question being investigated and how to interpret data

Adapted from Michie et al. (2014, pp. 250–253, 259–283)

LA. The five most applicable categories for virtual universities are defined in Table 4 with examples of how institutions could adopt them. Having such institutional policy and guidelines in place will add direction.

194

H. Jones and R. Fitzgerald

Table 4 BCW policy categories applicable to learning analytics implementation Policy category Communication/ marketing

Definition Using print, electronic, telephonic, or broadcast media

Guidelines

Creating documents that recommend or mandate practice. This includes all changes to service provision Establishing rules or principles of behavior or practice Designing and/or controlling the physical or social environment Delivering a service

Regulation Environmental/ social planning Service provision

Example Monthly digital LA newsletter including good practice examples and links to relevant research Good practice guidelines

Policy and procedural guidelines Facilitation of LA community of practice or research group Ensure all learning and teaching support staff have LA expertise

Adapted from Michie et al. (2014, p. 135)

Benefits and Challenges There are many benefits for teachers who engage with learning analytics. Research demonstrates that LA can improve student engagement, which has been identified as a challenge in the virtual domain (Karaoglan Yilmaz and Yilmaz 2021). It has also been identified as a factor in improved interaction and progress (Hilliger et al. 2019) and self-reflection (Corrin et al. 2013; Ifenthaler and Yau 2019). While student engagement has been defined in many ways over the past century (Groccia 2018), we use the definition from Kuh (2009, p.683), i.e., “the time and effort students devote to activities that are empirically linked to desired outcomes of college and what institutions do to induce students to participate in these activities.” The changes to curriculum and pedagogy that result from LA insights can be implemented either for the current offering of a course, providing immediate benefit for staff and students, or for the longer term. Both approaches have merit, and implementation will depend on the depth of change required and time available for staff development and engagement. Realization of benefits may need behavior change for students and staff, and institutions must ensure appropriate action is taken as a result of insights from LA if they progress down this route (Sønderlund et al. 2019). The socio-technological challenges that impact the effective implementation of LA are true of any AI or analytics endeavor. For example, ensuring that the data gathered is meaningful, reliable, and real time, is accessible, and of high quality (Webb 2020) can be a challenge. Ensuring users can analyze and interpret output is identified as both a technical and a technological challenge that can impede the adoption and utilization of LA by teaching academics (Ferguson et al. 2014; Macfadyen and Dawson 2012). Developing skills, supporting users, and building capability are sociological challenges that need to be considered when implementing

11

The Role of Analytics When Supporting Staff and Students in the. . .

195

LA, and these elements impact teacher engagement. Lack of involvement of stakeholders, due to low levels of knowledge of benefits of LA and what that involvement entails, is a continuing challenge (Hilliger et al. 2019). In their study of academics’ perceptions of LA, Howell et al. (2018) identified five areas of teachers’ concerns about implementation of LA: facilitating learning, ensuring ethical and appropriate use of data, and data integrity; informed consent from students and impact on students’ learning and mental well-being; academic workload issues; and including staff and students in all aspects of LA implementation. Despite these challenges they found that “academics perceived scope for learning analytics to be beneficial if there is collaboration between academics, students, and the university” (p. 1).Their study concludes with a suggestion that teachers should be involved in the development of policy and procedures for LA. Additionally, concerns about privacy, ownership, and the ethical use of data are key challenges (Ifenthaler and Yau 2019; Macfadyen and Dawson 2012). Developing ethical data strategies, creating clear processes regarding data storage and use, and ensuring users are aware of what data is being collected and what is being used need to be part of a mature strategic plan regarding the use of learning analytics for the virtual university (Wixom and Markus 2017). Achieving a careful balance to effectively engage with learning analytics will require planning and policy adjustment. Wixom and Markus (2017) suggest key principles to develop management practices that facilitate the development of “norms and acceptable data use” that include capturing stakeholder perspectives, monitoring and evaluating how data is being converted into insights and action, and creating a shared understanding and consensus on how data is being used.

Data, Ethics, and Privacy It is important when developing LA implementation for a virtual university to consider integration of a wide range of data and not rely solely on easy-to-access LMS data and LA reports. Other sources of data that can be integrated with LMS data to provide a more holistic picture of a student’s engagement with the institution include student evaluations of courses and teaching and interaction with library, student support, and clubs and societies. Virtual universities will also be wellpositioned to lead in cutting-edge approaches to LA such as multimodal analytics (Blikstein and Worsley 2016), eye-tracking (Vatrapu et al. 2013), and natural language processing (McNamara et al. 2017). Further examples of contemporary data-driven approaches are provided in ▶ Chap. 29, “Using Institutional Data to Drive Quality, Improvement, and Innovation.” Other considerations to include when determining how to roll out LA include accessibility of data, the types of visualizations to provide to staff and students, reports provided as part of a pull or push approach, and how to ensure data is provided in a timely manner that will empower staff and students to take appropriate actions. Promoting ethical use of data, including consideration of privacy issues, data ownership, and informed consent of students, will be an ongoing concern for virtual

196

H. Jones and R. Fitzgerald

universities. The importance of ethical use frameworks and concerns about privacy has been noted in higher education (Corrin et al. 2019; Drachsler and Greller 2016). However, these issues have yet to translate into clear practice (Jones 2016). Using the data strategy approach, the virtual university should develop clear guidance for teachers and other stakeholders (Welsh and McKinney 2015).

Embedding Learning Analytics as a Virtual University: A Plan of Action To embed LA in a strategic and ethical manner, virtual universities can consider implementing recommendations from a recent discussion paper on ethical considerations in the Australian higher education sector (Corrin et al. 2019), namely: 1. 2. 3. 4. 5. 6. 7.

Recognize that the ethics of learning analytics is very complex. Develop clear principles and guidelines on data use in learning and teaching. Actively engage with multiple stakeholders. Establish transparency and trust. Avoid reinventing the wheel. Get a move on. Develop processes to revisit and recast practice.

We complete this chapter with some guidelines on how to embed learning analytics through a set of design principles to consider as part of a learning strategy for the virtual university and summarize key areas that need to be considered before embarking on this journey. These principles are an important outcome from a recent doctoral study (Jones 2020). The aim of the principles is to provide insights and a guide that can be adopted and adapted in a range of virtual universities (McKenney et al. 2006). While each principle could be adopted in isolation, the most effective approach for any institution would be to consider these as part of a holistic approach (Table 5).

Conclusion and Future Directions This chapter has explored different ways in which LA can be used within virtual universities to inform and enhance learning and teaching through evidence-based approaches. We began with discussion of a range of LA frameworks that can be considered to inform widespread collaborative adoption of LA, highlighting the need to consider socio-cultural and pedagogical aspects, alongside the technological aspects. We reviewed the behavior change wheel as a theoretical framework to design a LA implementation plan and explored the benefits and challenges of institutional LA adoption. We finished with an outline of design principles for consideration and the support and professional learning that would be required to empower such adoption. There is much more that we can achieve with LA, and

11

The Role of Analytics When Supporting Staff and Students in the. . .

197

Table 5 Design principles for learning analytics (Jones 2020) Design principle Provide training and professional learning opportunities in all aspects of LA implementation in a range of modalities

Provide support and resources for all aspects of LA

Provide easy access to relevant and actionable LA data

Elaborations and examples • Contextualize professional learning opportunities and support around the areas of importance to staff, for example, student retention and success or use of nudges • Take a holistic approach to capacity building by integrating training on LA into pedagogical discussions • Provide training to build academics’ knowledge of the full affordances of the LMS, including the use of LA reports and tools appropriate to their context • Build knowledge, skills, and confidence in all aspects of accessing and interpreting student data and implementing appropriate actions • Provide a range of training opportunities including formal sessions, individual training, informal just-in-time learning, and social learning with and from peers/colleagues • Focus contextual discussions on the benefits of LA for students rather than for academics • Provide support through actions/tasks which can be undertaken by other staff, including support for contacting students at risk to offer pastoral care where needed and support to access and analyze data • Provide resources on how and why of using LA, including details of key personnel and roles; resources to build academics’ knowledge and use of the full affordances of the LMS, including LA reports and tools; information on the benefits of engaging with LA reports, particularly those that currently have low levels of use; and resources to empower staff to access, analyze, and interpret student data in the LMS • Provide timely and appropriate reports to help academics support students who may be at risk • Include insights with reports that could enhance course design and student engagement and learning • Provide access to data from other courses to enable comparisons • Provide overview of staff usage of LMS and LA reports and tools and opportunities for discussion to consider actions that could be taken as a result • Provide information that links the names of reports in the Event name column of log reports to the way that data is provided in the course sites (continued)

198

H. Jones and R. Fitzgerald

Table 5 (continued) Design principle Nurture a workplace culture that encourages and enables the use of LA through structures and discourse

Provide clear and timely communication of available reports, support, and any changes to systems Facilitate professional learning by dedicated staff with expert knowledge and skills in LA and pedagogical considerations

Elaborations and examples • Provide opportunities for social learning, networking, collaboration, and discussion with other staff • Recognize the time needed to engage in these processes in staff workload models • Include recognition and reward as a component of the implementation plan • Include clear and timely dissemination of information about any new tools and reports • Include effective communication of supports available across the institution • Ensure that those facilitating the implementation plan have the requisite knowledge and expertise to provide just-intime learning on all aspects of working in LMS as well as LA • Provide professional learning opportunities to build capabilities of staff in appropriate roles

virtual universities will be well-placed to lead further research and development of applications of LA through a considered data strategy that builds on the frameworks and incorporates and includes stakeholders to establish what Wixom and Markus (2017) suggest is shared understanding, consensus, and buy-in regarding conflicts and resolutions to identify the desired behaviors. These desired behaviors relate to the use of LA in ways that will improve student learning outcomes and the overall student learning experience.

References Bichsel, J. 2012. Analytics in higher education: Benefits, barriers, progress, and recommendations. E. C. f. A. Research. https://library.educause.edu/-/media/files/library/2012/6/ers1207.pdf?la¼en& hash¼B6E84D1B3A1A0921609BF64F298D741297DA3006 Blikstein, P., and M. Worsley. 2016. Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics 3 (2): 220–238. https://doi.org/10.18608/jla.2016.32.11. Buchanan, T., P. Sainter, and G. Saunders. 2013. Factors affecting faculty use of learning technologies: Implications for models of technology adoption. Journal of Computing in Higher Education 25: 1–11. https://doi.org/10.1007/s12528-013-9066-6. Colvin, C., T. Rogers, A. Wade, S. Dawson, D. Gasevic, S. Buckingham Shum, K. Nelson, S. Alexander, L. Lockyer, G. Kennedy, L. Corrin, and J. Fisher. 2016. Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. https://opus.lib.uts.edu.au/bitstream/10453/117173/1/AUS_OLT_LearningAnalytics_2016.pdf Corrin, L., G. Kennedy, and R. Mulder. 2013. Enhancing learning analytics by understanding the needs of teachers. In Electric Dreams 30th ASCILITE Conference, Sydney Australia. https:// www.ascilite.org/conferences/sydney13/about/proceedings.pdf

11

The Role of Analytics When Supporting Staff and Students in the. . .

199

Corrin, L., G. Kennedy, S. French, S. Buckingham Shum, K. Kitto, A. Pardo, D. West, N. Mirriahi, and C. Colvin. 2019. The ethics of learning analytics in Australian Higher Education: A discussion paper. https://melbourne-cshe.unimelb.edu.au/__data/assets/pdf_file/0004/ 3035047/LA_Ethics_Discussion_Paper.pdf Crehan, M., M. Munroe, and M. O’Keefe. 2022. Peer support, technology enhanced learning and the virtual university. In Technology enhanced learning and the virtual university, ed. M. Sankey, H. Huijser, and R. Fitzgerald. Springer. Drachsler, H., and W. Greller. 2016. Privacy and analytics: It’s a DELICATE issue a checklist for trusted learning analytics. In LAK ’16: The Sixth International Conference on Learning Analytics & Knowledge, Edinburgh United Kingdom. Dyckhoff, A.L., D. Zielke, M.A. Chatti, and R. Schroeder 2011. eLAT: An exploratory learning analytics tool for reflection and iterative improvement of technology enhanced learning. In EDM 2011 4th International Conference on Educational Data Mining, Eindhoven, Netherlands. http://citeseerx.ist.psu.edu/viewdoc/download?doi¼10.1.1.1032.3892&rep¼rep1& type¼pdf#page¼367 Ferguson, R., L.P. Macfadyen, D. Clow, B. Tynan, S. Alexander, and S. Dawson. 2014. Setting learning analytics in context: Overcoming the barriers to large-scale adoption. Journal of Learning Analytics 1 (3): 120–144. https://doi.org/10.18608/jla.2014.13.7. Gasevic, D., S. Dawson, and G. Siemens. 2015. Let’s not forget: Learning analytics are about learning. Techtrends 59: 64–71. https://doi.org/10.1007/s11528-014-0822-x. Groccia, J.E. 2018. What is student engagement? New Directions for Teaching and Learning Summer 2018 (154): 11–20. https://doi.org/10.1002/tl.20287. Gunn, C., J. McDonald, C. Donald, J. Milne, and M. Blumenstein. 2017. Building an evidence base for teaching and learning design using learning analytics. AkoAotearoa. https://ako.ac.nz/ assets/Knowledge-centre/NPF-15-008-Building-an-Evidence-Base-for-Teaching-and-Learn ing-Design-Using-Learning-Analytics-Data/RESEARCH-REPORT-Building-an-evidencebase-for-teaching-and-learning-design-using-learning-analytics.pdf Hilliger, I., M. Pérez-Sanagustín, M. Ortiz, P. Pesántez, E. Scheihing, Y.-S. Tsai, P.J. MuñozMerino, and T. Broos. 2019. Assessing institutional needs for learning analytics adoption in Latin American Higher Education. In 9th International Conference on Learning Analytics & Knowledge (LAK19), Tempe, Arizona. https://www.solaresearch.org/wp-content/uploads/2019/ 08/LAK19_Companion_Proceedings.pdf Howell, J., L.D. Roberts, K. Seaman, and D.C. Gibson. 2018. Are we on our way to becoming a “Helicopter University”? Academics’ views on learning analytics. Technology, Knowledge and Learning 23: 1–20. https://doi.org/10.1007/s10758-017-9329-9. Ifenthaler, D., and J.Y.-K. Yau. 2019. Higher education stakeholders’ views on learning analytics policy recommendations for supporting study success. International Journal of Learning Analytics and Artificial Intelligence for Education 1 (1): 28–42. https://doi.org/10.3991/ijai. v1i1.10978. Jones, H. 2015. The ‘I’s have it: Development of a framework for implementing learning analytics. In 32nd Australasian Society for Computers in Learning in Tertiary Education Conference: Globally Connected, Digitally Enabled (ASCILITE 2015), Perth, Australia. http://www. 2015conference.ascilite.org/wp-content/uploads/2015/11/ascilite-2015-proceedings.pdf. ———. 2016. Ethical considerations in the use of student data: International perspectives and educators’ perceptions. In Show me the Learning ASCILITE 2016, Adelaide, Australia. https:// 2016conference.ascilite.org/wp-content/uploads/ASCILITE-2016-full-proceedingsUpdated-1512.pdf ———. 2020. Using the behaviour change wheel to design and test a learning analytics adoption strategy at a Regional Australian University. Unpublished doctoral dissertation, University of Southern Queensland. Karaoglan Yilmaz, F.G., and R. Yilmaz. 2021. Learning analytics intervention improves students’ engagement in online learning. Tech Know Learn. https://doi.org/10.1007/s10758-021-09547-w

200

H. Jones and R. Fitzgerald

Klein, C., J. Lester, H. Rangwala, and A. Johri. 2019. Technological barriers and incentives to learning analytics adoption in higher education: Insights from users. Journal of Computing in Higher Education 31: 604–625. https://doi.org/10.1007/s12528-019-09210-5. Kuh, G.D. 2009. What student affairs professionals need to know about student engagement. Journal of College Student Development 50 (6): 683–706. https://doi.org/10.1353/csd.0.0099. LAK11. 2011. 1st International Conference on Learning Analytics & Knowledge. https://tekri. athabascau.ca/analytics/ Lockyer, L., E. Heathcote, and S. Dawson. 2013. Informing pedagogical action: Aligning learning analytics with learning design. American Behavioral Scientist 57 (10): 1439–1459. https://doi. org/10.1177/0002764213479367. Lodge, J., S.S.J. Alhadad, M. Lewis, and D. Gasevic. 2017. Inferring learning from big data: The importance of a transdisciplinary and multidimensional approach. Technology, Knowledge and Learning 22: 385–400. https://doi.org/10.1007/s10758-017-9330-3. Lodge, J., and Lewis, M. 2012. Pigeon pecks and mouse clicks: Putting the learning back into learning analytics. Future challenges, sustainable futures. Proceedings ascilite Wellington, 560–564. Macfadyen, L.P., and S. Dawson. 2012. Numbers are not enough. Why e-learning analytics failed to inform an institutional strategic plan. International Forum of Educational Technology & Society 15 (3): 149–163. https://www.jstor.org/stable/pdf/jeductechsoci.15.3.149.pdf. McNamara, D. S., Allen, L. K., Crossley, S. A., Dascalu, M., & Perret, C. A. (2017). Natural language processing and learning analytics. In C. Lang, G. Siemens, A. Wise, & D. Gašević (Eds.), Handbook of Learning Analytics (pp. 93–104). Society for Learning Analytics Research. https://doi.org/10.18608/hla17.008 McKenney, S., N. Nieveen, and J. van den Akker. 2006. Design research from a curriculum perspective. In Educational design research, ed. J. van den Akker, K. Gravemeijer, S. McKenney, and N. Nieveen, 67–90. Routledge. Michie, S., L. Atkins, and R. West. 2014. The behaviour change wheel: A guide to designing interventions. Silverback Publishing. Rehrey, G., L. Shepard, C. Hostetter, A. Reynolds, and D. Groth. 2019. Engaging faculty in learning analytics: Agents of institutional culture change. Journal of Learning Analytics 6 (2): 86–94. https://doi.org/10.18608/jla.2019.62.6. Saroyan, A., and K. Trigwewll. 2015. Higher education teachers’ professional learning: Process and outcome. Studies in Educational Evaluation 45: 92–101. https://doi.org/10.1016/j.stueduc. 2015.03.008. Sønderlund, A.L., E. Hughes, and J. Smith. 2019. The efficacy of learning analytics interventions in higher education: A systematic review. British Journal of Educational Technology 50 (5): 2594–2618. https://doi.org/10.1111/bjet.12720. Tsai, Y.-S., P.M. Moreno-Marcos, I. Jivet, M. Scheffel, K. Tammets, K. Kollom, and D. Gasevic. 2018. The SHEILA framework: Informing institutional strategies and policy processes of learning analytics. Journal of Learning Analytics 5 (3): 5–20. https://doi.org/10.18608/jla. 2018.53.2. Vatrapu, R., P. Reimann, S. Bull, and M. Johnson. 2013. An eye-tracking study of notational, informational, and emotional aspects of learning analytics representations. In LAK ’13: Proceedings of the Third International Conference on Learning Analytics and Knowledge, Leuven Belgium. Webb, R. 2020. 12 challenges of data analytics and how to fix them. https://www.clearrisk.com/ risk-management-blog/challenges-of-data-analytics-0 Welsh, S., and S. McKinney. 2015. Clearing the fog: A learning analytics code of practice. In Ascilite 2015: Globally Connected Digitally Enabled, Perth, Western Australia. http://www. 2015conference.ascilite.org/wp-content/uploads/2015/11/ascilite-2015-proceedings.pdf Wixom, B., and M. Markus. 2017. To develop acceptable data use and build company norms. MIT Centre for Information Systems Research https://cisr.mit.edu/publication/2017_0401_ AcceptableDataUse_WixomMarkus

Part V Learning Theories and Application of TEL

The 3C Merry-Go-Round: Constructivism, Cognitivism, Connectivism, Etc.

12

Chris Campbell and Tran Le Nghi Tran

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cognitivism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Constructivism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Connectivism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning Theories and TEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cognitivism and TEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Constructivism and TEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Connectivism and TEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Examples That Benefit the Virtual University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Echo360 Data: Engaging with Cognitivism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Graduate Certificate Course: Embedding Constructivism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PebblePad ePortfolio: Constructivism Through Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Using Microsoft Teams: Connectivism Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Explore Learning and Teaching Website: Connectivism for Staff Professional Learning . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

204 206 206 207 208 209 209 210 210 211 211 212 214 215 216 219 220 221 221

Abstract

The two learning theories cognitivism and constructivism have been around for many years, with constructivism gaining momentum in the sciences firstly and then more broadly across higher education. Connectivism is the newer theory with its grounding in constructivism, and while some may argue it is not a C. Campbell (*) Division of Learning and Teaching, Charles Sturt University, Albury, NSW, Australia e-mail: [email protected] T. L. N. Tran Learning Futures, Griffith University, Brisbane, QLD, Australia © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_12

203

204

C. Campbell and T. L. N. Tran

learning theory at all, others disagree and have moved this knowledge and debate forward. Although each theory has some detractors, by and large they are the three main theories taught in higher education courses across the country. This chapter will provide a detailed review of the recent literature for the three theories, provide current definitions, and report on any recent developments from the literature and research. This allows, for example, from the area of educational technology to be paramount. Thus, the second section of this chapter will provide authentic examples from various aspects of teaching online and providing online support. These include how websites such as Griffith University’s Explore Learning and Teaching website can provide for connectivist learning among academics. Other examples are also provided. Constructivism learning theory examples include both course development from a constructivist viewpoint as well as flipped learning examples. With the increasing use of video and active learning in courses, flipped learning provides an excellent way of using constructivist approaches in higher education and through online and virtual learning, via the virtual university. Keywords

Cognitivism · Constructivism · Connectivism · Virtual university · Learning theories · Technological-enhanced learning (TEL)

Introduction In today’s technology-driven world, especially with the rise of the virtual university fueled by the COVID-19 pandemic, it is timely to examine the intersection of learning theories and digital technologies to better inform technology-enhanced learning (TEL) practices (Parmaxi et al. 2021). Learning theories have been considered indispensable for effective and pedagogically sound teaching practices (Yilmaz 2008) and to support the learning process in virtual spaces supported by technologies (Capacho 2018). However, the myriad of instructional theories and the sheer volume of education technological innovations present challenges for educators who grapple with aligning effective instruction with using educational technologies (Havard et al. 2016). While the theories are distinct, it is also observed that there are overlaps in the principles of these learning theories(Hammad et al. 2020; Ng 2015; Sankey 2020), which adds more complexity to the situation of TEL in the virtual university due to the various challenges regarding the ambiguity of concepts, nonacceptance of students and teachers, as well as change management and technology-related issues (Narayan and Shailashri 2020). The need for further and ongoing research into learning theories and TEL, therefore, has been pressing even before the pandemic. Reported by Harasim (2017), “opportunities for educators to reflect on the implications of how we might shape and apply new communication technologies within our practice have been limited” (p. 2).

12

The 3C Merry-Go-Round: Constructivism, Cognitivism, Connectivism, Etc.

205

While it is often believed that there is no unified or “grand” theory behind TEL (Janelli 2018; Jones 2015), in reality, a variety of learning theories have been adopted in the implementation of TEL (Valtonen et al. 2022). The two learning theories cognitivism and constructivism have been around for many years, with constructivism gaining momentum in the sciences firstly and then more broadly across higher education (Klinger 2010). Connectivism is the newer theory with its grounding in constructivism, and while some may argue it is not a learning theory at all (Steffens 2015), others disagree and have moved this knowledge and debate forward (Kop and Hill 2008; Strong and Hutchins 2009). These learning theories were found to play an important role in bridging the gap between theory and practice in education (Banihashem et al. 2019); therefore, how these learning theories are adapted and fitted for the virtual university is of important consideration due to the rapid developments in TEL driven by the COVID-19 pandemic. Despite the increasing popularity of TEL, thanks to developments in educational technologies, the concept of a virtual university (also referred to as virtual teaching/learning environments, virtual learning communities, and flexible learning environments) is still at an early stage (Abdollahi 2018). A virtual university may utilize an online learning system or platform like Moodle, which provides a virtual learning environment that facilitates online material sharing, meetings, interaction, and assessment (Batara and Rapat 2020). Many benefits of a virtual university have been reported including flexibility, cost efficiencies, student employability, various pedagogical possibilities, and increased competitiveness (Widera and Martin 2018). The COVID-19 pandemic has accelerated the development of the virtual university on a global scale, and while both positive and negative experiences were reported by teachers and students, it was argued that the virtual university should be part of a hybrid education model for the post-pandemic world, complementing rather than substituting offline education (Horváth et al. 2022). Griffith University is a salient example of a virtual university in Australia with its digital campus, which is fully online, being the second largest of its six campuses (Griffith University 2021b). The University itself is located in South East Queensland, Australia, has 5 physical campuses, 50,000 students, and almost 4000 staff, and offers over 200 degrees (Griffith University 2021a). The digital campus allows students to study in a flexible mode both on and off campus, putting Griffith at the forefront of the virtual university drive so that it investigates and implements the best pedagogies to use when teaching online. This is in line with efforts to integrate learning theories into TEL reported in various countries like Iran and Austria (Montpetit and Sabourin 2016). As Griffith University has such a well-defined digital campus, it has advanced techniques to enhance students’ learning, thus examples from Griffith University have been used. This chapter provides a detailed review of the recent literature for the three theories of cognitivism, constructivism, and connectivism, providing current definitions, and will report on recent developments from the literature and research. This chapter will also draw on a number of case studies from work done at Griffith University, as a means to illustrate the principles being proposed in the later part of

206

C. Campbell and T. L. N. Tran

this chapter. These include how websites such as how the Explore Learning and Teaching website can provide support for connectivist learning among academics. Constructivism learning theory examples include both course development from a constructivist viewpoint as well as flipped learning examples. With the increasing use of video and active learning in courses, TEL provides an excellent way of using constructivist approaches in higher education through online and virtual learning at a virtual university. By reviewing how cognitivism, constructivism, and connectivism were adopted at Griffith University as a virtual university, the current chapter aims to further understanding into TEL, an area that deserves further research being under much criticism regarding the lack of a sound foundation in learning theories (Bligh 2020).

Literature Review Learning theories, which were developed to explain how learning takes place in the human brain and body, fall under the broad umbrella of learning psychology and the adjacent disciplines of sociology, pedagogy, and biology (Illeris 2018). There are a number of learning theories, for example, the database built by Culatta and Kearsley (2022) listed 54 theories relevant to human learning and instructions, learning concepts, and key domains of learning, as well as key theorists pertaining to these theories. There are relationships and linkages observed among these theories that were mapped out by Boettcher and Conrad (2021). Learning theories have been well studied and applied in different disciplines, for example, Kearsley and Shneiderman (1998) developed engagement theory, a framework for technology-based for teaching and learning with the focus on meaningful learning, which aligns well with the constructivist approach. Following this direction on the use of learning theories in TEL at the virtual university, the current chapter focuses on only three learning theories that are widely adopted in TEL, namely cognitivism, constructivism, and connectivism. The following section reviews each of the three theories followed by a brief description of how they have been adopted in teaching and learning in higher education.

Cognitivism Cognitivism was a learning theory that emerged in the 1950s (Dakich 2014, p. 153). In the cognitive perspective, the learner was seen as an information processing system (Crook and Sutherland 2017) and learning was defined as a structured computational process of acquiring and storing information, focused on the internal aspects of learning (Bruner 2009). Cognitivists saw knowledge acquisition as a mental activity that entails internal coding and structuring by the learner and the learner is as a very active participant in the learning process (Ertmer and Newby 2013b). However, the role of teachers is considered significant in modeling

12

The 3C Merry-Go-Round: Constructivism, Cognitivism, Connectivism, Etc.

207

behaviors for learners to learn under the influence of personal characteristics and environmental factors (Miller et al. 2019). Although cognitivism predates the advent of computers in learning and teaching, the field of educational technology provided very fertile ground for cognitivist researchers and instructional designers, with computers soon being the key learning technology for cognitivist learning theorists (Harasim 2012). During the heydays of cognitivism the 1960s–1970s, when computing technologies were still in their early days with various limitations, these emerging technologies were considered important and provided a strong underpinning for the cognitive revolution (Garnham 2009). Thus, the theory had a significant influence on instructional design (which was also emerging as a discipline of its own), and cognitivists anticipated that artificial intelligence and expert systems would replace the teacher while distance and online learning environments might automatize the learning process (Dakich 2014). While it has previously been suggested that the arguments for cognitivism remain valid today, there have been a series of setbacks in the prevalence of the theory since the early 1980s (Garnham 2009). Thus, this theory is explored less than the other theories in this chapter, although it does strongly underpin what was to come next.

Constructivism Constructivism is considered a synthesis of multiple learning theories that were diffused together, which originally dated back from Socrates’ time and assimilates both behaviorism and cognitivism (Amineh and Asl 2015; Hammad et al. 2020). Constructivism, while recognizing the complexity of knowledge and real-life learning through sense-making (AlDahdouh et al. 2015), defines learning as the active construction of new knowledge based on learners’ previous experience (Alzaghoul 2012). Unlike more traditional theories which see learning as the transmission of knowledge from teachers to learners, constructivism argues that human knowledge is constructed rather than transmitted (Phillips 1995). Constructivists argue that the learner is at the center of the learning process and shifts the focus from the studentcontent interactions of cognitive-behaviorist models to the critical role of student– student interaction. Social constructivism, an important branch of constructivism (Amineh and Asl 2015), emphasizes the diversity of viewpoints, cultural experiences, and the potential for divergent opinion realized through intercultural interactions (Anderson and Dron 2012). Unlike cognitivism, which views learning as insights, constructivism sees learning as a process implemented through personal findings, experiences, and interpretations (Sarısakaloğlu et al. 2015). While constructivism emerged much later than cognitivism and kept evolving (Raskin 2008), it gained acceptance fairly quickly. By about 2010, constructivism had become the dominant educational theory that lay the foundation for the majority of teaching methods of the twenty-first century, particularly those making early inroads into online learning (Carwile 2007). These include methods such as problem-based learning, authentic learning, and computer-

208

C. Campbell and T. L. N. Tran

supported collaborative learning (Ertmer and Newby 2013a) along with other teaching methods such as active learning and online activities that might be well used by those in a virtual university.

Connectivism The twenty-first century has seen tremendous changes in technologies, learners, and teaching methods that have impacted the learning process and motivated the emergence of new learning theories (Ertmer and Newby 2013a). Connectivism is seen as a recent development of constructivism in response to the current scenario of the intense use of technology in education. Siemens (2004) argued that connectivism is the most suitable to education in the digital age as the next generation of learning theories and is built upon constructivism. Connectivism sees learning as a networked activity negotiated through interactions between individuals, within an organization or virtual environments (Mattar 2018). Connectivists argue that learning can occur outside personal interaction, for example, information stored and manipulated by technological tools. While knowledge can be learned then unlearned, it can be retrieved and activated again whenever needed, which emphasizes the central role of the learner in connecting and constructing knowledge in a context that includes not only external networks and groups but also their own histories and predilections (Anderson and Dron 2012). On the other hand, instructors are still believed to play an important role in online network learning (Goldie 2016). Thanks to its relative recent emergence, connectivism aligns well with the changing learning environment as a natural and logical response to significant technological developments that affect learning (Corbett and Spinello 2020). Connectivism is also seen as a useful and powerful theory to help understand how learning can be best accommodated in a world of growing information complexity (Strong and Hutchins 2009). Fifteen years since its emergence, a literature review into recent works in connectivism indicated that while some critics may remain, on the whole, the theory seemed to be broadly and usefully applied in various disciplines (Downes 2019). The author pointed to previous research evidence on “the broad awareness and acceptance of the role of networks in learning, and significantly, the positive impact of network principles such as autonomy and interactivity” (Downes 2019, p. 124). Connectivism is therefore a good match for TEL in a virtual university thanks to its focus on learners’ development within a wider interconnected community (Downes 2022). It is worth noting that there are similarities and overlapping features among the cognitivist, constructivist, and connectivist viewpoints (Hammad et al. 2020; Ng 2015; Sankey 2020). For example, connectivism agrees with cognitivism that learning must be active, authentic, and connected to reality, and align with constructivism on the construction of knowledge through life engagement, participation, social, or cultural interaction (Duke et al. 2013). While there has been criticism towards connectivism as a learning theory when it first emerged in the early 2000s, there has been greater understanding and even growing acceptance of its merits and

12

The 3C Merry-Go-Round: Constructivism, Cognitivism, Connectivism, Etc.

209

influence in TEL in the last decade (Corbett and Spinello 2020; Crook and Sutherland 2017).

Learning Theories and TEL Learning theories were found to play multiple roles in TEL, including underpinning and guiding practices (Banihashem et al. 2019), which explained why they formed the basis for pedagogic strategies as argued by Sankey (2020). For example, Boettcher and Conrad (2021) argued that the 10 core principles and 14 best practices of online learning were grounded in various learning theories. This aligns well with the observation that the design of learning and teaching strategies draws a lot from learning theories, while educational technology served as the driving force to develop new teaching strategies informed by learning theories (Sankey 2020). With learning theories informing teaching practices, the adoption of TEL in the virtual university was argued to facilitate effective and enjoyable teaching and learning experiences (Boettcher and Conrad 2021).

Cognitivism and TEL In reality, the cognitively oriented use of technologies for learning in the 1950s was found to be of little usefulness due to the adoption of inappropriate tools without consideration of learner individual differences (Cooper 1993). Then the 1980s onwards saw a tendency toward acceptance of cognitivism in teaching and learning using technologies such as intelligent tutoring, hypertext, hypermedia, and expert systems (Cooper 1993; Crook and Sutherland 2017; Jonassen 1991). For example, the adoption of cognitivism in simulation studies that focused on the cognitive actions of learners for mistake identification and feedback provision led to the design of “cognitive tutors” (Koedinger and Corbett 2006). These intelligent tutoring systems were able to “adapt” to student activity to provide correction for mistakes based on modeling the student’s apparent understanding of the knowledge under instruction. While the main pedagogical approach emerging from cognitivism is the active pursuit of understanding, many early attempts to adopt technology in education mainly aimed to simply design better ways of information presentation (Mayes 2019), which may not be the most effective way to enhance learning. Cognitivism has also been found to be particularly influential among designers of multimedia learning materials (Mayer 2001). As cognitivists saw technology as a tool for creating instructional materials and learning environments that allow learners to construct their own cognitive representations, the theory has contributed significantly to the development of constructivist learning environments integrating new digital technologies (Gillani 2003). Some examples of cognitivist TEL included the development of intelligent tutoring systems used for adaptive learning and artificial intelligence (Bates 2019; Harasim 2012). However, while cognitivists held high hopes that instructional technologies will eventually teach most of the

210

C. Campbell and T. L. N. Tran

curriculum, including less-well-defined skills such as essay writing, there was still limited progress toward this ambitious goal after three decades of work (Dede 2008). While used less in today’s classroom, we do give one example that works well in the virtual university that is about using Echo360 Active Learning Platform (ALP).

Constructivism and TEL Constructivism was welcomed by many TEL practitioners for its potential to reposition technology from being the learner’s “tutor” to becoming the learner’s “tutee” that facilitate their learning through explorations using digital tools like online platforms (Crook and Sutherland 2017, p. 15). These technologies help successfully scaffold active, manipulative, intentional, complex, authentic, collaborative, and reflective learning experiences (Jonassen 1999). Constructivist instructional technologies were found to cover a broad range of sophisticated knowledge and skills, and there have been extensive research results providing evidence for the effectiveness of this pedagogical approach, for example, in solving complex problems and creating higher engagement as well as solid learning outcomes (Dede 2008). However, it is also worth noting that a learning theory may be translated differently into practice in different learning environments (Beetham and Sharpe 2019); hence, further studies onto the adoption of constructivism in a virtual university are needed.

Connectivism and TEL Thanks to the paradigm shift from cognitivism and constructivism to connectivism as theories advance (Duke et al. 2013), the instruction focus shifts from teaching to learning, from the passive transfer of knowledge to the active application of ideas to problem-solving (Ertmer and Newby 2013b). Foroughi (2015) presented a range of studies that adopted connectivism for TEL, with technologies like eLearning software or virtual reality for distance learning or massive open online courses (MOOCs). While the benefits of connectivist TEL have been acknowledged as a student-centered approach that foster learner autonomy, diversity, and openness, there are still challenges such as student reluctance, lack of structure and instructor guidance, which cast doubt on whether the potential educational benefits of TEL and connectivism will be realized (Bates 2019; Foroughi 2015). It has also previously been suggested that not all learners are part of a network (Glassner and Back 2020); hence, the adoption of connectivism in teaching and learning remains arguably contestable at times; therefore, further research into its application at a virtual university where people interact in a digital learning environment is essential. While learning theories have been well studied, their adoption in TEL remains under-researched while the current literature mainly focuses on the pedagogical implications of small-scaled theory testing in particular contexts (Mayes 2019). Scardamalia and Bereiter (2014), for example, criticized these major learning theories for not adequately engaging with how they inform TEL. Crook and Sutherland

12

The 3C Merry-Go-Round: Constructivism, Cognitivism, Connectivism, Etc.

211

(2017) pointed out that there was still little literature on the theoretical perspectives on TEL and how they result in innovative practices adopting digital technologies. On the other hand, it was found that TEL is not always enthusiastically embraced by schools and universities (Selwyn 2010). Despite the claim by connectivism proponents that it is the learning theory of the digital age (Downes 2022), such theory may become irrelevant to certain cohorts like nonautonomous learners who do not know how to self-direct their learning (Glassner and Back 2020). These gaps in both the TEL literature and practices highlight the need for innovative TEL implementation that are both theoretically and pedagogically sound (Corbett and Spinello 2020). Like the horses in a merry-go-round, the abovementioned theories are built upon each other, and despite the ups and downs they all undergo, they have formed a sound theoretical foundation for teaching and learning practices with technologies. It is noteworthy, however, that there is unlikely to be a single theory that will explain TEL, and while the latest theory of connectivism provides useful pedagogical principles, further development and testing is needed (Downes 2022; Goldie 2016). While the virtual university, like any other university, is considered an interconnected system, there are both challenges and opportunities identified for TEL, leaving core questions regarding the impact of technology innovations being unanswered (Siemens et al. 2020), for example, on TEL implementation in a virtual university context. The current chapter, therefore, focuses on the following research questions: 1. What types of digital technologies support learning and teaching practices in the virtual university? 2. What are the learning theories that underlie these digital technologies and their implementation at higher education institutions?

Examples That Benefit the Virtual University Below are a number of examples to illustrate how the students can be supported with their learning in the virtual university. Examples include engaging students using cognitivism theory with the Echo360 Active Learning Platform (ALP), two connectivism examples and two different constructivism examples. The two connectivism examples include using Microsoft Teams to enhance learning and accessing the Explore Learning and Teaching website which includes exemplars on using technologies to enhance teaching practices. One constructivism example includes embedding constructivism across a Graduate Certificate course while the other example includes ePortfolio use with the tool PebblePad. The examples generally have been embedded around the research that has been conducted in this space at the university.

Echo360 Data: Engaging with Cognitivism While cognitivism focuses more on student-content interaction, materials, and learning design, one tool that is designed for these types of activities is Echo360 ALP (2021). This is a platform that offers lecture capture, flipped classrooms, and

212

C. Campbell and T. L. N. Tran

campus video library that can be integrated into learning management systems by higher education institutes (Echo360 2021). This interactive platform allows staff to actively engage students in their courses, both in face-to-face mode as well as online (Duffy et al. 2017). One way of using Echo360 ALP is to ensure the interactive slides in Echo360 promote student-content and student-teacher interaction in large lectures. After gaining ethics approval, a survey of staff who use Echo360 ALP in their teaching was conducted in 2019. There were 53 survey completions with 81.13% (n ¼ 43) checking that it is an easy platform to use. Those who had barriers generally had not used it prior, which may explain the result. Echo360 ALP also provides learning analytics that can be used to inform design improvements and interventions. The cognitive processes and biases which play a fundamental role in the use of these data to make decisions about learning and teaching strategies were studied at Griffith University, revealing the importance of cognitive factors when designing the tools, professional learning, and institutional strategies as part of the implementation of learning analytics (Alhadad 2016). The study highlights the necessity of aligning the intended purpose and design features of learning analytics with consideration given to the cognitive processes in decisionmaking regarding learning and teaching, which confirms the critical role of cognitivism as an underlying learning theory behind successful TEL practices in a virtual university. From the staff survey one person suggested, “it is student’ centred and it provides lecture capture that students can download recording after class.” Also, another commented that “students have some flexibility around when to interact,” suggesting good flexibility in engaging with Echo360 ALP. One of the main advantages is student engagement and the ability to gauge the level of student understanding during class rather than after an assessment item has been submitted. While its use was previously encouraged at the university, it is used less today, and as staff are faced with many new technologies, it is one that while continuing to be used by some staff may not be used by all in the future. It may be that some staff may not see the value of using this theory to underpin student learning in their classes, although it may also be more about skill development and time constraints rather than this. Echo360 ALP offers the virtual university student-centered online synchronous activities. The flexibility in using Echo360 ALP offers affordances through its use to engage students in class online.

A Graduate Certificate Course: Embedding Constructivism One example of a course that has embedded constructivism is via programs such as the Graduate Certificate in Higher Education and the Graduate Certificate in University Learning and Teaching. The course employs VoiceThread, a shared video media platform that aligns with constructivism as reported previously (Millard 2010; Sun et al. 2013). While embedding constructivism was the fundamental theory underpinning the entire graduate certificate, one example is a fully online course 7032LFC Curriculum and Assessment Design for Learning, taught in Trimester

12

The 3C Merry-Go-Round: Constructivism, Cognitivism, Connectivism, Etc.

213

2 each year. This course has many features that allow for enhanced constructivism which are laid out here. Evaluation of the course was also undertaken as part of the normal course evaluation process, with a survey conducted online at the end of each course. Participation in the evaluation was small (n ¼ 9 in 2018; n ¼ 7 in 2019 and n ¼ 4 in 2020), in part due to small course numbers of approximately 35 each year; however, data collected provides examples of constructivism at work in the virtual university. Survey responses were analyzed in a qualitative manner and analyzed for themes. One focus of these courses was to present material in chunks, while another was to allow for reflection, which was utilized in various ways including through VoiceThread and also through PebblePad, two online technologies that facilitate student learning. Regular course announcements were used to chunk information, with participants reporting that they find the regular email announcements useful. Through the use of VoiceThread, the students were able to engage with each other in the curriculum and assessment design course. VoiceThread promotes student engagement, allows student to interact with content from others, and allows them to demonstrate their understanding of the content (McKeeman and Oviedo 2020). From the nine students who completed the survey for 7026LFC Leading Learning in Technology Enhanced Learning Environments, six said they used VoiceThread in the trimester with five completing an “Introduction” in VoiceThread or commented on another. All six checked that they had no difficulties in commenting in VoiceThread with one stating “the system was simple and easy to use” while another stated there were “straight forward instructions and low risk” comments. They were asked how they could use VoiceThread in their future teaching with responses including “creating a community of practice,” “it’s good for online teaching, [and] also to introduce the course before students start the course to give some introduction about the course,” with one stating they had already “used it to record a video with slideshow.” These potential activities suggest that they will embed constructivism into their courses. Students felt that the activities and resources explored during the courses assisted them to learn about the content, which was about curriculum and assessment design. One stated, “they were aligned and scaffolded,” while another stated they were: theoretically explained the ideas (backed by literature) via online means. The collaborative [online synchronous classes] sessions helped to explain more detail about both the knowledge and the assessment. (as well as short quizzes etc). PebblePad reflections really helped to consolidate knowledge.

As can be seen from both the VoiceThread and PebblePad reflections, knowledge is both constructed and consolidated, thus demonstrating constructivism in action in the TEL university. The VoiceThread example, while creating a community of practice, also allowed students to interact with content from the lecturer and with content from each other in a system that was online and easy to use. The PebblePad reflections allowed for both the construction and consolidation of learning through the reflection activities that were both formally assessed and formative assessment.

214

C. Campbell and T. L. N. Tran

Embedding constructivist learning into a virtual university allows for students to learn in various well-defined ways which have been explained in the example above. Ultimately, this allows for an improved student experience. The examples demonstrate that using activities such as above allows for this and a community of practice, which in turn increases a sense of belonging, all while studying virtually.

PebblePad ePortfolio: Constructivism Through Learning PebblePad is more than just a portfolio system. It is a learning journey platform that facilitates student-centered learning, along with providing targeted scaffolding (PebblePad 2021) that can be predetermined by the teaching staff to provide and allow for using PebblePad in a variety of meaningful ways. Assessment using the PebblePad platform can also be conducted. In June 2018, a student survey was conducted at the university to evaluate the implementation of PebblePad thus far. The survey data was analyzed and has been presented in several outputs. From the 12,634 students who were using PebblePad, 747 participated in the survey, which included some students who did not answer every question. While the students used PebblePad in many ways, some of these ways were based on constructivism, while others were not. For example, submitting an assessment item (n ¼ 442) would not have been based on constructivism, while reflecting on learning (n ¼ 265) may have been, as would completing a learning journal through a program of study (n ¼ 144) and perhaps some of the template completion (workbooks) (n ¼ 265) may also have been (Campbell and Duffy 2019). Students were asked what worked well when using PebblePad to support their studies. Responses were analyzed using coding for themes of the qualitative answers. While many answers were functional about how the tool supported learning, some responses show that the tool promoted constructivism. Examples include one quote “Reflecting on learning and working processes over the last week/two weeks and writing them down made me better appreciate what I had done in that time,” while another stated, “The reflective evaluation allowed me to understand what I was doing in the lab.” Interestingly, another stated, “I liked having a place to put my thoughts down to help self-reflect on the course content, without having to worry about anyone seeing my writing or my thinking process.” These examples show that reflection was used well by some students. This may also have allowed for construction of knowledge and feedback if work was submitted to teaching staff. Thus, through reflection, there is scaffolding of knowledge, both through discussion and via teaching staff feedback on the students’ reflection when reflecting over time. Some students used it to record experiments with one student reflecting, “PebblePad is good for recording experiments I had done, especially when given a question that could help with understanding the topic in further detail.” Another example of using PebblePad includes “Setting out goals and reflecting throughout the trimester worked well, as well as doing small but frequent writing tasks with a due date regarding my course helped me get a clearer idea of my personal goals.”

12

The 3C Merry-Go-Round: Constructivism, Cognitivism, Connectivism, Etc.

215

As can be seen from this constructivism example, platforms such as PebblePad assist students to actively construct their own knowledge, particularly when using techniques such as reflection, particularly when the platform allows students to see this change over time. Overall, the platform fits well into the TEL university of the future, for its ease of use, and that it is a larger platform and not just an ePortfolio system. PebblePad really does allow for the solution of complex problems and for achieving of the learning outcomes that are set. Using PebblePad in a virtual university across programs provides many benefits. Aside from the benefits described, PebblePad can be an entire solutions platform that enhances the student experience through allowing artifacts to be obtained and build on over a prior of time, such as throughout a 4-year undergraduate degree.

Using Microsoft Teams: Connectivism Example One example of connectivism is the use of Microsoft Teams. While Teams is on the Microsoft O365 platform and is multifaceted, some uses of Teams are an excellent example of connectivism as connections are able to be made to assist student learning. This can occur through the Teams channels and also via chat. Tagging is an important feature in Teams and the use of this supports the connections being made, particularly when just in time. Focus group interviews were conducted in July/August 2020 after gaining ethics approval for the project (GU Ref: 2020/356), staff were both surveyed with 55 responses and interviewed, with several focus groups conducted, with those being not reported here. These were transcribed and analyzed with some conclusions drawn. When implementing a new technology, the responses scored in surveys can be quite low, which means that a score of 74.55% (n ¼ 41) stating that Teams met their expectations in terms of providing a learning and teaching solution was considered a successful number. Those who did agree with this made some valuable comments on using Teams, with one stating that it, “provided a more dynamic platform than Bboard [BlackBoard LMS], two-way student communication, platform for group classes etc.” Another importantly stated it, “allowed easy connection with students.” While another suggested, “it streamlined communication, primarily, and made small group and one on one consultations and feedback sessions with students easier.” Another comment included: Teams provided a space for students to have discussions regarding course content and assessment items. Students more readily engaged with the Teams space than Blackboard Discussion Boards (both were available). I was able to introduce activities on Teams that students responded to and engaged with. Students also spontaneously established peer study groups using meetings within Teams.

While yet another staff member mentioned that it was a “student driven context.” Finally, another commented that it “provided a network for more frequently

216

C. Campbell and T. L. N. Tran

engagement (e.g., faster and consistent engagement with learning content in comparison to L@G and email).” With Teams being part of a bigger suite of tools offered by Microsoft called O365, it was worth asking staff about their experiences with the other tools. This is because although some tools might not assist with connectivism, others perhaps do. Thus, staff were asked if they had heard of a technology tool relating to O365 and if they had used it in a course. Importantly, from the survey results, 82.76% of respondents had used Teams in a course, while 75% had used Teams chat, and 74.14% had used Teams meetings. While using Teams, some staff also report that they used “Word as shared documents” (64.29%), OneDrive (55%), and Stream video (54.05%) to support their learning and teaching practices. Some staff felt that Teams is a good teaching solution with 83.02% of respondents indicating this, while one stated, “I needed something university-supported, and enabled us to implement, online, the interactive groupwork that is essential to breaking down fears/barriers/ laziness with learning how to do stats.” This suggests that connectivism occurs through the use of Teams. One staff member commented, “I get students to post questions to the Teams site and I answer them there. It means I don’t have to answer the same question by email many times,” which suggests that students are able to learn from each other’s posts. Another commented on the fact that it allows the course to “promote [a] collaborative approach to teaching and learning.” Teams has great value for the virtual university as it really allows staff and students to connect, with each other and across the university. This means that learning and teaching is enhanced through the creation of communities that can focus on various aspects of the course or program from first-year undergraduate students to prograduate research students. The example above shows evidence for providing well-supported learning and teaching practices using Teams with these translating to a virtual university well.

Explore Learning and Teaching Website: Connectivism for Staff Professional Learning At Griffith University, a customized website has been created called Explore Learning and Teaching (https://app.secure.griffith.edu.au/exlnt/). Figure 1 shows an example of the landing page, with staff able to access this site on a just in time basis to make the connections they need as they need them to support their learning and teaching. Fully searchable, by item type and by content this site allows staff to look at how teaching tools, specifically using educational technology are able to support learning and teaching. Faculty Sparks, one of the types of entries in the Explore Learning and Teaching website, involve a staff member describing the learning and teaching problem they were trying to overcome and stating what the challenge actually was. Using an evidence-informed approach, the website then described the solution taken as well as the outcomes and what technology was used to enable this approach. A video of the academic describing this is included as well as any supporting resources are linked

12

The 3C Merry-Go-Round: Constructivism, Cognitivism, Connectivism, Etc.

217

Fig. 1 Explore Learning and Teaching searchable landing page that is showing the most votes section (Griffith University 2022)

and along with any additional media (see Fig. 2). Staff are able to go to the website and search for solutions to their own learning and teaching problems and thus connect with potential solutions in a just in time way. This allows for them to make connections as they are needed.

218 Fig. 2 Partial example of a Griffith University Learning and Teaching Faculty Spark (Korf and Campbell 2020)

C. Campbell and T. L. N. Tran

12

The 3C Merry-Go-Round: Constructivism, Cognitivism, Connectivism, Etc.

219

The use of Explore Learning and Teaching website and Faculty Sparks showcases a salient example of connectivism in a higher education context in which faculty members become learners who know where to access support, retrieve resources, and activate learning through connections whenever needed. Their justin-time learning experience highlights the central role of the learner in connectivism as argued by Anderson and Dron (2012). While many educators struggled with the adoption of digital technologies (Harasim 2017), such initiatives like the Explore Learning and Teaching website helps develop effective theory-based and researchbased pedagogies in online teaching. These staff active engagement with the website suggests that connecting and constructing knowledge may occur not only via the Internet but also through the support networks and connection with colleagues at the university. This is beneficial to a virtual university as staff are able to connect to various solutions to improve their teaching in a just-in-time way. A website such as this in the virtual university would be able to include many solutions for staff to connect with to improve their teaching in a virtual university.

Discussion While understanding and knowledge about TEL matures, the appreciation of the importance of theory deepens, and it becomes clear that theory and practice must be aligned within a coherent and workable model of education for the virtual university in the twenty-first century (Mayes and de Freitas 2013). The above examples have shown how learning theories can be embedded in TEL and drive successful teaching and learning practices in a virtual university. This is consistent with the argument by Jung (2019) that cognitivism, constructivism, and connectivism have been well applied in various contexts in the virtual learning environment, each with their own strengths and limitations. While cognitivism is often considered a somewhat outdated theory (Rybakova and Whitt 2017), it can still inform recent technological platforms like Echo360 ALP which can result in increased student engagement and improved learning outcomes for large courses (Montpetit and Sabourin 2016). The authors from one study concluded that Echo360 ALP offered numerous ways to engage student intellectual and affective metacognition, which aligns well with the positive research results presented on the blending of learning theories and technologies for efficient teaching and learning in the virtual university. These confirm the argument by Harasim (2017) that learning theories are a dynamic and fluid part of knowledge that evolve with the new technologies that emerge and transform intellectual, social, and economic horizons. While research shows the key success factors with using digital technologies such as Echo360 ALP (Duffy et al. 2017), these key success factors include the academics themselves and how they use and engage students with the tools, support of professional staff with both technical and pedagogical assistance and vendor support to continue to improve the technology. This highlights the crucial

220

C. Campbell and T. L. N. Tran

role of instructors in TEL as insisted by various researchers (Hung 2001; Miller et al. 2019; Ng 2015) and aligns well with the argument by Hammad et al. (2020) that learning theories only explain how different learners learn without telling them how to learn, resulting in learners reluctance and struggle due to the lack of learner support (Bates 2019; Foroughi 2015). This example allows aligns well with the virtual university as key success factors will allow academics to succeed. The examples presented in the current chapter also align well with the argument by Havard et al. (2016) that education needs to build upon and integrate influential learning theories to reform TEL in the digital age, which is characterized by connectivity, collaboration, accessibility, and rapidly emerging technologies. The successful implementation of provided examples suggests that the virtual university may adopt various learning theories and educational technologies to suit their own instructional context and learning objectives, which is consistent with the viewpoint by Hung (2001) that learning theories may not necessarily be discordant regarding the adoption of TEL. It is therefore argued that there is no need to have a unified theory of TEL, as existing learning theories can be combined, modified, and/or directly applied by the virtual university (Janelli 2018). The examples presented in this chapter confirm that well-established learning theories can be extended to meet the new challenges posed by digital environment at higher education level (Malkawi and Khayrullina 2021), thus proving their importance for the virtual university of the future. The above analysis suggests that TEL may be well supported with learning theories that inform the design and implementation of digital technologies in learning at a virtual university. While in agreement with the arguments by Hammad et al. (2020) and Ng (2015) on the importance of context and the view of different pedagogical approaches as complementing rather than compete with each other, the authors argue that successful TEL implementations can be both technologically and theoretically driven, thanks to the evolution of both learning theories and digital technologies. This is reflected through the development of learning theories to reflect advances in educational technologies (Şahin 2012; Whyte 2011). Technology has been found to be an influencing factor to the development of learning theories (Masethe et al. 2017; Mechlova and Malcik 2012; Mounia et al. 2012). While previous studies may have criticized that TEL is mainly technological driven and under-theorized (Hamat and Embi 2010; Hammad et al. 2020; Johnson 1992), these examples suggest that it may not always be the case moving forward, thanks to the evolution of both established and emerging learning theories. This aligns well with the call for further research to address identified criticisms like the connectivist underconceptualization and interaction oversimplification with a focus on the key factors of learner autonomy, diversity, openness, and interactivity (Jung 2019).

Conclusion This chapter both introduces and describes how three major learning theories – cognitivism, connectivism, and constructivism – all contribute to TEL and the virtual university of the future. These learning theories have underpinned the application of

12

The 3C Merry-Go-Round: Constructivism, Cognitivism, Connectivism, Etc.

221

TEL at Griffith University as a virtual university through the use of Echo360, VoiceThread, PebblePad ePortfolio, Microsoft Teams, and the Griffith Explore Learning and Teaching website, and demonstrate how they can be used in a virtual university of the future. An important implication from all examples analyzed is that TEL may be implemented at different levels from a part or whole course delivered in a digital campus, for example, 7032LFC Curriculum and Assessment Design for Learning, to a university-wide scale in the case of the Explore Learning and Teaching website that applies for both face to face and online teaching and learning. Another implication is that learning theories can be combined, modified, and/or directly applied by the virtual university to inform the successful implementation of TEL through the use of various technological tools in a specific course or program like the Graduate Certificate. Concrete examples have been given using evidence-informed research from a current university that has well developed practices in teaching online. The examples given allow one to envision how TEL may be used in the virtual university of the future. It demonstrates how various platforms can be used to assist student learning via active learning processes to assist with engagement and learning. These concrete examples provide ways students can engage in the virtual university of the future.

Cross-References ▶ Academic Engagement in Pedagogic Transformation ▶ Innovation and the Role of Emerging Technologies ▶ Making Online Assessment Active and Authentic ▶ The Role and Application of Learning Theories in the Virtual University

References Abdollahi, D. 2018. A study of pedagogical aspects of a virtual university. International Journal of Educational and Psychological Researches 4 (1): 12–19. https://doi.org/10.4103/2395-2296. 22914. AlDahdouh, A., A. Osorio, and S. Caires. 2015. Understanding knowledge network, learning and connectivism. International Journal of Instructional Technology and Distance Learning. 12 (10): 3–21. Alhadad S.S.J. 2016. Attentional and cognitive processing of analytics visualisations: Can design features affect interpretations and decisions about learning and teaching? In Show me the learning. Proceedings ASCILITE 2016, ed. S. Barker, S. Dawson, A. Pardo, and C. Colvin, 20–32. ASCILITE. https://2016conference.ascilite.org/wp-content/uploads/ascilite2016_ alhadad_full.pdf Alzaghoul, A.F. 2012. The implication of the learning theories on implementing e-learning courses. The Research Bulletin of Jordan 11: 27–30. Amineh, R.J., and H.D. Asl. 2015. Review of constructivism and social constructivism. Journal of Social Sciences, Literature and Languages 1 (1): 9–16. Anderson, T., and J. Dron. 2012. Learning technology through three generations of technology enhanced distance education pedagogy. European Journal of Open, Distance and e-Learning 2: 1–14.

222

C. Campbell and T. L. N. Tran

Banihashem, S.K., K. Aliabadi, A.S. Pourroostaei, N. AhmadAbadi, M. Reza, and A. Delavar. 2019. Investigation on the role of learning theory in learning analytics. Interdisciplinary Journal of Virtual Learning in Medical Sciences 10 (4): 14–27. https://doi.org/10.30476/IJVLMS.2019. 84294.1001. Batara, C., and C.I. Rapat. 2020. Design and implementation of virtual university based on ICT. International Journal of Research 8 (4): 144–152. https://doi.org/10.29121/granthaalayah.v8. i4.2020.19. Bates, A. 2019. Teaching in a digital age. 2nd ed. Tony Bates Associates Ltd. https://pressbooks. bccampus.ca/teachinginadigitalagev2/ Beetham, H., and R. Sharpe. 2019. Theory into practice: Approaches to understanding how people learn and implications for design. In Rethinking pedagogy for a digital age, ed. H. Beetham and R. Sharpe, 243–250. New York: Routledge. Bligh, B. 2020. Theory disputes and the development of the technology enhanced learning research field. Studies in Technology Enhanced Learning 1 (1): 115–169. Boettcher, J.V., and R.-M. Conrad. 2021. Teaching online: The big picture. In The online teaching survival guide: Simple and practical pedagogical tips, 3rd ed. San Francisco: Wiley. Bruner, J.S. 2009. The process of education. Cambridge: Harvard University Press. Campbell, C., and M. Duffy. 2019. Engaging students in a wide-scale educational technology implementation: Investigating student attitudes. In ASCILITE 2019 conference proceedings: Personalised learning. Diverse goals. One heart, ed. Y.W. Chew, K.M. Chan, and A. Alphonso, 375–379. Australasian Society for Computers in Learning in Tertiary Education (ASCILITE). http://2019conference.ascilite.org/assets/proceedings/ASCILITE-2019-Proceedings-Final.pdf Capacho, J. 2018. Validation of learning theories in their relationship with information and communications technology. Turkish Online Journal of Distance Education 19 (2): 166–188. Carwile, J. 2007. A constructivist approach to online teaching and learning. Inquiry, 12 (1), 68–73. Cooper, P.A. 1993. Paradigm shifts in designed instruction: From behaviorism to cognitivism to constructivism. Educational Technology 33 (5): 12–19. Corbett, F., and E. Spinello. 2020. Connectivism and leadership: Harnessing a learning theory for the digital age to redefine leadership in the twenty-first century. Heliyon 6 (1): 1–9. https://doi. org/10.1016/j.heliyon.2020.e03250. Crook, C., and R. Sutherland. 2017. Technology and theories of learning. In Technology enhanced learning, ed. E. Duval, M. Sharples, and R. Sutherland, 11–27. Springer. https://doi.org/10. 1007/978-3-319-02600-8_2. Culatta, R., and G. Kearsley. 2022. Learning theories. https://www.instructionaldesign.org/theories/ Dakich, E. 2014. Theoretical and epistemological foundations of integrating digital technologies in education in the second half of the 20th century. In Reflections on the history of computers in education, ed. A. Tatnall and B. Davey, 150–163. Berlin, Heidelberg: Springer. Dede, C. 2008. Theoretical perspectives influencing the use of information technology in teaching and learning. In International handbook of information technology in primary and secondary education, ed. J. Voogt and G. Knezek, 43–62. New York: Springer. Downes, S. 2019. Recent work in connectivism. European Journal of Open, Distance and e-learning 22 (2): 113–132. ———. 2022. Connectivism. Asian Journal of Distance Education 17 (1): 58–87. http://www. asianjde.com/ojs/index.php/AsianJDE/article/view/623 Duffy, M., James, J., Campbell, C., & Williams, J.C. 2017. Key success factors to implementing an active learning platform. In Proceedings ASCILITE 2017: 34th international conference on innovation, practice and research in the use of educational technologies in tertiary education, ed. H. Partridge, K. Davis, and J. Thomas (Eds.), 63–67. University of Southern Queensland. https://2017conference.ascilite.org/wp-content/uploads/2017/11/ASCILITE-2017Proceeding.pdf Duke, B., G. Harper, and M. Johnston. 2013. Connectivism as a digital age learning theory. In The international HETL review, ed. K. Petrova, L. Stefani, S. Tegginmath, and B. Todhunter, 4–13. New York: The International HETL Association. Echo360. 2021. Echo360 for higher education. https://echo360.com/higher-ed/

12

The 3C Merry-Go-Round: Constructivism, Cognitivism, Connectivism, Etc.

223

Ertmer, P.A., and T.J. Newby. 2013a. Behaviorism, cognitivism, and constructivism: Connecting “yesterday’s” theories to today’s contexts. Performance Improvement Quarterly 26 (2): 43–71. ———. 2013b. Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly 26 (2): 43–71. https:// doi.org/10.1111/j.19378327.1993.tb00605.x. Foroughi, A. 2015. The theory of connectivism: Can it explain and guide learning in the digital age? Journal of Higher Education Theory and Practice 15 (5): 11–26. Garnham, A. 2009. Cognitivism. In The Routledge companion to philosophy of psychology, ed. S. Robins, J. Symons, and P. Calvo, 121–132. Abingdon: Routledge. Gillani, B.B. 2003. Learning theories and the design of e-learning environments. Lanham: University Press of America. Glassner, A., and S. Back. 2020. Connectivism: Networks, knowledge, and learning. In Exploring heutagogy in higher education, 39–47. Springer. https://doi.org/10.1007/978-981-15-4144-5_3. Goldie, J.G.S. 2016. Connectivism: A knowledge learning theory for the digital age? Medical Teacher 38 (10): 1064–1069. https://doi.org/10.3109/0142159X.2016.1173661. Griffith University. 2021a. About Griffith. https://www.griffith.edu.au/about-griffith ———. 2021b. Digital campus. https://www.griffith.edu.au/about-griffith/campuses-facilities/ digital?city¼online ———. 2022. ExLNT: Explore learning and teaching. https://app.secure.griffith.edu.au/exlnt/ #o¼most-votes Hamat, A., and M.A. Embi. 2010. Constructivism in the design of online learning tools. European Journal of Educational Studies 2 (3): 237–246. Hammad, R., Z. Khan, F. Safieddine, and A. Ahmed. 2020. A review of learning theories and models underpinning technology-enhanced learning artefacts. World Journal of Science, Technology and Sustainable Development 17 (4): 341–354. https://doi.org/10.1108/WJSTSD-062020-0062. Harasim, L. 2012. Cognitivist learning theory. In Learning theory and online technologies, 54–66. New York: Routledge. Harasim, L. 2017. Introduction to learning theory and technology. In Learning theory and online technologies, 1–14. Routledge. http://ebookcentral.proquest.com/lib/uql/detail.action? docID¼958148 Havard, B., M.L. East, L. Prayaga, and A. Whiteside. 2016. Adaptable learning theory framework for technology-enhanced learning. In Handbook of research on applied learning theory and design in modern education, ed. E. Railean, G. Walker, A. Elci, and L. Jackson, 632–654. IGI Global. https://doi.org/10.4018/978-1-4666-9624-2.ch018. Horváth, D., K. Ásványi, A. Cosovan, T. Csordás, J. Faludi, D. Galla, Z. Komár, É. Markos-Kujbus, and A.E. Simay. 2022. Online only: Future outlooks of post-pandemic education based on student experiences of the virtual university. Society and Economy 44 (1): 2–21. https://doi.org/ 10.1556/204.2021.00026. Hung, D. 2001. Theories of learning and computer-mediated instructional technologies. Educational Media International 38 (4): 281–287. https://doi.org/10.1080/09523980110105114. Illeris, K. 2018. An overview of the history of learning theory. European Journal of Education 53 (1): 86–101. https://doi.org/10.1111/ejed.12265. Janelli, M. 2018. E-learning in theory, practice, and research. Educational Studies Moscow 4: 81–98. https://doi.org/10.17323/1814-9545-2018-4-81-98. Johnson, J.K. 1992. Advancing by degrees: Trends in Master’s and doctoral programs in educational communications and technology. TechTrends 37 (2): 13–16. Jonassen, D.H. 1991. Objectivism versus constructivism: Do we need a new philosophical paradigm? Educational Technology Research and Development 39 (3): 5–14. ———. 1999. Constructivist learning environments on the web: Engaging students in meaningful learning. Singapore: The Educational Technology Conference and Exhibition. Jones, C. 2015. Theories of learning in a digital age. In Networked learning. An educational paradigm for the age of digital networks, 47–78. Springer. https://doi.org/10.1007/978-3-31901934-5_3.

224

C. Campbell and T. L. N. Tran

Jung, I. 2019. Connectivism and networked learning. In Open and distance education theory revisited, 47–55. Singapore: Springer. Kearsley, G., and B. Shneiderman. 1998. Engagement theory: A framework for technology-based teaching and learning. Educational Technology 38 (5): 20–23. http://www.jstor.org/stable/44428478 Klinger, C.M. 2010. Behaviourism, cognitivism, constructivism, or connectivism? Tackling mathematics anxiety with ‘isms’ for a digital age. In Proceedings of the 16th international conference of adults learning mathematics – A research forum, ed. G. Griffiths and D. Kaye, 154–161. London: Adults Learning Mathematics. Koedinger, K.R., and A. Corbett. 2006. Cognitive tutors: Technology bringing learning sciences to the classroom. In The Cambridge handbook of the learning sciences, ed. K. Sawyer, 61–78. Cambridge: Cambridge University Press. Kop, R., and A. Hill. 2008. Connectivism: Learning theory of the future or vestige of the past? The International Review of Research in Open and Distance Learning 9 (3): 1–13. https://doi.org/ 10.19173/irrodl.v9i3.523. Korf, A., and C. Campbell. 2020. Flight procedures training laboratory for Griffith Aviation. Retrieved from https://app.secure.griffith.edu.au/exlnt/entry/7128/view Malkawi, E., and M. Khayrullina. 2021. Digital learning environment in higher education: New global issues. SHS Web of Conferences 92: 1–8. https://doi.org/10.1051/shsconf/20219205019. Masethe, M.A., H.D. Masethe, and S.A. Odunaike. 2017. Scoping review of learning theories in the 21st century. In Proceedings of the World Congress on engineering and computer science, ed. S.I. Ao, C. Douglas, and W.S. Grundfest, vol. 1, 25–27. San Francisco: Newswood Limited. Mattar, J. 2018. Constructivism and connectivism in education technology: Active, situated, authentic, experiential, and anchored learning. Revista Iberoamericana de Educación a Distancia 21 (2): 201–213. https://doi.org/10.5944/ried.21.2.20055. Mayer, R. 2001. Multimedia learning. Cambridge University Press. https://doi.org/10.1017/ CBO9781139164603. Mayes, T. 2019. Learning theory and the new science of learning. In Rethinking pedagogy for a digital age, ed. H. Beetham and R. Sharpe, 17–31. New York: Routledge. Mayes, T., and S. de Freitas. 2013. Technology-enhanced learning: The role of theory. In Rethinking pedagogy for a digital age: Designing for 21St century learning, ed. H. Beetham and R. Sharpe, 41–54. London: Taylor & Francis Group. McKeeman, L., and B. Oviedo 2020. Supporting resilience through meaningful, digital performance-based projects. In Teaching, technology, and teacher education during the COVID-19 pandemic: Stories from the field, ed. R.E. Ferdig, E. Baumgartner, R. Hartshorne, R. Kaplan-Rakowski, and C. Mouza. Association for the Advancement of Computing in Education (AACE). https://www.learntechlib.org/p/216903/ Mechlova, E., and M. Malcik. 2012. ICT in changes of learning theories. In 2012 IEEE 10th international conference on emerging eLearning technologies and applications (ICETA), ed. J. Uramova, M. Kubina, P. Segec, and P. Paluch, 253–262. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICETA.2012.6418326. Millard, M. 2010. Analysis of interaction in an asynchronous CMC environment. In Proceedings of the web science 2010: Extending the frontiers of society on-line, ed. W. Hall, J. Hendler, C. Lagoze, C. Pope, and A.T. Schreiber, 1–7. Online. Miller, C.L., M. Manderfeld, and E.A. Harsma. 2019. Learning theories: Social cognitivism. All Resources 143: 1–4. https://cornerstone.lib.mnsu.edu/all/143 Montpetit, C., and S. Sabourin. 2016. Assessing the impact of an “Echo360-active learning platform”- enabled classroom in a large enrolment blended learning undergraduate course in genetics. In Show me the learning. Proceedings ASCILITE 2016, ed. S. Barker, S. Dawson, A. Pardo, and C. Colvin, 440–445. Adelaide: ASCILITE. Mounia, A., R. Ajhoun, and L. Ensias. 2012. Impact of technological advancement on pedagogy. Turkish Online Journal of Distance Education 13 (1): 224–237. Narayan, H.L., and V. Shailashri. 2020. Conceptualization of e-learning theories and approaches in the context of further and higher education. International Journal of Applied Engineering and Management Letters 4 (2): 253–264. https://doi.org/10.5281/zenodo.4429449.

12

The 3C Merry-Go-Round: Constructivism, Cognitivism, Connectivism, Etc.

225

Ng, W. 2015. Theories underpinning learning with digital technologies. In New digital technology in education, 73–94. Springer. https://doi.org/10.1007/978-3-319-05822-1_4. Parmaxi, A., A. Nicolaou, E. Kakoulli Constantinou, M.V. Soulé, S. Papadima Sophocleous, and M. Perifanou. 2021. Learning theories and teaching methodologies for the design of training in digital competence for language teachers: A narrative review. In Learning and collaboration technologies: New challenges and learning experiences, ed. P. Zaphiris and A. Ioannou, vol. 12784, 125–139. Cham: Springer. https://doi.org/10.1007/978-3-030-77889-7_9. PebblePad. 2021. Pebble learning. https://www.pebblepad.co.uk/ Phillips, D.C. 1995. The good, the bad, and the ugly: The many faces of constructivism. Educational Research 24 (7): 5–12. Raskin, J.D. 2008. The evolution of constructivism. Journal of Constructivist Psychology 21 (1): 1–24. https://doi.org/10.1080/10720530701734331. Rybakova, K., and K.C. Whitt. 2017. Academically productive talk in a college classroom. New England Board of Higher Education. https://nebhe.org/journal/academically-productive-talk-ina-college-classroom/ Şahin, M. 2012. Pros and cons of connectivism as a learning theory. International Journal of Physical and Social Sciences 2 (4): 437–454. Sankey, M. 2020. Putting the pedagogic horse in front of the technology cart. Journal of Distance Education in China 5: 46–53. https://doi.org/10.13541/j.cnki.chinade.2020.05.006. Sarısakaloğlu, A., T. Atay-Avşar, and Z. Acar. 2015. Communication barriers in online teaching and online learning with digital media, in the framework of teaching and learning theory approaches. In International conference on communication, media, technology and design, 57–67. https:// www.academia.edu/download/41290206/ICCMTD_-_2015_ARTICLE_AYNUR_TULAY_ ZULFIYE.pdf Scardamalia, M., and C. Bereiter. 2014. Knowledge building and knowledge creation: Theory, pedagogy, and technology. In Cambridge handbook of the learning sciences, ed. K. Sawyer, vol. 2, 2nd ed., 397–417. Cambridge: Cambridge University Press. Selwyn, N. 2010. Schools and schooling in the digital age: A critical analysis. New York: Routledge. Siemens, G. 2004. Connectivism: A learning theory for the digital age. http://www.elearnspace.org/ Articles/connectivism.htm Siemens, G., J. Rudolph, and S. Tan. 2020. “As human beings, we cannot not learn”. An interview with Professor George Siemens on connectivism, MOOCs and learning analytics. Journal of Applied Learning and Teaching 3 (1): 108–119. https://doi.org/10.37074/jalt.2020.3.1.15. Steffens, K. 2015. Competences, learning theories and MOOCs: Recent developments in lifelong learning. European Journal of Education 50 (1): 41–59. https://doi.org/10.2307/26609252. Strong, K., and H.M. Hutchins. 2009. Connectivism: A theory for learning in a world of growing complexity. Impact: Journal of Applied Research in Workplace e-Learning 1 (1): 53–67. Sun, Y., J. Yu, and F. Gao. 2013. Shared video media: A new environment to support peer feedback in second language learning. In Society for information technology & teacher education international conference 2013, ed. R. McBride, and M. Searson, 1746–1751. Association for the Advancement of Computing in Education (AACE). https://www.learntechlib.org/p/48359 Valtonen, T., S. López-Pernas, M. Saqr, H. Vartiainen, E.T. Sointu, and M. Tedre. 2022. The nature and building blocks of educational technology research. Computers in Human Behavior 128: 107123. https://doi.org/10.1016/j.chb.2021.107123. Whyte, S. 2011. Learning theory and technology in university foreign language education: The case of French universities. Arts and Humanities in Higher Education 10 (2): 213–234. https://doi. org/10.1177/1474022210364783. Widera, S., and I. Martin. 2018. Bavarian virtual university – Best practice for a network of higher education online. In Exploring the micro, meso and macro. Proceedings of the European distance and E-learning network 2018 annual conference, ed. A. Volungeviciene and A. Szűcs, 384–390. European Distance and E-Learning Network. https://doi.org/10.38069/ edenconf-2018-ac-0051. Yilmaz, K. 2008. Constructivism: Its theoretical underpinnings, variations, and implications for classroom instruction. Educational Horizons 86 (3): 161–172. https://www.jstor.org/stable/42923724

The Role and Application of Learning Theories in the Virtual University

13

Iwona Czaplinski and Henk Huijser

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Active Learning in the Context of the “Three Cs”: Social Constructivism, Cognitivism, and Connectivism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

228 230 232 238 241 242 242

Abstract

This chapter uses a case study to explore the role and application of learning theories in the virtual university. In the wake of Covid-19, there is an identified need for learning design to be theoretically informed and evidence-based. For students, the ability to find and critically reflect on knowledge and information is a key skill in contemporary digital environments, as is the ability to act on that knowledge and apply it. In this chapter, we argue that this requires self-directed and self-regulated learning which needs to be explicitly taught and/or designed into learning activities. To do this effectively requires strong evidence-based and theoretical underpinnings. Overall, the findings in the case study presented in this chapter show the importance of ongoing, planned, and structured collaboration between discipline-based lecturers and educational experts. It is important in the I. Czaplinski (*) Curriculum Standards and Quality, Queensland University of Technology, Brisbane, QLD, Australia e-mail: [email protected] H. Huijser Learning and Teaching Unit and Faculty of Engineering, Queensland University of Technology, Brisbane, QLD, Australia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_13

227

228

I. Czaplinski and H. Huijser

virtual university to place the design of theoretically informed and justified technology-enabled learning activities at the heart of its learning and teaching practice that will engage learners. Keywords

Learning engagement · Learning theories · Self-directed learning · Self-regulated learning · Connectivism · Communities of practice · Pedagogy

Introduction Contemporary educational practices within higher education have increasingly embraced new technologies and taken up the most salient affordances for learning they offer, a process that has been significantly accelerated as a result of Covid-19. Within this rapidly changing context however, there are increasing concerns about learner engagement, as related to behavioral, cognitive, emotional, and social engagement elements (Deng et al. 2020). In their applied model of learner engagement, Carroll et al. (2021) make a useful distinction between influencing factors for engagement – at an individual, task, and environmental level – on the one hand, and engagement outcomes – at the cognitive, behavioral, emotional, and physiological level – on the other (p. 759). They further conceptualize this as mediated via Csikszentmihalyi’s concept of flow (p. 759). This is particularly important in the context of the virtual university as the various elements of learner engagement become an important way to measure progress of learners, which in turn can be used as a way to develop responsive learning environments. Given the absence of direct visual cues in face-to-face or blended learning environments, the virtual university would need to rely extensively on other data, such as digital data, in order to measure both learner engagement and by extension learning outcomes. This shows the important relationship between learning design and data to measure the outcomes (or results) of that learning design. Given the rapid move online in the wake of Covid-19, there is a need to consistently collect data to evaluate learning designs to ensure they are theoretically informed and evidence-based. For example, all elements of Deng et al.’s (2020) measures of learner engagement can be seen as related to active learning approaches, which in turn are founded on social constructivist approaches to learning and teaching, and by extension to learning design in the virtual university. As Hartikainen et al. (2019) have noted, “constructivism can be and has been used as a guide for forming instructional strategies [read: learning designs] that aim to enhance deep understanding” (p. 2). They go on to argue that “active learning as an instructional approach aims to enable constructivist learning by emphasizing students’ self-construction of knowledge, and students’ responsibility for their own learning” (p. 2). We can thus draw clear links between social constructivism, learner engagement, and active learning. However, even though these are all particularly relevant to the virtual university, they are certainly not new approaches in

13

The Role and Application of Learning Theories in the Virtual University

229

themselves, and their implementation is often challenging in a higher education context where a teacher-centered lecture/tutorial model is still the norm in many disciplinary contexts. Overall, contemporary educational practices within higher education, despite having embraced new technologies and taken up the most salient affordances for learning they offer, have shown more limited success in transforming learning environments to promote learner autonomy (i.e., self-regulation and self-direction) and provision of effective, efficient, and enjoyable learning experiences (Harasim 2017; Czaplinski 2020; Kirschner and Hendrick 2020). As noted, this is by no means restricted to online environments, but in the context of the virtual university, this is where the focus of this chapter lies. The Covid-19 pandemic and subsequent challenges for both teachers and learners caused by the sudden shift toward emergency online teaching and learning have created a new urgency around the importance of theoretically informed and evidence-based learning design for online environments (Rapanta et al. 2020). The importance of learning design in stimulating active learning, as well as selfdirected and self-regulated learning, is reinforced by broader changes related to the “information society” (Webster 2014). The unprecedented speed of technological development has inconspicuously transformed our world into a “compliant society” (Harasim 2017), in which technology increasingly dominates the ways knowledge is coconstructed and shared, and the opportunities for accessing information (and by extension knowledge) determine personal and societal prosperity. In short, knowledge has become, and will remain, a fundamental “economic resource” (Drucker 1994) that will determine the future of our society. The ability to critically reflect on information and knowledge, and then act on it without necessarily being instructed to do so, is therefore a fundamental skill in contemporary digital environments. This may apply even more so with the emergence of generative AI (Venaruzzo et al. 2023). As we will argue in this chapter however, self-directed and self-regulated learning do not simply happen but need to be explicitly taught, which requires strong pedagogical foundations and deliberative learning design. Most contemporary higher education students still acquire their knowledge through formal education, characterized by officially accredited curricula, recommended (prescribed) approaches to learning and teaching, and institutionally designed learning environments, even if this is slowly changing with the emergence of microcredentials and digital badges (Fitzgerald and Huijser 2021). All these components are becoming increasingly complex, with a dense network of material (e.g., textbooks), digital (e.g., learning platforms), and hybrid (e.g., modern scientific laboratories) resources. Pedagogical transformation should therefore aim to embed technologies in the learning process to make the technology invisible and seamless (Bax 2011) and to enable learners’ agency and autonomy. However, as Goodyear (2021) observes “technology becomes educational by virtue of its relation to activity, rather than through a priori classification or because of its intrinsic features” (p. 2). In other words, pedagogy needs to precede design. It is crucial, therefore, that pedagogical change and adaptation in the virtual university is built on a solid, theoryinformed foundation, which is why there are a number of chapters in this volume with a focus on learning theories, including this one.

230

I. Czaplinski and H. Huijser

Learning theories attempt to both explain the learning process and to provide potential solutions to educational problems, such as learning and/or instructional design (Harasim 2017). Ertmer and Newby (2013) have observed that theories of human learning are a “reliable source of verified instructional strategies, tactics, and techniques [. . .], provide the foundation for intelligent and reasoned strategy selection, [. . .] indicate how specific techniques/strategies might best fit within a given context and with specific learners, and [. . .] allow for reliable prediction” (p. 44). Thus, learning theories are the backbone of learning/instructional design, and any design decision should be based on sound understanding of a learning theory and its applications. However, diverse learning theories differ in their understandings of specific concepts, and teachers and learning designers should be aware of these differences, and to be able to adjust their practice according to learners’ needs and academics’ educational competence. Using the concept of active learning, as understood within the theoretical frames of Social Constructivism, Cognitivism, and Connectivism, this chapter draws on a doctoral study into the ways academic teachers from STEM disciplines made sense of pedagogical concepts and how they applied them in their discipline-specific learning designs and pedagogical practices. This serves as a case study into the role and application of learning theories, with associated implications for the virtual university. The study shows that understandings of learning theories and related pedagogical concepts, as well as the application of these concepts, are often inconsistent, which in turn impacts on all elements of learning design, including the design of learning activities, the use of technology, and pedagogical practices. In this chapter, we explore the wider implications of the role and applications of learning theories for learning design, and ultimately student learning outcomes, in the virtual university.

Active Learning in the Context of the “Three Cs”: Social Constructivism, Cognitivism, and Connectivism George Siemens (2004), in his seminal paper on connectivism, noted that, Behaviorism, cognitivism, and constructivism are the three broad learning theories most often utilized in the creation of instructional environments. These theories, however, were developed in a time when learning was not impacted through technology. (p. 1)

Two of these Cs have been discussed in ▶ Chap. 12, “The 3C Merry-Go-Round: Constructivism, Cognitivism, Connectivism, Etc.,” by Campbell and Tran in this volume. First, they note that within cognitivist theory, learning is seen as a structured process of acquiring and storing information. The learner is seen as a very active participant in the learning process, while the teacher models appropriate learning behaviors according to the context. Second, they explain that within constructivism, learning is seen as the active construction of new knowledge based on learners’ previous experience.

13

The Role and Application of Learning Theories in the Virtual University

231

Connectivism has provoked considerable debate because of its focus on changes in the learning context, rather than learning itself. Indeed, questions have been raised about whether connectivism is actually a learning theory as such (Czaplinski 2020; Goldie 2016; Kop and Hill 2008; Sahin, 2012; Utecht and Keller 2019). Stephen Downes (2007, cited in Kop and Hill 2008) has confronted some of those critiques by arguing that other learning theories are “cognitivist,” “in the sense that they depict knowledge and learning as being grounded in language and logic” (p. 7). He contrasts this with connectivism, which he calls “connectionist”: “knowledge is, in this theory, literally the set of connections formed by actions and experience” (p. 7). He goes on to argue that, in connectivism, there is no real concept of transferring knowledge, making knowledge, or building knowledge. Rather, the activities we undertake when we conduct practices in order to learn are more like growing or developing ourselves and our society in certain (connected) ways. (p. 7)

Thus, connectivism can be seen as a shifting of the context, rather than a learning theory, or what Kop and Hill (2008) have called a paradigm shift. The rise of social media has only exacerbated that shift since then (Willems et al. 2018). An important part of this “connectivist” paradigm shift is the development and emergence of new pedagogies, and in particular pedagogies that are relevant to the virtual university. These pedagogies are characterized by a learning context in which “control is shifting from the tutor to an increasingly more autonomous learner” (Kop and Hill 2008, p. 11). The focus on the autonomous learner is relevant from our perspective, as it provides a link to active learning, which can be seen as an underlying principle of at least two of the “three Cs,” but it is often interpreted in a variety of different ways (Lombardi et al. 2021). In a broad sense, it refers to “learning by doing,” and it is often “nested within the two familiar pedagogical approaches of student-centred and inquiry-based learning” (Lombardi et al. 2021, p. 9), to which we can add problembased learning (PBL) (Kek & Huijser, 2017), project-based learning (Dahl 2018), and work-integrated learning (Dorasamy and Rampersad 2018). Social constructivism is the main learning theory providing the broad rationale for most of these approaches and has become fairly dominant in higher education in recent decades (Kirschner and Hendrick 2020). Within social constructivism, learning is seen as occurring in a (social) context and through new experiences (i.e., learning by doing or experiential learning), which explains the emphasis on group work in approaches such as PBL. Related to this is the changed role of the teacher from a knowledge holder to a learning facilitator (McWilliam 2009). The notion of “activity” in active learning comes from a rather different perspective in cognitivist theory, where the emphasis is on the active mental processing on the part of the learner. Within this context, the teacher’s role is to ascertain “how the learner engages in the learning process such that this learning process may be enhanced” (Nagowah and Nagowah 2009, p. 280). Importantly, “cognitive psychologists view the learner’s role as an active and creative activity

232

I. Czaplinski and H. Huijser

rather than a passive one” (ibid, p. 280). In that sense, like social constructivism, cognitivism can be seen as being aligned with student-centered learning as well as with active learning. Central to the idea of active learning is learner activity, and the notion of an increasingly autonomous learner, as mentioned above. Within these approaches, it is often implied that self-directed learning will “naturally” happen with a little bit of guidance. However, there is considerable evidence to suggest that learners need to be explicitly taught self-directedness (Czaplinski 2020). This is particularly relevant to the context around which the concept of connectivism is based, including the virtual university, as it requires learners to not only recognize opportunities for learning within “the network,” but to also then act on such opportunities and take advantage of their affordances. This requires well-developed self-directed learning skills, and by extension teachers who know how to teach such skills. This is a key challenge in the context of the virtual university, as the exploratory case study we report on in this chapter, as an example, demonstrates.

Case Study We draw here on a case study that is based on the first author’s doctoral research as an example of the importance of learning theories and pedagogical knowledge in designing for learning, with a specific focus on active learning, which is relevant to the virtual university, as noted. The study was based on a recognition that learning processes have left the bricks and mortar environment to various extents (whether this be a university learning space or the confinement of one’s house), and have migrated into networked, virtual spaces. The virtual university can be seen in this sense as part of a broader learning ecology (Kek et al. 2022). Research about learning networks (Goodyear et al., 2004; Goodyear and Carvalho 2016) and networked learning (Goodyear et al. 2016) has identified the need for a broader understanding of the concept of learning, one that encompasses formal, institutional, and informal, personal learning networks supporting connectivity and interactions (Hodgson and McConnell 2018). This is relevant for our purposes, because it relates both to active learning and the need for learning theories (e.g., connectivism) to inform learning design. The study investigated STEM academic teachers’ (lecturers and tutors) selfreflections on their experience in designing and teaching to promote active learning. The focus on STEM constituted a “convenience” sample, as the first author worked as a learning designer in a Science and Engineering Faculty. A series of semistructured questions were designed to enable an in-depth investigation of variables impacting on respondents’ pedagogical choices, decisions, and practices. To this end, the following questions were formulated: 1. What is your experience in teaching? 2. Do you have formal training/education in teaching? 3. Are you familiar with any educational theories?

13

The Role and Application of Learning Theories in the Virtual University

233

4. 5. 6. 7. 8. 9. 10. 11.

Do you search for additional information about pedagogy? Would you like to be offered any specific course/training on active learning? How do you understand the concept of active learning? Can you give some examples of activities in active learning? What was challenging for you in this approach? What was positive for you with your teaching approach? How do you design your courses/units? How do you decide which technological tool you will use for your teaching, and how will you use it?

These questions were specifically designed to enable respondents’ guided selfreflection and to solicit their in-depth opinions about their perceptions of their effectiveness as academic teachers. Given that one of the study’s objectives was to develop a contextualized learning design that would have the potential to effectively assist undergraduate STEM students in becoming active learners, two large, first year units/ courses, teaching highly diverse student cohorts, were a natural choice for the study. Of the 29 academic teaching staff in both units, 8 provided responses to the questionnaire and 6 took part in focus groups, 3 female and 3 male. In total, ten academic teachers who taught in these two units/courses self-selected to participate in the study, including four lecturers and six tutors. All respondents, except for one tutor, were experienced academic teachers, with their experience ranging from 3 to 40 years of teaching in a range of contexts. Considering the differences between lecturing and tutoring responsibilities, the questions were adjusted to the respondents’ academic role. That is, while lecturers were asked all 11 questions, tutors were only asked the first 5 questions, in line with their different responsibilities. The data were collected over a period of 3 weeks and on the premises of the university. The interviews were audio-recorded, transcribed by one of the authors, and coded to preserve the anonymity of the respondents. Deductive content analysis (Elo and Kyngäs 2008; Elo et al. 2014) was used to identify recurring patterns. This method led to identification of the following four overarching patterns which reflected participants’ responses: 1. There is a relationship between the position held by academic teachers (i.e., lecturer, tutor), the level of familiarity with learning theories, and the interest in deepening the educational knowledge. 2. The disciplinary expertise of academic teachers strongly impacted on their perception of learning theories, learning design, and teaching practice. 3. For STEM educators, learning, especially active learning, was always associated with hands-on, problem-solving activities with technology playing a supporting, rather than an enabling, role. 4. Personal satisfaction from successfully teaching disciplinary knowledge was highly motivational for all respondents. Below, we discuss these four overarching patterns in more detail.

234

I. Czaplinski and H. Huijser

The first four interview questions investigated respondents’ preparedness to teach and their practice of deepening their knowledge of pedagogy. The difference in academic roles defined the level of respondents’ engagement with pedagogical training and collaboration with educational experts. Tutors appeared to feel disconnected from the pedagogical/educational aspect of the unit. None of them had any formal training in learning and teaching, and only two had attended short workshops for sessional staff. Furthermore, in their teaching practice they were guided by their lecturers, but none of them was involved at a deeper level in, for example, designing/reviewing the unit. When asked about exploring pedagogical aspects of their practice, one respondent replied: “I do not think I need it,” while another observed: “I don’t have the time for that.” It is important to note here that tutors often have limited access to professional development as part of their precarious employment conditions. By contrast, all lecturers had obtained formal training in teaching, predominantly through university-organized professional development programs, or through recognition programs such as HEA (Higher Education Academy) Fellowships. However, when asked about their familiarity with educational theories, the respondents gave general answers and quoted some theoretical concepts, but in a rather random order. For example, Respondent 3 stated: Not sure, I vaguely remember some names of models, but not theories. Constructive alignment, collaborative learning, active learning, these names come to my mind without really being able to describe them.

Another respondent mentioned: “some names that come to mind are: constructivist approach to learning, flipped learning theory, action-based learning, scaffolding. But I cannot name them all now.” In short, it appeared that the respondents’ familiarity with learning theories was not fully solidified thus creating an impression of an amalgamated model of learning theories. Research on teacher education has revealed that many educational beliefs that are commonly held by academic teachers are not research-underpinned nor supported by empirical evidence (Dekker et al. 2012; Double et al. 2020). These beliefs are difficult to change as they are disciplineindependent (Dekker et al. 2012), and do not self-correct over time (Double et al. 2020). They include “neuromyths,” about the brain and its influence on learning (Dekker et al. 2012). This is a potential worry, as teachers infer their beliefs from positive teaching experiences (Dekker et al. 2012; Double et al. 2020), and they may directly attribute their students’ academic performance to the teaching approach they applied (Double et al. 2020). This impression of an amalgamated application of learning theories was confirmed by responses to the next four questions, which investigated in more detail the ways respondents understood and applied the concept of active learning in their practice. Overall, respondents associated the concept of active learning with an act of doing/performing a learning task, or completing a hands-on activity, as opposed to sitting in the classroom (or in front of a computer screen) and listening to the explanation from the lecturer/tutor. Respondent 1 explained: “For me, it’s something

13

The Role and Application of Learning Theories in the Virtual University

235

that makes them do things, promote doing something, not only presenting the content; it’s a hands-on activity.” Respondent 2, a lecturer with a long experience of teaching, tutoring, and helping students with learning disabilities, provided a definition of active learning that focused on the process of learning itself: A stimulus, an impulse is transmitted by the nervous system to the brain, and next, resulting from another stimulus, an impulse is retrieved from the brain. This impulse is information. The process is composed of three phases: sending, integration, transmission back. As this involves the brain’s activity, it is active learning.

It appears that disciplinary expertise as a scientist and experience in teaching students with learning disabilities influenced Respondent 2’s focus on the process of learning from a cognitivist learning theory perspective. Preferably, active learning builds on previous knowledge to coconstruct new knowledge, as explained by Respondent 3: This is a situation when students do not passively acquire knowledge but they are piecing together elements of the knowledge, and they construct new knowledge. Students can apply this constructed knowledge in new contexts to become more confident.

This response confirms that Respondent 3’s ideas around the learning process were couched in social constructivist learning theory, even if this was not explicitly mentioned. Finally, an important element of active learning is the contextualization of learning activities within real-world settings, to give students a taste of working within an industry (Jackson 2017). Respondent 4 described this concept in the following terms: As my students will go to resource industry, I am building my teaching and assessment around real-world data. This is not a catch phrase, I am using the real world data students will be using. I have a lot of interaction with industry, so I give them tasks like in their real workplace. I give questions based on knowledge of the activities of the company. They are motivated by the task and I provide them with real data. It is always straightforward. My activities are always motivated by real-world goals. This poses challenges for students, they need to do the real-work tasks.

Regarding the examples of active learning activities designed by the respondents, as expected, most common examples encompassed the following: hands-on practical activities in the lab (e.g., using sophisticated equipment, computational calculations, and taking measurements); problem-solving tasks and conceptual manipulation of possible solutions (“what if” thinking); scenarios and discussion groups; and smallgroup real-world problem-solving activities. Interestingly, Respondent 2 also mentioned the affective factor, the academic teacher’s passion for science (i.e., the passion for the discipline), as a motivator for students to wanting to learn and solve problems: I like to give them problems which require a certain degree of problem solving and creativity. Concepts are like recipes. To understand the concept, they need to understand the definition,

236

I. Czaplinski and H. Huijser

the terminology and the application. Aspects of this can be boring, but they will inform problem-solving activity. Going into transmission and application – you can only succeed if you engage your class with active learning. So, I use psychological manipulation. I love science and I love solving science problems. I try to convey that passion when I interact with students. That makes them want to learn. From a personal perspective, students recognise the passion, and they start admiring, they want to do work and thus they become active learners.

Respondent 2 thus appeared to be influenced by cognitivist learning theory in describing the learning process in “scientific” terms (e.g., stimulus, transmission, and application). Questions 5–8 (related to Theme 3) focused on challenges associated with implementing active learning in practice. Not surprisingly, respondents perceived student engagement in learning as the most challenging aspect of their practice, which is even more challenging in the virtual university (Paulsen and McCormick 2020). The interviews revealed that the challenge of student engagement was complex and reflected respondents’ different theoretical perspectives on the concept of active learning. For example, Respondent 1, inspired by an inquirybased learning approach, and using lab simulation to promote students’ active learning, noted the risk of students focusing on task performance and missing an opportunity to holistically reflect on the overarching problem they were solving: The biggest difficulty is that students can get absorbed, bogged down in the practical details, large topic experiments, technical details of what to do, instead of getting the big picture. They miss the ‘what’ you want them to learn. The simulation part is not the important part, it’s the result of applying the theory. They get caught up in technical details.

For Respondent 2, who seemed to perceive active learning from a cognitivist perspective, the most challenging element was to arouse students’ interest in the content and stimulate their willingness to engage in intellectual enquiry: For me, number one is dealing with my frustration and disappointment that there is a portion of students who really does not care. There are always some students who do not care. The frustration is definitely an obstacle for me.

For Respondent 3, who described active learning from a social constructivist perspective, the challenge lay with teaching strategies that would enable active learning: I find the challenge is always understanding where the group is at the start. You do not make any assumption about how much students know. When I am expecting students to discuss in a group, I am providing them with the scaffold. [. . .] Also, as the educator, feeling comfortable with the silence. When you ask question and there is no response. You might start to listen to students, and to give them time to reflect. [. . .] Finally, often they are thinking that their recall is their knowledge; they do not understand that application is the real test for active learning. The trick is to have plenty of activities to practice that and to provide feedback on that process.

The reported challenges seemed to be outweighed by the personal satisfaction from seeing students engaged in learning, completing activities, asking questions, and

13

The Role and Application of Learning Theories in the Virtual University

237

coming to the class, regardless of whether that is in a face-to-face or online context. For example, Respondent 3 described the feeling with the following words: I can’t imagine doing this any other way. [. . .] When the students have taken the theory, they practiced, and they have the “aha” moment. They achieved the learning. They are happy to have a go and to take the risk. I love a noisy classroom, that’s the sign that they are discussing and learning.

If the personal satisfaction from seeing students actively engaged in the classroom was so highly valued by lecturers, as this study shows, then the next question is how to ensure that active learning is “designed into” learning in the virtual university. In other words, how do we create “noisy classrooms” in the virtual university? Furthermore, if learning design for active learning relies on an in-depth knowledge and understanding of learning theories and related pedagogical approaches, the design process may benefit significantly from support from and collaboration with educational experts such as learning designers and/or academic developers (Tay et al. 2023). The data analysis showed that none of the respondents had a learning and teaching mentor. As for collaboration with educational experts, consultations would occur irregularly, predominantly at the beginning of the teaching semester, during design/revision of the unit phases, or when encountering some specific problems/challenges. It appears that there was little ongoing, planned, and structured collaboration between lecturers and educational experts (Fitzgerald et al. 2020). As respondent remarked: I have consulted some literature, mainly to find out about others’ experiences in teaching similar content. As my discipline is very much content-heavy, I design the unit around the content first, that is the central element. Next, I would consult literature and search for a teaching strategy.

This was echoed by other respondents. The starting point was always the content to teach, spread across the teaching period, mapped against learning outcomes and assessment. One respondent who predominantly taught first year students also mentioned designing additional activities (e.g., online questionnaires), distributed before the commencement of the semester to collect information about enrolled students’ actual level of disciplinary competence. This order of designing is mirrored in the process of embedding technology-based tools in learning activities and teaching practice. Respondents’ descriptions of their practice were highly similar in this respect and consisted of using technology-based tools as a support for content transfer rather than a medium of active learning. That is, respondents were using standard tools provided by the formal, institutionally designed environments (e.g., LMS Blackboard, Turnitin). Occasionally, due to highly specialized disciplinary content, respondents would use specific, commercial software, to provide students with the opportunities to use technologies used in industry. However, respondents reported facing institutional hurdles such as lack of funds to purchase commercial products, shortages in technological support, or

238

I. Czaplinski and H. Huijser

know-how to take full advantage of the software, which are key issues to consider in relation to the virtual university. In other words, what systems and processes are in place to ensure the smooth and flexible adoption of appropriate technologies to ensure active learning in an online environment? For instance, Respondent 3 who trialed MS OneNote in one of the units reported the challenges that effectively discouraged further use of the software: I use Blackboard, like everyone else. I also used OneNote in one of my units. That was an interesting experience. One Note offered more flexibility, but there were issues with accessing the sites from outside the University login, so we reverted to Blackboard.

Thus, technological obstacles can effectively discourage pedagogical innovation and prevent technological transformation, especially in the context of formal, institutionalized education (Tay et al. 2023). This case study has provided an indication of the role and application of learning theories in learning design, with a particular focus on active learning and the use of technologies (in the context of connectivism). This case study was set in a particular faculty and is thus a discipline-specific example. However, the findings related to the use and application of learning theories are likely to be transferable to other disciplinary contexts. Furthermore, the findings can also provide some elements to consider when we translate the blended context of the case study to the virtual university. In the next section, we discuss the findings of this case study with regard to the application of learning theories to pedagogical practice to promote active learning within the virtual university.

Discussion The first overarching pattern, identified in the case study, of the relationship between the position held by academic teachers (i.e., lecturer, tutor) and the level of familiarity with learning theories is important. For academic teaching, the institutional parameters that define academic roles might need to be set up differently in the virtual university, as the quality of teaching and learning design, regardless of the academic role, impacts on the overall student experience. The findings indicate a discrepancy between pedagogical preparedness to teach, on the one hand, and capacity to improve one’s teaching skills by deepening educational knowledge and improving teaching practice on the other. This discrepancy may be magnified when applied to the virtual university as it involves a potentially different skill set in the form of learning design founded upon appropriate learning theories (Pham and Ho 2020). While there was some expectation for lecturers to engage in teaching and learning design-related professional development programs, there was no such expectation (nor budget) for sessional tutors to do so. The literature (Kahu and Picton 2019) indicates that good teacher-student relationships have “academic and affective dimensions” (p. 23), which also suggests a

13

The Role and Application of Learning Theories in the Virtual University

239

link to Carroll et al.’s (Carroll et al. 2021) earlier mentioned applied model of learner engagement, especially with regard to the emotional level. Furthermore, according to Kahu and Picton (2019), good teachers are “super helpful, really caring, approachable, and hands-on, interactive teachers” (pp. 25–27). The “interactive” characteristic is particularly important when the aim is to develop an active learning environment, as this relies on teacher-student and peer-to-peer interaction, in line with social constructivist principles. The abovementioned attributes of good teachers benefit significantly from expert knowledge of the learning process, which is then “designed into” both learning activities and learning facilitation on the teacher’s part. Thus, teachers in the virtual university would benefit considerably from appropriate professional development related to learning design and facilitation for active learning and learner engagement, as this ultimately has the potential to have a strong, positive impact on students. Moreover, connecting teachers to each other and creating space for ongoing opportunities to discuss pedagogical approaches, for example, in the form of communities of practice and professional conversations (Pleschová et al. 2021), can pave the way for more theoretically informed teaching approaches. In other words, teachers need positive stimulation to deepen their educational expertise and pedagogical knowledge, which such conversation can provide. Teachers’ comments in this study suggest that this is a challenge in their current environments, which the virtual university would thus be well-advised to address. Regardless of the many valid reasons shaping this challenge (e.g., workload, discipline-based research focus, etc.), in the longer term, there is a risk that student learning experiences will be negatively impacted by a lack of expert teaching, including expert use of technologies to enable active learning. With regard to disciplinary experts, the case study also showed that teachers’ learning and teaching design was strongly influenced by their disciplinary expertise which made them lean toward social constructivist ideas of teamwork or hands-on experiential learning. By contrast, fundamental concepts of connectivism, such as creating knowledge through connections and sharing it with others, which are important potential elements of learning in the virtual university, were much less applied by respondents. The second overarching finding is also important, as it points to the challenges associated with the notion of expertise and the assumption that expertise extends to all disciplines, including in-depth knowledge about learning theories and expertise in learning design informed by such theories. With increased complexity of the educational landscape, the teaching academic’s role has been transformed in many ways. An academic teacher is no longer only focused on “delivering” content and imparting knowledge but is also expected to design learning for different environments, including virtual environments. Thus, contemporary academic teachers often fulfill a triple role: content expert, designer of learning activities, and facilitator of knowledge transfer (Czaplinski 2020). Becoming a designer of learning and facilitator of knowledge transfer requires an additional area of expertise, or near-expert competence in learning design and teaching methods (Konnerup et al. 2018; Sweller et al. 2003). However, becoming an expert requires an extensive knowledge that allows one to see patterns in the environment, quickly retrieve information from long-term memory, and identify (creative) solutions (Bransford et al. 2000).

240

I. Czaplinski and H. Huijser

Moreover, disciplinary experts are not necessarily skillful teachers, and they may even have “forgetten” the difficulties they have overcome in order to become experts in their disciplines (Bransford et al. 2000). Expertise is founded on domain-specific knowledge and entails a deep change in cognition methods (Sweller et al. 2003), which influences one’s perception. This makes it difficult for an expert to cognitively go back and see the problem as a novice (Kirschner and Hendrick 2020) or a lessknowledgeable learner. It is therefore important for a discipline expert to be afforded the time and resources to learn about another discipline in order to be able to make evidence-based pedagogical decisions (Czaplinski 2020), as related to both learning design and facilitation. This is not to say that one cannot be a good teacher without in-depth expertise and knowledge related to learning theories, but rather that learning design and facilitation is an iterative process that benefits from ongoing professional conversations and development, particularly in fast-changing technology-rich environments, such as the virtual university. The third overarching finding in the case study related to academic teachers’ perception of technology through lenses of its pedagogical virtue (Goodyear 2021) rather than through their professional (i.e., discipline expertise) and personal life experiences. All respondents seemed to ground their practice of learning and teaching designs in social constructivist and cognitive learning theories, while connectivism seemed to be completely ignored. This may be reinforced by the earlier-mentioned common educational beliefs that are not necessarily evidencebased but do not self-correct over time, such as “neuromyths” (Double et al. 2020; Dekker et al. 2012). Such educational myths may also apply to the educational virtue of particular technologies, as their potential for enabling learning can be missed, misinterpreted, or misused. This provides a plausible explanation for the third finding and suggests that more salient technological stimuli, such as disciplinespecific specialized software, attracted respondents’ attention, and were selected for application. In case of more challenging applications, the technology was often ignored or quickly abandoned. This is potentially problematic if there are sound pedagogical reasons, for example, based on connectivist theory, to include certain technologies as a part of active learning approaches in the virtual university. The last finding might serve as a potential focus in the virtual university to engage academic teachers in designing technology-enhanced learning activities where technology becomes invisible and seamless (Bax 2011). This would involve creating enough space for such engagement for all teachers (including lecturers and tutors) and learning designers, including space to continuously reflect on pedagogy and learning theories that would in turn inform choice around applications of technologies for learning. Such a conversational space, for example, in the form of a community of practice, is important to nurture as a collaborative space where teachers and learning designers engage in designing learning experiences that are grounded in learning theories and supported by evidence-based examples of good practice (Fitzgerald et al. 2020). This model would form the basis for designing teaching for active learning and, in this way, would effectively assist students in becoming self-directed, autonomous learners (Czaplinski 2020), as suggested earlier.

13

The Role and Application of Learning Theories in the Virtual University

241

Finally, such a collaborative space would also promote ways in which the pedagogical virtue of learning technologies (Goodyear 2021) would be used not only to support or enhance, but also to design effective and enjoyable (Kirschner and Gerjets 2006) technology-enabled learning activities in the virtual university.

Conclusion and Future Directions This chapter has presented an argument that a good knowledge of learning theories and opportunities to develop pedagogical collaborations are indispensable for teachers in design learning activities and applying teaching practice that effectively promote learning engagement and active learning within the virtual university. The findings from the case study lead to four key suggestions for pedagogy-related approaches in the virtual university. First, if we recognize the importance of active learning, as well as self-directed and self-regulated learning, then deliberative learning design, based on a good understanding of learning theories, is important. The virtual university should therefore offer, and perhaps structurally embed, learning design-related professional development targeting both lecturers and sessional tutors. Second, designing learning and facilitating knowledge transfer requires near-expert competence in learning design and teaching methods. The virtual university should therefore ensure that discipline experts are afforded enough time and resources to keep developing their expertise and knowledge related to learning design and facilitation through ongoing professional conversations and development. The third finding relates to choices about technologies, which refers to a combination of perceived “pedagogical virtue” and often persistent educational myths. The suggested solution for the virtual university similarly involves providing enough time and resources to allow for iterative professional conversations and development to ensure that relevant and up to date technologies are adopted and applied. Again, this process needs to be grounded in sound pedagogical reasoning behind choices being made, and ideally it would lead to the design of learning activities that make the use of technology invisible and seamless (Bax 2011) (the fourth finding). Overall then, the findings in the case study show the importance of ongoing, planned, and structured collaboration between lecturers and educational experts, for example, in the form of communities of practice. It is therefore important for the virtual university to ensure affordances are made for such communities to develop, for example, in the form of sufficient workload allocations. The above-described phenomenon of amalgamating different learning theories and relative lack of consistency in applying such learning theories to learning design and teaching poses potential risks if the ideal is to develop learning designs for active learning. Effective collaboration between discipline experts and learning designers offers the potential to create learning design characterized by pedagogical virtue of learning technologies (Goodyear 2021). In the virtual university, this would be used not just to support or enhance, but to place the design of effective and enjoyable (Kirschner and Gerjets 2006) technology-enabled learning activities at the heart of its practice to

242

I. Czaplinski and H. Huijser

engage learners in active learning approaches, couched in pedagogical practice that is aligned with appropriate learning theories. This will ultimately ensure selfdirected and self-regulated learners with an ability to leverage the affordances of networks and ever-changing technologies.

Cross-References ▶ Models of Professional Development for Technology-Enhanced Learning in the Virtual University ▶ The 3C Merry-Go-Round: Constructivism, Cognitivism, Connectivism, Etc. ▶ The Virtual University: Moving from Fiction to Fact

References Bax, S. 2011. Normalisation revisited: The effective use of technology in language education. International Journal of Computer-Assisted Language Learning and Teaching 1 (2): 1–15. https://doi.org/10.4018/ijcallt.2011040101. Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How people learn: Brain, mind, experience, and school. Expanded Edition. National Academy Press. Carroll, M., S. Lindsey, M. Chaparro, and B. Winslow. 2021. An applied model of learner engagement and strategies for increasing learner engagement in the modern educational environment. Interactive Learning Environments 29 (5): 757–771. https://doi.org/10.1080/ 10494820.2019.1636083. Czaplinski, I (2020). An analysis of learning networks of STEM undergraduate students to promote active learning. PhD Thesis by monograph. Queensland University of Technology. https://doi. org/10.5204/thesis.eprints.201510. Dahl, B. 2018. What is the problem in problem-based learning in higher education mathematics. European Journal of Engineering Education 43 (1): 112–125. https://doi.org/10.1080/ 03043797.2017.1320354. Dekker, S., N.C. Lee, P. Howard-Jones, and J. Jolles. 2012. Neuromyths in education: Prevalence and predictors of misconceptions among teachers. Frontiers in Psychology 3: 429. https://doi. org/10.3389/fpsyg.2012.00429. Deng, R., P. Benckendorff, and D. Gannaway. 2020. Learner engagement in MOOCs: Scale development and validation. British Journal of Educational Technology 51 (1): 245–262. https://doi.org/10.1111/bjet.12810. Dorasamy, N., and R. Rampersad, eds. 2018. Critical perspectives on work-integrated learning in higher education institutions. Cambridge Scholars Publishing. Double, K.S., Y.L. Chow, E.J. Livesey, and T.N. Hopfenbeck. 2020. Causal illusions in the classroom: How the distribution of student outcomes can promote false instructional beliefs. Cognitive Research: Principles and Implications 5 (3). https://doi.org/10.1186/s41235-02000237-2. Drucker, P.F. 1994. Post-capitalist society. Harper Business. Elo, S., and H. Kyngäs. 2008. The qualitative content analysis process. Journal of Advanced Nursing 62 (1): 107–115. https://doi.org/10.1111/j.1365-2648.2007.04569.x. Elo, S., M. Kääriäinen, O. Kanste, T. Pölkki, K. Utriainen, and H. Kyngäs. 2014. Qualitative content analysis: A focus on trustworthiness. SAGE Open 2014: 1–10. https://doi.org/10.1177/ 2158244014522633.

13

The Role and Application of Learning Theories in the Virtual University

243

Ertmer, P.A., and T.J. Newby. 2013. Behaviourism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly 26 (2): 43–71. Fitzgerald, R., and H. Huijser. 2021. Exploring industry-university partnerships in the creation of short courses and micro-credentials. In Back to the future: ASCILITE ’21: Proceedings ASCILITE 2021, ed. S. Gregory, S. Warburton, and M. Schier, 340–344. https://eprints.qut. edu.au/227133/1/103454821.pdf. Fitzgerald, R., H. Huijser, D. Meth, and K. Neilan. 2020. Student-staff partnerships in academic development: The course design studio as a model for sustainable course-wide impact. International Journal for Academic Development 25 (2): 134–146. https://doi.org/10.1080/ 1360144X.2019.1631170. Goldie, J.G.S. 2016. Connectivism: A knowledge learning theory for the digital age? Medical Teacher 38 (10): 1064–1069. https://doi.org/10.3109/0142159X.2016.1173661. Goodyear, P. 2021. Navigating difficult waters in a digital era: Technology, uncertainty and the objects of informal lifelong learning. British Journal of Educational Technology 1–18. https:// doi.org/10.1111/bjet.13107. Goodyear, P., and L. Carvalho. 2016. Activity centred analysis and design in the evolution of learning networks. In Proceedings of the 10th International Conference on Networked Learning, ed. S. Cranmer, N.B. Dohn, M. de Laat, T. Ryberg, and J.A. Sime, 193–199. http://www. networkedlearningconference.org.uk/abstracts/pdf/P13.pdf. Goodyear, P., S. Banks, V. Hodgson and D. McConnell. 2004. Advances in research on networked learning. United Kingdom: Springer. Goodyear, P., L. Carvalho, and N.B. Dohn. 2016. Artefacts and activities in the analysis of learning networks. In Research, boundaries, and policy in networked learning, ed. T. Ryberg, C. Sinclair, S. Bayne, and M. de Laat, 93–110. Springer. https://doi.org/10.1007/978-3-319-31130-2_6. Harasim, L. 2017. Learning theory and online technologies. 2nd ed. Routledge. Hartikainen, S., H. Rintala, L. Pylväs, and P. Nokelainen. 2019. The concept of active learning and the measurement of learning outcomes: A review of research in engineering higher education. Education Sciences 9 (4): 276. https://doi.org/10.3390/educsci9040276. Hodgson, V., and D. McConnell. 2018. The epistemic practice of networked learning. In Proceedings of the 11th international conference on networked learning 2018, ed. M. Bajić, N.B. Dohn, M. de Laat, P. Jandrić, and T. Ryberg, 445–464. http://networkedlearningconference.org.uk/abstracts/papers/mcconnell_59.pdf. Jackson, D. 2017. Developing pre-professional identity in undergraduates through work-integrated learning. Higher Education 74 (5): 833–853. https://doi.org/10.1007/s10734-016-0080-2. Kahu, E.R., and C. Picton. 2019. The benefits of good tutor-student relationships in the first year. Student Success 10 (2): 23–33. https://doi.org/10.5204/ssj.v10i2.1293. Kek, M.Y.C., F. Padró, and H. Huijser. 2022. Towards an interconnected university ecology for a hypercomplex world. In Student support services, ed. H. Huijser, Megan Yih Chyn A. Kek, and F. Padró, 871–883. Springer. Kirschner, P.A., and P. Gerjets. 2006. Instructional design for effective and enjoyable computersupported learning. Computers in Human Behaviour 22: 1–8. https://doi.org/10.1016/j.chb. 2005.01.004. Kirschner, P.A., and C. Hendrick. 2020. How learning happens. Seminar works in educational psychology and what they mean in practice. Routledge. Konnerup, U., T. Ryberg, and M.T. Sørensen. 2018. The teacher as designer? What is the role of ‘learning design’ in networked learning? In Proceedings of the 11th international conference on networked learning 2018, ed. B. Milan, N.B. Dohn, M. de Laat, P. Jandric, and T. Ryberg, 331–339. Kop, R., and A. Hill. 2008. Connectivism: Learning theory of the future or vestige of the past? The International Review of Research in Open and Distributed Learning 9 (3). https://doi.org/10. 19173/irrodl.v9i3.523.

244

I. Czaplinski and H. Huijser

Lombardi, D., T.F. Shipley, and Astronomy Team, Biology Team, Chemistry Team, Engineering Team, Geography Team, Geoscience Team, and Physics Team. 2021. The curious construct of active learning. Psychological Science in the Public Interest 22 (1): 8–43. https://doi.org/10. 1177/1529100620973974. McWilliam, E. 2009. Teaching for creativity: From sage to guide to meddler. Asia Pacific Journal of Education 29 (3): 281–293. https://doi.org/10.1080/02188790903092787. Nagowah, L., and S. Nagowah. 2009. A reflection on the dominant learning theories: Behaviourism, cognitivism and constructivism. International Journal of Learning 16 (2): 279–285. Paulsen, J., and A.C. McCormick. 2020. Reassessing disparities in online learner student engagement in higher education. Educational Researcher 49 (1): 20–29. https://doi.org/10.3102/ 0013189X19898690. Pham, H., and T. Ho. 2020. Toward a ‘new normal’ with e-learning in Vietnamese higher education during the post COVID-19 pandemic. Higher Education Research & Development 39 (7): 1327–1331. https://doi.org/10.1080/07294360.2020.1823945. Pleschová, G., T. Roxå, K.E. Thomson, and P. Felten. 2021. Conversations that make meaningful change in teaching, teachers, and academic development. International Journal for Academic Development 26 (3): 201–209. https://doi.org/10.1080/1360144X.2021.1958446. Rapanta, Ch., L. Botturi, P. Goodyear, L. Guàrdia, and M. Koole. 2020. Online university teaching during and after the Covid-19 crisis: Refocusing teacher presence and learning activity. Postdigital Science and Education 2. https://doi.org/10.1007/s42438-020-00155-y. Siemens, G. (2004). Connectivism: A learning theory for the digital age. Elearnspace.org, 14–16. https://citeseerx.ist.psu.edu/document?repid¼rep1&type¼pdf&doi¼f87c61b964e32786e06c 969fd24f5a7d9426f3b4 Sweller, J., P.L. Ayres, S. Kalyuga, and P.A. Chandler. 2003. The expertise reversal effect. Educational Psychologist 38 (1): 23–31. https://doi.org/10.1207/S15326985EP3801_4. Tay, Z.A., H. Huijser, S. Dart, and A. Cathcart. 2023. Learning technology as contested terrain: Insights from teaching academics and learning designers in Australian higher education. Australasian Journal of Educational Technology 39 (1): 56–70. https://doi.org/10.14742/ajet. 8179. Utecht, J., and D. Keller. 2019. Becoming relevant again: Applying connectivism learning theory to today’s classrooms. Critical Questions in Education 10 (2): 107–119. https://files.eric.ed.gov/ fulltext/EJ1219672.pdf. Venaruzzo, L., Ames, K., & Leichtweis, S. (2023). Embracing AI for student and staff productivity: An ACODE whitepaper based on the ACODE 88 workshop and roundtables. https://www. acode.edu.au/pluginfile.php/13426/mod_resource/content/4/ACODE88-Whitepaper.pdf. Webster, F. 2014. Theories of the information society. Routledge. Willems, J., C. Adachi, F. Bussey, I. Doherty, and H. Huijser. 2018. Debating the use of social media in higher education in Australasia: Where are we now? Australasian Journal of Educational Technology 34 (5): 135–149. https://doi.org/10.14742/ajet.3843.

Adapting and Creating New Theories Through the Ongoing Research of Technology-Enhanced Learning

14

Nathaniel Ostashewski

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Networked Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Authentic Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Community of Inquiry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

246 248 251 254 257 258 259

Abstract

With the changes that digitization has ushered in to all facets of our society and lives since the early 2000s, it is no surprise that education has changed significantly over that time. The most significant change for education has come in the form of digital tools, resources, and connectivity. Higher education and Universities in particular have been challenged to address these changes as fast as they have appeared in our global society. Moving from brick and mortar placed-based education to all kinds of online and hybrid blends of education delivery has been both costly and difficult to implement. The biggest challenges for the new technology-enabled education delivery model adopted by universities over the past 10 years are no longer technical or infrastructure based, but rather pedagogical. These challenges coupled with the increased global demand for Higher Education, some of which will need to be met with increases in virtual and online university offerings, highlight the importance of pedagogical challenges that leverage the benefits that technology-enabled education provides. N. Ostashewski (*) Open, Digital, and Distance Education Program, FHSS, Athabasca University, Athabasca, AB, Canada e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_14

245

246

N. Ostashewski

This chapter explores some of the needed changes and adaptations of existing models and pedagogical approaches for education delivery. Specifically, this chapter examines how learning theories and practices, such as networked learning, authentic learning, and the Community of Inquiry, can be implemented to enable quality virtual higher education. Based on an exploration of theory and recent practices being implemented, this chapter describes how these three pedagogical approaches have been used in online course delivery that provides quality sustainable education for virtual students. Keywords

Pedagogy · Networked learning · Authentic learning · Community of Inquiry · Pedagogical approaches · Technology-enabled learning

Introduction In order to provide quality learning in virtual higher education settings, educators cannot simply replicate teaching practices found in brick-and-mortar classrooms. Online or virtual educational settings, while similar to face-to-face education settings, differ in ways that need to be understood well by educators and the institutions they instruct for. One of these ways in which they are different centers on the role technology plays in virtual education. Technology-enabled learning has been around for some time in the education setting, but using technology to enhance face-to-face education is significantly different than when it is the basis for all educational interaction as needed for the virtual university setting. This was never so evident as during the shift to remote teaching and learning that occurred around the globe in the Covid-19 pandemic of 2020. Millions of educators needed to shift from in-person teaching to technology-mediated learning overnight and while a small percentage of educators were able to cope, most were not (Dhawan 2020). When considering quality virtual education, it is important to be clear about what benefits and challenges exist for learners. Some of the words commonly used to describe the benefits of online learning are convenient, flexible, and accessible. For example, virtual learning does not usually follow a schedule that requires course work to be done at a certain time of day. As a student this allows course work to be completed when it is convenient and fits into the learner’s own schedule. Virtual learning is also flexible with respect to location. Course work can be done from anywhere you can connect to the internet. By exploring some of the differences of virtual learning and face-to-face learning, we can perhaps better understand the dynamics of communication and interaction within these settings. The first most obvious differentiating factor between virtual learning and face-toface learning is physical presence and what that makes possible for the learners and the educator. In the face-to-face learning environment, students have the benefit of having the instructor in the same room and everyone uses expressions, body language, and other non-verbal cues to clarify or enhance the meaning of the

14

Adapting and Creating New Theories Through the Ongoing Research. . .

247

interactions. Likewise, educators can gauge learners’ understanding of material based on non-verbal cues. In the virtual environment, students and instructors must express themselves clearly and concisely, usually in writing, in order to make sure the information is communicated as it is intended, without the benefit of these non-verbal cues. Another differentiating factor is that most communication in face-to-face learning occurs in a synchronous manner, while much of virtual learning is often asynchronous. In face-to-face learning, both learners and educators are physically present allowing for real-time communication to occur. In the virtual learning environment, participants are not physically present, and much of the interaction that normally occurs between learners and educators becomes asynchronous. Table 1 presents other differences between virtual learning and face-to-face learning that help in understanding the role of communication and interaction: Again, it’s important to note that learners may very well find a virtual course that does not match the characteristics mentioned here, and the same holds true for faceto-face learning. However, in general, these differences between online learning and traditional face-to-face learning hold true. To summarize, convenience, flexibly, and accessibility are the hallmarks of virtual education delivery; however, just as in faceto-face learning, the role of the educator is pivotal in making the virtual learning experience a success (Appana 2008; Singh and Thurman 2019). In a study of virtual higher education students and what they ranked as important to their learning success, Martínez-Argüelles, Blanco Callejo, and Castán Farrero (2013) reported that the core operation of the learning, or the teaching to be specific, was at the top of the list. This identification of teaching being key is directly tied to quality of the virtual education and speaks to what virtual institutions need to pay attention to be successful. Martínez-Argüelles et al. (2013) state: Table 1 Attributes of virtual and face-to-face learning Virtual learning Online is continuous; log in any time Attendance not always readily apparent Student-centered learning Less step-by-step instruction; learner independence Peer-to-peer learning is detailed and thorough Choice of whether or not to engage at certain times Less social pressure; sense of anonymity; level playing field Less reliance on major tests or memorization of content Encourages continued learning and networking

Face-to-face learning Face-to-face instruction has a beginning and end time (i.e., class time) Attendance/physical presence is very obvious Instructor-driven learning More directed learning; specific instructions/ procedures provided by instructor Peer-to-peer learning not as formalized and not heavily-weighted Must engage (to some degree) while being instructed Social issues and pressures more prevalent; more hierarchies formed Often features major tests which can cause anxiety in students When the course is over, it is over

248

N. Ostashewski

[U]niversities that operate online should bear in mind that, when it comes to assessing the quality of the e-service they provide, their students pay a great deal of attention to the teaching that a university offers. To be precise, students focus above all on the lecturers’ knowledge, experience and pedagogical capacity; on the quality of the feedback they get from tutors on activities they, the students, carry out; and on the speed and efficiency of having their queries solved (p. 279).

In a meta-analysis of 61 peer-reviewed articles in the online learning literature, Tan, Chan, and Mohd Said (2021) identified key learner perceptions of online instruction related to quality virtual education. Three factors for quality online learning were identified in this analysis: quality instruction, social interaction, and instructional and technical support. In an early study describing elearning benchmarks, Ossiannilsson and Landgren (2012) described a framework for quality virtual learning. Their framework describes the following elements as integral to quality elearning: personalization, flexibility, accessibility, transparency, interactiveness, participation, and productivity. Other critical success elements relevant to virtual higher education Ossiannilsson and Landgren (2012) describe include issues of pedagogy, open educational resources (OER), and other learning materials, and teachers’ competences and skills. What these three studies (and many others in the virtual learning literature) articulate is that to achieve and sustain quality in virtual learning there needs to be opportunities for learners to actively participate in flexible, meaningful, productive ways led by educators who are knowledgeable about the topic being studied as well as how quality virtual education is delivered. In the following sections of this chapter, we will examine how three pedagogical approaches can provide educators with the tools to provide high quality virtual learning. These approaches are relatively new in the education space having only been identified late in the twentieth century because of the Internet and the affordances of the digital revolution and computer-mediated communication. Now let’s consider the three pedagogical approaches – networked learning, authentic learning, and the Community of Inquiry – and what they can bring to the quality discussion.

Networked Learning While there have been many technologies that have dramatically changed the way humans carry on their lives, such as fire, the inclined plane, or the printing press, it can be argued that the Internet holds a place in this list. Wikipedia defines the Internet as: Global system of interconnected computer networks that uses the Internet protocol suite (TCP/IP) to communicate between networks and devices. It is a network of networks that consists of private, public, academic, business, and government networks of local to global scope, linked by a broad array of electronic, wireless, and optical networking technologies.

Prior to the digitization of data mass media relied on analogue technologies. Print, radio, film, and television have been pivotal in shaping communities and countries as

14

Adapting and Creating New Theories Through the Ongoing Research. . .

249

they develop. The Internet – because of its networking affordances across a connection of computer networks spanning the globe – has connected anyone and everyone to each other with the ability for personal communication. Using these networks for learning is the basis for Networked Learning as educational theory and practice. In 2004, networked learning was described by Goodyear et al. (2004) as a theory for the digital age which has implications for learners who are connected by networks. Networked learning is Learning in which information and communications technology (ICT) is used to promote connections: between one learner and other learners; between learners and tutors; between a learning community and its learning resources. (Goodyear et al. 2004, p. 1)

Taking a closer look at this definition one can see that interaction – between learners, between learner and teacher, and between learners and content – forms the basis of connections. Interaction has always been valued in education. John Dewey, in 1938, stated that interaction is the defining element of education that occurs when the student transforms the information passed to them from another, and constructs it into knowledge with personal application and value (Dewey 1938). Some time later, Moore (1989) originated the identification of interaction types in distance education practice when he described the three forms of interaction in DE as learner-content interaction, learner-instructor interaction, and learnerlearner interaction. In addition, interactivity has been described as fundamental by educational theorists who focus on the creation of the learning communities (Garrison and Anderson 2000). The tools that support networked learning which were at one time much less capable than those available today and better able to foster connections between learners and instructors. The connections that can be supported by online or virtual networks are what is most important about this pedagogical approach for the virtual university setting. In their seminal work, Anderson and Garrison (1998) conceptualized the interactions that happen in learning in order to make clear the importance of connections which are needed for deep and meaningful learning (see Fig. 1). Anderson and Garrison expanded on the work that Moore had done to identify six types of interaction: student-content interaction, student-student interaction, studentteacher interaction, teacher-teacher interaction, teacher-content interaction, and content-content interaction. In a virtual university, support for all of the types of key interactions that encompass any and all of the learner touchpoints with the institution, the instructor, and other learners is crucial. While there are considerable opportunities where networked learning can support learners and learning in a virtual university, the affordances of the network and the active learning that it supports is the most important. Active learning “engages students in the process of learning through activities and/or discussion in class, as opposed to passively listening to an expert. It emphasizes higher-order thinking and often involves group work” (Freeman et al. 2014). Examining the interactions identified in Fig. 1, active learning as the structure by which to implement networked learning provides an example for consideration. Central to networked learning

250

N. Ostashewski

Fig. 1 Modes of interaction in distance education. (Adapted from Anderson and Garrison (1998))

approaches is the importance of human interactions in the teaching and learning activities that form a virtual education experience. Benefits and challenges for networked learning implementation are like with any other pedagogy – support and experience are needed to be successful. The most evident benefits of networked learning approaches include the opportunity for collaboration and shared knowledge building that are part of active learning designs. In both theory and practice, networks enable connections that are meaningful for the learning process. When educators implement learning that takes advantage of these connections, including the ongoing building of new connections, learners benefit significantly. Networked learning assumes a constructivist approach to learning, and as such engages learners in their own learning as opposed to being passive listeners to lectures. Freeman et al. (2014) conducted a meta-analysis of 225 studies comparing “constructivist versus exposition-centered course designs” across all disciplines and reported that constructivist learning activities were significantly more effective at supporting learning. Networked learning is not without its challenges as Anderson and Garrison (1998) pointed out in their book chapter Learning in a networked world: New Roles and Responsibilities. They describe how the role of the learner changes from a passive learner to an active participant where computer-mediated communication is present. Networked learning brings with it a challenge of preparing learners for this new active role, particularly when learners have come from more instructivist learning environments. Evident also are the challenges with educators and learners using the tools of the network in meaningful ways. Despite the conviction of society on the digital acumen of younger learners, using networks to support your own

14

Adapting and Creating New Theories Through the Ongoing Research. . .

251

learning is not the same as texting, emailing, or simply posting images and videos. Perhaps one way in which to understand the challenges of networked learning approaches is to consider the popular social media platform Twitter. Posting a tweet (a short sentence) seems relatively simple to accomplish, however using hastags (#) or reposts to aggregate or share out to an existing network of followers – for the purpose of education – is not so simple. At the same time Twitter users who are experienced and can use the platform well have access to a multitude of users and groups that can aggregate information in real time about any topic of interest. Pinterest and Instagram are examples of similar platforms that make use of images in the same way, sharing and aggregating information that readily is used to educate the user. Beyond simply the cost of access to the Internet and the devices that needed to access are the challenges of any networked technology in terms of time to develop levels of expertise that result in meaningful activity. In summary, networked learning lays the groundwork for learners to actively engage with their learning. Without support from the institution and the educator, learners can quickly be cast adrift to get frustrated and quit. For this reason, learning technologies should be kept to a few effective systems that are fully supported to not overwhelm learners who avail themselves of what a virtual university experience can provide: flexibility, access, and personalization (Dhawan 2020).

Authentic Learning As we ended the previous discussion highlighting the advantages of a virtual university, we now turn our attention toward a pedagogical approach that directly impacts the personalization of the experience – authentic learning activities. The term authentic learning more aptly refers to the intentional structuring of authentic learning activities within a situated learning design. Collins (1988) described situated learning as “learning knowledge and skills in contexts that reflect the way the knowledge will be useful in real life.” In essence, authentic refers to the presentation of real-life situations where the topic being studied is being applied. When learning is being designed for adults (andragogy), learning activities that have real-life application or are able to be situated in the learner’s own context are particularly motivating. (Hartnett et al. 2011) When considering authentic learning, we are best able to perhaps to articulate what it provides for learners. According to Rule (2006), who conducted an analysis of 45 authentic learning journal articles, authentic learning can be described using four components: 1. The activity involves real-world problems that mimic the work of professionals in the discipline with presentation of findings to audiences beyond the classroom. 2. Open-ended inquiry, thinking skills, and metacognition are addressed. 3. Students engage in discourse and social learning in a community of learners. 4. Students are empowered through choice to direct their own learning in relevant project work (Rule 2006, p. 2)

252

N. Ostashewski

Considering the affordances of the virtual educational setting, one of the key benefits of a well-designed instructional environment is its ability to include “opportunities for simulated apprenticeships as well as a wealth of learning support activities” (Reeves 1993, p. 107). Researchers and educators studying situated learning have accepted that the digital resources can provide an alternative to the real-life setting, and that such technologies can be used without sacrificing the authentic context which is such a critical element of situated learning. McLellan (1996) similarly pointed out that while knowledge needs to be learned in context (according to the situated learning), that context can be the actual work setting, a highly realistic or ‘virtual’ surrogate of the actual work environment, or an anchoring context such as a video or multimedia program (p. 8). This is the basis for a wide variety of digital representations of contexts – from fully immersive 3D worlds like Second Life to online simulation environments, or simply as case studies organized in designed contexts presented in online videos. Colleges that deliver trade apprenticeship programs are clear examples where authentic or situated learning is the predominant learning environment. In apprentice programs learning by engaging in the real-life situations is paramount for the preparation of trade certification. University education, a more broad and often less career-specific educational path, is less able to provide direct situational settings for learning activities. However, in the following paragraphs we explore some of the benefits and challenges authentic learning can bring to learning, and how it can contribute to quality in a virtual university setting. The design of authentic learning opportunities provides several benefits for learners and for the institution itself. Personalizing of course activities is one example where providing authentic or real-life-type activities contributes to learning success. Having learning activities that are applicable to the particular context of the learner, in essence allows for contextualizing the learning material by the learner for the learner’s particular situation. Not only does this support constructivist and collaborative learning, but also actively engages the learner. The active meaningful engagement is further supported by adding to learner motivation for course participation. The increased engagement and motivation is perhaps the most important benefit of authentic learning, whether the activities are structured assessments or online discussions that form part of a course. One challenge for educators in virtual education settings is the online discussion forum and making the participation of learners meaningful. Many educators speak about the challenges in making forums something more than a reiteration of the correct answer by each learner. Authentic discussions can address this challenge. Research conducted by Hartnett, St. George, and Dron (2011) points to three specific manners in which authentic discussions can support learner motivation. Hartnett et al. (2011) state that by structuring discussions to be relevant, offering options, and with frequent communication, learner motivation to participate is supported. They recommend the following in the design of authentic online discussions. 1. The relevance and value of the task (e.g., online discussions) need to be clearly identified and linked to learning objectives to help learners understand how the

14

Adapting and Creating New Theories Through the Ongoing Research. . .

253

activity can aid in the realization of personal goals, aspirations, and interests, both in the short and longer term. 2. Offering meaningful choices (i.e., not just option choices) to learners that allow them to pursue topics that are of interest to them, the perceived value of the activity is further enhanced. 3. Establishing frequent, ongoing communication with learners, where they feel able to discuss issues in an open and honest manner, practitioners are in a better position to accurately monitor and respond to situational factors that could potentially undermine learner motivation. (Hartnett et al. 2011). One group of researchers has spent considerable time researching and implementing situated learning through the use of authentic learning tasks. Jan Herrington, Tony Herrington, Ron Oliver, and Tom Reeves collaborated on in-depth research describing what situated learning looks like, first the classroom setting, and eventually the online environment. In the virtual education setting what is most evident about situated learning is that the use of authentic tasks encourages and supports immersion in self-directed and independent learning (Herrington 2006). These learner attributes are important success factors for virtual learning settings. Their work collectively has clarified how authentic learning impacts learners and can be summarized as follows: In-depth qualitative studies revealed that the most successful learning environments employing authentic tasks: are customer-oriented, offering education as a process rather than a product; they do not necessarily seek to provide real experiences or photo-realistic simulations, but provide ‘cognitive realism’; and they accept the need to assist students to become accustomed to learning in what might be a totally different way, and to assist with the necessary ‘suspension of disbelief’ that is sometimes required in such learning environments. (Herrington 2006, p. 4).

The “learning in a different way” that Herrington refers to seems to be the new roles Anderson and Garrison (1998) highlighted regarding the onset of the networked learning environments. Exactly what is situated learning and how can it be implemented? According to Herrington and Oliver (2000) situated learning environments have the following characteristics: 1. Provide authentic contexts that reflect the way the knowledge will be used in real life. 2. Provide authentic activities. 3. Provide access to expert performances and the modelling of processes. 4. Provide multiple roles and perspectives. 5. Support collaborative construction of knowledge. 6. Promote reflection to enable abstractions to be formed. 7. Promote articulation to enable tacit knowledge to be made explicit. 8. Provide coaching and scaffolding by the teacher at critical times. 9. Provide for authentic assessment of learning within the tasks. (p. 4).

254

N. Ostashewski

These characteristics provide an overview of the learning environment but how does this work in terms of learning activities and structuring those for a virtual education setting? The Herrington, Oliver, and Herrington (2007) team engaged in design-based research projects to test their critical elements of an authentic learning design for online learning. The research and practice they share in Authentic learning on the web: Guidelines for course design (Herrington et al. 2007) describes three steps for the creation of authentic learning. 1. Develop an authentic scenario that is capable of carrying the concepts and skills associated with the course of study. 2. Create authentic activities for learners to complete over the term of the course. 3. Support learners over the term of the course by: (a) Providing access to expert performances. (b) Providing multiple roles and perspectives. (c) Providing opportunities for articulation. The role of the educator changes in this authentic environment to more of a guide on the side or expert learner in the community of learners that makes up the course participants. One of the challenges of authentic learning is for educators to have enough expertise and creativity with the design of learning activities that implements this approach. As with many other educational approaches, educator training and experiences are what best prepares them for working toward authentic activities and environments. However, the benefits of this approach which is student-centered means that for virtual higher education, where course design is likely done using a systems or team design approach, is significant and worth the effort. One unintended consequence of designing online programs with authentic learning activities is that they form a barrier for plagiarism and student cheating. For a virtual institution, one of the significant digital age challenges is the ease in which learners can submit assignments they have not completed themselves as their own. Numerous online repositories offer learners completed assignments in exchange for their own assignments, or for a fee to have the assignment completed for the learner. Almost any course that has been online for any length of time, even as short as one month, often can be found in some online assignment repository. This creates significant challenges for a virtual university and by incorporating authentic activities (or learner collaborative activities) into course designs, some of this plagiarism can be averted.

Community of Inquiry The third pedagogical approach we explore is the Community of Inquiry (CoI). The CoI is perhaps the most important and encompassing approach when considering quality of education delivery in a virtual higher education university. The CoI was originally described as a consequence of research and practice in early text-based online learning delivery. Originating from quality education practices, the practical

14

Adapting and Creating New Theories Through the Ongoing Research. . .

255

inquiry model, and the intersection of distance education theory, the CoI brings attention to the new online roles of the educator and learner. The Community of Inquiry (CoI) theoretical framework (Garrison et al. 2001) is the most widely referenced (cited over 8200 times) and arguably the most widely used model for online-based technology-enabled learning due to its simplicity and versatility as a framework (Fig. 2). Creating communities of inquiry in online education courses is a well-researched pedagogical approach. Early research of the model focused on describing and understanding social presence (Richardson and Swan 2003) as a new way to approach teaching beyond direct transmission models of education delivery. For practitioners, the CoI is about engaging learners in ways that allow for them to take the lead in their own education path, guided by the instructor, supported by the content and peers. This is in line with the move away from instructivist, institutioncentered approaches and represents the revolution in education that is happening around the globe, as educators more and more come to understand the implications of digital information and communication. Therefore, both as practitioner and researcher model, the CoI provides a clearly articulated way virtual education should be implemented for higher education institutions.

Fig. 2 The Community of Inquiry Framework. (CC-SA The Community of Inquiry Website (https://www.thecommunityofinquiry.org/framework))

256

N. Ostashewski

The CoI framework is a collaborative-constructivist process model that describes the essential elements of a successful online higher education learning experience rooted in Dewey’s educational philosophy and social constructivism (Garrison 2017). In a more practical sense, the CoI is a dynamic model of the necessary core elements for both the development of community and the pursuit of inquiry, in any educational environment (Swan et al. 2009). The framework includes three elements called presences: cognitive presence (CP), social presence (SP), and teaching presence (TP). 1. Social Presence: The ability of participants to identify with the community (e.g., course of study) communicate purposefully in a trusting environment, and develop interpersonal relationships by way of projecting their individual personalities. 2. Cognitive Presence: The extent to which learners are able to construct and confirm meaning through sustained reflection and discourse in a critical community of inquiry. 3. Teaching Presence: The design, facilitation, and direction of cognitive and social processes for the purpose of realizing personally meaningful and educationally worthwhile learning outcomes. From the viewpoint of an institution or an educator, the CoI provides a model from which to consider all aspects of the virtual learning course. Teaching presence provides a clear description of the role of the online or virtual educator as the presider of the course. Teaching presence is defined (in the CoI) as the design, facilitation, and direction of cognitive and social processes for the purpose of realizing personally meaningful and educationally worthwhile learning outcomes (see Table 2). Teaching presence begins before the course commences as the teacher, acting as instructional designer, plans and prepares the course of studies, and it continues during the course, as the instructor facilitates the discourse and provides Table 2 Principles of teaching presence A. Design Social presence Cognitive presence B. Facilitating discourse Social presence Cognitive presence C. Direct instruction Social presence Cognitive presence

Establish climate that will create a community of inquiry Establish critical reflection and discourse that will support systematic inquiry

Sustain community through expression of group cohesion Encourage and support the progression of inquiry through to resolution

Evolve collaborative relationship where students are supported in assuring increasing responsibility for their learning Ensure that there is resolution and metacognitive development

Adapted from Garrison (2006)

14

Adapting and Creating New Theories Through the Ongoing Research. . .

257

direct instruction when required. Through adequate teaching presence, formal learning that facilitates personally relevant and educationally defined outcomes is achieved. Implementing a CoI approach in virtual settings should be the goal of all virtual courses, as the benefits of this approach take advantage of the asynchronous interactions of learners in ways that allow learners to participate as they best determine they can and need to support their learning. For example, instructors should understand that their role in asynchronous learning can support the educational needs of learners by: (a) Ensuring student comfort in course discussions (b) Solid instructor leadership to focus discussions (c) Supporting a sense of belonging through interactions and discussion At the higher education level, more than at any other level of formal education, learners know best how they can learn. The CoI approach meets that goal of learnerdirected, learner-centered educational delivery.

Discussion As discussed at the beginning of this chapter, research on virtual learning in universities has identified several elements that students have identified as important for their satisfaction and success. These elements are lecturers’ knowledge, experience, and pedagogical capacity; quality of the feedback they get on activities students carry out; and on the speed and efficiency of having their questions being answered. All three of these elements are related to the ability of a lecturer or their institution being able to provide timely responses in support of student learning – at any time and for any reason. This need for support has numerous implications for a virtual university that can enable or hamper the three pedagogical approaches described in this chapter. One implication for virtual universities is the need for robust and reliable technological tools. The first of these tools is a well-developed learning management system (LMS) that allows for many different types of learner interactions. The LMS should be able to support a wide variety of learner-instructor, learner-content, and learner-learner activities. For learner-centric approaches to be effective, the technologies that they operate on must be reliable and have a wide range of well-supported capabilities. To best support learners as they progress through virtual-delivered programs, both asynchronous tools (threaded discussion boards) and synchronous tools (live chat, live videoconferencing) should be available to learners. As on-campus social events and activities would not be likely possible for a virtual institution, an internal social media platform should be made available. There learners can participate in regularly scheduled online events, activities, opportunities for learners to engage with each other outside of the courses that make up their program of study. The technology tools that are made available – not only for course

258

N. Ostashewski

delivery, but also for learner connections – are of crucial importance for a virtual university. A second implication for virtual universities as they consider what is needed to support the three pedagogical approaches presented in this chapter is the need for effective and responsive support systems. A virtual university needs support systems for both learners and instructors, especially since there is a large dependency on technological tools. Rather than classrooms and physical spaces being managed, virtual institutions need to focus on enabling online use of LMS, email, and synchronous tools that form the spaces for educational activities. Instructional supports for educators such as instructional design team assistance, a wide range of online library resources, professional development for both technologies and pedagogical approaches for instructors, and responsive IT technical support are needed. Similarly, learners also need to be able to access IT technology supports (such as a call center), training opportunities on how to utilize the LMS and other tools, and ongoing opportunities for engagement with other educational program and personal wellness supports. And most importantly, instructors need to be competent in not only their field of expertise, but also in the technological pedagogical content knowledge (Koehler and Mishra 2009) that is needed to support online learners effectively.

Conclusion In this chapter, we have examined three approaches to education that support the delivery of quality higher education in a virtual setting. While all three of these approaches vary considerably, they share common elements. One common thread that runs through them all is that they focus on a learner-centered approach and include a variety of supports for learner – learner interactions. There are activities that exist in all elements of human learning activities: we as a learner choose what to learn and often find resources and others with whom to discuss or enable the learning. Formal education settings are about being able to provide learning paths that are common to larger groups of students, primarily because of the cost involved in development and delivery of education. Networking on meaningful topics related to a course of study within a community supports learner diversity of thought, collaboration, and engagement in ways that are simply not possible in educational delivery approaches of the past. In an age where information is no longer the limiting element for new knowledge access, processes such as collaboration and cooperation in education settings are the future requirements. The centrality of human interaction, in our conception of networked learning, carries with it some pedagogical commitments and beliefs about learning. In short, there is no point to networked learning if you do not value learning through co-operation, collaboration, dialog, and/or participation in a community. (Goodyear et al. 2004, p. 2)

Another common thread of the three learning approaches presented in this chapter is their description of learner as an active participant, rather than a passive one. This

14

Adapting and Creating New Theories Through the Ongoing Research. . .

259

requires much more effort and self-regulation on behalf of the learner and is one challenge that many educators face when bringing these types of approaches into practice. McConnell et al. (2012) make clear this point: Networked learning places a high value on cooperation and collaboration in the learning process; self-determination; difference; trust; investment of self in the networked learning process; and the role technology plays in connecting and mediating. (p. 291)

Networked learning is at the center of authentic learning and the CoI, as without the network affordances, the rest would not be possible. The role of instructors and learners are different in the age of digital education and technology-enabled learning approaches. Engaging learners and active learning needs to be the primary goal of educators and the institutional system that support them. Collaboration opportunities and authentic assessments should be evident in all courses within a program. A consistent message to learners about the importance of their role as active learners to achieve success is key to all three pedagogical approaches. And educators at all levels of the institution should understand that designing effective learning needs to focus on discourse and collaboration as the key elements to achieve high-quality online learning. In summary, virtual higher education can best support learners applying the following elements integral to the approaches discussed in this chapter are: 1. Networked learning supports active engagement with peers and instructor in a course. 2. Situated learning with authentic activities further support engagement and motivation. 3. The Community of Inquiry approach puts learners at the center of the process with support being provided by resources and other learners of a community (in addition to just the instructor) working to understand the same topic. These give support for meaningful learner engagement with course topics in a scaffolded design manner. In some sense, the use of these three approaches can provide a scaffolded active learning opportunity for learners to be engaged as much as they are able to with the course topic. Giving voice to other learners in an instructional-like role allows them to contribute to the learning of their peers, while at the same time becoming more confident in their own topic knowledge.

References Anderson, T., and D.R. Garrison. 1998. Learning in a networked world: New roles and responsibilities. In Distance learners in higher education: Institutional responses for quality outcomes. Madison: Atwood. Appana, S. 2008. A review of benefits and limitations of online learning in the context of the student, the instructor and the tenured faculty. International Journal on E-learning 7 (1): 5–22.

260

N. Ostashewski

Collins, A. 1988. Cognitive apprenticeship and instructional technology, Technical report no. 6899. Cambridge, MA: BBN Labs, Inc. Dewey, J. 1938. Experience and education. New York: Collier Books. Dhawan, S. 2020. Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems 49 (1): 5–22. Freeman, S., S.L. Eddy, M. McDonough, M.K. Smith, N. Okoroafor, H. Jordt, and M.P. Wenderoth. 2014. Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences USA 111: 8410–8415. Garrison, D. R., Anderson, T., and Archer, W. 2000. Critical inquiry in a text-based environment: Computer conferencing in higher education model. The Internet and Higher Education 2(2–3), 87–105. Garrison, D.R. 2006. Online collaboration principles. Journal of Asynchronous Learning Networks 10 (1): 25–34. ———. 2017. E-learning in the 21st century: A community of inquiry framework for research and practice. 3rd ed. London: Routledge. Garrison, D.R., T. Anderson, and W. Archer. 2001. Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education 2 (2–3): 87–105. Goodyear, P., S. Banks, V. Hodgson, and D. McConnell. 2004. Research on networked learning: An overview. In Advances in research on networked learning, 1–9. Springer Science + Business Media, Inc. Hartnett, M., A. St. George, and J. Dron. 2011. Examining motivation in online distance learning environments: Complex, multifaceted, and situation-dependent. The International Review of Research in Open and Distance Learning 12 (6): 20–38. https://doi.org/10.19173/irrodl. v12i6.1030. Herrington, J. 2006. Authentic E-Learning in higher education: Design principles for authentic learning environments and tasks. In World conference on e-learning in corporate, government, healthcare, and higher education (ELEARN) 2006, 13–17 October 2006, Honolulu, Hawaii, USA. Herrington, J., and R. Oliver. 2000. An instructional design framework for authentic learning environments. Educational Technology Research and Development 48 (3): 23–48. Herrington, J., R. Oliver, and A. Herrington. 2007. Authentic learning on the web: Guidelines for course design. In Flexible learning in an information society, 26–35. IGI Global. Koehler, M., and P. Mishra. 2009. What is technological pedagogical content knowledge (TPACK)? Contemporary Issues in Technology and Teacher Education 9 (1): 60–70. Martínez-Argüelles, M.J., M. Blanco Callejo, and J.M. Castán Farrero. 2013. Dimensions of perceived service quality in higher education virtual learning environments. International Journal of Educational Technology in Higher Education 10 (1): 268–285. McConnell, D., V. Hodgson, and L. Dirckinck-Holmfeld. 2012. Networked learning: A brief history and new trends. In Exploring the theory, pedagogy and practice of networked learning, ed. L. Dirckinck-Holmfeld, V. Hodgson, and D. McConnell. New York: Springer. McLellan, H., ed. 1996. Situated learning perspectives. Englewood Cliffs: Educational Technology Publications. Moore, M. 1989. Three types of interaction. American Journal of Distance Education 3 (2): 1–6. Ossiannilsson, E., and L. Landgren. 2012. Quality in e-learning–a conceptual framework based on experiences from three international benchmarking projects. Journal of Computer Assisted Learning 28 (1): 42–51. Reeves, T.C. 1993. Evaluating interactive multimedia. In Multimedia for learning: Development, application, evaluation, ed. D.M. Gayeski, 97–112. Englewood Cliffs: Educational Technology Publications. Richardson, J., and K. Swan. 2003. Examining social presence in online courses in relation to students’ perceived learning and satisfaction. Journal of Asynchronous Learning Networks 7 (1): 68–88.

14

Adapting and Creating New Theories Through the Ongoing Research. . .

261

Rule, A.C. 2006. The components of authentic learning. The Journal of Authentic Learning 3 (1): 1–10. Singh, V., and A. Thurman. 2019. How many ways can we define online learning? A systematic literature review of definitions of online learning (1988–2018). American Journal of Distance Education 33 (4): 289–306. Swan, K., D. Garrison, and J.C. Richardson. 2009. A constructivist approach to online learning: The community of inquiry framework. In Information technology and constructivism in higher education: Progressive learning frameworks, ed. C. Payne, 43–57. IGI Global. https://doi.org/ 10.4018/978-1-60566-654-9.ch004. Tan, K.H., P.P. Chan, and N.E. Mohd Said. 2021. Higher education students’ online instruction perceptions: A quality virtual learning environment. Sustainability 13 (19): 10840.

Social Media: Friend and Foe

15

Stefanie Panke

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evolution, Features, Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Drawbacks and Concerns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pedagogical Potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Recommendations for Virtual Universities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alternatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

264 266 269 271 271 275 276 278 279 279

Abstract

Social media tools are extremely easy to use and at the same time very hard to use as well. How should the virtual university traverse the treacherous waters of usergenerated content networks with potential for viral distribution, where one’s reputation can rise and fall in the blink of a click? Should educational organizations sail down the endless stream of tweet, post, reel, like, share, react, and comment, or should they steer clear of channels and feeds? The chapter traces the rise of social media; documents common traits, platforms, and genres; and discusses both potential and pitfalls. It presents alternative platforms and provides pedagogical strategies and use cases. It concludes with recommendations for the virtual university that center effective, fair, and ethical use.

S. Panke (*) School of Social Work, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_15

263

264

S. Panke

Keywords

User-generated content · Social networks · Wikis · Blogs · Podcasts · Informal learning · Seamless learning · Misinformation · Social influence · Web 2.0

Introduction Social media is a term that is broadly used to describe any number of technological systems related to collaboration and community. While it appears that a specific definition may be elusive, social media is often described by example. Social networking sites, blogs, wikis, multimedia platforms, virtual game worlds, and virtual social worlds are among the applications typically included (Tess 2013). As of 2014, mobile devices officially outnumber people on the planet. According to the data portal Statista, the power of social networking is such that the number of worldwide users is expected to reach some 3.02 billion monthly active social media users by 2021, around a third of Earth’s entire population (Statista 2020). How can we begin to understand what this means for learning and teaching? Both formal and informal learning experiences shape what we know and how we learn, and social media permeate both modes of learning. While attending a virtual university, study settings encompass many different environments: Learning happens at home, at work, with friends, in the coffee shop. Similarly, the tools and technologies students use to learn will include both formal university channels and informal sources for information and communication, the latter typically via social media platforms. Social media sites can offer a range of learning opportunities, to access expert advice, encounter challenges, defend opinions, and amend ideas in the face of criticism (Sharples et al. 2016). Social media channels permeate the daily lives of millennials. The availability of social media through smartphones and mobile apps is a significant contributing factor (Talib 2018). In the spirit of meeting learners where they are, many online programs offered by universities have embraced social media channels as teaching tools or administrative communication platforms. However, it is not easy for organizations to navigate the social media landscape and leveraging its potential while avoiding its pitfalls (Khan et al. 2021). Given the prevalence of social media among relevant audiences, administrative communication, and marketing are understandably drawn to these tools for purposes such as student recruitment, donor communication, and alumni relations. Virtual and traditional campuses rush to claim their stake on the most prominent social media platforms. Virtually every campus communication department feeds content into Twitter, Facebook, YouTube, Instagram, and other platforms. These social media channels feature predominantly carefully curated content that has been vetted by communication professionals. Some organizations regulate staff and departmental use through policies and include guidelines for acceptable social media use in the student code of conduct. According to the content analysis by Pomerantz et al. (2015), policies required that those affiliated with the institution

15

Social Media: Friend and Foe

265

post appropriate content, represent the unit appropriately, and moderate conversations with coworkers and external agencies. In addition to administrative communication, social media tools play a significant role in teaching and learning. Sometimes, social media and formal learning converge productively. Other times, the lecture competes for attention with the latest TikTok video or snapchat message. Privacy breaches and data ownership concerns, distraction, cognitive overload, harassment, and tribalism are negative outcomes associated with social media use. Consequently, many teachers appear to struggle with the tension between possible pedagogical use and the tempting distraction of this technology (Van Den Beemt et al. 2020). Rather than connecting learners in a “global village,” social media content is algorithmically targeted to users so that their feed reinforces their world view rather than expand or challenge it (Harris 2020). At the same time, messages can unintendedly reach viral distribution and produce visceral reactions, often referred to with the dysphemism “shitstorm.” Herrmann, Lindvig and Aagaard (2021) observed that within the last decade the techno-optimism has waned in the scholarly community, and practitioners have started debating whether and how to regulate the use of mobile devices and social media. Some campuses have banned social media use in teaching altogether and instead use platforms that embrace similar principles in a sheltered space. How can virtual universities leverage social media effectively, responsibly, and purposefully? Clearly, this question goes beyond a technical how-to of handling specific channels or tools. As Mike Caulfield described it: “Now everyone has access, anyone can publish, and it is people’s attention that is the scarce resource. [. . .] Participation has to grapple much less with access and much more with how one invests time productively, ethically, fairly” (Interview for AACE Review, cf. Panke 2018). This chapter intends to map the territory of social media and discuss its usage in higher education, providing a compass based on case studies, best practice examples, reports, and research. The focus is on the opportunities of social media for the virtual university, especially with regard to support of digital well-being and digital citizenship in an online program. The structure is as follows: 1. Section one introduces social media features, platforms, and genres. 2. Section two outlines problems with social media usage on the societal, individual, and organizational levels. 3. Section three presents the potential for social media in education both in terms of broad pedagogical concepts and in form of concrete examples of effective social media use in teaching and learning. 4. Section four discusses restrictive regional and institutional policies and lists alternative platforms that emulate features of social media in organizationally controlled environments. 5. The conclusion recommends tools and approaches for conceptualizing social media communication channels for the virtual university.

266

S. Panke

Evolution, Features, Platforms The early days of the Internet were characterized by the fact that the pioneers were not only the users of what was offered on the web but, as a rule, also the authors of a web presence. Instead of professional services, individual curation and creativity were the drivers of the growing world wide web, in line with the vision of early pioneers. During the late 1990s, as the Internet increasingly became part of society’s everyday media experience, the use of the technology was simultaneously hampered by new limitations: Since web design had become more and more professionalized, it was very difficult to create a state-of-the-art website without profound knowledge of content-management systems, HTML, Cascading Style Sheets (CSS), and script languages. Individuals needed to spend a lot of time, and considerable technical skills were required to maintain and update a website. Users who were not versed in technology faced great difficulties when they tried to play an active part in authoring, linking, and commenting. For that reason, the web was perceived by many users only and exclusively as a medium for retrieving information. Today, however, there exist many different providers and programs that allow individuals to utilize the web to suit their own intentions without any prior technical knowledge. The lifecycle of software has become more dynamic (“perpetual beta”) and more closely aligned with the needs and requirements of users. Marketing principles like “the long tail” (Anderson 2006) emphasize the importance of niches to create the best possible fit between supply and demand. This is facilitated by open application programming interfaces (APIs), which allow to derive “mash-ups” from various data sources so that the users’ needs can be considered across the boundaries of different services and websites. Simple tools and web services, such as blogs, wikis, content sharing, and social bookmarking, simplify the presentation of one’s own topic to large audiences – an opportunity available to many more people than ever before (Table 1). Users are turning to these new and simple technologies that function as open systems without any rigid role and workflow concepts, since they support creative interaction, enhancement, and change. Since the early 2000s, web technology was shaped by a co-evolution of innovative online services and innovative forms of usage. This co-evolution has resulted in a set of features that are characteristic across social media sites: • Social tagging describes the personalized and collective way of organizing and sharing content on social media. Social tagging comprises both concepts (# symbol) as well as individual people, groups, organizations, or businesses (@ symbol). Tagging combines individual and collective knowledge management, retrieval, and signaling, and can be compiled into folksonomies (Yang et al. 2012). • User-generated content is any video, photo, review, post, comment, etc., created by the user uploaded to the Internet. User-generated content is perceived as authentic and credible, which makes it a valuable marketing tool for educational organizations.

15

Social Media: Friend and Foe

267

Table 1 Most influential platforms in social media worldwide. (Data compiled from various reports by Statista (2021), retrieved from: https://www.statista.com/) Platform Twitter

Facebook

Instagram

YouTube

Description The online social networking service enables users to send short 280-character messages (“tweets”), acquire a regular audience (followers), use hashtags to organize content, and engage in discourse by retweeting and contextualizing information The online social networking service Facebook enables users to create a profile where they can share text (posts), photos, and media with a personal audience (friends) or a public audience. Users engage in discourse by sharing posts (reposting) and joining interest groups The online photo and video-sharing social networking service Instagram enables users to upload media that can be edited with filters. Users acquire a personal audience (followers) and use geotagging and hashtags to organize content. Users engage in discourse by sharing posts both within Instagram and to a variety of other social media platforms such as Facebook and Twitter The online video-sharing social networking service YouTube enables users to post videos, acquire an audience (subscribers), use hashtags to organize content, and engage in discourse by commenting on and liking/disliking videos

Popularity One of the leading social networks worldwide. As of the fourth quarter of 2020, Twitter had 192 million daily active users worldwide

Educational use Many academics use Twitter as a platform to disseminate their research and engage in discussions The use as teaching tool is documented in Bista (2015)

Facebook is the biggest social media network worldwide. As of the fourth quarter of 2020, Facebook had 2.8 billion global active users

Many academics use Facebook to stimulate discussion and encourage participation and community building The use of Facebook as a teaching tool is documented in Camus et al. (2016)

Instagram is one of the leading photos and videosharing social networks worldwide. A forecast from October 2020 estimates that there would be 1.2 billion global users by 2023. With over 120 million active Instagram users, the United States is the app’s leading market base

Educators use Instagram to illustrate complex concepts, foster student engagement with course material, and encourage discussion The use of Instagram as an educational tool is documented by Carpenter et al. (2020)

YouTube is one of the leading video-sharing social networks worldwide. In 2021, YouTube’s user base in the world amounts to approximately 2.1 billion users

Educators use YouTube to distribute lecture recordings and other video learning content The use of YouTube as a teaching tool is documented by Roodt (2013)

(continued)

268

S. Panke

Table 1 (continued) Platform Description TikTok/Douyin The online video-sharing social networking service TikTok (called Douyin in China) enables users to post short-form videos ranging from 15 seconds to 3 minutes. Users acquire a regular audience (followers), use hashtags to organize content, and engage in discourse by reposting videos and commenting SnapChat The online photo and multimedia sharing social networking service Snapchat enables users to share videos and photos (stories), privately message impermanent photos (snaps), and acquire a personal audience (friends). Users engage in discourse by sharing photos and stories WhatsApp The online instant messaging social media service WhatsApp enables users to send text messages, make voice and video calls, share content, and acquire a personal audience (friends). Users engage in discourse by forwarding and sharing information WeChat The messaging service WeChat is a multipurpose application that is used to send messages, make voice and video calls, conduct e-payments, as well as share and retrieve information

Popularity TikTok is one of the fastest growing videosharing social networks worldwide. In the second quarter of 2021, TikTok achieved approximately 205 million downloads

Educational use Educators use TikTok to promote educational innovation and create engagement The use of TikTok as a teaching tool is discussed by Escamilla-Fajardo et al. (2021)

Snapchat is one of the leading photos and videosharing social networks worldwide. During the fourth quarter of 2020, Snapchat reported 265 million daily active users worldwide

Educators use Snapchat to enhance student engagement, connect students to resources, and encourage participation The use of Snapchat as a teaching tool is documented by Fenn and Reilly (2020)

WhatsApp is one of the leading instant messaging social network worldwide. As of March 2020, WhatsApp had two billion monthly active global users

Educators use WhatsApp to reach out to students, encourage flexible learning, and offer easy access to material The use of WhatsApp as a teaching tool is documented by Gon and Rawekar (2017)

As of March 2021, Tencent’s instant messenger service, WeChat, was the most popular mobile app in China with over one billion monthly active users

Li et al. (2021) described the use of WeChat in Language Learning

• Social navigation is recommendations being informed and guided by information about what other people have been accessing online. The user profile and user history are no longer an individual record but also used as data to enrich the experience of other users. • Memes are oftentimes humorous combinations of image and text that are spread across social media. Coined by Richard Dawkins (1976) as the cultural equivalent

15

Social Media: Friend and Foe

269

of a gene, memes originally refer to ideas, symbols, or practices that spread by natural selection. Similarly, in the social media sphere, memes describe feelings, experiences, and behavior. In an academic environment, memes can foster connection with students, increase memorization, and generate visual representations of certain topics (Wiggins 2019).

Drawbacks and Concerns Over the past decade, EdTech researchers and practitioners alike have become more focused on the addictive and absorbing qualities of social media. When devising an effective social media strategy or deciding whether or not to deploy social media platforms as teaching tools, the digital well-being of the target audience comes into play. In a blog post on digital well-being, Chryssa Themelis described the perceived risks of social media: “Personal relations, may become less meaningful due to lack of face-to-face communication and dialogue even though sharing (photos, tweets and documents) is a prominent cultural norm” (Themelis 2018). One set of negative effects of social media relate to social comparison, a psychological process in which people evaluate themselves by comparing their own attitudes, abilities, and traits with others. Social media channels come equipped with ubiquitous comparison information and readily accessible feedback in the form of followers, likes, comments, shares, etc. Such information allows people to form impressions of others quickly in a salient and visible way, which can promote social anxiety (Jiang and Ngien 2020). A study by Stanford and New York University researchers documented that quitting Facebook improved mental health in the short-term (Allcott et al. 2020). Widely reported leaks from Facebook’s internal research suggest a substantially negative effect of Instagram on the mental health of female teenagers, with a qualitative study of female college students documented participants frequently compared their looks or the number of likes/comments with others and were concerned with how others perceived their appearance on Instagram (Baker et al. 2019). Social media create a constant segue of social influence into our lives. What we believe, what we consume, how we act, and how we feel are largely influenced by others. Thus, the makeup of our online communities is both crucial and significantly different from offline social networks. Homophily, the psychological tendency to surround ourselves with others who share our perspectives and opinions about the world, is an organizing principle underpinning many social media sites in which algorithms amplify our preference for sameness. Social media therefore limit the exposure to diverse perspectives and favor the formation of groups of like-minded users framing and reinforcing a shared narrative, that is, echo chambers (Cinelli et al. 2021). These echo chambers collide frequently in predictable patterns of everincreasing polarization. Digital humanist Nishant Shah describes Web 3.0 as “an entertainment-hate complex” (Shah 2020): “To be online is to hate – to express it, to be the victim of it, to share it in outrage, or at least to witness it in growing glee, as people rave, rant, and rage with all their might.”

270

S. Panke

In this emotionalized state, facts are deconstructed to perspectives on reality that will often depend on your social network, and it becomes harder to discern factually correct and incorrect statements online. Websites containing hoaxes and misleading information pop up across the Internet and are often shared on social media to increase their reach – by both human users and artificial bots, deliberately or unintentionally spreading disinformation. Fake news, the term to describe this phenomenon, became itself politicized and controversial, used both to criticize mainstream media and to refer to problematic content online. The World Health Organization (WHO) has referred to the scope and speed of the spread of false information linked to COVID-19 as an “infodemic” that needs swift addressing. This is not a new concern: The arrival of the Zika virus in 2007 generated frantic activity on social media focusing on the algorithmic increase in the spread of the disease and its concerning complications. Sharma et al. (2017) analyzed the discourse in the United States and found that the misleading posts were far more popular than the posts dispersing accurate, relevant public health information. Tech companies have been deploying three approaches to combat fake news: Deplatforming users, demonetizing content or channels, and labeling content as misinformation. It is however an open and extremely divisive question if social media companies are equipped to arbitrate misinformation effectively without suppressing productive dissent and freedom of speech (Prasad 2021). Most social media platforms are commercial products and leverage user data for business purposes. Concerns related to data ownership, net neutrality, and privacy are interrelated with the “walled garden” metaphor, defined as “closed or exclusive information services, content, or media on platforms” (Paterson 2012, p. 97). In online advertising, the two companies Facebook and Google are essentially capturing the complete market, complemented by regional alternatives such as Baidu in mainland China and Yandex in Russia. The social media platform WeChat combines communication app, e-payments, and news distribution. Other examples of walled gardens are the Apple ecosystem, or the overwhelming market share in e-commerce of the platforms Amazon and Alibaba. As these examples demonstrate, walled garden is an issue that transcends social media. It is amplified by the vast amount of personal information that users share with and on social media platforms and the fact that most services are owned by a handful of large companies (for a critique of Facebook, see Sen et al. 2017). As Jaron Lanier (2021) pointed out in a podcast interview, whenever advertising is the business model, users are the product: The problem is not the Internet or social media in a broad sense but rather specifically the use of the algorithms. When Google and Facebook and others went to the advertising business model anytime anybody did anything, anytime anybody connected with somebody else it was financed by third party whose motivation was to manipulate what happened. Then the whole business model was about how to manipulate more and more. What that results in is people being directed rather than exploring and that makes the world small. That is fundamental. You cannot make these algorithms better. You can’t say we want a better form of constant incremental manipulation of every person. The whole concept from the start is poison. (Lanier 2021; podcast excerpt transcribed by the author)

15

Social Media: Friend and Foe

271

One cannot consider the pedagogical potential of social media without acknowledging the massing indicators of negative effects on societal discourse and individual well-being that have led some educators to wariness or even aversion toward social media channels and companies. As Prasad (2021) predicted: “In 50 years, social media in 2021 will look like the tobacco industry in 1960 – they knowingly offered an addictive product, and, worse, hid the damage the addiction caused, while actively tried to deepen the dependency.” Is social media content truly “heroin of the mind” (Prasad 2021) or is the initial excitement that educator felt for the potential of networked learning with the rise of web 2.0 and learning 2.0 still justified? The following section makes the case for productive social media use in teaching and learning through pedagogical concepts and use cases.

Pedagogical Potential For at least two decades, contemporary educational research has advocated the blurring of boundaries between formal and informal learning to create seamless learning experiences, a term created by Kuh (1996). Seamless learning refers to the integration of learning experiences across formal and informal contexts, individual and social spaces, as well as face-to-face and online settings, with the emphasis on student-centered learning focuses the institutional mission on enabling learning by whatever means are available, convenient and comfortable for the learner (Bell 2000). As such the idea of seamless learning is closely related to the concept of “Personal Learning Environment” (PLE). Personal learning environments (PLE) are “an idea of how individuals approach the task of learning” (Couros 2010) and describe “the activities and milieu of a modern online learner” (Martindale and Dowdy 2010). PLEs comprise tools, communities, and services learners use to direct their own learning and pursue educational goals. They migrate the management of learning from the institution to the learner (Downes 2007). Mobile technologies and social media can foster seamless learning and empower learners to steward their own personal learning environments. Social media serve as self-organized meeting spaces for informal learning and peer connection. For example, Masserini and Bini (2021) analyzed survey data from first-year students and found that joining student Facebook groups lowered the drop-out rate.

Examples Social media refer to a wide array of platforms, which shift with growing or waning popularity, and genres that transcend the technological features of one single platform into a narrative technique. When thinking about the pedagogical potential of social media, it pays off to distinguish between the specific commercial platforms and the generic genres that have emerged in this space. Table 2 highlights selected examples of social media transcending formal and informal learning that exemplify

272

S. Panke

Table 2 Examples of pedagogical applications for different social media genres, platforms, and tools Pedagogical application Citizen Science

Genre/ platform Mobile apps

Community Engagement Learning Exchange

Weblog

Wikimedian in residence

Wiki

Wikipedia Edit-athon

Wiki

Pressbooks and Podcast

Podcast

Description A powerful example of seamless, mobile, networked, and personal learning is Citizen Science. Citizen Science enlists the public in collecting large quantities of data across an array of habitats and locations over long spans of time. Citizen Science projects often use social media to recruit, retain, and train volunteers. Moreover, mobile apps such as iNaturalist and Zooninverse allow for community building, inquiry, education, and data gathering. Unger et al. (2021) describe the use of iNaturalist with firstyear undergraduate biology majors How can educators leverage blogs to encourage reflective and critical discourse? Panke and Stephens (2018) used a single case study to build understanding of digital citizenship in the Community Engagement Learning Exchange (CELE), a multi-author blog that engaged public officials and citizens with varying experiences and backgrounds in a shared discourse on civic engagement. Having ground rules, focusing on learning and information sharing, and not putting people down were qualities that the bloggers associated with their CELE blog experience The University of Edinburgh is hosting a “Wikimedian in Residence” since 2016 to embed the creation of Open Educational Resources (OER) in the curriculum and support collaboration between the university and Wikimedia UK. Students benefit from the opportunity to engage in scholarship that lasts beyond the assignment and does something for the common good (McAndrew and Johns 2019) In a concerted effort to edit and improve a specific topic or type of content, this Wikipedia editing event includes basic editing training, research, and instruction (Lauro 2020) Panke et al. (2019) document the OER textbook Local Government in North Carolina that is implemented with the WordPress-based tool PressBooks and

Educational setting Higher education/ informal learning

Higher education/ informal learning

Higher education/ informal learning

Higher education/ informal learning Higher education/ informal learning (continued)

15

Social Media: Friend and Foe

273

Table 2 (continued) Pedagogical application

Genre/ platform

RealTimeWWII

Twitter

Eva’s story

Instagram

Esports

Twitch

Fanfiction

AO3

Description linked to data visualizations with tableau, augmented reality elements, and a podcast channel. The podcast and the textbook are complimenting one another, and each year a student intern is contributing another season to the audio repository @RealTimeWWII, a Twitter account with over 350,000 followers. The goals of the project are to educate followers about the sequence of events in World War Two and to give a sense of what the war felt like to ordinary people. The author Alwyn Collinson is a curator at the Museum of London. He bases his tweets on eyewitness accounts, photographs, and videos, giving the impression that his tweets are coming straight from the time With over one million followers, Eva. Stories is a high-budget production of Instagram videos and images that are based on the diary of Eva Heyman – a 13-year-old Hungarian who chronicled the 1944 German invasion of Hungary. Eva’s Instagram diary employs hashtags and emojis and is intended to engage and educate a younger audience about the Holocaust Defined as a form of web-based, competitive video gaming, esports are characterized as a global leisure activity with opportunities to motivate learners, based on the success of platforms such as Twitch (Kukulska-Hulme et al. 2020) Fanfiction can be based on any fictional work such as TV series, novels, movies, or podcasts. The popular fanfiction repository Archive of Our Own (AO3) counts 40,000 different fandoms. Fanfiction offers agency and participation in community spaces that are largely protected by pseudonymity. While occasionally writers also connect to their readers through social media sites such as Tumblr, most accounts lack identifying information and do not disclose gender or age (Panke 2020)

Educational setting

Informal learning

Informal learning

Peerconnection

Informal learning

274

S. Panke

the pedagogical potential for seamless learning across a variety of platforms, domains, and audiences. Blogs, wikis, and podcasts are three distinct genres of social media long-form content that play a substantive role in teaching and learning settings. From a tertiary education perspective, they offer significant potential for aligning with learning objectives such as critical thinking, literacy, research, creative expression, and academic writing.

Blogs Since their first appearance in the 1990s, blogs have become an integral part of everyday Internet culture. Bloggers come and go, the entirety of blogs, called “blogosphere,” continues to grow, competing with traditional journalism as demonstrated by the influence of platforms such as medium.com. Reese et al. (2007) summarized the distinct characteristics of blogs as “ease of use, low barriers to creation and maintenance, dynamic quality, easy interactivity and potential for wide distribution.” Based on a meta-analysis, Sim and Hew (2010) identified the educational purposes of blogs as: a learning diary, documentation of everyday life, expression of moods and preferences, communication, assessment, and task management. Christie and Morris (2021) discussed blogs as an authentic assessment technique. Hou et al. (2020) demonstrated blogs as infrastructures to promote higher-order thinking. Kaçar (2021) describes a multilingual blogging environment. Wikis Wikis are highly permeable, yet robust editing systems. Instead of pre-structuring the publishing process through a complex and precise editorial workflow, the editor, reviewer, and publisher functions are combined into one role: Anyone may access and edit them at any time. In the wiki syntax hyperlinks can lead to the creation of a new page if it does not yet exist. Wikis form an incremental, non-hierarchical environment. Another central feature is the transparency of discourse in connection to the creation of shared artifacts, as a result of the fact that all versions of a document are stored and that a discussion page complements each article. The versioning feature allows an immediate restoration of any previous version of a page. Administrators have a specific status within the wiki community. In contrast to standard users, they are able to delete pages, to (temporarily) block out editorial activity, and to suspend users from the community. However, any user may add wikipages to a personal watchlist, and the user will then be notified of changes. A summary of the potential of wikis in teaching may be found in Ferris and Wilder (2006): Educators may use wikis to provide customized electronic portfolios, to facilitate collaborative activities such as web-writing or problem-solving, for information sources or case libraries, for the submission of student assignments, and for project spaces (for the latter cf. Xu 2007). Collective authoring is not something that students will do naturally. Initiating a wiki is a real challenge that calls for some pedagogical efforts (Cubric 2007). Openly accessible wiki projects are usually meant to survive considerable changes of the stock of their users and articles over a long and indeterminate period

15

Social Media: Friend and Foe

275

of time. According to Roth (2007), the viability of a wiki is a coin with two sides, which might be described as population dynamics (recruitment, retention, exclusion, or leave) and content dynamics (growth, stabilization, quality), and both sides co-evolve. Examples of long-lasting community learning wikis outside of the Wikimedia Foundation are few and far between. In contrast to the pedagogically and organizationally dauting task of establishing and promoting a standalone wiki, the use of Wikipedia as a teaching tool has been successfully implemented in many classrooms, focusing on the improved consumption of information and the collaborative construction of knowledge (Sigalov and Nachmias 2017). Fact-checking, editing, and creating Wikipedia articles required students to engage with source material and allowed them to advance their research, writing, and digital literacy skills (Di Lauro and Johinke 2017).

Podcasts First coined in 2004, the term podcasting typically refers to the process of providing audio episodes on a regular schedule with a specific Real Simple Syndication (RSS) feed that allows listeners to capture the content on mobile devices such as smartphones or tablets. A multitude of applications, such as Apple Podcast, Stitcher, Google Podcast, Pocket Cast, Overcast, and Spotify, allow for disseminating and accessing podcast feeds. Creating podcasts with true educational value is challenging – it requires content that is substantive, engaging, and relevant to specific teaching and learning goals. While there is no tried-and-true “podcast pedagogy,” most practitioners and researchers agree that the personal tone and authentic narrative of podcasting enhances learner motivation (O’Donoghue et al. 2008). Van Baalen-Wood and Boggs (2015) reported a perception of increased connectedness and satisfaction with online learning among both instructors and students through podcasting. Instead of producing their own podcast content, instructors can also choose to integrate existing podcasts into the earning environment. In second language learning, podcasting can provide an unlimited amount of authentic target-language input (Yeh 2017). Lastly, student podcasting can be an opportunity for authentic assessment of research, language, and composition competencies. Yeh et al. (2021) conducted a study of Taiwanese university students’ podcast production. The results indicated significant gains in speaking fluency and accuracy.

Recommendations for Virtual Universities Should universities wield or curtail their social media presence? More important than answering this question decisively is to make space for debate – both about and on social media. In 1914 the polish-german communist Rosa Luxemburg coined the aphorism that freedom is always, and exclusively, freedom of the one who thinks differently. Supporting academic freedom means to defend the freedom of the person who disagrees with you. So far, higher education institutions are largely missing the opportunity to foster productive debate on social media. Instead, university social

276

S. Panke

media policies appear to favor institutional reputation over the tenets of academic freedom (Kwestel and Milano 2020). Within the virtual classroom, educators should provide nuance and help students chart their own path through purposeful activities instead of either vilifying or hailing social media. As an icebreaker, instructors can invite students to draw a map of their social media landscape. Which platforms do they use? Who is and is not in their network? What are examples of accounts that make meaningful contributions to their learning network? How do they do this? It is possible to acknowledge the role of social media for informal learning, while being at the same time cautious about digital well-being and social division. University students form a group that is particularly vulnerable in terms of their mental health. Instructors can help students reflect upon a balanced use of social media by encouraging them to go offline for a day or two and to document what they miss – and gain. Playful engagement with social media through informal activities (esports, fanfiction) can encourage peer connection and reduce stress. Last but not least, students need to learn how to evaluate the information that reaches them through their social media streams (He et al. 2019). The “4 moves” or SIFT (stop, investigate the source, find better coverage, trace the original context) model encourages students to deploy simply tactics that take 30 seconds or less and thus can become an unconscious habit, like looking in the car’s rearview mirror before changing lanes (Panke 2018). Despite common complaints about the walled garden architectures of commercial platforms, few higher education institutions spend their effort on non-profit platforms. One of the most accessed websites worldwide, and one of the most academically aligned social media platforms, is Wikimedia. Results of Edit-a-thons and Wikimedian in Residence programs show how seamless learning, authentic assessment, and social media can converge productively (Lauro 2020).

Alternatives For virtual universities that operate on an international scale, it is crucial to consider that many of the most prominent social media sites are blocked in mainland China (for a continuously updated overview of blocked sites and services, see Wikipedia 2021). Furthermore, some campuses discourage, limit, or prohibit the use of social media for teaching and learning in the accepted sue policies or social media policies. An example is the social media policy of Griffith University, Australia: Staff should avoid using platforms that are inherently personal by nature (such as Facebook and Instagram) to maintain their professional responsibilities towards students. Such platforms overstep the divide separating the professional and personal and thus can invite difficulties arising from the power disparity inherent in the staff-student relationship. (Griffith Social Media Guidelines 2018)

15

Social Media: Friend and Foe

277

As an alternative, faculty can leverage learning management integrations and productivity tools that allow for access control yet emulate features and traits of social media. • VoiceThread offers multiple modes of interaction through video, audio, and text. The environment promotes autonomy and learner engagement due to the opportunities for private reflection and public discussion using additional multimedia tools. Delmas (2017) documented how VoiceThread is an effective tool to foster a sense of community in learning. VoiceThread was also significant because it helped students feel connected to each other and created a more engaging learning environment. • Microsoft Office 365 Teams is a workplace communication channel that offers services such as video conferencing, file storage, and application integration. An option of Class Teams is available for students where live calls, facilitation of learning and assignments, and collaborative class efforts can take place with their peers and their instructor. Ellison and Arora (2013) described how Teams is helpful for creating a social learning environment. For example, students thought the implementation of Teams in a classroom setting was positive, because it allowed them to learn more about their peers. Additionally, students liked that the tool was similar to social media websites like Facebook and Twitter and felt that made learning more engaging. • FlipGrid is used by teachers to create grids that facilitate video discussions. Teachers can post a topic and students can record and post a video response which would then be displayed in a grid. It is often used as an icebreaker or to generate equal opportunities to contribute. It can be beneficial to students who are not comfortable speaking in front of groups, or do not feel welcomed in large group settings, to remain an active participant without being in the spotlight. Green and Green (2018) document how Flipgrid can be utilized to make online discussions more engaging and personal, particularly in asynchronous/remote courses. For example, with Flipgrid, students can see each other and feel a stronger connection with their peers. • Online writing applications such as Microsoft OneNote, Google Docs, Etherpad, and Framapad offer some of the collaboration features that make the collective development of text and media in Wiki environments so fruitful, while allowing the instructor to restrict access to class participants only. • Slack is as a hybrid between messaging and team collaboration environment with features such as chat channels, file sharing (through Google drive), polls and to-do lists (chiefly through third party apps), and short video messages (“clips”). Educators have explored its use as a student-centered communication tool that complements learning management systems (LMS) (Tuhkala and Kärkkäinen 2018). While students and educators report benefits, there are also issues with losing significant messages or being overwhelmed with too many messages in different channels (Darvishi 2020). • Contemporary learning management systems (LMS) – e.g., Canvas or Moodle – have adopted many social media features and have become more responsive and

278

S. Panke

mobile-app-based. Many LMS products center user dashboards with constant update streams, incorporate wikis, blogs, chats, and instant messaging features and offer advanced collaboration tools beyond the discussion forum. While these alternative platforms offer promising potential for collaborative learning, it is unclear if the same positive outcomes of social media such as informal peer connections, seamless learning, and the growth of a personal learning network can be achieved. The idea of seamlessly connecting different learning environments and bridging formal and informal learning spaces is at least hampered if not disconnected when learning management systems and productivity tools replace social media in teaching and learning. Furthermore, it is an open question if the prevalence of restrictive policies or organizational guidance has a moderating effect on students’ social media use.

Conclusion and Future Directions In current media reporting as well as in the academic literature two narratives have emerged that are polar opposites: (1) Social media offer opportunities for global connection, discourse and personal learning networks and are beneficial for the individual, learning communities, and society as a whole. It is thus imperative for education to leverage social media platforms. (2) Social media are addictive (“dopamine rush”), distract from (higher) learning, push people into echo chambers, poison societal cohesion, and threaten democracy. It is thus only logical to ban social media from classrooms. Each position is true, but partial. Rather than taking sides, this chapter outlined both the problems and the potential of using social media tools for learning: Social media present unprecedented opportunities and challenges for educational organizations. On the one hand, promising and creative use cases point to pedagogical potential; on the other hand, negative outcomes on the individual and societal levels serve as cautionary tales. While it might be tempting to err on the side of caution and avoid or curtail the use, virtual universities cannot ignore that social media fulfill important social functions for students. The widespread, daily use of social media is a reality in student lives. To ignore channels, platforms, and genres is to pass up on teaching moments for productive use. Given that most university agents – administrators, faculty, and students – already engage in various forms of social media use, what are sensible approaches to teaching and learning in the virtual classroom? Productive use, civility, and fact-checking are important learning outcomes related to social media that virtual universities have a mandate to promote. Establishing connections between social media and common good projects such as Citizen Science and Open Educational Resources provide a way for universities to steward students’ social media consumption toward scientific engagement, open debates, and discourse beyond tribalized bubbles.

15

Social Media: Friend and Foe

279

Effective social media pedagogies can foster critical consumption and intellectual curiosity. Social media tools offer opportunities to teach principles of academic discourse in a space that transcends the (digital) walls of the institution. At the same time, social media environments can easily become echo chambers that make institutional walls more impermeable by reinforcing codes and memes that exclude rather than invite dissent and outside voices. In the end, it is our choices that show what we truly are, far more than our technologies.

Cross-References ▶ Innovation and the Role of Emerging Technologies ▶ Open Educational Practice as an Enabler for Virtual Universities

References Allcott, H., L. Braghieri, S. Eichmeyer, and M. Gentzkow. 2020. The welfare effects of social media. American Economic Review 110 (3): 629–676. Anderson, C. 2006. The long tail: Why the future of business is selling less of more (1st ed.). Baker, N., G. Ferszt, and J. G. Breines. 2019. A qualitative study exploring female college students’ Instagram use and body image. Cyberpsychology, Behavior and Social Networking 22 (4): 277–282. Bista, K. 2015. Is Twitter an effective pedagogical tool in higher education? Perspectives of education graduate students. Journal of the Scholarship of Teaching and Learning 15 (2): 83–102. Bell, S. J. 2000. Creating learning libraries in support of seamless learning cultures. College & Undergraduate Libraries 6(2), 45–58. Camus, M., N. Hurt, L. Larson, and L. Prevost. 2016. Facebook as an online teaching tool: Effects on student participation, learning, and overall course performance. College Teaching 64 (2): 84–94. Carpenter, J., S. Morrison, M. Craft, and M. Lee. 2020. How and why are educators using Instagram? Teaching and Teacher Education 96: 1–12. Couros, A. 2010. Developing personal learning networks for open and social learning. Emerging Technologies in Distance Education 2010: 109–127. Christie, H., and N. Morris. 2021. Using assessed blogs to enhance student engagement. Teaching in Higher Education 26 (4): 573–585. Cinelli, M., G. D. F. Morales, A. Galeazzi, W. Quattrociocchi, and M. Starnini. 2021. The echo chamber effect on social media. Proceedings of the National Academy of Sciences 118 (9). Cubric, M. 2007. Wiki-based process framework for blended learning. Proceedings of the International Symposium on Wikis 11–24. Darvishi, S. 2020. The use of Slack as a social media in higher education: Students perceptions of advantages and disadvantage of Slack during learning process. Dawkins, H. R. 1976. The selfish gene. Oxford University Press. Delmas, P. M. 2017. Using VoiceThread to create community in online learning. TechTrends 61: 595–602. Di Lauro, F., and R. Johinke. 2017. Employing Wikipedia for good not evil: Innovative approaches to collaborative writing assessment. Assessment & Evaluation in Higher Education 42 (3): 478–491. Downes, S. 2007. Learning networks in practice. Emerging Technologies for Learning 2 (4): 20.

280

S. Panke

Ellison, A., and M. Arora. 2013. Harnessing the power of Office 365 to provide a social learning environment through a new Student Portal. Materials and Applied Chemistry 1. https://doi.org/ 10.7250/eunis.2013.010. Escamilla-Fajardo, P., M. Alguacil, and S. Lopez-Carril. 2021. Incorporating TikTok in higher education: Pedagogical perspectives from a corporal expression sport sciences course. Elsevier 28: 1–13. Fenn, P., and P. Reilly. 2020. Problematizing the use of snapchat in higher education teaching and learning. The Journal of Social Media for Learning 1 (1). Ferris, S. and H. Wilder 2006. Uses and potentials of wikis in the classroom. Innovate 2 (5). http:// www.innovateonline.info/index.php?view¼article&id¼258. Accessed 15 July 2015. Gon, S., and A. Rawekar. 2017. Effectivity of E-learning through WhatsApp as a teaching learning tool. MVP Journal of Medical Sciences 4 (1): 19–25. Green, T., and J. Green. 2018. Flipgrid: Adding voice and video to online discussions. TechTrends 62: 128–130. Griffith Social Media Guidelines. 2018. Internal document. Griffith University. Harris, T. 2020. Can truth survive big tech? Interview. Rebel Wisdom Podcast. https://www. youtube.com/watch?v¼wHQQFOv7QgQ. Accessed 5 Feb 2022. He, Y., P. Cook, P. D. Boruff-Jones, and M. T. Bradley. 2019. Combating digital polarization: Teaching undergraduates web literacy using “four moves and a habit”. LOEX 2019 annual conference. https://hdl.handle.net/2022/26759. Accessed 15 Oct 2021. Herrmann, K. J., K. Lindvig, and J. Aagaard. 2021. Curating the use of digital media in higher education: A case study. Journal of Further and Higher Education 45 (3): 389–400. Hou, H. T., T. F. Yu, F. D. Chiang, Y. H. Lin, K. E. Chang, and C. C. Kuo. 2020. Development and evaluation of mindtool-based blogs to promote learners’ higher order cognitive thinking in online discussions: An analysis of learning effects and cognitive process. Journal of Educational Computing Research 58 (2): 343–363. Jiang, S., and A. Ngien. 2020. The effects of Instagram use, social comparison, and self-esteem on social anxiety: A survey study in Singapore. Social Media + Society. Kaçar, I. G. 2021. Academic knowledge formation through blogs: An innovative and multilingual teaching approach. In Futuristic and linguistic perspectives on teaching writing to second language students, 118–140. IGI Global. Khan, M., A. Minbashian, and C. MacCann. 2021. College students in the western world are becoming less emotionally intelligent: A cross-temporal meta-analysis of trait emotional intelligence. Journal of Personality 89 (6): 1176–1190. Kuh, G. D. 1996. Guiding principles for creating seamless learning environments for undergraduates. College Student Development 37 (2): 135–148. Kukulska-Hulme, A., E. Beirne, G. Conole, E. Costello, T. Coughlan, R. Ferguson, E. FitzGerald, M. Gaved, C. Herodotou, W. Holmes, C. Mac Lochlain, M. Nic Giolla Mhichíl, B. Rienties, J. Sargent, E. Scanlon, M. Sharples, and D. Whitelock. 2020. Innovating pedagogy 2020: Open university innovation report 8. The Open University. Kwestel, M., and E. F. Milano. 2020. Protecting academic freedom or managing reputation? An evaluation of university social media policies. Journal of Information Policy 10: 151–183. Lanier, J. 2021. Was the internet a horrible mistake? Honestly with Bari Weiss. Podcast. https://www. honestlypod.com/podcast/episode/2a738715/was-the-internet-a-horrible-mistake. Accessed 15 Dec 2021. Lauro, F. D. 2020. ‘If it is not in Wikipedia, blame yourself:’ Edit-a-thons as vehicles for computer supported collaborative learning in higher education. Studies in Higher Education 45 (5): 1003–1014. Li, F., S. Fan, Y. Wang, and J. Lu. 2021. Chinese university students’ experience of WeChat-based English-language vocabulary learning. Education Sciences 11 (9): 554. Masserini, L., and M. Bini. 2021. Does joining social media groups help to reduce students’ dropout within the first university year? Socio-Economic Planning Sciences 73: 100865.

15

Social Media: Friend and Foe

281

Martindale, T., and M. Dowdy. 2010. Personal learning environments. Emerging Technologies in Distance Education 177: 193. McAndrew, E., and J. Johns. 2019. Wikimedian in residence. In Wikimedia at the LILAC information literacy conference. O’Donoghue, M., A. Hoskin, and T. Bell. 2008. Guidelines for podcast production and use in tertiary education. Proceedings Ascilite Melbourne 2008. Panke, S. 2018. Improving civic discourse by fostering web literacy: An interview with Mike Caulfield. AACE Review. Retrieved August 15, 2021 from https://www.aace.org/review/ interview-mike-caulfield/. Accessed 15 Dec 2021. ———. 2020. Playful learning: The allure of fanfiction. AACE Review. https://www.aace.org/ review/playful-learning-the-allure-of-fanfiction/. Accessed 15 Dec 2021. Panke, S., and J. Stephens. 2018. Beyond the echo chamber: Pedagogical tools for civic engagement discourse and reflection. Journal of Educational Technology & Society 21 (1): 248–263. Panke, S., R. Morse, and S. Stone. 2019. Digital citizenship meets open educational resources: Local government open textbook project. In Proceedings of E-learn: World conference on E-learning in corporate, government, healthcare, and higher education, ed. S. Carliner, 901–912. New Orleans: Association for the Advancement of Computing in Education (AACE). Paterson, N. 2012. Walled gardens: The new shape of the public internet. In Proceedings of the 2012 iConference (iConference ’12), 97–104. New York: Association for Computing Machinery. Pomerantz, J., C. Hank, and C. R. Sugimoto. 2015. The state of social media policies in higher education. PLOS ONE 10 (5): e0127485. Prasad, V. 2021. Down with the social media platforms. MedPage Today. Retrieved September 21, 2021 from https://www.medpagetoday.com/opinion/vinay-prasad/93976. Accessed 15 Dec 2021. Reese, S. D., L. Rutigliano, K. Hyun, and J. Jeong. 2007. Mapping the blogosphere: Professional and citizen-based media in the global news arena. Journalism 8 (3): 235–261. Roodt, S. 2013. Using YouTube to support student engagement for the net generation in higher education. Academic Conferences International Limited. Roth, C. 2007. Viable wikis: Struggle for life in the wikisphere. In Proceedings of the 2007 international symposium on wikis, ed. A. Désilets and R. Biddle, 119–124. Montreal: ACM. Sen, R., S. Ahmad, A. Phokeer, Z. Farooq, I. Qazi, D. Choffnes, and K. Gummadi. 2017. Inside the walled garden: Deconstructing Facebook’s free basics program. ACM SIGCOMM Computer Communication Review 47 (5): 12–24. Sim, J. W. S., and K. F. Hew. 2010. The use of weblogs in higher education settings: A review of empirical research. Educational Research Review 5 (2): 151–163. Shah, N. 2020. Feel free to unfriend. The Indian Express. Retrieved July 15, 2021 from https:// indianexpress.com/article/express-sunday-eye/feel-free-to-unfriend-social-media-6546128/. Accessed 15 Dec 2021. Sharma, M., K. Yadav, N. Yadav, and K. C. Ferdinand. 2017. Zika virus pandemic—Analysis of Facebook as a social media health information platform. American Journal of Infection Control 45 (3): 301–302. Sharples, M., R. de Roock, R. Ferguson, M. Gaved, C. Herodotou, E. Koh, . . ., and L. H. Wong. 2016. Innovating pedagogy 2016: Open University innovation report 5. Institute of Educational Technology, The Open University. Sigalov, S. E., and R. Nachmias. 2017. Wikipedia as a platform for impactful learning: A new course model in higher education. Education and Information Technologies 22 (6): 2959–2979. Statista. 2021. Online portal for statistics. statista.com/ [last accessed, June 2023]. Statista. 2020. Social media usage worldwide. https://www.statista.com/study/12393/socialnetworks-statista-dossier/. Accessed 15 Dec 2021. Talib, S. 2018. Social media pedagogy: Applying an interdisciplinary approach to teach multimodal critical digital literacy. E-Learning and Digital Media 15 (2): 55–66. Tess, P. A. 2013. The role of social media in higher education classes (real and virtual)–A literature review. Computers in Human Behavior 29 (5): A60–A68.

282

S. Panke

Themelis, C. 2018. Digital well-being: Are devices overwhelming or extending our minds?. AACE Review. https://www.aace.org/review/digital-well-being-are-devices-overwhelming-orextending-our-minds/ [last accessed, June 2023]. Tuhkala, A., and T. Kärkkäinen. 2018. Using Slack for computer-mediated communication to support higher education students’ peer interactions during Master’s thesis seminar. Education and Information Technologies 23 (6): 2379–2397. Van Den Beemt, A., M. Thurlings, and M. Willems. 2020. Towards an understanding of social media use in the classroom: A literature review. Technology, Pedagogy and Education 29 (1): 35–55. Van Baalen-Wood, M., and C. Boggs. 2015. Is anybody there? Podcasting in online learning. In EdMedia+ innovate learning. Association for the Advancement of Computing in Education (AACE), pp. 175–181. Unger, S., M. Rollins, A. Tietz, and H. Dumais. 2021. iNaturalist as an engaging tool for identifying organisms in outdoor activities. Journal of Biological Education 55 (5): 537–547. Wiggins, B. E. 2019. The discursive power of memes in digital culture: Ideology, semiotics, and intertextuality. Routledge. Wikipedia. 2021. List of websites blocked in mainland China. https://en.wikipedia.org/wiki/List_ of_websites_blocked_in_mainland_China. Accessed 15 Dec 2021. Xu, L. 2007. Project the wiki way: Using wiki for computer science course project management. Journal of Computing Sciences in Colleges 22 (6): 109–116. Yang, L., T. Sun, M. Zhang, and Q. Mei. 2012. We know what@ you# tag: Does the dual role affect hashtag adoption? In Proceedings of the 21st international conference on World Wide Web, 261–270. Yeh, C. C. 2017. An investigation of a podcast learning project for extensive listening. In Asianfocused ELT research and practice: Voices from the far edge, 87. Yeh, H. C., W. Y. Chang, H. Y. Chen, and L. Heng. 2021. Effects of podcast-making on college students’ English speaking skills in higher education. Educational Technology Research and Development 1–23.

The Future of the Learning Management System in the Virtual University

16

Stephen Marshall and Michael David Sankey

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What Is an LMS? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evolving the LMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LMS 3.0? Reconceiving the LMS as a Platform for Learning in the Future Virtual University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Positioning and Enacting the Learning Platform for a Future Virtual University . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

284 286 290 294 298 300 300 300

Abstract

The single system model of an LMS, reflecting a relatively passive use of digital technologies, is increasingly out of step in the rapidly evolving global higher education environment. This proposes a model that moves from a single systems conception of the LMS to one informed by concepts of learning ecosystems and platforms. We define the LMS and the scope of its functions in order to frame how it might evolve in the future. A conception of the LMS as a learning platform is presented drawing on the successful models used by large Internet companies to provide an infrastructure designed to support change through innovation at the boundary while also ensuring it remains manageable at its core and operates in a way that is aligned to the strategic objectives of the university. S. Marshall (*) Centre for Academic Development, Victoria University of Wellington, Wellington, New Zealand e-mail: [email protected] M. D. Sankey Director Learning Futures and Lead Education Architect, Charles Darwin University, Darwin, NT, Australia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_16

283

284

S. Marshall and M. D. Sankey

Keywords

Learning management system · Virtual learning environment · LMS · VLE · Learning platform

Introduction The Learning Management System (LMS), or Virtual Learning Environment (VLE) if you live in the UK, is widely regarded as a key element of the modern university learning infrastructure with many surveys of students emphasizing the value of the LMS and seeking more from its use (Flavin 2020a). And for the Virtual University, the importance of systems like this very much determines how a student will experience their University. The LMS business was worth some US$14.43 billion in 2021 and is expected to grow annually at just over 14% as demand for educational services extends beyond the academic and government segments where most activity currently occurs (Fortune Business Insights 2021). Universities constitute a significant fraction of that market and typically operate LMS software offered by the major higher educational vendors, including Blackboard (both Learn and Ultra), Canvas, Moodle, and Desire2Learn (Hill 2023). In addition to these major players, a vast array of other LMS products are available, aimed at school education, corporate training, and supporting specific learning models, languages, and local cultures. Alternatives, such as the Google Classroom environment (Fenton 2017; Francom et al. 2021) have not been widely adopted by universities. Blackboard and Moodle were early leaders in the LMS market for higher education, rapidly replacing the use of in-house solutions in the late 2000s (Davis et al. 2009). One of the earliest proprietary systems was WebCT, developed at the University of British Columbia in 1995 (Turnbull et al. 2019) and subsequently acquired by Blackboard in the mid 2000s. At its peak, in the USA and Canada at least, Blackboard dominated the University LMS market with a 43% market share, more than three times that of the next three vendors, D2L Brightspace (14%), Instructure Canvas (14%), and Moodle (13%) (Lang and Pirani 2016). More recently, Canvas and to a lesser extent Brightspace have gained market share primarily at the expense of Blackboard which has shrunk to less than half its peak share from the mid 2000s (Hill 2023). Figure 1 shows the state of the US and Canadian LMS market in 2020; similar patterns are evident in other western countries. The dominance of the university space by four vendors and their products in Fig. 1 reflects the reality that universities are conservative in their selection and management of LMSs and that any change is a major disruption to teaching that will only occur after very careful analysis and planning (Flavin 2020b; Lang and Pirani 2016). This is not just due to the cost of the product itself, but more so due to the costs associated with change management and ultimately the return on that investment (ASU 2018). In other words, the gain from making a change, has to outway the pain of making that change. In 2016, more than half of the Universities surveyed by the Educause Centre for

16

The Future of the Learning Management System in the Virtual University

285

Fig. 1 LMS market share (Hill 2023)

Analysis and Research indicated a commitment to their current platform for a period of more than 6 years, and two-thirds had no plans to make any changes looking forward for the next 3 years (Lang and Pirani 2016). Flavin (2020b, p. 48) observes: “The purchase of a VLE is not only a significant investment. It is a long-term commitment, and once a university has a relationship with a particular vendor it is difficult to dislodge that relationship. Far from innovating, technology, in this instance, entrenches.” The result of this conservatism and entrenchment is that LMSs have been criticized for the lack of sustained impact on learning: What is clear is that the LMS has been highly successful in enabling the administration of learning but less so in enabling learning itself. Tools such as the grade book and mechanisms for distributing materials such as the syllabus are invaluable for the management of a course, but these resources contribute, at best, only indirectly to learning success. Initial LMS designs have been both course- and instructor-centric, which is consonant with the way higher education viewed teaching and learning through the 1990s. Higher education is moving away from its traditional emphasis on the instructor, however, replacing it with a focus on learning and the learner. Higher education is also moving away from a standard form factor for the course, experimenting with a variety of course models. These developments pose a dilemma for any LMS whose design is still informed by instructor-centric, one-size-fits-all assumptions about teaching and learning. They also account for the love/hate relationship many in higher education have with the LMS. The LMS is both “it” and “not it”—useful in some ways but falling short in others. (Brown et al. 2015, p. 2)

The single system model of an LMS, reflecting a relatively passive use of digital technologies, is increasingly out of step in the rapidly evolving global higher

286

S. Marshall and M. D. Sankey

education environment. Weaknesses of the current generation of LMSs include the need to be updated rapidly in response to technological developments, the need for better interoperability between systems, the poor state of learning data, the lack of cohesion and transferability between systems, and the lack of personalization in the learning experience (Redmond and Macfadyen 2020). This is compounded somewhat by a shift by most of major LMS vendors to a software as a service (SaaS) model of provision, over recent years, rather than individual institutions running these platforms on their own server infrastructure. The shift to a mode of operation that assumes access to a high-quality Internet connection also exacerbates inequalities arising from lack of access, particularly when high volumes of bidirectional data are needed such as for conferencing. The risk of sustaining a model of the LMS operated as a vendor-defined commodity infrastructure is that the university ceases to have agency over its strategy (Carr 2003). Virtual universities seeking to actively lead educational experiences in the future need to purposefully engage in the evolution of our learning systems to ensure they continue to drive strategic growth and respond to the needs of our societies. This chapter proposes a model that moves from a single systems conception of the LMS to one informed by concepts of learning ecosystems (Redmond and Macfadyen 2020) and platforms (Denning 2016; Flavin 2020b). In building the case for this model, we first need to define the LMS and the scope of its functions in order to frame how it might evolve in the future.

What Is an LMS? The term LMS is complemented by alternatives such as Course Management System (CMS, often confused or conflated with Content Management System that may also be known as a CMS), Personal Learning Environment (PLE), Virtual Learning Environment (VLE), or Learning Content Management System (LCMS) (Flavin 2020a; Turnbull et al. 2019). The use of these terms reflects the evolution of the way that technology is used at scale to enable technology enhanced learning to occur within the university and the prioritization of the learner (PLE), mode of learning (CMS), or infrastructure (LCMS) in the thinking of those leading and enacting these systems in the university. Despite these considerations, the term LMS continues to dominate in the literature (except in the UK, where VLE is used as a synonym) and will be used in this chapter expansively to cover systems that others describe using these related terms. The modern LMS enables universities to communicate efficiently and reliably with all enrolled students on a course by course (unit by unit) basis, distribute learning resources in line with copyright and other legal requirements, manage communication, collaboration, feedback, and assessment processes, monitor student activities for educational, pastoral care, and administrative reporting purposes, and, of increasing importance, offer a modern learning experience that supports student learning in line with university and faculty aspirations, including access to a wide range of educational technologies (Sinclair and Aho 2018).

16

The Future of the Learning Management System in the Virtual University

287

The modern LMS is also a complex ecosystem of interconnected technologies providing a range of services to faculty, students, and universities. Common connections include systems operating content management, including copyright compliance; visual media recording and delivery; assessment and feedback processes; student records management; collaboration tools; social media (see ▶ Chap. 15, “Social Media: Friend and Foe” in this volume); and student services and support. Interestingly, more recently, the technology that is making significant inroads into academic practices have been the advent of productivity and communication tools, such as Office 365 Teams, Slack and Trello, and most importantly in the COVID environment the use of synchronous video collaboration tools such as Zoom and Teams (Hill 2021). Beyond this, there is the vast array of general and educational tools and services available from hundreds of vendors and able to be used by staff and students in learning activities and assessment (Fig. 2). Brown (2017) similarly illustrates the complexity of the evolution of university learning environments. The functionality they identify includes the LMS within a web of systems enabling course material delivery, content discovery and creation, data warehousing, analytics, dashboards, student advising, student progress monitoring, assessment, adaptive learning, social networking, and competency-based learning. All of these need to address a complex array of requirements including accessibility and universal design, collaboration, personalization, and interoperability. This complexity

Fig. 2 The ecology of tools used for technology enhanced learning

288

S. Marshall and M. D. Sankey

only increases when institutions choose to operate multiple types of LMS in order to meet the needs of specific disciplines, learner populations, and business models. Figure 3 summarizes the key features of a modern LMS (Brown 2017; Brown et al. 2015; Long and Mott 2017; Turnbull et al. 2019). This figure illustrates the way that the LMS builds on a foundation of a robust information technology infrastructure which is built upon in the higher layers, presenting an easily used management interface and systems that allow learning designs to be created and enacted. A combination of resources, collaboration, and communication tools provide the affordances of these designs, complemented by systems promoting learner progress and finally providing ways of demonstrating and celebrating learner success at the top of the hierarchy. This layered and hierarchical view represents a vendor-centric system design model with high levels of dependency and control operating vertically, such as is typically provided by the current generation of systems. The information technology infrastructure needed for a modern LMS reflects the complexity of modern organizations as well as the evolution of the systems that support their operation. The architecture of the LMS must have coherence with the university’s enterprise software architecture. Integration is essential, addressing technical features such as identity management, functional connections with a range of learning and support tools, and most importantly with the information infrastructure. This latter is perhaps the most important as it conveys the university’s assumptions regarding the learning activities it undertakes and supports the smooth operation of the systems enabling student enrollment, access to learning, and recognition of outcomes. All of these technical aspects also have to operate in an increasingly hostile Internet environment requiring highly resilient systems able to operate securely and reliably in order to protect the privacy of students and the viability of the university.

Fig. 3 Summary of the hierarchical architectural elements of the modern LMS

16

The Future of the Learning Management System in the Virtual University

289

The LMS is more than the university’s technological infrastructure. It must also be viable over a wide range of user devices and contexts, particularly for the Virtual University. Mobility is increasingly a defining feature of all aspects of life, and the desktop computer has long been overtaken by more portable devices such as the smartphone for many information access activities and as a medium for content development, and the web browser is now merely one of a suite of apps used to experience learning. All of these must be usable by wide range of users without requiring extensive training. The other foundational requirement of an LMS is that it enables the management of learning in an organizationally relevant and effective way. This is dominated by administrative aspects reflecting the connections to enrollment and student record systems that support availability management of learning before, during, and after formal learning, and enable users to access functions and information appropriate to their different, and increasingly multiple, system roles. Modern LMSs also are aligned to curriculum management systems and need to operate in ways that ensure learning is structured and sustained in line with formal approvals and institutionally defined learning designs. Enabling accessible learning design is an increasingly explicit feature of the modern LMS. It must support the creation of a course structure, supported by templates and reusable elements, and reflecting the range of discipline differences in pedagogical framing. Sequenced or scaffolded learning activities aligned to learning objectives and curriculum need to be able to be selected from a library of templated examples as well as being created as needed. These activities are supported and enacted by learning resources, rich media, collaboration, and assessment tools, the provision of which reflects perhaps the most user-visible aspects of the LMS. In some institutions, this is supported by the adoption of minimum or threshold standards as to how these environments should be developed and delivered at the course/unit level (refer to ▶ Chap. 30, “The Role of Standards and Benchmarking in Technology-Enhanced Learning”). LMS provision of learning resources in a range of formats, both directly, and with support from integrated content management tools is a basic function. This has become more complex however with the legally mandated needs to support accessibility (USDoJ 1990) and the global reach of education leading to the need to support a wide range of languages and script systems. Tools supporting learners collaborating with staff and peers in the use of these resources have also now grown in complexity and ambition. As well as basic communication of notices, discussion tools, and profiles supporting direct communication with peers, support staff, and teachers, there is now the expectation that learners are supported in forming communities, engaging in group work, and creating shared artifacts in collaboration. Pleasingly, tools are slowly entering the marketplace that assist the LMS to meet these requirements. An example of this would be the Blackboard Ally that, despite it being marketed by a major LMS company, is actually LMS agnostic. Historically, the LMS was little more than a management tool for submission and the communication of grades. Active engagement in assessment processes is now

290

S. Marshall and M. D. Sankey

one of the most important features of a modern LMS and learning environment. Modern learning designs depend on a range of summative and formative activities, scaffolded by feedback, and framed and contextualized by learning objectives into the curriculum plan. The formats have expanded well beyond the multiple-choice test and submission of written material to include modern tools such as video and audio. The process of assessment is increasingly subject to university processes requiring the provision of rubrics, marking criteria, academic integrity tools, and formal moderation processes. Assessment is also a major component of the toolset used by the LMS to enable learner progress to be enabled, supported, reported, and managed. The first generation of the LMS included basic progress features such as announcements, course calendars, and the ability to release material sequentially, but this has now been expanded to ensure systematic support for range of learner capabilities and needs. The major driver is the use of analytics to identify students diverging from planned pathways and expectations, resulting in support interventions. These analytics are also increasingly used to manage the entire learning operation through dashboards and university processes that monitor key performance indicators in real time. The last layer of the modern LMS is the recognition of learner progress and achievement and its communication internally for purposes of qualification award, and externally for the learner to progress onto future opportunities. The rise in the value of generic attributes (Bond et al. 2017) means that learners are increasingly using reflective accounts of their activities and assessments, supported by the products of that work to show their capabilities beyond their grades. This section has explored the current state of the LMS. For it to be of value in the virtual university of the future, it cannot remain static, nor, worse still, continue to deflect and paralyze change in TEL. The LMS must evolve.

Evolving the LMS Because the LMS is anchored in semester-based sections of instructor-led courses, anything resembling an innovation is largely “bolted on” rather than transformational. Although this is heading in the right direction, simply adding collaboration and assessment tools to the LMS leaves core learning processes and roles largely unchanged. (Long and Mott 2017, p. 23)

The role of the LMS in virtual universities of the future is as a tool supporting user and organizational management of their learning across a range of contexts and a more expansive conception of the system (Fig. 3). The traditional LMS was conceived as a tool to support the administration of a campus-based university education. The first generation of LMS software is very much defined by infrastructural concerns such as the management of access to information and the management of content and its display to authorized users. Use of LMS was dominated by content distribution and typically not regarded by many academics as an essential component of teaching (Schoonenboom 2014). Features relating to learning design and

16

The Future of the Learning Management System in the Virtual University

291

Fig. 4 Shift from old to new conception of the LMS

experience were rudimentary and consisted of provision of basic communication and assessment tools (Fig. 4). The second generation of LMS tools recognized the limitations of the pedagogical aspects and provided mechanisms for additional tools to be integrated into the LMS as supplementary features (as seen in Fig. 1). Examples include the integration of tools such as Turnitin (academic integrity tools) for assessment of written work, Talis for management of copyright and inclusion of library resources, and Panopto/ Echo 360 for management of video resources. The integration of these tools, while perceived by users as additions to the LMS functionality, in reality is somewhat limited. Most of the functions are implemented by linking to external systems, with the LMS operating simply as a portal for identity management and as a channel for information interchange (such as grade information). Through both generations of LMS, the pedagogical experience has remained passive and aligned to a transmission pedagogy that is increasingly questionable in a more information dense and connected world subject to rapid change, hence the rise of more productivity and collaboration-based systems such as Office 365, Sharepoint, Teams, Slack, and Trello that are now extensively used in modern workplaces, and the increasing attention being paid to active learning pedagogies. Future directions for LMS signaled in the literature relevant to the future virtual university include greater collaboration and partnerships between universities, increased deployment of systems designed to use the cloud, artificial intelligence, improved analytics, and increased use of models drawn from social media (see ▶ Chap. 15, “Social Media: Friend and Foe” in this volume) and games (Brown et al. 2020; Turnbull et al. 2019).

292

S. Marshall and M. D. Sankey

Mobile access to LMS features enabling active learning, social and gamified learning, and microlearning are recognized as a key feature of the modern LMS, with all of the major systems having a combination of responsive design features and mobile apps to enable this (Lang and Pirani 2016). Other ideas from the game space, including badges and other social features aimed at building and sustaining communities of learning, are increasingly mainstream features of the LMS (Ellis et al. 2016). Cloud hosting of key university infrastructure is a major trend, enabling responsiveness to changing levels of demand, resilience in the face of threats to business continuity or disruption, and a reduction in the complexity of managing an internal information technology environment. This is evident in the shift from in-house operation of the LMS which was 59% for Blackboard Learn in 2015 compared to that for Instructure Canvas where nearly 80% of universities used the vendormanaged software as a service cloud environment (Lang and Pirani 2016), also known as software as a service (SaaS). There is now evidence of a wide range of e-learning frameworks and implementations apparent in the cloud (Khan and Salah 2020) including all of the major university educational infrastructure vendors. The real value of this shift requires systems designed to go beyond mere virtual hosting to enable rapid scalability and continuous development while maintaining the high levels of system availability expected from global organizations. Linked with the shift to cloud computing is the increased ability for these providers to introduce more cognitive services, such as artificial intelligence (AI) to assist both staff and students in the practice of learning and teaching. AI has the potential to move the LMS from a teacher-focused institutional infrastructure to the “exoskeleton of the mind” (Long and Mott 2017, p. 22) promised by Engelbart (1962) and suggested by the work of Skinner (1958). Server-based AI features can be designed into the LMS in a wide variety of ways depending on the underlying AI model and the scope of the intended outcomes and activities (Szulc 2019). Artificial intelligence-enhanced learning management systems (Szulc 2019) have the potential to enable adaptive learning designs (Xie et al. 2019), responsive learning outcomes (Guan et al. 2020), personalized learning (Alamri et al. 2021), and direct engagement with learners through intelligent tutoring (Guan et al. 2020). Similarly, adaptive learning systems that also draw on AI data are personalized learning platforms that adapt to students’ learning strategies, the sequence and difficulty of the task abilities, and the time of feedback and students’ preferences (Xie et al. 2019). Paramythis and Loidl-Reisinger (2003) provide a good overview of the steps needed to enact an adaptive learning experience, stating that “a learning environment is considered adaptive if it is capable of: monitoring the activities of its users; interpreting these on the basis of domain-specific models; inferring user requirements and preferences out of the interpreted activities, appropriately representing these in associated models; and, finally, acting upon the available knowledge on its users and the subject matter at hand, to dynamically facilitate the learning process” (p. 182). Although these systems have yet to be anything more

16

The Future of the Learning Management System in the Virtual University

293

than limited research pilots, there is some evidence that adaptive learning systems may have value for weaker students in particular (Katuk et al. 2013). A more ambitious use of AI, and a feature now well used in the corporate world, involves the educational use of chatbot technology. Chatbots are “. . . artificial narrow intelligence (ANI) programmes designed to interact through text, or voice, with users in a human-like way, answering questions and performing tasks” (Schmulian and Coetzee 2019, p. 2751). Chatbots can be used as tutoring systems that “engage the students in sustained reasoning activity and to interact with the student based on a deep understanding of the students behavior” (Corbett et al. 1997, p. 849). The first Intelligent Tutoring System “SCHOLAR” was designed to support geography learning and was capable of generating interactive responses to student statements (Carbonell 1970). More recent systems can recommend information of interest (Syed et al. 2017), and advanced systems can simulate the behavior of a human tutor online (Ennaji et al. 2020) adapting as needed to the individual needs and goals of students (Paladines and Ramírez 2020). Currently, most of the systems reported in the literature are designed to help students learn knowledge and correct mistaken understanding in the STEM field (Mousavinasab et al. 2018). These intelligent tutoring systems are able to use artificial technology embedded in tools such as Facebook Messenger (Schmulian and Coetzee 2019), and researchers are exploring the use of smart personal assistants such as Amazon Alexa, Apple Siri, or Microsoft’s Cortana, which can provide support during learning activities (Winkler et al. 2021) and are less constrained to text as the medium of communication. The current generation of AI software is primarily driven by large data sets, which use this data to improve student outcomes and increase the efficiency of university systems, and has seen an explosion in the collection of such data in the form of learning and academic analytics. Siemens (2013, p. 1382) provides a good general definition of the concept: “Learning analytics is the measurement, collection, analysis, and reporting of data about learners and their contexts, for the purposes of understanding and optimizing learning and the environments in which it occurs.” It is important to recognize the difference between what is framed as academic analytics and as learning analytics. van Barneveld et al. (2012) distinguish academic analytics as the use of information to manage and improve the performance of the institution and its constituent organizational units. This includes the use of data to analyze admission, course success, graduation, employment, and citizenship (Prinsloo et al. 2015). They frame learning analytics in a narrower sense: “focused on the learner, gathering data from course management and student information systems in order to manage student success, including early warning processes where a need for interventions may be warranted” (van Barneveld et al. 2012, p. 6). Both types of analytics are important for the LMS as universities expand the use of information from analytics in real time to manage the system and the experiences of learners and educators. Particularly as systems evolve to include a range of external service providers, there is the need to monitor system performance proactively to ensure that learners are receiving the desired quality of service, and that the business model being enacted is successfully operating. In contrast, despite a vast

294

S. Marshall and M. D. Sankey

amount of research and investment in systems, there is as yet only limited evidence of a positive impact on student learning outcomes arising from the use of these systems (Sønderlund et al. 2019). Also increasingly challenging the first generation of analytics technologies is the awareness of the need to respect the privacy of individuals using the LMS (Zeide and Nissenbaum 2018). This suggests that system management is likely to show greater use of analytics in the near term, while direct value for learning is likely to require more work. All of these technologies will likely influence the ongoing development of the LMS, initially through stand-alone services integrated into courses and programs, and then as fully integrated tools operated by the vendor. However, while all of these areas of active development reflect important new capabilities for universities, teachers, and learners, they are all still essentially sustaining the same operational, business, and pedagogical model that was defined by the first generation of the LMS. Increasingly they are imposing an unsustainable cost not only in financial terms, but also in terms of complexity, maintainability, and in sustaining the capability to change. The focus for the virtual university needs to be on its ability to generate options for future action that move beyond this, to stimulate the development of new models of provision, and to achieve more than incremental improvements in existing activities in a way that reduces the costs of change on both people and organizations. This leads us to the reconception of the LMS as a Learning Platform.

LMS 3.0? Reconceiving the LMS as a Platform for Learning in the Future Virtual University Organizationally, the reality of the LMS is a complex web of systems integrated locally with others operated by a range of vendors (as seen in Fig. 1). University systems have evolved from single functional products deployed locally, into interconnected services that enact business functions using complex information architectures. Increasingly these have moved from locally hosted servers to unbundled services operated in remotely located computing hubs operated by companies like Amazon and Microsoft. This is more than a shift of hardware to the virtual cloud environment or outsourcing of complex technical functions, with many vendors moving from hosted software solutions to SaaS, to maintain more control over their product. It reflects a desire to have systems that not only sustain current activities but also allow for rapid shifts in focus, scale, and context without the historical constraints of sunk investment in traditionally constructed systems, reflecting a more ecological (Redmond and Macfadyen 2020) understanding of the university. This shift suggests the possibility of a further leap in our conception of the LMS designed to encourage agility, responsiveness and diversity of learning models, pedagogies, and contexts, while still retaining coherence, sustainability, and management of the whole – a platform rather than merely a system. Parker et al. (2016, p. 10) define platforms as “a new business model that connects people, organizations and resources through technology in an interactive

16

The Future of the Learning Management System in the Virtual University

295

ecosystem in which incredible amounts of value can be created and shared.” Zysman and Kenney (2016) define platforms as “multisided digital frameworks that shape or intermediate the terms on which participants – often but not always buyers and sellers – interact with one another” (p. 6). They also identify the important role that platforms play in realizing the productivity benefits promised by information and communication technologies by increasing the scale and scope of the impact people can achieve through their work within platforms as opposed to disconnected silos. Platforms can be simply technological artifacts used to support product development and supply chains, but in this chapter the concept is being used in a more sociotechnological sense that incorporates organizational processes and norms (Tilson et al. 2012). The most visible examples of platforms are the app environments provided by Apple and Google, where a significant platform of tools and services enables much smaller developers to create custom products. This is more than just a programming environment, including as it does the infrastructure and standards that enable services for sharing information privately and securely, engaging in e-commerce, and using cloud-based cognitive tools such as digital assistants within diverse contexts and applications. It is important to emphasize that platforms depend on standards – and standardization has influenced the development of the platform concept – but standards are not platforms (Gawer and Cusumano 2013). Much of the analysis of platforms is framed by commercial opportunities. The platform represents a powerful opportunity to turn single product revenue streams into market-dominating environments that generate multiple streams (Zhu and Furr 2016). In this more general application, a platform is a means of enabling other businesses to interoperate and generate new businesses rapidly within a coherent space. This conception goes beyond the operation of a content or product distribution system, to something able to be shaped by those engaging with it through their direct interactions with each other (Zhu and Furr 2016). Creating platforms in the commercial space is a process that Denning describes as “taking something that was previously a standalone product and creating a platform with shared infrastructure and base level of functionality, then inviting investors to create extensions and adaptations on top of that platform” (Denning 2016, p. 5). This is, however, not the only way to use this idea as Parker et al. observe: “practically any industry in which information is an important ingredient is a candidate for the platform revolution” (2016, p. 10). If we shift our thinking to multiple outcomes for learners and consider the possibility that the platform could be placed within a university, or group of collaborating partner universities, and the “vendors” operating within the platform are the disciplines and programs offered to learners, we arrive at a model that potentially aligns with the intellectual and social goals of the university as much as it does with the economic and technological drivers valued by commercial organizations. This alignment is apparent in the characteristics of platforms identified by Doll and Murphy (2020). As well as observing that platforms are not necessarily disruptive innovations, nor simply an integrated infrastructure such as that provided by the Internet or a traditionally framed LMS, they identify a platform as:

296

• • • • •

S. Marshall and M. D. Sankey

Connecting people both inside and outside the organization Placing an emphasis on different resources and the exchange of knowledge Creating value through platform interactions Able to streamline existing offerings as well as enable new ones Creating and sustaining an environment for collaboration by all users of the platform

These represent a strong foundation for enabling the learning activities of any university but are fundamental when considering the virtual university. The characteristics of “platforms” also align well to the dominant models of university change and operation, as the pressure to do more with technology to replace human interaction increases. The platforms benefit from network effects (Gawer and Cusumano 2013; Katz and Shapiro 1985) arising as a consequence of a feed-forward or positive feedback loop where the value of a system grows exponentially as the system grows in scale. Also known as Reed’s Law which states that the power of a network, especially one enhancing social networks, multiplies more rapidly as the number of different groups using the network increases (Reed 1991). Network effects are described as direct if they arise from increasing numbers of similar users, and indirect if they arise from increasing diversity of users (Katz and Shapiro 1985). Both types of effect are clearly of value to a university seeking to strengthen its learning platform. Direct effects respond to the scale of the offerings and the number of staff and learners using the platform, gaining value as more choices are provided. Similarly, indirect effects provide value by supporting a greater diversity of learners, contexts, and modes of learning, creating the drivers for growth in the scope of the university and enabling responsiveness to new opportunities and changes in the dominant and established contexts. Architecturally, platforms are “a core on which others can build modules that are designed to extend the service possibilities of the platform” (Eaton et al. 2011, p. 2). They need a stable core and a variable periphery (Gawer 2014) in order to provide the structure necessary for sustaining the platform and to create the opportunity for ongoing development. This structure allows modular innovation to be decoupled from architectural innovation (Henderson and Clark 1990) allowing the platform to change at both the micro- and meso-level without requiring significant organizational disruption to activities not directly being changed. Technological modularity “allows interdependent components of a system to be produced by different producers, with limited coordination required” (Jacobides et al. 2018, p. 2260). This increases the value of the platform as more vendors and options are added and allowing a distributed network of actors to “tune” the capabilities of the platform at its boundaries (Eaton et al. 2015). This structure necessarily reflects the operation of standards, but these need to be seen as enabling interoperability and cohesion, rather than imposing a rigid structure. Standards play a critical role along with architectural oversight of the use of these to ensure the platform goals are sustained (Gulati et al. 2012).

16

The Future of the Learning Management System in the Virtual University

297

Platforms are ecological in their conception and in how they influence organizational change. Ecological change models derive value from the use of competition and selection as pressures on activity, working through dynamic interactions between diverse actors (Postman 1992). Ecological systems shift the power mechanisms from hierarchies to roles, increasing the efficiency of activities and collaborations and enabling more rapid change to occur (Jacobides et al. 2018), that can enact a network of compatible patterns of activity, rather than hierarchical control. Architecturally, the adopting platform perspective reframes the features of the LMS presented in Figs. 2 and 3 into service channels, with the infrastructure redefined as the architectural standards used to translate services into specific modularized products (Fig. 5). A feature of the platform conception is that it is designed with the goal of attracting producers through opportunities and advantages, rather than with the goal of imposing a predefined efficient single model of activity as the traditionally conceived LMS has tended to do. This philosophical orientation encourages learning designs to be attempted that meet diverse needs, not only in terms of the pedagogical designs, but also with regard to diverse learner populations and contexts. This shift also arises through the realignment of the relationship between the university and technology partners. Rather than a supplier model, the platform moves the control to the university and frames the vendors within that architecture as modules delivering

Fig. 5 Learning platform conception of LMS

298

S. Marshall and M. D. Sankey

the standardized services. The new conception opens the door for greater interoperability with collaboration platforms offered by Microsoft, Google, and others without losing strategic control of the future models of education operated by the university.

Positioning and Enacting the Learning Platform for a Future Virtual University Successful innovators operate in a zone of proximal development, offering change but in terms that are intelligible in relation to current practice. (Flavin 2020b, p. 128)

The university in many ways reflects the dilemma identified by Christensen (1997) in the title of his work focusing on the failure of established organizations in the face of changing technologies. The long success of the university model ties it to established models of learning, learner communities, and operational models associated with that history. The LMS has reflected this in its evolution and suffered the consequence of being both the architect and victim of a static model of education. This dynamic reflects the action of the paradoxical relationships of change and control (Tilson et al. 2012). The paradox of change implies the need for digital platforms to simultaneously remain not only stable to sustain their operation, but also flexible enough to change and grow. The paradox of control reflects the need to balance competing pressures for central and distributed control to provide space for change to occur while not destroying the platform in the process of doing so. These are challenges of governance and leadership. They speak to the need for universities to strategize carefully how they position and enact their technology enhanced learning plans and evolution toward the future virtual university (refer to ▶ Chap. 30, “The Role of Standards and Benchmarking in Technology-Enhanced Learning”). Platforms in the commercial environment depend on the activities of owners, providers, producers and consumers (Parker et al. 2016), language that can easily be translated to reflect the university, technology partners, academics, and students. Importantly, when applying the concept of platforms within established organizations, there is evidence that effective governance mechanisms require participation from the owner, the management of the university, and those on the periphery of the platform (Schreieck et al. 2018). Changing conceptions of the LMS for the future need to be founded on a narrative of supporting the virtual university in its enaction of vision driven by building on the strengths of the university aligned to the goals of intellectual excellence and higher education, rather than a technocratic vision of innovation and novelty (Kahl and Grodal 2016). Gawer and Cusumano (2013, p. 429) summarize the effective practices needed for platform leadership which are expressed below in a form shifted to the language of the university:

16

The Future of the Learning Management System in the Virtual University

299

1. Develop a vision of how a product, technology, or service could become an essential part of a larger university learning ecosystem. 2. Build the right technical architecture and “connectors” aligned to the processes and services of the university. 3. Build a coalition around the platform: Share the vision and rally technology partners, academics, and students into cocreating a vibrant ecosystem together. 4. Evolve the platform while maintaining the collegial and intellectual values and direction of the university by improving the ecosystem’s vibrancy. Platforms respond best to a model of governance that reflects their ecosystemic roots. They need to be led in ways that maximize complementarity and aligned interests within the community of owners, providers, producers, and consumers (Gawer 2014), using narrative and sense-giving (Marshall 2018b; see also ▶ Chap. 4, “Laying and Maintaining the Foundations for Quality,” in this volume). These are well suited to the academic values and collegial systems of the research university. Adoption and use of an LMS is strongly related to the role and professional identity of the academic (Liu and Geertshuis 2021). Key aspects affecting adoption and use include the change management approach used to evolve the LMS (Marshall 2018c); the support of generalized skills and confidence in using Internet applications; the profile of teaching and the organizational commitment to its importance; and the provision of capability development that helps shift the conception of teaching (Liu and Geertshuis 2021). A platform treating academics as active, collaborating partners rather than clients or users is a powerful mechanism to enact the collegial university model of distributed leadership. The future virtual university needs systems that reflect engagement with diverse learners and a focus on expanding the reach and impact of the university into new contexts. The learner population will be more dynamic and seeking far more than certification as outcomes. Increasingly there will be the need to provide continuous educational experiences responding in real time to changing needs for learners, employers, and society. Many of the new affordances will reflect changed patterns of work and the dynamic networks learners participate in for their social lives and employment. LMS conceptions in this future environment will still include the management of content and learner information, but this will operate in support of a wider range of learning models informed by different work practices and within a rapidly evolving web of commercial relationships and business models (Newfield 2019). Finally, the future virtual university needs an LMS that is itself manageable, capable of sustaining the evolution of existing operations into new and uncharted spaces that are filled with risks and uncertainty. This suggests that the core must be strongly framed by clear standards and business processes that are designed to protect users, the university, and all of their information, but not so rigid as to prevent innovation at the boundaries of the organization and of pedagogical knowledge.

300

S. Marshall and M. D. Sankey

Conclusion Globally, there has been a massive growth in different models for tertiary education and a complex ecosystem of commercial educational service providers (Hill 2020). Hill identifies three major segments in this commercial space, ranging from the providers of core tools and media who have a relatively passive role and influence, through Online Program Enablement (OPE) where service providers work, to within a university in a hybrid business model, and to Online Program Management (OPM) providers who work essentially in an outsourced model of educational delivery. The concept of the learning platform is already recognized by companies like EdX (Agarwal 2017) who are using the MOOC as the starting point for their vision of a platform, building out from their initial offerings to grow a conception of an alternative platform to the university. The dependence of their initial offering on the university as a supplier is not in any way an obstacle; they benefit from the legitimacy and energy of those collaboration as they evolve the platform toward an educational version of Uber or Amazon. The aspirations of technology companies to create platforms for university education raise serious issues that will need to be addressed by institutions working heavily in the virtual space, particularly with the shift to vendor platforms. At the heart of this is the loss of agency with regard to the learning experience. As companies like Amazon have demonstrated, modern aggregation platforms can rapidly define expectations and have significant market dominance and control over suppliers and “partners” operating within their ecosystem. An example is the current tension between universities and publishers over access to scholarly research (Hiltzik 2020). This latter experience is particularly relevant given the clear ambitions of companies such as Pearson to grow the value of their educational ecosystem into their own platforms (Marshall 2018a). Futurist Avin Toffler stated “The illiterate of the twenty-first century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn” (Toffler 1970, p. 414). The failed universities of the twenty-first century will be those who fail to apply this advice to their own learning models, and instead allow the key systems and models to be controlled and shaped by external commercial interests who through their platforms come to define and own the future virtual university.

Cross-References ▶ Laying and Maintaining the Foundations for Quality ▶ Social Media: Friend and Foe ▶ The Role of Standards and Benchmarking in Technology-Enhanced Learning

References Agarwal, A. 2017. A platform for digital education. In 2017 Platform strategy summit: MIT initiative on the digital economy, 20–23. https://ide.mit.edu/sites/default/files/2017-MITPlatform-Summit-Report.pdf

16

The Future of the Learning Management System in the Virtual University

301

Alamri, H.A., S. Watson, and W. Watson. 2021. Learning technology models that support personalization within blended learning environments in higher education. TechTrends 65: 62–78. https://doi.org/10.1007/s11528-020-00530-3. ASU. 2018. Return on investment overview: What is return on investment and how should postsecondary institutions be using it? Issue brief. Arizona State University and Boston Consulting Group. https://www.everylearnereverywhere.org/wp-content/uploads/ROI-Strat egy-Toolkit.pdf Bond, C.H., R. Spronken-Smith, A. McLean, N. Smith, S. Frielick, M. Jenkins, and S. Marshall. 2017. A framework for enabling graduate outcomes in undergraduate programmes. Higher Education Research and Development 36 (1): 43–58. Brown, M. 2017. The NGDLE: We are the architects. Educause Review, July/August 2017, 11–18. Brown, M., J. Dehoney, and N. Millichap. 2015. The next generation digital learning environment: A report on research. Louiseville: Educause. Brown, M., M. McCormack, J. Reeves, D.C. Brooks, and S. Grajek. 2020. 2020 EDUCAUSE horizon report teaching and learning edition. Louiseville: Educause. Carbonell, J.R. 1970. AI in CAI: An artificial-intelligence approach to computer-assisted instruction. IEEE Transactions on Man-Machine Systems 11: 190–202. https://doi.org/10.1109/ TMMS.1970.299942. Carr, N. 2003. IT doesn’t matter. Harvard Business Review 81 (5): 41–49. Christensen, C.M. 1997. The innovator’s dilemma: When new technologies cause great firms to fail. Boston: Harvard Business Review Press. Corbett, A.T., K.R. Koedinger, and J.R. Anderson. 1997. Intelligent tutoring systems. In Handbook of human-computer interaction, ed. M. Helander, T.K. Landauer, and P. Prabhu, 849–874. New York: Springer. https://doi.org/10.1016/B978-044481862-1.50103-5. Davis, B., C. Carmean, and E.D. Wagner. 2009. The evolution of the LMS: From management to learning. Santa Rosa: E-Learning Guild. Denning, S. 2016. Can new disruption research suggest defenses against threats and opportunities for innovators? Strategy and Leadership 44 (3): 3–8. Doll, D.C., and D.J. Murphy. 2020. Platforms and strategies: Understanding how companies can align platforms and strategies. Masters thesis, Copenhagen Business School. https://researchapi.cbs.dk/ws/portalfiles/portal/62184168/889995_Understanding_how_companies_can_ align_platforms_and_strategies.pdf Eaton, B., S. Elaluf-Calderwood, C. Sørensen, and Y. Yoo. 2011. Dynamic structures of control and generativity in digital ecosystem service innovation: The cases of the Apple and Google mobile app stores. London School of Economics and Political Science 44: 1–25. Eaton, B.D., S. Elaluf-Calderwood, C. Sørensen, and Y. Yoo. 2015. Distributed tuning of boundary resources: The case of Apple’s iOS Service System. MIS Quarterly 39 (1): 217–243. Ellis, L.E., S.G. Nunn, and J.T. Avella. 2016. Digital badges and micro-credentials: Historical overview, motivational aspects, issues, and challenges. In Foundation of digital badges and micro-credentials: Demonstrating and recognizing knowledge and competencies, ed. D. Ifenthaler, N. Bellin-Mularski, and D.-K. Mah, 3–22. Geneva: Springer. Engelbart, D.C. 1962. Augmenting human intellect: A conceptual framework, SRI summary report AFOSR-3223. Menlo Park: Stanford Research Institute. Ennaji, M., H. Boukachour, M. Machkour, and M. Ben Salah. 2020. Mutual human-computer tutoring using agents. In Proceedings of the 6th IEEE Congress on Information Science and Technology (CiSt). https://doi.org/10.1109/CIST49399.2021.9357194. Fenton, W. 2017. Google Classroom absent from college courses. Inside Higher Ed, June 21, 2017. https://www.insidehighered.com/digital-learning/article/2017/06/21/google-classroom-not-col lege-classroom Flavin, M. 2020a. Virtual library environment? VLEs in practice. In Re-imagining technology enhanced learning, Digital education and learning. Cham: Palgrave Macmillan. https://doi.org/ 10.1007/978-3-030-55785-0_3.

302

S. Marshall and M. D. Sankey

———. 2020b. Reboot the messenger: A narrative for technology enhanced learning. In Re-imagining technology enhanced learning, Digital education and learning. Cham: Palgrave Macmillan. https://doi.org/10.1007/978-3-030-55785-0_7. Fortune Business Insights. 2021. Learning management system (LMS) market size. https://www. fortunebusinessinsights.com/industry-reports/learning-management-system-market-101376 Francom, G.M., A. Schwan, and J.N. Nuatomue. 2021. Comparing Google Classroom and D2L Brightspace using the technology acceptance model. TechTrends 65: 111–119. https://doi.org/ 10.1007/s11528-020-00533-0. Gawer, A. 2014. Bridging differing perspectives on technological platforms: Toward an integrative framework. Research Policy 43: 1239–1249. https://doi.org/10.1016/j.respol.2014.03.006. Gawer, A., and M.A. Cusumano. 2013. Industry platforms and ecosystem innovation. Journal of Product Innovation Management 31 (3): 417–433. Guan, C., J. Mou, and Z. Jiang. 2020. Artificial intelligence innovation in education: A twenty-year data-driven historical analysis. International Journal of Innovation Studies 4 (4): 134–147. Gulati, R., P. Puranam, and M. Tushman. 2012. Meta-organization design: Rethinking design in interorganizational and community contexts. Strategic Management Journal 33 (6): 571–586. Henderson, R.M., and K.B. Clark. 1990. Architectural innovation: The reconfiguration of existing product technologies and the failure of established firms. Administrative Science Quarterly 35: 9–30. Hill, P. 2020. OPM market landscape and dynamics: Fall 2020 updates. https://philonedtech.com/ opm-market-landscape-and-dynamics-fall-2020-updates/ ———. 2021. The future of learning platforms: LMS and video to share top billing. https:// philonedtech.com/the-future-of-learning-platforms-lms-and-video-to-share-top-billing/ ———. 2023. State of higher ed LMS market for US and Canada: Year-end 2022 edition, 31 January. https://philonedtech.com/state-of-higher-ed-lms-market-for-us-and-canada-yearend-2022-edition/ Hiltzik, M. 2020. UC notches a massive win in its quest to save scientific publishing. https://www. latimes.com/business/story/2020-06-17/uc-open-access-scientific-publishing Jacobides, M.G., C. Cennamo, and A. Gawer. 2018. Towards a theory of ecosystems. Strategic Management Journal 39: 2255–2276. https://doi.org/10.1002/smj.2904. Kahl, S.J., and S. Grodal. 2016. Discursive strategies and radical technological change: Multilevel discourse analysis of the early computer (1947–1958). Strategic Management Journal 37: 149–166. https://doi.org/10.1002/smj.2453. Katuk, N., J. Kim, and H. Ryu. 2013. Experience beyond knowledge: Pragmatic e-learning systems design with learning experience. Computers in Human Behavior 29 (3): 747–758. Katz, M.L., and C. Shapiro. 1985. Network externalities, competition, and compatibility. American Economic Review 75 (3): 424–440. Khan, M.A., and K. Salah. 2020. Cloud adoption for e-learning: Survey and future challenges. Education and Information Technologies 25: 1417–1438. https://doi.org/10.1007/s10639-01910021-5. Lang, L., and J.A. Pirani. 2016. The 2015 enterprise application market in higher education learning management systems, Research report. Louisville: ECAR. Liu, Q., and S. Geertshuis. 2021. Professional identity and the adoption of learning management systems. Studies in Higher Education 46 (3): 624–637. https://doi.org/10.1080/03075079.2019. 1647413. Long, P.D., and J. Mott. 2017. The N2GDLE vision: The “next” next generation digital learning environment. Educasue Review, July/August. https://er.educause.edu/-/media/files/articles/ 2017/7/erm17412.pdf Marshall, S. 2018a. Internal and external stakeholders in higher education. In Shaping the university of the future. Singapore: Springer. https://doi.org/10.1007/978-981-10-7620-6_4. Marshall, S.J. 2018b. Sense-giving and leadership. In Shaping the university of the future. Singapore: Springer. https://doi.org/10.1007/978-981-10-7620-6_21. ———. 2018c. Making sense of change. In Shaping the university of the future. Singapore: Springer. https://doi.org/10.1007/978-981-10-7620-6_17.

16

The Future of the Learning Management System in the Virtual University

303

Mousavinasab, E., N. Zarifsanaiey, S.R. Niakan Kalhori, M. Rakhshan, L. Keikha, and M. Ghazi Saeedi. 2018. Intelligent tutoring systems: A systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments 29 (1): 142–163. https://doi.org/10. 1080/10494820.2018.1558257 Newfield, C. 2019. Unbundling the knowledge economy. Globalisation, Societies, and Education 17 (1): 92–100. https://doi.org/10.1080/14767724.2019.1602353. Paladines, J., and J. Ramírez. 2020. A systematic literature review of intelligent tutoring systems with dialogue in natural language. IEEE Access 8: 164246–164267. https://doi.org/10.1109/ ACCESS.2020.3021383. Paramythis, A., and S. Loidl-Reisinger. 2003. Adaptive learning environments and e-learning standards. In Second European conference on e-learning, vol. 1, no. 2003, pp. 369–379. Parker, G.G., M.W. Van Alstyne, and S.P. Choudary. 2016. Platform revolution: How networked markets are transforming the economy and how to make them work for you. New York: W.W. Norton and Company. Postman, N. 1992. Technopoly: The surrender of culture to technology. New York: Vintage Books. Prinsloo, P., E. Archer, G. Barnes, Y. Chetty, and D. van Zyl. 2015. Big(ger) data as better data in open distance learning. International Review of Research in Open and Distance Learning 16 (1): 284–306. Redmond, W.D., and L.P. Macfadyen. 2020. A framework to leverage and mature learning ecosystems. International Journal of Educational Technology 15 (5): 75–99. Reed, D.P. 1991. The law of the pack. Harvard Business Review 79 (2): 23–24. Schmulian, A., and S.A. Coetzee. 2019. The development of Messenger bots for teaching and learning and accounting students’ experience of the use thereof. British Journal of Educational Technology 50 (5): 2751–2777. https://doi.org/10.1111/bjet.12723. Schoonenboom, Judith. 2014. Using an adapted, task-level technology acceptance model to explain why instructors in higher education intend to use some learning management system tools more than others. Computers and Education 71: 247–256. https://doi.org/10.1016/j.compedu.2013. 09.016. Schreieck, M., M. Wiesche, and H. Krcmar. 2018. Multi-layer governance in platform ecosystems of established companies. Academy of Management Proceedings 2018 (1): 10068. Siemens, G. 2013. Learning analytics: The emergence of a discipline. American Behavioral Scientist 57 (10): 1380–1400. Sinclair, Jane, and Anne-Maria Aho. 2018. Experts on super innovators: Understanding staff adoption of learning management systems. Higher Education Research and Development 37 (1): 158–172. https://doi.org/10.1080/07294360.2017.1342609. Skinner, B.F. 1958. Teaching machines. Science 128 (3330): 969–977. Sønderlund, A.L., E. Hughes, and J. Smith. 2019. The efficacy of learning analytics interventions in higher education: A systematic review. British Journal of Educational Technology 50: 2594–2618. https://doi.org/10.1111/bjet.12720. Syed, T.A., V. Palade, R. Iqbal, and S.S.K. Nair. 2017. A personalized learning recommendation system architecture for learning management system. In IC3K 2017 – Proceedings of the 9th international joint conference on knowledge discovery. Knowledge engineering and knowledge management, 275–282. Szulc, J. 2019. Models of e-learning systems architecture using AI components. In E-learning and STEM education, ed. W.E. Smyrnova-Trybulska, 295–320. Katowice-Cieszyn: Studio Noa for University of Silesia. Tilson, D., C. Sørensen, and K. Lyytinen. 2012. Change and control paradoxes in mobile infrastructure innovation: The Android and iOS mobile operating systems cases. In 45th Hawaii International Conference on System Science (HICSS 45), Maui. Toffler, A. 1970. Future shock. London: Bodley Head. Turnbull, D., R. Chugh, and J. Luck. 2019. Learning management systems: An overview. In Encyclopedia of education and information technologies, ed. A. Tatnall. Cham: Springer. https://doi.org/10.1007/978-3-319-60013-0_248-1.

304

S. Marshall and M. D. Sankey

USDoJ. 1990. Americans with disabilities act. https://www.ada.gov/pubs/adastatute08.htm van Barneveld, A., K.E. Arnold, and J.P. Campbell. 2012. Analytics in higher education: Establishing a common language. (ELI paper 1). Retrieved from https://net.educause.edu/ir/ library/pdf/ELI3026.pdf Winkler, R., M. Söllner, and J.M. Leimeister. 2021. Enhancing problem-solving skills with smart personal assistant technology. Computers and Education 165: 104148. https://doi.org/10.1016/ j.compedu.2021.104148. Xie, H., H.C. Chu, G.J. Hwang, and C.C. Wang. 2019. Trends and development in technologyenhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers and Education 140: 103599. Zeide, E., and H. Nissenbaum. 2018. Learner privacy in MOOCs and virtual education. Theory and Research in Education 16 (3): 280–307. https://doi.org/10.1177/1477878518815340. Zhu, F., and N. Furr. 2016. Products to platforms: Making the leap. Harvard Business Review 94 (4): 72–78. Zysman, J., and M. Kenney. 2016. The next phase in the digital revolution: Platforms, abundant computing, growth and employment. Berkeley roundtable on the international economy. Working paper 2016-3. Berkeley: BRIE.

Part VI New and Emerging Forms of Assessment

Making Online Assessment Active and Authentic

17

Mathew Hillier

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Active and Authentic Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Assessment to Improve Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aligned Curriculum to Improve Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aligned Educational Environment to Improve Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What Is Taught and How It Is Taught Matters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Addressing the Challenges of Delivering Effective Assessment Online . . . . . . . . . . . . . . . . . . . . . . . Scalability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Active and Authentic Assessment in the Virtual University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluating Active and Authentic Assessment Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Examples of Active and Authentic Assessment Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . An Online Quiz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interactive Knowledge Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Object-Based Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Online Interactive Oral Assessment for Summative Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . Virtual Immersive Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Remote Labs and Simulated Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Virtual Work-Integrated Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

308 310 311 313 314 315 317 317 318 319 320 321 321 332 334 336 338 341 343 346 348 348

Abstract

Demands on universities to produce graduates with twenty-first century capabilities have increased, and it is through the system of assessment that evidence M. Hillier (*) Office of the Pro-vice Chancellor of Learning and Teaching, Macquarie University, Sydney, NSW, Australia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_17

307

308

M. Hillier

of student achievement is evaluated. This chapter outlines why active and authentic assessment can meet this need as well as how a virtual university can take steps to enable the active and authentic assessment of twenty-first century skill sets. This includes alignment of curriculum, pedagogy, and the importance of the judicious selection of technology infrastructure with suitable enabling affordances. The chapter presents a series of examples of how active and authentic assessment design can be realized through the creative use of new and emerging toolsets. An evaluation of each featured assessment method is provided against Bloom’s revised taxonomy, a triad of forces acting on assessment and the SAMR model representing the extent to which the affordances of technology can be leveraged to create innovative assessment designs. Keywords

Assessment · Constructive alignment · Technology affordances · Learning · Active · Authentic · Scalable

Introduction Early in the twenty-first century there was recognition that the skill sets required of people to be successful in work, social, and political life were changing. In the United States of America, the “Partnership for 21st century skills” framework, explored by Trilling and Fadel (2009), identified skills in learning and innovations, digital literacy, and life and career skills (see Fig. 1, left). In Australia, the “Assessment and Teaching of 21st century Skills” consortium conducted an international meta study and compiled commonly identified skills under four categories of “ways of thinking,” “ways of working,” “tools for working,” and “living in the world” Binkley, Erstad, Herman, Raizen, Ripley, Miller-Ricci, and Rumble (2012) (see Fig. 1, right). In both cases, the sets of identified twenty-first century skills are seen as additional to foundational skills in language, numeracy, and social literacy. The importance of twenty-first century skills have been reinforced in a 2020 report from the World Economic Forum (2020). Based on an international survey of employers, the report provided a list of skills foreseen to be in demand in 2025 (see Fig. 2). The 2020 report also covered the first year of the COVID-19 pandemic that caused much disruption to the education system and employment market; however, the identified skill sets are largely an evolution from those identified in previous WEF jobs surveys conducted in 2018 (2018) and 2016 (2016). The COVID-19 pandemic and the looming forth industrial revolution that is seeing increasing use of automation, robotics, and generative artificial intelligence demonstrate that graduates require lifelong learning skill sets to be able to adapt and thrive in a rapidly changing world. It is in the mission of most universities to produce graduates fit for both work and social life, and in that light, they would be well advised to pay heed to the needs

17

Making Online Assessment Active and Authentic

Partnership for 21st Century Skills (Trilling & Fadel, 2009) Learning and Innovation ● Critical Thinking & Problem Solving ● Creativity & Innovation ● Communication & Collaboration Information, Media & Technology ● Information Literacy ● Media Literacy ● ICT (Information, Communications & Technology) Literacy Life & Career ● Flexibility & Adaptability ● Initiative & Self-Direction ● Social & Cross-Cultural Skills ● Productivity & Accountability ● Leadership & Responsibility

309

Assessment and Teaching of 21st Century skills (Binkly et al 2012) Ways of Thinking ● Creativity and innovation ● Critical thinking, problem solving, decision making ● Learning to learn, Metacognition Ways of Working ● Communication ● Collaboration (teamwork) Tools for Working ● Information literacy ● ICT literacy Living in the World ● Citizenship – local and global ● Life and career ● Personal and social responsibility – including cultural awareness and competence

Fig. 1 Two frameworks outlining twenty-first century skills

Fig. 2 Top 10 skills expected to be in demand in 2025 (WEF 2020. p. 36)

identified in Figs. 1 and 2. Universities often do so by defining an institutional set of graduate attributes. At the individual program (degree) and unit (subject) level, they define increasingly specific sets of intended learning outcomes relevant to the discipline and the focus of the subject to be learnt. It is through the design and implementation of learning activities and assessment tasks where student attainment of all those learning outcomes is actually evidenced. This makes assessment the key component in how universities assure the community that graduates can do what they claim they can do. For that to be true, assessment design must be fit for purpose and aligned to serve the needs of assessing the desired twenty-first century learning outcomes.

310

M. Hillier

Active and Authentic Assessment The characteristics of twenty-first century skills are such that traditional methods such as the reproduction of textbook knowledge, multiple-choice tests, and essays are likely to fall short of what is needed to enable learning and assessment of complex problem-solving, innovation, and creativity. As such, enhanced approaches to assessment are needed. Analysis of educational research (Hartikainen et al. 2019; Duchatelet et al. 2020) has shown that tasks designed to be active (i.e., Freeman et al. 2007) and authentic (i.e., Wiggins 1989) are thought to improve student learning outcomes. It will be argued over the following sections that such an approach is better suited to assessing complex, dynamic, twenty-first century capabilities. Active learning is defined by Bonwell and Eison (1991) as occurring when “students are doing things and actively thinking about what they are doing” (p iii.) and where “students must engage in the higher order tasks of [applying], analysis, synthesis and evaluation” (ibid.). We can also add “creation” to fill out the higher levels of Bloom’s taxonomy as represented in Fig. 3 (Bloom et al. 1956; Anderson and Krathwohl 2001). This translates into needing assessment designs where students are doing, creating, and cognitively engaging with the activity rather than regurgitating information from memorization. This engenders a degree of rigor in the intellectual engagement of students in assessment tasks. Tasks designed to assess the higher order of Bloom’s are also more in tune with the types of tasks needed to assess twenty-first century skills (as per Figs. 1 and 2). A simple example of active learning is designing a presession preparation task that requires bouts of reading and then responding to linked questions in turn, rather

Fig. 3 Bloom’s revised taxonomy after Anderson and Krathwohl (2001). (Image: Creative Commons (CC BY 2.0), Vanderbilt University Center for Teaching, 2016)

17

Making Online Assessment Active and Authentic

311

than just reading alone. An active assessment example is the use of a role-play simulation in which the student must participate in order to respond to an assessment task. However, having students engaged in doing things and actively thinking about a task only gets us part way along the road of holistic learning. If the task itself is largely artificial or trivial, then it may not challenge the student in ways reflective of the complexity required of professional practice. The types of capabilities highlighted in the twenty-first century skills frameworks outlined earlier points to the need for greater relevance and authenticity in the design of assessment tasks. A number of researchers have examined the idea of authentic assessment (Wiggins 1989; Bosco and Ferns 2014; Crisp 2009; Iverson et al. 2008 and Villarroel et al. 2018). Wiggins (1989), as one of the earliest to define authenticity in terms of the assessment of student learning, argued that authenticity in a test comes from it being representative of challenges in the discipline. Wiggins (2014) went on to provided 27 characteristics for authentic assessment based on his work in 1989 (see Fig. 4). In simple terms, authentic assessment tasks are designed with the characteristics, processes, and expected outcomes that are, to various extents, reflective of the world of “work” and the “social world” where problems can be messy, dynamic, and complex. Authentic tasks are in stark contrast to contrived tasks designed to only assess educational attainment. A reflection of the complexity of the discipline, profession, and society should therefore be represented in the activities and problems presented as part of the education program. This provides the learner with an appreciation as to the relevance of what and how they are being taught in the program in terms of professional practice. However, Wiggins (2014) clarified that “authentic” should not be conflated with “hands on” (practical) or “real world,” leaving room for tasks that are cognitive or simulated. It is also worth noting that there is a firm place in an education program for activities that teach foundational knowledge and improve skills by practice, provided relevant authentic tasks are also included later in the program that build upon such knowledge to create connections with professional practice. Both the characteristics of “active” and “authentic” are not dichotomies but are instead continuums from passive to active and from contrived to authentic (see Fig. 5). At this point, we can differentiate “active” and “authentic” assessment design. An “active” assessment task moves students away from passive memorization towards the need to take action and engage in problem-solving, while an “authentic” assessment task goes beyond contrived activities by utilizing the characteristics of complex problems found in professional practice. The assessment of twenty-first century skills would be well served by task designs that are both active and authentic.

Assessment to Improve Learning Curriculum is multifaceted (Glatthorn et al. 2005) and assessment in particular has multiple roles in an education program. This includes purposes that are diagnostic, formative, and summative (Crisp 2012). Delivering the benefits of authentic and

312

M. Hillier

Twenty Seven Characteristics of Authentic Assessment A. Structure & Logistics 1. Are more appropriately public; involve an audience, panel, etc. 2. Do not rely on unrealistic and arbitrary time constraints 3. Offer known, not secret, questions or tasks. 4. Are not one-shot – more like portfolios or a season of games 5. Involve some collaboration with others 6. Recur – and are worth retaking 7. Make feedback to students so central that school structures and policies are modified to support them B. Intellectual Design Features 1. Are “essential” – not contrived or arbitrary just to shake out a grade 2. Are enabling, pointing the student toward more sophisticated and important use of skills and knowledge 3. Are contextualized and complex, not atomized into isolated objectives 4. Involve the students’ own research 5. Assess student habits and repertories, not mere recall or plug-in. 6. Are representative challenges of a field or subject 7. Are engaging and educational 8. Involve somewhat ambiguous (ill-structures) tasks or problems C. Grading and Scoring 1. Involve criteria that assess essentials, not merely what is easily scored 2. Are not graded on a curve, but in reference to legitimate performance standards or benchmarks 3. Involve transparent, de-mystified expectations 4. Make self-assessment part of the assessment 5. Use a multi-faceted analytic trait scoring system instead of one holistic or aggregate grade 6. Reflect coherent and stable school standards D. Fairness 1. identify (perhaps hidden) strengths [not just reveal deficits] 2. Strike a balance between honoring achievement while mindful of fortunate prior experience or training [that can make the assessment invalid] 3. Minimize needless, unfair, and demoralizing comparisons of students to one another 4. Allow appropriate room for student styles and interests [ – some element of choice] 5. Can be attempted by all students via available scaffolding or prompting as needed [with such prompting reflected in the ultimate scoring] 6. Have perceived value to the students being assessed. Note: Clarifications added by Wiggins in 2014 are in square brackets. Fig. 4 Characteristics of authentic assessment from Wiggins (2014)

17

Making Online Assessment Active and Authentic

Authentic

Reading a complex case study

313

Group project for industry partner

to Contrived

Viewing a lecture on theory Passive

to

Discussion assessment on theory Active

Fig. 5 “Active” and “authentic” are a continuum

active assessment means a recognition that assessment usually needs to do “double duty” (Boud 2000), where the multiple purposes of assessment must be balanced. In addition to using assessment to measure learning after it has occurred (assessment of learning), when assessment is active and authentic, this can encourage learning via the act of doing the assessment (formative assessment and assessment for learning). Learning as part of doing assessment provides students with opportunities to iterate and improve their performance (Tai et al. 2018). If tasks are scaffolded appropriately, Yan and Boud (2021) argue that assessment tasks can enable leaners to learn how to judge their own performance (assessment as learning). Fostering evaluative judgment skills can enable critical self-evaluation that is an important skill for graduates to be able operate as independent professionals. While a discussion of effective feedback practices (timely, targeted, and actionable) is beyond the scope of this resource, it is necessary to note that “feed-forward” needs to be part of the design. Assessment practice must be active and authentic to enable feedback to be framed in a manner that is actionable by students and relevant for the enhancement and demonstration of twenty-first century skills. Using looped patterns or iterative stages of assessment work and feedback can provide students with multiple opportunities to realize and implement the improvement suggestions received from feedback (Henderson et al. 2018).

Aligned Curriculum to Improve Learning A constrictively aligned curriculum (Biggs 1996) is one where the components of the curriculum including assessment tasks, learning activities, and learning resources are designed to support the student’s achievement of the intended learning outcomes. The format and nature of assessment has consequences. Ramsden (1992) argues that assessment largely defines the curriculum from a student’s point of view. Boud (2000) similarly argues that assessment is seen by students as an indicator as to what their teachers see as important. It is therefore important that assessment is constrictively aligned to the desired learning outcomes because by its attentiongrabbing nature, well-aligned assessment tasks will reinforce those desired outcomes with students. Conversely, a disincentive for students can arise where a program may define intended learning outcomes that reflect those espoused by WEF (2020), but where too much of the assessment is focused on trivial or passive activities reminiscent of the lower levels of Bloom’s (i.e., recall and memorization). As such,

314

M. Hillier

moving towards the use of aligned, active, and authentic assessment will provide incentives and scaffolding for students to achieve learning outcomes that are reflective of desirable twenty-first century skills. If active learning and assessment is desired, then instructional methods and tasks need to be deliberately designed to ensure that active engagement is also required by the student, in order to complete the task. Furthermore, as Ajjawi et al. (2020) point out, the constructive alignment of assessment tasks needs to be clearly articulated to students so that they can see the connections for themselves. This enables students to become active participants in the assessment process rather than a process being done to them. This includes acknowledging the inevitability that assessment tasks are never 100% authentic. Therefore, to avoid a cynical rejection by students of assessment as being irrelevant, it is important to explain to students the limitations of the assessment tasks, the extent to which the task is authentic, where misalignment may occur and how the taught curriculum can be translated into new contexts outside the institutional learning environment. As such, the constructive alignment of active and authentic assessment needs to both happen and be seen by students to happen.

Aligned Educational Environment to Improve Learning Biggs (1993) recognized that the education endeavor is a complex system of factors beyond what is taught in classrooms. Students, teachers, institutional rules, and technologies are elements that when in equilibrium will lead to learning. This means that for positive change to be enacted, all elements, including institutional aspects, curriculum, assessment, and pedagogy need to be in alignment in order for desired learning outcomes to be achieved (Biggs 1996). To illustrate the importance of alignment, Ripley (2007, p. 10) outlines that assessment modalities such as invigilated paper-based exams done in isolation are a major barrier to curriculum change due to the limited affordances of the medium. Tradition, social inertia, quality control frameworks, along with ideas such as “teaching-to-the-test” (TTTT) (Phelps 2012) and “wash back” (Anderson 2007; Longo 2010), can help explain how old testing paradigms can hold back the designed and taught curriculum. The TTTT phenomena is where teachers focus on aspects that they know will be on a high stakes test (assessment). This can be particularly problematic in environments where student performance on narrowly focused standardized tests can have implications for the tenure of the teacher. Wash back is the impact on the focus of teaching activity, learning, and the taught curriculum due to the nature of the test (assessment). While deleterious wash back may occur via TTTT, Andrews et al. (2002) and Hillier and Fluck (2017b) argue that TTTT could also be a force for change via positive wash back where the characteristics of the test have been carefully designed to align with the desired learning outcomes. In this case, suitably aligned curriculum with active and authentic assessment will encourage teachers and students to focus on the desired twenty-first century learning outcomes.

17

Making Online Assessment Active and Authentic

315

What Is Taught and How It Is Taught Matters Returning again to Biggs’s (1993) idea of education being a complex system of factors, we must recognize that information and communication technologies (ICT) have become a part of contemporary education practice. ICT has a role in both curriculum and pedagogy when it comes to assessing twenty-first century skills. The concept of curriculum has been extensively defined in the literature (Glatthorn et al. 2005) as a complex construct including intended, taught, written, assessed, and hidden elements. The definition of pedagogy is similarly been contested in the literature (Watkins and Mortimore 1999; Young 2011) as the art, science, theory, and profession of teaching with varying philosophical stances taken. To clearly differentiate the concepts for the purposes of this discussion, the definition of “curriculum” will be taken to mean “what is taught” while “pedagogy” will mean “how it is taught.” The distinction is important because technology is intimately a part of pedagogy in an online program, but technology and related skills are also a component of the curriculum in most, if not all disciplines that aim to produce work and world ready graduates as per twenty-first century skills (Trilling and Fadel 2009; Binkley et al. 2012). To illustrate the point, if we consider the desired learning outcome of “to be able to write a useful computer programme under real world conditions,” then an invigilated pen-on-paper exam (the pedagogy) on a computer programming language (the curriculum) is not as active or authentic as would be an invigilated exam on the same topic done using a computer equipped with a software development kit and an internet connection. The concept of constructive alignment (Biggs 1996) of desired learning outcomes, curriculum elements, assessment tasks, and support resources extends to the choice and use of technology tools in assessment. In this case, the alignment of the curriculum (computer programming) and pedagogy elements (a computerized test) that includes the use of authentic tools of the trade (a software development kit and internet access) arguably results in a better evaluation of the student’s ability to write a useful computer program under real-world conditions (the intended learning outcome). The design of an assessment task, along with the context and audience, are all significant factors in enabling students to demonstrate the learning outcomes. Returning to the recent example, if we can move away from a traditional exam and instead use an authentic task such as an industry-focused group project conducted online, we push the pedagogy towards greater relevance. Students will gain practical experience in an environment that more closely resembles the scenarios they will face in their future careers and this improves their chances of achieving the desired learning outcomes. In terms of pedagogy, Fluck and Hillier (2014) and Hillier and Fluck (2017b) have argued that assessment technology needs to better enable the assessment of twenty-first century skills (e.g., WEF 2020). The use of technology in assessment must not be merely for administrative convenience. Instead, the affordances of technology infrastructure used for learning and assessment must allow active and

316

M. Hillier

authentic design to be possible. A virtual university that selects assessment platforms with limited pedagogical affordances will in turn limit the scope for assessment designers to create authentic tasks. This can occur where committees selecting tools relegate pedagogy in favor of administrative, cost, or efficiency concerns. It is therefore necessary for a virtual university to pay particular attention to the selection of assessment technologies that allow for design flexibility. Fluck and Hillier (2014) argue that enabling assessment designers to embed contemporary technology tools into assessment will better enable active and authentic designs to be created, particularly in the online assessment space. The vast majority of professions and occupations now utilize software tools as part of their “tools of the trade” toolbox. In assessment, this would include allowing assessment designers and students the use of multiple rich media sources, large data sources, discipline-based software (e.g., chemistry construction kits, mathematical modeling, and simulation tools), and “e-tools of the trade” (e.g., spreadsheets, programming development environments, computer-aided design tools, and modern office suites) within the assessment task itself. This is contrasted to a toolset focused on educational testing and efficiency (e.g., a locked-down multiple-choice online exam tool). A rich toolset will serve to broaden the pedagogical landscape of assessments and exams and so offers the potential to “unblock” the way towards curriculum transformation through positive wash back (Hillier and Fluck 2017b). Therefore, when defining technology selection requirements, a virtual university must prioritise features that enable the integration of modern industry and discipline technology tools within the design of both formative and high-stakes summative assessment. This will reinforce and support an active and authentic pedagogical strategy. The broader learning environment also matters when it comes to enabling active and authentic assessment. In many universities, technology has been increasingly used to augment on-campus learning and support the administration of education. While in a virtual university, the technology platform is the campus (i.e., the virtual campus). The interface students have with a virtual university and an online education program is mediated by technology. The affordances of the institutional technology environment are integral to supporting all aspects of learning and assessment, including facilitating connections and collaboration between students, teachers, and the wider society. The affordances of the selected technical infrastructure can enable or inhibit the ability for students to learn the practice of the discipline and the profession. In some disciplines, a technology interface presents a layer of abstraction from the professional world, when considering occupations such as a scientist working in a chemistry laboratory or nurse working in primary health care. It must also be acknowledged that purely face-to-face education programs that lack technology integration no longer represent the way work and social lives are lived out in the contemporary society, where information technology is a common medium of communication and is a “tool of the trade.” Examples of this reality can be observed anywhere knowledge work exists such as the financial services sector or in an international engineering project. In the case of a virtual university, the selection of disciplines and programs to be taught as well as the learning technologies selected will impact their student’s ability to achieve the

17

Making Online Assessment Active and Authentic

317

desired capabilities fit for practice. In suitably selected disciplines, an approach to assessment must be deployed that takes into consideration both the limits and opportunities of the technology environment, but also the competing demands such as integrity and resourcing.

Addressing the Challenges of Delivering Effective Assessment Online In defining what are considered to be desirable and feasible assessment strategies in an institutional setting, competing needs and requirements include authenticity, scalability, and validity. The triad of competing factors, forces, or needs acting on assessment are represented in Fig. 6. While authenticity (and by extension “active”) has been covered earlier, scalability and validity will be outlined next. The two additional forces of scalability and validity are outlined.

Scalability Available financial resources, time, and staff to student ratios, especially in large enrolment units, all place limitations on what is feasible. This means that consideration of the efficiency of an assessment design and the associated process is required in terms of the workload it places on staff and students. Scalability is similarly impacted by the complexity of assessment designs, the degree to which grading is manual or automated, as well as the available resources, time, technology tools, and staff-to-student ratios. As such, scale and efficiency need to be a part of the assessment design brief.

Fig. 6 A triad of forces acting on desirable and feasible assessments. (Based on Hillier and Fluck (2017a). Permission granted for reproduction)

318

M. Hillier

Validity We need to be sure that assessment is reliably measuring what is intended, that the university is accrediting the correct student as competent and that the circumstances under which assessment is undertaken can be verified. This means that academic integrity and security measures must be considered when designing and deploying assessments. We must be able to verify the identity of the student, the authorship of any products produced, the nature of access to any resources or assistance used by the student, and the conditions under which the task was undertaken. Further, the assessment must be available when required and the assessment data kept secure. The Information Assurance (DoD 2006) five pillars of “availability, integrity, authentication, confidentiality, and non-repudiation” (p. 9) and the model of Cyber Forensics Assurance (Dardick 2010) both provide sets of elements and considerations that need to be in place to ensure assessments are verifiable. The security aspects of assessment are expanded upon in ▶ Chap. 20, “Authenticity, Originality, and Beating the Cheats.” Beyond security, the importance of ensuring the accuracy and validity of assessment items and results means that educational measurement theory and practice will continue to play an important role (e.g., item response analysis, Wu et al. 2016). Ensuring that assessment is valid also means that it must be fair and equitable and so the accessibility of assessment designs matter. This means that virtual universities should be looking to frameworks such as World Wide Web Consortium (W3C) accessibility standards (Henry 2021) and the universal design for learning guidelines (CAST 2018) to guide the construction of online assessment. The well-being of students and stress levels placed upon students are also important elements in delivering valid assessment because troubled students cannot perform at their best. This means for valid assessment to be possible, students will need contextual support to be in place. This includes pastoral care, access to financial aid for students in low socioeconomic groups to acquire the necessary equipment and network connectivity, and access to reasonable adjustments so that each student has an equitable opportunity to demonstrate their competence. The three competing forces, broadly labeled as authenticity, validity (or integrity), and scalability, together make it difficult to achieve all three elements outlined above in a single assessment task. The second half of this chapter provides a series of illustrative examples to demonstrate how these factors play out in different assessment designs. Each factor will push and pull on the shape of an assessment design. To illustrate, we can look at the available resources both human and financial as well as the enabling technologies used for teaching and assessment. For example, the choice of the learning management system (LMS) and the processes established to support learning and teaching functions, the capabilities, skills, resources available to teachers and students as well as their attitudes towards issues such as data security and privacy all serve to shape the selection, design, and use of online assessment systems. The affordances of the selected education technologies such as LMS, testing tools, and lecture recording platforms as well as ability to include software “tools of the trade” from disciplines and industry are enablers to doing assessment differently. Selecting and supporting a particular LMS at an institution provides

17

Making Online Assessment Active and Authentic

319

efficiencies for the institution that can enable scalability, but this also sets boundaries on what is possible in learning and assessment design. As such, assessment designers need to balance the competing needs of authenticity, scalability, and integrity in a similar manner to the concept of the resource constraint “iron triangle” (Atkinson 1999) of “quick, good, cheap – pick two” in project management and product development. We can also see this playing out in the current marketplace of commercial online testing products where providers often target scalability and security at the expense of authenticity, as is evidenced by the large number of locked-down quizzing systems available for sale. To further illustrate the tensions, let us take the example of an end of session examination comprised of psychometrically verified multiple-choice questions. Such an assessment can be considered to be scalable, and if done in a highly secure, invigilated environment may provide high levels of integrity; however, such tests frequently lack the characteristics of authenticity in consideration of the complexities of work and social contexts. As argued earlier, assessment activities that involve rich, complex, messy projects, such as those undertaken in work-integrated learning settings, do reflect the world of work and may offer a good degree of identity verification. However, it is often the case that such complex assessment designs do not scale to a large number of students because there are limits on the availability of host organizations and because such programs are labor- and time-intensive to manage. There needs to be a mix of assessment across a learning program that together achieve the desired coverage of characteristics. Tasks across a program can be mapped and rated against the three criteria represented in Fig. 6 (triad of forces) so that informed choices can be made about the mix of tasks. The remainder of this chapter focuses on authenticity, and by extension active assessment, but does not ignore the others. A detailed discussion of assessment integrity and security is examined in ▶ Chap. 20, “Authenticity, Originality, and Beating the Cheats.”

Active and Authentic Assessment in the Virtual University In exploring what an active and authentic assessment design may look like, we must also consider the sophistication of the desired learning outcome. Bloom’s (1956) cognitive taxonomy refers to lower order activities, such as memorizing facts, and higher order activities, such as integration and synthesis of ideas, theories, and practices. While it is acknowledged that foundational knowledge is required in a discipline to operate, a university-level education program must also include the higher order capabilities so that graduates can demonstrate professional competence. As outlined previously, the affordances of the available technology tools frame what is possible and this may temper the creativity of an assessment designer. It is also common for the adoption of new methods and tools to occur in an evolutionary rather than a revolutionary manner. Initially small changes are made, possibly replicating old ways of working but using new tools, and then as those using the

320

M. Hillier

Fig. 7 The SAMR model. (Creative Commons Attribution-Share Alike 4.0 International license)

tools learn more about the affordances of the tools, increasingly sophisticated patterns of use emerge that sees entirely new ways of working come into existence. When exploring possibilities for active and authentic assessment design, it is useful to reflect on the level of technological integration and design sophistication required of assessment designers and students as well as the technology affordances required to bring about such designs. The SAMR model (Puentedura 2013), as seen in Fig. 7, provides a conceptual framework that allows for a qualitative evaluation as to the degree that technology affordances have been leveraged to enable sophisticated or innovative assessment design. The model represents a progression up four levels, starting with substitution, then to augmentation, to modification, and finally to redefinition. In the case of a new virtual university, an opportunity exists to leapfrog the practices of older institutions; however, the staff joining such an institution are likely to bring their practices with them and therefore support and professional development of staff will still be required. The implication of the ideas from the SAMR model for active and authentic assessment design in a virtual university is that it is likely that a sophisticated capability will be required to leverage judiciously selected technology with enabling affordances in order to be able to deliver on the assessment of twenty-first century capabilities.

Evaluating Active and Authentic Assessment Methods In exploring possible active and authentic assessment methods, it is useful to qualitatively evaluate each approach against the frameworks introduced so far. These are:

17

Making Online Assessment Active and Authentic

321

• Bloom’s revised taxonomy: The extent to which the proposed method allows for the assessment of higher order capabilities that are expected in higher education and reflective of twenty-first century skills. • The assessment forces triad: The balance of desirable and feasible features in terms of active/authentic task design, scalability, and validity/security. • The SAMR model: The extent to which the proposed method represents or allows for the leveraging of the affordances of technology to enable sophisticated task design or re-imagination.

Examples of Active and Authentic Assessment Design The remainder of this resource will provide some inspiration for educational leaders and assessment designers at a virtual university as to what is already possible with existing tools and a dose of creativity. Each of the examples selected include both active and authentic assessment design elements in action. The designs range from those that could be implemented using common quiz tools to others that will require more effort or particular technologies. Each example is accompanied by a qualitative evaluation and notes of where the activity could sit on Bloom’s revised taxonomy, the likely balance of factors in the assessment triad and the degree of technologically enabled innovation on the SAMR model.

An Online Quiz A quiz tool is typically available in most LMSs or as a standalone tool. The affordances of the given quiz software set the boundaries on the extent to which the assessment designer can create active and authentic questions. A suitably flexible quizzing platform may include capabilities such as the integration or linking of multiple media resources, the ability to receive files or recorded media as a response. The platform may have an extensible architecture that could allow designers to add capabilities via plugins. In this set of quiz examples, the Moodle LMS quiz was used to provide known boundaries and because it is the most commonly used LMS platform in the world, it will therefore provide value to many readers. However, the functionality represented here is common to many other LMS quizzing tools and independent testing platforms. A quiz tool provides very high levels of scalability, given its capability for online submission, automated marking, and near infinite capacity once established on a scalable cloud server. In most cases, quizzes will be cost effective where an institution has chosen a suitable LMS as a standard platform and where automated marking has been used where suitable. On the assessment triad, a quiz ticks the scalability box. When used alone, quiz tools provide only a moderate level of integrity control (for example, if randomization is used). A quiz will need to be matched with appropriate security measures to ensure academic integrity requirements can be addressed to a reasonable extent. The real challenge is to think about how a quiz

322

Create Evaluate Analyse Apply Understand Remember

M. Hillier Authenticity Redeinition Modiication Augmentation Scalability

Integrity

Substitution

Fig. 8 Qualitative evaluation of using a standard online quiz for assessment

can be used to host active and authentic tasks, given that it can be tempting to fall into old habits such as creating simple multiple-choice questions or setting standard essay response tasks. At least the first two levels of SAMR can be achieved using features common to most quiz platforms. However, SAMR’s third level and the fourth level “redefinition” may be possible provided the features of the platform support creative task design and complex constructed responses. It may be necessary via the use of plugins to provide suitable additional capabilities. It is important to note that where tasks target the higher levels of Bloom’s, the more diverse the expected student responses will be, and therefore the less such responses will to be amiable to automated evaluation. While varying levels of Bloom’s can be represented in quiz questions, it is worth noting that the lower levels are more at risk of academic integrity breaches such as collusion and the use of unauthorized resources, particularly when online quiz tools are used for summative assessment in non-invigilated contexts. The use of additional add-on security measures such as remote invigilation or lockdown browsers may enhance security, but these measures will also impact the extent to which authentic task designs can be delivered. A qualitative evaluation of using an online quiz tool with standard or basic features (without plugins or add-on security measures) against the three frameworks is presented in Fig. 8. An additional evaluation of an extended quiz is presented following additional examples later in this section. A quiz tool that provides a diverse range of question types will allow the assessment designer to move away from traditional multiple-choice (MCQ) and essay response questions, opening up possibilities for innovative question and task design. All quiz tools come with a basic set of questions types – typically 10–15 variations on selected response items, such as multiple choice, true/false, sorting, matching, and constructed response items, such as fill-in-the-blanks, numeric, short text, and longer text responses. Some platforms also allow expansion via plugins (see Focus 1 for an example). Focus 1: Basic and Extended Question Types in the Moodle LMS quiz

The basics: There are 16 standard question types. This includes image-based hot spots, numerical, calculated, and long text. The standard features also (continued)

17

Making Online Assessment Active and Authentic

323

Focus 1 (continued)

include the ability for students to attach files to a question that could contain a complex constructed response, an audio or video response. See https://docs. moodle.org/en/Questions for the full standard list. Extended capabilities: The Moodle quiz platform allows additional plugins and so the landscape of possibilities is expanded further. At the time of writing, the Moodle community provided an additional 50 question types as plugins. These include music theory, computer programming, mathematical modeling, handwritten formula recognition, spreadsheet formats, chemistry molecule editors, a drawing canvas response item, and media response question types designed for language assessment. See https://moodle.org/plugins/?q¼type: qtype for a dynamic list of available question type plugins.

Using the basic capabilities of most quiz platforms, the teacher can attach material to a quiz question prompt when creating the question by adding a link using the text editor or adding an attachment. This material could include a mini case, scenario, or data and be of any file format (e.g., PDF, a data set, or graphic). The teacher can then ask students to analyze, reflect, compare, or contrast. See Fig. 9 showing an example of embedding a link to a PDF containing a case study. It is also a good idea to contextualize the question to the course and discipline. The teacher may ask students to include theory taught in the course, to draw upon

Fig. 9 Moodle quiz with a link to a reference PDF resource in a question stem

324

M. Hillier

Fig. 10 Moodle quiz with a multimedia resource in a question stem

their own experience, or link their response to recent events in the industry in their response. Rich media material can also be used in question stem. Material can include files or embedded media such as video or audio. The quiz can be used to collect a response that could be text, numeric, or a selected response item (Fig. 10). Many quiz platforms provide one or more calculated question types. A calculated question will provide the ability to present the student with a set of randomly generated variables or elements (from a predefined range) within the question. This makes it harder for direct collusion between students to occur because each student will get a different set of values in place of the question variables (Fig. 11). Complex formulae can be used to generate a large array of possible answers according to the set of values presented to each student. A calculated question therefore allows the teacher to generate a collection of similar questions that can be used for practice or in summative assessment. Such questions require students to apply a taught method to solve a problem. The questions are automatically marked with automated feedback also possible. Quiz platforms may have variations on each question type that provide slightly differing capabilities. An example of this variation in Moodle is explored in Focus 2. Focus 2: Calculated Question Types in the Moodle LMS quiz

In the case of Moodle, there are three types of calculated question types that include “calculated” (as per Fig. 11), “calculated multiple choice,” and “calculated simple.” Further information is available on the Moodle calculated question types: https://docs.moodle.org/en/Questions.

17

Making Online Assessment Active and Authentic

325

Example - calculated question - display for student A

Example - calculated question - display for student B

Fig. 11 Examples of a calculated question in a Moodle quiz

Specialist platforms for mathematical or computer programming focused problems are also available. Computer algebra platforms such as STACK (Sporring and Sangwin 2019; Sangwin 2021) cater for mathematical and STEM questions (Fig. 12) and code evaluation toolsets such as CodeRunner (Lobb 2014, 2021; Lobb and Harlow 2016) for computer programming (Fig. 13). These additional tools provide increasingly sophisticated capabilities with a programming language used to construct complex, interactive questions that are evaluated using a computerized evaluation engine. The two tools featured above can also be added as plugins to an LMS quiz tool (such as Moodle). Some quiz platforms permit the construction of multipart or multielement questions that are also referred to as embedded answers or “cloze” questions. See Fig. 14 for an example. The cloze question provides for a degree of flexibility and creativity in building a multielement or compound question. A cloze question may include a mix of selected elements (radio button or drop-down selection list) and constructed convergent fields (text or numbers). The question type may include differentiated scoring according to response sets defined by the question designer and some platforms will also allow the differential weighting of the components. See Focus 3 for further information on creating cloze questions in Moodle. Focus 3: Creating Embedded Answers Cloze Questions in Moodle

In Moodle, building an embedded answer (cloze) question requires the use of a limited set of syntax to define the question elements where multiple variables each with differentiated marking can be created. It is possible to build cloze questions using the text editor or the graphical user interface tools available in the Moodle Quiz editing interface. Numerical, selected response, and short (continued)

326

M. Hillier

Fig. 12 Example of a STACK graph question in a Moodle quiz. (Source: STACK “DEMO: Using JSXGraph for diagrams and interactivity”. https://stack-demo.maths.ed.ac.uk/demo/)

Focus 3 (continued)

text input can be automatically assessed by creating response sets for each input field. Further information on cloze questions is available in: https://docs.moodle.org/en/Embedded_Answers_(Cloze)_question_type

The next step up in interactivity is to move from selected response and closed focus text response questions towards having the student take action to utilize provided resources or tools to address the task. The resource could be a spreadsheet file containing data or simulation tool that students will need to manipulate or build upon to arrive at an answer. In an example of guided constructed enquiry, the teacher provides a key piece of starting information that the student will use to carry out a task in order to arrive at their response. Students may be asked to respond using one of the many standard question types in a quiz or the student can also be asked to respond via text where they must explain the outcome or findings (Fig. 15).

17

Making Online Assessment Active and Authentic

327

Fig. 13 Example of a CodeRunner question in a Moodle quiz. (Source: CodeRunner https:// coderunner.org.nz/)

Where an open-ended enquiry and response is appropriate, then the teacher can ask the student to conduct an analysis task using a supplied resource such as a spreadsheet file or data set and then explain their findings (Fig. 16). In another example (Fig. 17), the teacher provides a data file along with a starting hint. The student must undertake an analysis task using a discipline software tool. Depending on the expected knowledge level of the cohort regarding the tool, the teacher can decide to exclude the hint. The student then responds with a number that can be automatically marked. Stepping up the interactivity further, the student now needs to construct and submit a unique response or digital artifact. For example, the student may need to create a digital artifact using a software application commonly used in the discipline. The teacher can direct students to use an external software application to do a programming task, spreadsheet task, or a drawing task. As the level of interactivity increases and in the interests of equity of access, it is advisable to be flexible regarding the use of software tools to complete the task. For example: “use any drawing tool capable of exporting a PNG,” rather than insisting on specific name brand proprietary tool. It is also important to provide clear instructions on what the student must do.

328

M. Hillier

Fig. 14 Example of Moodle quiz cloze question

Where the quiz platform supports file uploads to individual questions, students can be asked to upload one or more files containing their constructed response directly to the quiz before progressing to the next question. See Fig. 18 for an example showing the provision of a starter file and spaces for text and file upload response. Focus 4 provides additional instructions on how to enable direct file uploads into a quiz within Moodle. Focus 4: File Uploads During a Moodle Quiz

In Moodle, file uploads within the quiz can be done using an essay question type where the question is configured by setting “Allow Attachments” to “enabled” and “Require attachments” set to at least “1.” When using a quiz in this manner, the file containing the student’s complex constructed response will become part of a quiz submission. The teacher can also choose to require an accompanying text response. If a text response is not required, then ensure “Require text ¼ Text input is optional” is selected when setting up the question.

17

Making Online Assessment Active and Authentic

329

Fig. 15 Moodle quiz question with instructions to use SciLab

Fig. 16 Moodle quiz with link to a spreadsheet in the question stem

If file uploads are not possible within the LMS quiz, then a suitable “assignment” submission mechanism could be used to receive the file instead. However, when using a file upload that sits outside of the quiz, the advantages of timing and enforced or desired sequencing of submission may not be possible.

330

M. Hillier

Fig. 17 Moodle quiz question with a data file link and instructions in the stem

Fig. 18 Moodle quiz question with spreadsheet attachment and file upload

Another example in Fig. 19 shows a computer programming task. In this case, the teacher has provided some instructions to use a discipline software tool and an outline of the requirements of the expected response. Similarly, the response may require the student to explain what they did and to submit the product of their creation. Where the quiz platform permits the use of on-the-fly recording, the teacher can have students respond using multimedia. This technique can be used for oral or video

17

Making Online Assessment Active and Authentic

331

Fig. 19 Moodle quiz question using Scratch and file upload

responses to a question. Responses received as an audio recording could be of value in language assessment or as a means to assessing fluency of an idea via a spoken medium. A short video response or presentation could be useful where nonverbal and visual aspects of communication may be assessed. The on-the-fly media recording also adds an element of identity verification to the response because the voice or video of the respondent is captured as part of their response. Figure 20 shows the use of an onthe-fly video recording tool within a quiz question for a “selfie” ID check. Details of on-the-fly media recording within a Moodle quiz are available in Focus 5. Given the additional technical requirements of having a microphone and video camera as well as the need for some additional network capacity, it is advisable to warn students of any special technical requirements as part of the assessment instructions. In the interests of equity of access, it is recommended to be flexible in the format and method that response files are received. Focus 5: On-the-Fly Recording in a Moodle Quiz

In recent versions of Moodle, a set of recording buttons are included on the default Atto editor tool bar. These can be used by students to record short (less than 3 min) audio or video clips on-the-fly without leaving the quiz. As of (continued)

332

M. Hillier

Fig. 20 Example of Moodle quiz on-the-fly multimedia capture

Focus 5 (continued)

writing, the recording tool currently requires the use of Firefox or Chrome on a laptop or desktop computer. Users of other devices will need to use a secondary tool to undertake the recording and then upload the recording file separately (see Focus 4). To enable the use of a media recorder in a Moodle quiz, the essay question type must be used and the settings must be configured with “Response format ¼ HTML editor with file picker.” Other settings can remain at their defaults.

A qualitative evaluation of using an online quiz tool with extended features (e.g., via plugins) against the three frameworks is presented in Fig. 22. This should be compared to the basic quiz as shown in Fig. 8 where the ability to allow for complex constructed responses through active and authentic tasks is constrained (Fig. 21). Additional examples of active and authentic assessments using linked or embedded tools in a LMS quiz can be found in Crisp (2010) and Hillier (2019, 2020).

Interactive Knowledge Maps Concept and knowledge maps have the potential to improve student learning and understanding by promoting meaningful learning and critical thinking. The “Knowledge Maps” tool (see Valan 2020) is a web-based platform developed at University of New South Wales, Australia, where it has been used in medical education. The tool can be integrated with the Moodle LMS. The platform can be used to create, edit, and share maps, while also providing automated feedback on students’ submissions. The Knowledge Maps tool allows learning about the

17

Making Online Assessment Active and Authentic

333

Fig. 21 Qualitative evaluation of using an extended online quiz for assessment

Fig. 22 Author interface to create knowledge map questions

relationships between concepts. The Knowledge Maps tool provides teachers with an assessment editing mode (Fig. 22). Teachers can create an “expert map” as a model answer and then remove nodes or linking phrases to create the student activity. Beginner students can select from a drop-down menu of answer choices (Fig. 23). Increased difficulty can be created by removing any number of the nodes and connectors or by using free text responses. Ho et al. (2019) reported the use of knowledge maps in two pilots in a medical course, where students responded to scaffolded maps with missing concepts by selecting from a menu of options (Fig. 23). Students reported that they learnt more with the maps compared to learning without the maps. Student-led activities can also be undertaken with students using knowledge maps to constructed maps and submit the map as an assessment response. Software such as Cmap (IHMC 2021) can also be used to create compatible knowledge maps. The knowledge map tool allows for the mid-levels of Bloom’s knowledge assessment and at least the first two levels the SAMR model. In terms of the assessment triad, the design includes good scalability once maps are implemented and the potential for automated assessment. The level of authenticity depends on the nature and complexity of the knowledge maps that are created along with supporting

334

M. Hillier

Fig. 23 A scaffolded knowledge map task showing a drop-down menu of answer choices

resources such as a case study or scenario. A judgment on the validity of using knowledge maps compared to methods such as structured quizzes is perhaps less certain and will be dependent on the richness of the task design, but the research by Ho et al. (2019) provides reason to value the approach. Knowledge maps would be subject to academic integrity risks in cases where all students receive the same problem set. However, it would be possible to create multiple maps and allocate one to each student to minimize the possibility for collusion. Verification of academic integrity will similarly depend on the nature of any additional security regime that is implemented. A qualitative evaluation of the knowledge maps assessment method is presented in Fig. 24.

Object-Based Assessment Theophilos (2021) outlines how objects and artifacts can be used in online assessment to enrich assessment and facilitate deep learning. Physical objects that may be available to students at campus-based institutions are not accessible to those attending online programs. Theophilos (2021) cites that using objects and digital representations of objects can enable “active learning” (Freeman et al. 2014) by allowing students to “construct meaning through interactions” (Hannan et al. 2013), make “observations to complex abstract concepts” (Chatterjee and Hannan 2015) and the “ability to visualise ideas” (Schönborn and Anderson 2006). Digitized objects or whole buildings can be modeled and presented in images, maps, diagrams, or 3D models with questions able to cover all levels of Bloom’s cognitive taxonomy (Fig. 25). Students can also construct digital objects by scanning objects using mobile devices to produce a 3D representation, image, or model. Software is available that allows 3D models to be built up from a series of separate images (Fig. 26).

17

Making Online Assessment Active and Authentic

Fig. 24 Evaluation of knowledge maps in online assessment

Fig. 25 Examples of digital objects associated with assessment questions

335

336

M. Hillier

Fig. 26 Creating a virtual 3D object from multiple images

Newer mobile devices are also increasingly starting to include light detection and ranging (LiDAR) sensors allowing increasingly sophisticated digital scanning in the hands of students. While as at writing in 2023 this tends to be only in the flagship phones from major brands, it may eventually filter down into lower end models. The use of digital objects in assessment allows mid to upper levels of Bloom’s knowledge assessment and for designers to climb to the top tier “redefinition” of the SAMR model depending on the mix of technology and associated task design. In terms of the assessment triad, the design includes only moderate scalability given the complexity of creating and managing digital objects. Once resources are developed, scalability then depends on student’s access to suitable devices for viewing and creating virtual objects. Using virtual objects or having students create their own can open up opportunities for higher levels of authenticity. Validity is less certain, given the novelty of the methods used and will be dependent on the task design. Identity verification is absent from the virtual object-based assessment method itself and will depend on using a linked or associated security system or protocols. A qualitative evaluation of the objectbased method for online assessment is presented in Fig. 27.

Online Interactive Oral Assessment for Summative Assessment The online interactive oral assessment (OIOA) (Sotiriadou et al. 2020; Logan et al. 2020, 2021) can be used to create interactive, authentic assessment experiences for students and offers a viable alternative to traditional written exams. The OIOA approach can trace its origins to the Socratic method utilizing the format of an interactive, argumentative conversation centered on asking and answering questions within the scaffold of a scenario. The interactions stimulate a critical exploration of the student’s knowledge of both theory and its application in the scenario being explored. The OIOA approach can be deployed online and at

17

Making Online Assessment Active and Authentic

337

Fig. 27 Evaluation of object-based tasks for online assessment

scale. The approach has been used at Griffith University over a period of 6 years with classes of over 750 students (Logan et al. 2021). Communities of practice have also been formed at other institutions in Australia, Singapore, Ireland, and the United States of America. An evidence-based evaluation of the assessment approach found benefits in enhanced student engagement, employability, and academic integrity in undergraduate and postgraduate courses. This approach enables the fostering and evaluation of twenty-first century skills with integrity. OIOA is a method that carries a range of characteristics cited by Bretag et al. (2019) as being least likely to be cheated on by students – that of reflection, viva, personalized, in-class, and focused on the real world. The features of OIOA allow the preservation of academic integrity in high stakes assessment due to audio, video of the student, recording of the session, and visual ID checks. In contrast to traditional viva assessment, OIOA utilizes an authentic task design with the opportunity to use a realistic scenario that includes scaffolded but free-flowing exploratory conversation. The online context makes it possible for the student to support their response via the use of discipline and professional software tools or resources (Sotiriadou et al. 2021). Griffith University (Learning Futures 2020; Logan et al. 2021) provided practical examples of scenario-based activities used in OIOA that include: an oral defense of a report, an oral linked to previous assessments, a performance review after a group project, a defense of an organization’s performance, a media statement and questioning, a time-released case study, a scenario based on a workplace context, a group project pitch, a simulated job interview, a presentation to a committee or board of directors, a briefing to a government minister, a crisis response media interview, annual stakeholder meeting, a job interview, a client interview or pitch, and a problem analysis presentation to a panel. The format provides flexibility to assessment designers in creating scenarios representative of authentic contexts. This can encourage students to connect with a task that has relevance to future professional practice. Clear processes, rubrics, and practice runs are important so that OIOA sessions can be an efficient and a positive experience for students and staff. Training for staff in the examination team for pre- and post-moderation of the examination covering assessment standards, criteria, and the nature of information, questioning, and prompting (Pearce and Chiavaroli 2020) is critical to ensuring both scalability and also fairness. Some important elements to success include:

338

M. Hillier

• Encouraging student engagement, for example, using a rubric to guide students to mark a recording of the teacher’s performance on the same task. This is effective in having students think about the criteria and standards of the task and to allow students to become familiar with how an interactive oral session will occur. • Preserving academic integrity by leveraging the characteristics of the task, that is dynamic, personalized, with verification of the student’s context and identify via voice and video. • The use of authentic scenario design makes for a lively and satisfying assessment experience for students and staff. The task design requires students to engage in higher order cognitive processes (re Bloom’s) and represents a significant augmentation (re Puentedura 2013) of a traditional face-to-face scenario, role-play, or oral assessment that brings it into the online space. Using a digital recording of the event occurs along with in-person identify verification serves to support assessment security aims. The logistical design of the assessment procedures developed by the Griffith University team supports scalability as demonstrated in its use in classes of 750 or more students. Against the frameworks introduced in this chapter, the OIOA is evaluated highly. The OIOA approach can be used to assess Bloom’s higher level skills, provided a suitable design for the scenarios, prompting, and questioning has been used. The OIOA approach can enable the upper levels of SAMR to be achieved depending on the design of the tasks. In terms of the assessment triad, the OIOA design strikes an effective balance in tacking the triple challenges of effective assessment design, that of achieving authenticity in assessment, developing methods to scale to large student numbers, and in upholding assessment integrity through guiding students in preparation and utilizing the features of contemporary technologies. Scalability is perhaps the bigger challenge of using this method, given the need to involve increasing numbers of staff as student numbers also increase; however, technical tools enable efficiencies in managing schedules and online conferencing tools allows access to students at a distance. Authenticity can be very high if realistic scenarios are used in the task design. Security can be high if synchronous ID checks are done to confirm student identity and where video is used to confirm and record the conditions and context under which the student is undertaking the task. A qualitative evaluation of the online interactive oral assessment method is presented in Fig. 28.

Virtual Immersive Environments The use of game-like 3D multiuser virtual environments (MUVE) or a virtual world in education allows online universities to provide learners with the ability to interact online with other learners and virtual objects via an avatar. Depending on the platform used, students may interact with the virtual space via a computer screen or with the use of a virtual reality headset. A number of examples exist in language education (Grant et al. 2013; Grant 2017), business (Barber 2015), pharmacy (Linegar 2012), hospitality (Patiar et al.

17

Making Online Assessment Active and Authentic

339

Fig. 28 Evaluation of online interactive oral assessment

2017), agriculture (Walker 2015), and construction (Landorf 2016). In language teaching, the “Virtually enhanced languages” platform (Grant et al. 2013; Grant 2015, 2017) has been used at Monash University for tertiary level Chinese language and culture learning for a number of years. The interactive, immersive environment addresses challenges of access to realistic scenarios and native speakers when students are learning foreign languages. An online 3D multiuser virtual environment (MUVE) allows learners to interact between themselves using language and with non-player characters (a programed character in the virtual environment) using various situated scenarios. The activities take place within scenes housed in a virtual Chinese city that includes a restaurant, shops, airport, doctor’s office, train station, houses, parks, and a farmer’s market. The environment allows free movement within the 3D city world. See Figs. 29 and 30 for examples. Students engage in guided lessons and activities in practical sessions with the teacher present or as self-practice outside of class times. Formative assessment is possible, given that the interactions with the non-player characters are logged and can be viewed by the teacher at a later time. A shared set of technical and pedagogical resources was also developed on free to access basis with latter iterations using the open-source technology “Open Sim” that works online and to a limited extent in an offline format (see Grant 2017). It is also possible to allow students to build their own 3D immersive spaces and objects. Free builder tools that include a graphical user interface and that require no programming skills to create 3D spaces and objects. Virtual environments now come in many formats. The increasing use of mobile devices allows the use of augmented reality (AR) where information is overlaid on a view of the world. This contrasts to virtual reality (VR) that typically involves enclosed and immersive environments separated from the real world. The decreasing cost of AR and VR capable mobile devices places these technologies increasingly within reach of many students, in addition to being available on desktop and laptop computers as well. Lower-cost headsets and even the basic, low cost “Google cardboard” (https://arvr.google.com/cardboard/) VR viewing adaptor for smart phones allow more immersive interfaces for engagement with virtual environments and objects when compared to using such environments on a 2D computer screen. One example is the basic training of emergency responders in laryngoscopy (clearing an airway) by combining 3D printing and an AR application. It provides an interactive and flexible approach that allows training at a distance and

340

M. Hillier

Fig. 29 Chinese island farmer’s market in a virtual world. (Source: Virtually enhanced languages)

Fig. 30 Chinese island train station. (Source: Virtually enhanced languages)

for a relatively low cost compared to previous methods that required specialist equipment and on-site training (Cowling and Birt 2018). MUVEs, AR, and VR can be used to design tasks and assessments that target Bloom’s higher level skills and enable assessment designers to “redefine” what is possible (SAMR) to develop tasks not previously possible. High levels of authenticity can be achieved by combining realistic scenarios with immersive, interactive interfaces. Scale can be a challenge in the initial build out of VR and AR problem sets for students (although this is becoming easier as new build tools become

17

Making Online Assessment Active and Authentic

341

Fig. 31 Evaluation of using virtual immersive environments for assessment

available). Low fidelity AR and VR is moderately scalable, provided students have access to a suitable mobile device. However, access to high-fidelity immersive environments remains a challenge in providing higher specification headsets, access to suitable network bandwidth for students, and the required server capacity in the case of MUVEs. However, the costs of devices and hosting will reduce over time. The level of validity and authentication available in AR/VR will depend on the media and interface features of the platform being used. AR and VR can include identity verification if a voice and video connection is also used during an assessment task in connection with the real-time presence of an assessor. However, fully virtualized MUVEs may face identity verification challenges if the student’s voice and face are substituted by a digital avatar. Figure 31 presents a summary of a qualitative evaluation of using immersive online environments for assessment with respect the three frameworks.

Remote Labs and Simulated Equipment A virtual university is likely to be without physical laboratories or equipment. It is possible to provide access to equipment using internet-connected labs such as those demonstrated by Lustig et al. (2018) and Lustig et al. (2019). Connected virtual labs can be used to design tasks and assessments that target Bloom’s higher level skills and enable designers to obtain the mid-levels of (SAMR), depending on the methods used and the design of the tasks. An example of a photovoltaic experiment where the user is able to control the equipment via the internet is shown in Fig. 32. This approach provides opportunities for online students to undertake practical assessments using equipment that is not locally available. However, this presents severe limitations to scalability because control of the equipment is only possible for one user at a time. Simulated equipment (Fig. 33) overcomes the capacity problem by allowing a large number of users to conduct software-simulated experiments at the same time. Students can conduct a simulated laboratory practical experiment, extract data, conduct data analysis, and write a laboratory report. The simulation can also be undertaken in conjunction with a LMS quiz (Fig. 34) where responses can be collected via quiz using one or more questions using numerical, text, or a selected response items.

342

M. Hillier

Fig. 32 Internet-connected photovoltaic laboratory experiment. (Source: http://kdt-29.karlov.mff. cuni.cz/index_c_en_js.html)

The internet-connected labs and virtualized labs can be used to assess Bloom’s higher level skills, although free-ranging “creation” is less likely with the predefined boundaries of virtualized experiments. Designers can activate the third level of SAMR “modification,” which can be achieved depending on the design of the tasks and the sophistication of the simulation. In terms of the assessment triad, scalability remains a challenge for internet-connected lab equipment, but this is largely solved when equipment is fully virtualized as a simulation. Online equipment access and simulations can provide for efficiencies in managing lab schedules. Internet-connected hardware can expand student access to authentic devices.

17

Making Online Assessment Active and Authentic

343

Fig. 33 Simulated photovoltaic laboratory experiment. (Source: http://phet.colorado.edu/en/ simulation/photoelectric)

Simulated and remote access may also open up access to experiments that would be considered too risky or dangerous to be conducted by novice students. Authenticity can be very high if realistic scenarios and high-quality simulated equipment are developed. Validity and identity verification may face similar challenges as an online quiz, given the remote access nature of the systems in use. Utilizing virtual labs in high stakes assessment would require additional identity verification and monitoring mechanisms to be put in place. A summary of a qualitative evaluation of using simulated lab equipment in online assessment is presented in Fig. 35.

Virtual Work-Integrated Learning Capacity and logistical overheads make arranging on-site work-integrated learning experiences costly. During the COVID-19 pandemic, many such experiences were suspended. Utilizing virtual techniques can provide students and virtual universities access to internship and work placement opportunities in many parts of the world. Male and Valentine (2019) outline that virtual work-integrated learning is where: . . .students undertake learning activities that involve industry but are not true employment (paid or unpaid). Students complete authentic tasks, using authentic tools and/or processes, and engage face-to-face or electronically with real or simulated workplaces and/or practitioners.

344

M. Hillier

Fig. 34 Embedded remote lab experiment within a Moodle quiz question. (Source: Transforming Assessment)

Male (2018) outlined how virtual work-integrated learning has been used in the engineering discipline at the University of Western Australia. The experience consisted of authentic projects, simulated scenarios (Fig. 36) and online meetings with industry partners via online conferencing.

17

Making Online Assessment Active and Authentic

345

Fig. 35 Evaluation of using simulated lab equipment for assessment

Fig. 36 Simulated work-integrated learning experience

Tasks and assessments can, depending on their design, target Bloom’s higher level skills and enable designers to “redefine” what is possible (SAMR) with the use of simulated experiences. Similar examples of work-integrated learning simulations include for health practitioner safety training (Sheen 2018), virtual hospitality work experience (Patiar et al. 2017), building construction (Landorf 2016), virtual on-farm field trips (Barber 2015), and experiential learning in accounting using a simulated city economy (Walker 2015). Virtualized work-integrated learning can be used to assess the highest levels of Bloom’s capabilities and enable the upper levels of SAMR to be achieved depending on the design of the tasks, projects, and scenarios presented to students. In terms of

346

M. Hillier

Fig. 37 Evaluation of using virtual work-integrated learning for online assessment

the assessment triad, scalability is moderate. It is more scalable than traditional work-integrated learning but will still face challenges due to the inherent complexity of the experience and this is especially the case as long as industry partners are involved. This may be alleviated if the main mode of interaction is via a simulation. Technical tools can provide for efficiencies in managing schedules, and internetconnected conferencing expands the reach to students and allows a single industry partner to interact with groups of students with relatively few overheads. Validity and identity verification can be high if real-time interaction is included via conferencing tools; however, fully simulated experiences may still face identity verification challenges. Figure 37 provides a summary of a qualitative evaluation of using virtual work-integrated learning for assessment in online programs.

Conclusion New virtual universities have the opportunity to reconceptualize assessment in light of the affordances of modern technologies to better assess the capabilities required of twenty-first century graduates. A virtual university will need to pitch their assessment to enable student learning of higher order capabilities (i.e., the higher order of Bloom’s revised taxonomy), judiciously select technology tools with suitable affordances, and leverage those affordances with the aim of redefining what is possible (i.e., aiming for the redefinition level of the SAMR model) in evidencing student’s twenty-first century capabilities. This resource has demonstrated to educational leaders and practitioners what is already possible by taking advantage of existing technologies available in the spheres of education and specialist disciplines. Summarized in Fig. 38 are the collective results of a qualitative evaluation of the featured assessment methods. This analysis provides an indication as to how each of the featured methods can address twenty-first century skills by aiming for the upper levels of Bloom’s and the level of sophistication required in leveraging technology tools to achieve these outcomes in an online environment. Lastly, the evaluation demonstrated that each method has a different balance between the competing needs of authenticity,

17

Making Online Assessment Active and Authentic

347

Basic quiz Create Evaluate Analyse Apply Understand Remember

Bloom's Revised

Authenticity Redeinition Modiication Augmentation Scalability

Integrity

Assessment forces triad

Substitution

SAMR

Extended quiz Create Evaluate Analyse Apply Understand Remember

Bloom's Revised

Authenticity Redeinition Modiication Augmentation Scalability

Integrity

Assessment forces triad

Substitution

SAMR

Knowledge maps Create Evaluate Analyse Apply Understand Remember

Bloom's Revised

Authenticity Redeinition Modiication Augmentation Scalability

Integrity

Assessment forces triad

Substitution

SAMR

Object based assessment Create Evaluate Analyse Apply Understand Remember

Bloom's Revised

Authenticity Redeinition Modiication Augmentation Scalability

Integrity

Assessment forces triad

Substitution

SAMR

Online interactive oral assessment Create Evaluate Analyse Apply Understand Remember

Authenticity Redeinition Modiication Augmentation Scalability

Integrity

Fig. 38 Summary qualitative evaluation of featured assessment methods

Substitution

348

M. Hillier

Fig. 38 (continued)

integrity, and scalability. It is therefore necessary for a virtual university to manage these demands by using a diversity of assessment approaches across their online programs.

Cross-References ▶ Authenticity, Originality, and Beating the Cheats

References Ajjawi, R., J. Tai, T.L. Huu Nghia, D. Boud, L. Johnson, and C.-J. Patrick. 2020. Aligning assessment with the needs of work-integrated learning: The challenges of authentic assessment in a complex context. Assessment & Evaluation in Higher Education 45 (2): 304–316. https:// doi.org/10.1080/02602938.2019.1639613.

17

Making Online Assessment Active and Authentic

349

Anderson, T.R. 2007. Bridging the educational research-teaching practice gap. Biochemistry and Molecular Biology Education 35 (6): 471–477. https://doi.org/10.1002/bmb.20135. Anderson, L., and D. Krathwohl, eds. 2001. A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Boston: Allyn & Bacon. Andrews, S., J. Fullilove, and Y. Wong. 2002. Targeting wash back—A case-study. System 30 (2): 207–223. https://doi.org/10.1016/S0346-251X(02)00005-2. Atkinson, R. 1999. Project management: Cost, time and quality, two best guesses and a phenomenon, its time to accept other success criteria. International Journal of Project Management 17 (6): 337–342. https://doi.org/10.1016/S0263-7863(98)00069-6. Barber, S. 2015. Development of 4D farms to improve student learning and safety, Transforming Assessment, 6 May. http://transformingassessment.com/events_6_may_2015.php. Biggs, J.B. 1993. From theory to practice: A cognitive systems approach. Higher Education Research and Development 12 (1): 73–85. https://doi.org/10.1080/0729436930120107. Biggs, J.B. 1996. Enhancing teaching through constructive alignment. Higher Education 32 (3): 347–364. https://doi.org/10.1007/BF00138871. Binkley, M., O. Erstad, J. Herman, S. Raizen, M. Ripley, M. Miller-Ricci, and M. Rumble. 2012. Defining twenty-first century skills. In Assessment and teaching of 21st century skills, ed. P. Griffin, B. McGaw, and E. Care, 17–66. Springer Netherlands. https://doi.org/10.1007/ 978-94-007-2324-5_2. Bloom, B.S., M.D. Engelhart, E.J. Furst, W.H. Hill, and D.R. Karthwohl. 1956. Taxonomy of educational objectives: The classification of educational goals: Handbook 1 cognitive domain. Green and Co LTD: Longmans. Bonwell, C.C., and J. A. Eison. 1991. Active learning: Creating excitement in the classroom, AEHE-ERIC Higher Education Report No. 1. Washington, DC: George Washington University. ISBN 1-878380-08-7. https://archive.org/details/activelearningcr0000bonw/page/n3/mode/2up. Bosco, A.M., and S. Ferns. 2014. Embedding of authentic assessment in work-integrated learning curriculum. Asia-Pacific Journal of Cooperative Education 15 (4): 281–290. Boud, D. 2000. Sustainable assessment: Rethinking assessment for the learning society. Studies in Continuing Education 22 (2): 151–167. https://doi.org/10.1080/713695728. Bretag, T., R. Harper, M. Burton, C. Ellis, P. Newton, K. van Haeringen, S. Saddiqui, and P. Rozenberg. 2019. Contract cheating and assessment design: Exploring the relationship. Assessment & Evaluation in Higher Education 44 (5): 676–691. https://doi.org/10.1080/ 02602938.2018.1527892. CAST. 2018. Universal design for learning guidelines, version 2.2. http://udlguidelines.cast.org. Chatterjee, H.J., and L. Hannan. 2015. Engaging the senses: Object-based learning in higher education. Routledge & CRC Press. Retrieved October 18. Cowling, M., and J. Birt. 2018. Pedagogy before technology: A design-based research approach to enhancing skills development in paramedic science using mixed reality. Information 9 (2): 29. https://doi.org/10.3390/info9020029. Crisp, G. 2009. Towards authentic e-assessment tasks. EdMedia + innovate learning, Association for the Advancement of Computing in Education, 22 Jun, Honolulu, HI, USA, 1585–1590. http://www.editlib.org/p/31689/. Crisp, G.T. 2010. Interactive E-assessment-practical approaches to constructing more sophisticated online tasks. Journal of Learning Design 3 (3): 1–10. Crisp, G.T. 2012. Integrative assessment: Reframing assessment practice for current and future learning. Assessment and Evaluation in Higher Education 37 (1): 33–43. https://doi.org/10. 1080/02602938.2010.494234. Dardick, G. S. 2010. Cyber forensics assurance. 8th Australian Digital Forensics Conference. 30 November. https://doi.org/10.4225/75/57B2926C40CDA. DoD. 2006. DoD directive 3600.1: Information Operations. U.S. Department of Defense. 14 August. https://irp.fas.org/doddir/dod/info_ops.pdf. Duchatelet, D., P. Bursens, S. Usherwood, and M. Oberle. 2020. Beyond descriptions and good practices: Empirical effects on students’ learning outcomes of active learning environments in political science curricula. European Political Science 19 (3): 327–335. https://doi.org/10.1057/ s41304-020-00259-w.

350

M. Hillier

Fluck, A., and Hillier, M. 2014. EExams transforming curriculum. Now IT’s Personal, 26th Australian Computers in Education Conference, 151–158. http://acec2014.acce.edu.au/sites/ 2014/files/attachments/eExams%20paperd%20-%20REV2b.docx. Freeman, S., E. O’Connor, J.W. Parks, M. Cunningham, D. Hurley, D. Haak, C. Dirks, and M.P. Wenderoth. 2007. Prescribed active learning increases performance in introductory biology. CBE Life Sciences Education 6 (2): 132–139. https://doi.org/10.1187/cbe.06-09-0194. Glatthorn, A.A., F. Boschee, and B. M. Whitehead, eds. 2005. Chapter 1: The nature of curriculum. In Curriculum leadership: Development and implementation, 2–35. Sage. Grant, S. 2015. Virtually enhanced language teaching. Transforming Assessment, 1 April. http:// transformingassessment.com/events_1_april_2015.php. Grant, S. 2017. Virtually enhanced languages (website) http://virtuallyenhancedlanguages.com/. Grant, S.J., H. Huang, and S.E. Pasfield-Neofitou. 2013. Language learning in virtual worlds: The role of foreign language and technical anxiety. Journal For Virtual Worlds Research 6 (1): Article 1. https://doi.org/10.4101/jvwr.v6i1.7027. Hannan, L., R. Duhs, and H. Chatterjee. 2013. Object-based learning: A powerful pedagogy for higher education. In Museums and higher education working together, ed. A. Boddington, J. Boys, and C. Speight, 159–168. Taylor and Francis. Hartikainen, S., H. Rintala, L. Pylväs, and P. Nokelainen. 2019. The concept of active learning and the measurement of learning outcomes: A review of research in engineering higher education. Education in Science 9 (4): 276. https://doi.org/10.3390/educsci9040276. Henderson, M., Boud, D. J., Molloy, E., Dawson, P., Phillips, M., Ryan, T., and Mahoney, P. (2018). Feedback for learning: Closing the assessment loop: Final report. Australian Government Department of Education and Training. http://newmediaresearch.educ.monash.edu.au/feed back/wp-content/uploads/ID16-5366_Henderson_Report_2018.pdf. Henry, S.L., ed 2021. W3C accessibility standards overview. Web Accessibility Initiative (WAI). https://www.w3.org/WAI/standards-guidelines/. Hillier, M. 2019. E-exam examples: From paper-equivalent to post-paper. Transforming Exams. https://transformingexams.com/files/BYOD_eexams_examples_2017-2018.pdf. Hillier, M. 2020. Moodle quiz: Towards post-paper, April, Transforming Exams. https:// transformingexams.com/files/Hillier-moodle_quiz_examples.pdf. Hillier, M., and Fluck, A. 2017a. The assessment triad and e-exams: Integrity, authenticity and scalability. Presentation at the assessment integrity and the role of examinations forum, 22 August, Melbourne, Australia. Hillier, M., & Fluck, A. 2017b. Transforming exams—How IT works for BYOD e-Exams. Me, Us, IT!: Proceedings ASCILITE 2017, 100–105. http://2017conference.ascilite.org/wp-content/ uploads/2017/11/Concise-HILLIER.pdf. Ho, V.W., M. Meng, G.-J. Hwang, N. Pather, R.K. Kumar, R.M. Vickery, and G.M. Velan. 2019. Knowledge maps: An online tool for knowledge mapping with automated feedback. Medical Science Educator 29 (3): 625–629. https://doi.org/10.1007/s40670-019-00736-y. IHMC. 2021. Cmap [computer software application]. Florida Institute for Human & Machine Cognition, https://cmap.ihmc.us/. Iverson, H.L., M.A. Lewis, and R.M. Talbot. 2008. Building a framework for determining the authenticity of instructional tasks within teacher education programs. Teaching and Teacher Education 24 (2): 290–302. https://doi.org/10.1016/j.tate.2007.09.003. Landorf, C. 2016. Developing a 4-dimensional interdisciplinary learning environment. Transforming Assessment, 3 February. https://transformingassessment.com/events_3_february_ 2016.php. Learning Futures. 2020. Assessment type: Examination – Oral (interactive). Griffith: Griffith University. https://app.secure.griffith.edu.au/exlnt/entry/9569/view. Linegar, D. 2012. An animated story about the Pharmatopia project, Oztron Pty. Ltd, Australia. https://oztron.com/2012/10/23/animated-story-pharmatopia-project/. Lobb, R. 2014. CodeRunner: Authentic e-assessment for computer programming. Transforming Assessment, 3 September. https://transformingassessment.com/events_3_september_2014.php.

17

Making Online Assessment Active and Authentic

351

Lobb, R. 2021. CodeRunner [computer software]. https://coderunner.org.nz/. Lobb, R., and J. Harlow. 2016. Coderunner: A tool for assessing computer programming skills. ACM Inroads 7 (1): 47–51. https://doi.org/10.1145/2810041. Logan, D., P. Sotiriadou, and R. Jobst. 2020. Authentic online oral assessment – An examination replacement. Transforming Assessment, 30 April. http://transformingassessment.com/events_ 30_april_2020.php. Logan, D., P. Sotiriadou, A. Daly, and R. Guest. 2021. Interactive Oral assessment; an authentic and integral alternative to examination [multi-media resource]. Griffith: Griffith University. https://tinyurl.com/IO-assessments. Longo, C. 2010. Fostering creativity or teaching to the test? Implications of state testing on the delivery of science instruction. The Clearing House: A Journal of Educational Strategies, Issues and Ideas 83 (2): 54–57. https://doi.org/10.1080/00098650903505399. Lustig, F., D. Schauer, and M. Ožvoldová 2018. E-laboratory project [website]. https://www.ises. info/. Lustig, F., P. Brom, and P. Kuriščák. 2019. Professional and hobby hands-on-remote experiments. AIP Conference Proceedings 2152 (1): 030020. https://doi.org/10.1063/1.5124764. Male, S. 2018. Virtual work integrated learning in engineering. Transforming Assessment, 5 September http://transformingassessment.com/events_5_september_2018.php. Male, S., & Valentine, A. (2019). Virtual work integrated learning for engineering students. Australian Government Department of Education and Training. https://ltr.edu.au/resources/ ID15-4951_Male_Final_Report_2019.pdf. Patiar, A., P. Benckendorff, and K. Wood. 2017. Virtual field trips to enhance employability skills in hospitality. Transforming Assessment, 5 April. http://transformingassessment.com/events_5_ april_2017.php. Pearce, J., and N. Chiavaroli. 2020. Prompting candidates in oral assessment contexts: A taxonomy and guiding principles. Journal of Medical Education and Curricular Development 7: 2382120520948881. https://doi.org/10.1177/2382120520948881. Phelps, R.P. 2012. The effect of testing on student achievement, 1910–2010. International Journal of Testing 12 (1): 21–43. https://doi.org/10.1080/15305058.2011.602920. Puentedura, R.R. 2013. SAMR: Moving from enhancement to transformation. Ruben R. Puentedura’s Weblog, 29 May. http://www.hippasus.com/rrpweblog/archives/000095.html. Ramsden, P. 1992. Learning to teach in higher education. London: Routledge. Ripley, M. 2007. E-assessment: An update on research, policy and practice. Future Lab. http:// archive.futurelab.org.uk/resources/documents/lit_reviews/Assessment_Review_update.pdf. Sangwin, C. 2021. STACK (software), https://www.ed.ac.uk/maths/stack/. Schönborn, K.J., and T.R. Anderson. 2006. The importance of visual literacy in the education of biochemists. Biochemistry and Molecular Biology Education 34 (2): 94–102. https://doi.org/10. 1002/bmb.2006.49403402094. Sheen, J. 2018. Risk aware: Enhancing students’ clinical competence in risky environments through blended simulation-based learning. Transforming Assessment, 2 May. http://trans formingassessment.com/events_2_may_2018.php. Sotiriadou, P., D. Logan, A. Daly, and R. Guest. 2020. The role of authentic assessment to preserve academic integrity and promote skill development and employability. Studies in Higher Education 45 (11): 2132–2148. https://doi.org/10.1080/03075079.2019.1582015. Sotiriadou, P., A. Daly, and D. Logan. 2021. Interactive orals: A key to authenticity, employability and risk management [presentation]. Australian International Education Conference, 7 October, Gold Coast. https://drive.google.com/file/d/1CdtmxsZLvrAIw2KBdpx1r-QOLZOILrSG/view. Sporring, E.M., and C. Sangwin, eds. 2019. STACK online assessment: A collection of case studies. The University of Edinburgh. https://www.maths.ed.ac.uk/~csangwin/stack/2019-cate-casestudies.pdf. Tai, J., R. Ajjawi, D. Boud, P. Dawson, and E. Panadero. 2018. Developing evaluative judgement: Enabling students to make decisions about the quality of work. Higher Education 76 (3): 467–481. https://doi.org/10.1007/s10734-017-0220-3.

352

M. Hillier

Theophilos, M. 2021. Object-based learning for digital assessment. Transforming Assessment, 4 August http://transformingassessment.com/events_4_august_2021.php. Trilling, B., and C. Fadel. 2009. 21st century skills: Learning for life in our times. Jossey-Bass. ISBN 978-0-470-55362-6. Valan, G. 2020. Online knowledge maps for assessment and feedback. Transforming Assessment, 4 March. http://transformingassessment.com/events_4_march_2020.php. Villarroel, V., S. Bloxham, D. Bruna, C. Bruna, and C. Herrera-Seda. 2018. Authentic assessment: Creating a blueprint for course design. Assessment & Evaluation in Higher Education 43 (5): 840–854. https://doi.org/10.1080/02602938.2017.1412396. Walker, J. 2015. A 3D virtual business simulation for experiential learning in first year accounting. Transforming Assessment, 5 August. http://transformingassessment.com/events_5_august_ 2015.php. Walker, R., and Handley, Z. 2016. Designing for learner engagement with computer-based summative exams. Transforming Assessment, 2 March. http://transformingassessment.com/ events_2_march_2016.php. Watkins, C., and P. Mortimore. 1999. Pedagogy: What do we know? In Understanding pedagogy: And its impact on learning, ed. P. Mortimore. SAGE. WEF. 2016. Future of jobs report 2016. World Economic Forum. https://reports.weforum.org/ future-of-jobs-2016/. WEF. 2018. The future of jobs report 2018. World Economic Forum. https://www.weforum.org/ reports/the-future-of-jobs-report-2018/. WEF. 2020. The future of jobs report 2020. World Economic Forum. https://www.weforum.org/ reports/the-future-of-jobs-report-2020/. Wiggins, G. 1989. A true test: Toward more authentic and equitable assessment. The Phi Delta Kappan 70 (9): 703–713. JSTOR. Wiggins, G. 2014. Authenticity in assessment, (re-)defined and explained. 26 January 26. Granted, And... https://grantwiggins.wordpress.com/2014/01/26/authenticity-in-assessment-re-definedand-explained/ Wu, M., H.P. Tam, and T.-H. Jen, eds. 2016. Educational measurement for applied researchers: Theory into practice. 1st ed. Springer. https://doi.org/10.1007/978-981-10-3302-5_1. Yan, Z., and D. Boud. 2021. Conceptualising assessment-as-learning. In Assessment as learning: Maximising opportunities for student learning and achievement, ed. Z. Yan and L. Yang. Routledge. Young, N. 2011. Pedagogy: A lexical oddity. Teacher 5 (2) https://research.avondale.edu.au/teach/ vol5/iss2/6/.

Peer and Collaborative Assessment

18

Tiffany Gunning, Chie Adachi, and Joanna Tai

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contemporary and Future Goals of Peer and Collaborative Assessment . . . . . . . . . . . . . . . . . . . . . . Individual Student Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Course Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Staff and University Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Designing Peer and Collaborative Assessment for the Virtual University . . . . . . . . . . . . . . . . . . . . . Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The TBA/PCA Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Assessment Rubrics That Target Employability Skills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Academic Preparedness and Programmatic Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Student Preparedness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Macro-level View of Peer and Collaborative Assessment Within the Virtual University . . . . . The Self- and Peer Assessment Project: 2015–Present . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Elements of a Strategic Virtual University for Peer and Collaborative Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

354 357 358 359 360 360 361 361 362 364 364 365 365 366 367 367 367 368

T. Gunning (*) Deakin Learning Futures, Deakin University, Geelong, Australia e-mail: [email protected] C. Adachi Queen Mary University of London, London, UK e-mail: [email protected] J. Tai Centre for Research in Assessment and Digital Learning (CRADLE), Deakin University, Melbourne, Australia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_18

353

354

T. Gunning et al.

Opportunities for Innovative Practice and Scholarship for Peer and Collaborative Assessment Within the Virtual University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

368 370 371 371

Abstract

The ability to work collaboratively in digitally connected ways has never been more important and is closely linked with the capabilities required for successful peer and collaborative assessment. A thoughtfully designed peer and collaborative assessment process supports student development in a broad range of personal, interpersonal, and technical skills that are transferrable across contexts. Peer and collaborative assessments are often highly structured learning activities that aim to guide and empower students to refine their skills in reviewing the work of their colleagues against criteria and participating in effective feedback processes. The chapter argues that peer and collaborative assessment should play a mainstream role in the assessment strategies of the virtual university. Drawing on the existing literature and extensive experience, key considerations associated with the preparation and implementation of peer and collaborative assessment across the virtual university are explored. Case studies are presented to demonstrate how student confidence and ability to assess the work of others critically and honestly can be fostered through development of evaluative judgement and engagement in quality feedback processes. These learning outcomes should be scaffolded across programs and measured through a combination of selfassessment, peer assessment, and teaching teams’ judgements. The ultimate success of peer and collaborative assessment activities is underpinned by institutional-level policy and provision of technology to engage and support teaching teams. The limitations and future implications for practice and scholarship in peer and collaborative assessment will also be discussed in relation to the notion of the virtual university. Keywords

Peer assessment · Collaborative assessment · Teamwork · Self- and intra-team peer assessment · Evaluative judgement

Introduction The ability to give and receive feedback on your own and others’ work, skills, behaviors, or attitudes relies on the application of a complex cluster of personal, interpersonal, and technical skills. This chapter explores how students can be supported to develop and apply these skills, through the careful design of peer and collaborative assessment (PCA). Higher education has both the opportunity and

18

Peer and Collaborative Assessment

355

responsibility to prepare graduates to be ethical and socially responsible leaders in their communities. Thus, the goal of PCA within the virtual university is to prepare graduates; to learn from each other; to critically analyze their own work and the work of others; to take responsibility for their actions, holding others accountable for theirs; and to be inclusive and demand inclusivity and equity for all. A benefit of PCA in the virtual university, beyond learning outcomes, is the provision for students to connect with their peers and develop a sense of belonging to their learning community, as they interact and learn from each other. Student cohorts in the virtual university are often diverse and separated from their peers by time and space. Peer and collaborative assessments provide students with the opportunity to develop capabilities to learn in communities of practice (Lave and Wenger 1991),and establish networks across a global environment with peers who differ in age, culture, gender, and life experiences. The definition of “peer assessment” has matured over time, being recently defined as, “students judging and making decisions about the work of their peers against particular criteria” (Adachi et al. 2018b). For application in the virtual university, we expand on this definition to acknowledge the feedback process (Boud et al. 1999) within the online learning environment and define peer assessment as: A reciprocal evaluation and feedback process completed in a virtual learning environment, that supports students to judge and make decisions about the work, skills, behaviours, and attitudes of their peers, against pre-established criteria and levels of achievement within a program of assessment.

With a focus on learning, peer assessment requires students to work through a complex process of critically evaluating others and then providing respectful, thoughtful, and constructive feedback. In return, students receive feedback from their peers, providing them with the experience to receive, evaluate, and use feedback to improve their individual performance (Panadero 2016). “Collaborative assessment” is a much broader term usually linked to a collaborative learning activity, defined as: A learning phenomenon where individuals in a social constellation (e.g., group, team, or community) within a physical and/or virtual environment, interact on the same or different aspects of a shared task to accomplish implicit or explicit shared and individual learning goals (e.g., domain-specific knowledge or skills, social skills, etc.) . . . An agent(s) within or outside of the social constellation diagnoses and/or evaluates the constellation’s and/or individual’s accomplishment(s) against criteria and standards. (Strijbos 2016)

The design of collaborative assessment can incorporate assessment of self, an individual, a team, or a combination depending on the intended learning outcomes. It can be designed as both formative and summative tasks or enable spontaneous learning through reflection. Collaborative assessment can only occur when individuals interact during a shared task, whereas peer assessment could occur in a different but overlapping range of activities, including when individuals complete an individual task with the benefit of peer feedback. For this chapter, we narrow the focus of

356

T. Gunning et al.

collaborative assessment within the context of the virtual university to overlap with peer assessment and define it as: Students interacting with others in a virtual environment on the same or different aspects of a shared task, to demonstrate accomplishment of learning outcomes, while also judging and making decisions about the work, skills, behaviours, and attitudes of peers, against pre-established criteria and levels of achievement within a program of assessment.

While the application and goals of PCA can be diverse, both types of assessment aim to stimulate active learning. The complexities associated with the design and implementation of PCA in the virtual university are explored throughout this chapter. Drawing on the literature, the learning intentions associated with the use of PCA are discussed, focusing on how this process supports student development of evaluative judgement (Tai et al. 2018) and feedback literacies (Molloy et al. 2020). When combined with a programmatic assessment design (Van Der Vleuten et al. 2015), this chapter explains how students can be supported to develop this complex set of skills, from the first through to the final year of their study. Thus, we argue that PCA can support the development of desirable “twenty-first-century skills” (Jorre de St Jorre and Oliver 2018; Tai and Adachi 2019), in preparation for employment and lifelong learning. A case study from a large Australian university follows, to demonstrate PCA in action in an online environment. This case study details how a self- and intra-team peer assessment strategy was used across diverse disciplines to engage and empower students during team-based assessments (TBA). Evaluation of achievement forms a key component of this case study, as students develop the necessary personal, interpersonal, and technical skills to work with others. The study reflects on the challenges including engagement, fairness, psychological safety, and technology and provides potential solutions and recommendations for implementation of PCA strategies in the virtual university. Peer and collaborative assessments are often considered alternative forms of assessment, as the focus is placed on the student, rather than the academic, being the assessor. Thus, another advantage of PCA is that it actively involves students in the teaching, learning, and assessment process (Freeman et al. 2014). The case study expands by illustrating how institutional-level support can foster the implementation of alternative forms of assessment in the virtual university. The study draws on examples, as it reflects on the challenges related to availability of technology, providing recommendations to enable PCA in an online environment. To provide a bridge between current and emerging technologies and strategies, a focus on participatory research of PCA in practice is recommended. It is vital for the virtual university to stay abreast of and add to the body of knowledge of PCA to ensure ongoing improvement in teaching practice and student learning in the online environment. The chapter concludes with a summation of why PCA should be a widely adopted assessment strategy within the virtual university. The authors contend that PCA should play a mainstream role in the assessment strategies of the virtual university

18

Peer and Collaborative Assessment

357

and to develop and refine student capacity to collaborate effectively and equitably with others in a global community.

Contemporary and Future Goals of Peer and Collaborative Assessment Peer and collaborative assessment has several future-focused purposes, in addition to being a complementary approach to traditional teacher-led summative assessment. This section discusses the ways in which PCA can contribute to current assessment regimes for certification and accreditation purposes. It then moves to discuss the potential for PCA to contribute to student learning and development for beyond the university. The concepts of evaluative judgement (Tai et al. 2018) and feedback literacy (Sutton 2012) are introduced. Strategies that a virtual university could employ to ensure the development of these capabilities are then outlined, focusing on student and staff capabilities, course design, and university systems. Peer and collaborative assessment, like all forms of assessment, can have more than one purpose, and these purposes might differ for various stakeholders (Adachi et al. 2018b; Boud and Falchikov 2006; Falchikov 2007). Teachers might benefit from additional perspectives on student work to inform summative assessment while also implementing PCA as a learning activity for students to better understand professional standards and develop professional behaviors. Students might perceive learning benefits in understanding the variety of ways a task might be completed and gain a deeper understanding of the criteria and topic, through the process of assessing others. Depending on the topic of the assessment and its summative value, PCA might also be viewed as achievable marks, contributing to a final grade. External accreditation bodies and employers might further view PCA as important since it provides specific evidence of graduate capabilities. When considering how and in which context to implement PCA, the following competing purposes and motivations should be kept in mind. Some goals might be achieved at the expense of others, and some purposes may influence what happens in practice – for example, summative contribution to marks may make the process less about learning and more about ensuring that criteria are met, even at a superficial level. The main tension that can damage the success of PCA is likely to be about students’ motivations to achieve their best grades and provide useful and accurate feedback to others (Sridharan and Boud 2019; Zhou et al. 2020). Although the social obligation that students in co-located synchronous settings feel to each other (and thus distort marks) might not exist to the same extent in a virtual university, careful design and explicitly outlining the purposes of PCA are still important for success, including student satisfaction. The learning purposes of PCA should be a key driver in contemporary and digital education settings (Boud et al. 1999). This aligns with the aspirational educational goals of the virtual university, in developing graduates who contribute in an ethical and responsible way to society. Peer and collaborative assessment can develop

358

T. Gunning et al.

personal, interpersonal, and technical skills, some of which might combine in graduate attributes around communication, teamwork, and in-depth domain-specific knowledge and capabilities. This can contribute to graduates’ employability, defined as where “students and graduates can discern, acquire, adapt and continually enhance the skills, understandings and personal attributes that make them more likely to find and create meaningful paid and unpaid work that benefits themselves, the workforce, the community and the economy” (Oliver 2015). This definition transcends settings, equipment, or locations, and so employability is globally relevant and important to be developed in the virtual university. Peer and collaborative assessments are also highly appropriate in preparing learners for collaborative work futures, where the creation and application of knowledge are likely to occur in team settings within and across interdisciplinary teams and broader communities of practice (Lave and Wenger 1991). Collaborative assessment will be vital to prepare students for these contexts of future employment, where understanding when work is of sufficient quality is a team effort. The development of capabilities in critical thinking and discerning quality work has been argued to be a significant outcome of peer assessment (Tai and Adachi 2019). To achieve these learning goals, several factors need to be considered: how students as individuals are prepared for successful participation in assessment processes (especially their feedback literacies, evaluative judgement, and ability/willingness to work with others) and, more broadly, how course design, staff capability, and university systems support and justify students’ participation in PCA processes.

Individual Student Capabilities Feedback is a critical part of PCA, and therefore, how feedback can be done well between students is a significant consideration. Feedback literacies are the metafeedback practices and orientations which can contribute to successful feedback processes. While initially conceptualized for students as feedback information receivers, interpreters, and users (Carless and Boud 2018; Sutton 2012), more recent work has expanded the notion of feedback literacies to include what staff must do as generators and designers of feedback (Winstone et al. 2017). However, in the context of PCA, the student often adopts both the role of feedback information recipient and provider; thus, feedback literacies in this context must transgress previous boundaries, moving toward a more fluid situation where students are aware of the feedback environment in which they operate and how they interact with others – including objects and tools (Chong 2021; Gravett 2020). Within these interactions, students must be able to appreciate the reasons for feedback, make judgements about the quality of feedback information, manage and acknowledge emotional aspects of participating in feedback processes, and ultimately take appropriate actions as a result of the feedback process (Carless and Boud 2018). In addition to explicit teaching on feedback, practice with feedback processes is one of the best ways to develop feedback literacies; through multiple opportunities students will gain a better understanding of what is required to

18

Peer and Collaborative Assessment

359

participate in feedback (Tai et al. 2021), and this may prepare them for future situations in which they are instigators of feedback processes. The students’ ability to appropriately mark PCA is also highly important, as they are often still developing their capabilities in each subject or skill. Without developing students’ understanding of quality, PCA is in danger of merely being an exercise in “grade guessing” (Boud and Falchikov 1989). Educators should be explicit about what counts as quality work for a particular task and ensure that students share this understanding of quality, through developing students’ evaluative judgement (Tai 2018). This capacity to make judgements about the quality of work is ideally developed through a range of learning activities prior to PCA. This includes the discussion of criteria, rubrics, and comparison of exemplars to contribute to student learning about what constitutes quality, as well as opportunities to practice making and articulating the rationale for judgements themselves (Tai et al. 2018). While peer and collaborative learning activities require some evaluative judgement capability, these activities can also reciprocally contribute to the development of evaluative judgement through those opportunities for practice (Tai 2018). While evaluative judgement is said to be context- and subject-dependent, the transferable aspects of knowing how to develop an understanding of quality and articulate judgements are also highly important in a constantly evolving world.

Course Design Consideration must be paid to the ways in which assessments across individual modules (i.e., at the course or program level) progressively build students’ capabilities in PCA. In initial entry modules, PCA might require additional scaffolding and integration into learning activities. Toward the later part of the course, students may require less guidance to successfully participate in and execute PCA. To do this well, a programmatic approach to assessment should be considered, which can also support students’ psychological safety in the process of assessment and feedback, build their confidence, and provide a sense of coherence in what they are learning in the virtual university (Schuwirth and Van der Vleuten 2011, 2019). A programmatic approach to PCA will help build the sense that students are developing skills in PCA and increasing their responsibility as learners across a program of study. In this way, we can ensure that students are well equipped for life beyond the university. Thus, carefully building in opportunities which become increasingly challenging is important to promote learning and development. With the rise of “e-cheating,” particularly for virtual universities, a programmatic assessment approach can also help teaching teams consider which are the most important assessment moments, where assessment security is required for the award of the degree. Not all assessments need to be secured to the same extent, but signature assessments, which could include PCA, across the degree could be a focus of security, assuring student identity, and that students did complete the task themselves (Dawson 2020). This is increasingly important given the digital modes of assessment likely to be used in both standard and virtual universities.

360

T. Gunning et al.

Staff and University Systems Though technology may be a significant affordance and mediator of PCA (Tai and Adachi 2020), ultimately people and systems also need to be able to draw on and support technology use. Across the virtual university, people will need to be aware of what PCA is and how it is enacted, including its goals, purposes, and pitfalls. Staff involved in the design of curriculum and assessment will need appropriate knowledge and skills to be able to implement PCA successfully. Complete assessment redesign may require a longer lead time for regulatory approval and quality assurance, and so staff may need to make creative adaptations of existing curriculum/ assessment to achieve PCA (Bearman et al. 2017). Student support services and those dealing with student complaints will also need to be familiar with the processes of PCA and where and how intervention might occur when students bring their concerns to those groups. In addition, university technology support staff will need to be familiar with any technology harnessed for PCA and how to troubleshoot it both from student and staff perspectives. At a university governance level, administrators will need to be familiar with PCA designs for approval in curriculum. Furthermore, university policy might circumscribe how students can be assessed (formatively and summatively) and how this contributes to grades that represent an individual student’s learning, participation, and contribution toward PCA. Without an official expectation for PCA, implementation is less likely to occur, since what is absent from the guidelines can sometimes be interpreted as not allowed. Conversely, if templates and guides for assessment design do include PCA as an option, then more people might consider using it since it is explicitly allowed.

Designing Peer and Collaborative Assessment for the Virtual University While the virtual university can provide resources and strategies to support student collaboration in the online environment, teaching teams are unlikely to have the means to observe and analyze student collaboration in action and thus ensure the integrity of the assessment. Students are best able to comment on the skills, behaviors, and attitudes of their peers as they self-manage their collaboration. While this is an important skill for students to develop, without support mechanisms, students may struggle or take advantage and contribute minimal effort to the collaborative process. Students often refer to collaborative projects as being “unfair” (Goldfinch and Raeside 1990; Lejk et al. 1996) when not all peers contribute equally to the process. This type of free-riding behavior is a common concern raised by students (Hall and Buzwell 2012). In addition, teaching teams can lose confidence in collaborative tasks when student contribution cannot be assured, and thus learning outcomes can misalign with learning intentions (Meijer et al. 2020). The following case study from a STEM-based faculty within a large Australian university explores how PCA was implemented across diverse disciplines to engage, assure, empower, and support the development of a broad range of student learning

18

Peer and Collaborative Assessment

361

outcomes during team-based assessment. Team-based assessment in this context required students to collaborate within a project team to produce and submit an artifact for assessment by an academic, with all team members receiving the same team mark. The challenges experienced and resulting solutions are explored, to aid in the design and implementation of PCA as a mainstream method of learning and assessment in the virtual university.

Case Study In response to increasing negative feedback and concerns from students and teaching teams that summative team-based assessments were unfair and difficult to manage, a STEM-based faculty initiated an intervention project (Gunning et al. 2022). The resulting strategy enabled a TBA to be linked with an online self- and intra-team peer assessment, a specific form of PCA. The strategy was generalizable across programs and disciplines, as the skills being assessed in the PCA were generic and independent from the discipline, while the TBA remained discipline-specific. The strategy was also scalable from small (7 students) to large cohorts (over 700 students) due to a purpose-built online tool minimizing administrative burden, thus providing teaching teams across the faculty with a consistent, time-efficient approach to managing TBA. Analysis of the PCA output from 39 subjects, inclusive of more than 5900 students over a 12-month period in 2020, demonstrated that 94.4% of students completed the PCA strategy during summative TBA. The strategy empowered students to hold their peers accountable during the team process, with an average of 10.3% of students identified by their peers as demonstrating teamwork skills and behaviors below the required minimum standard. The outputs of PCA enabled academics to validate student engagement in TBA and assure alignment of learning intentions with the learning outcomes (Gunning et al. 2022).

The TBA/PCA Strategy The strategy, shown in Fig. 1, illustrates the combination of TBA and PCA processes. Student teams were required to complete a collaborative team project (a), with the product submitted (b) for marking by the academic (d). Each student then completed a self- and intra-team peer assessment, anonymously and asynchronously, against a set of pre-defined teamwork skills and behavior criteria, using a purposebuilt online tool (FeedbackFruits GME 2023)(c). The peer assessments were shared with peers for reflection purposes and with the teaching team to provide oversight of engagement and contribution to the team process (e). The resulting feedback drew attention to those students who appeared to be disengaged and/or were underperforming, enabling teaching teams to investigate, intervene, and individualize team marks where warranted (f). Completion of the summative task was encouraged through the application of a non-completion penalty.

362

T. Gunning et al.

Fig. 1 The TBA/PCA strategy combines a discipline-specific team-based assessment with a generic self- and intra-team peer assessment and the sequence of complimentary formative and summative tasks

Early student feedback to the project team highlighted that the vast differences in student preparedness had the potential to derail the strategy. The project team had initially and incorrectly assumed that students would have basic skills and confidence to evaluate the skills and behaviors of others, give and receive feedback, and understand the importance of the underlying skills being developed when collaborating with others. In response, the project team designed multiple complimentary formative and summative tasks to develop student efficacy, confidence, and learning, to support PCA (Fig. 1.).

Assessment Rubrics That Target Employability Skills In addition to encouraging engagement in the TBA, the PCA strategy provided teaching teams with the ability to focus student learning on specific transferable employability skills. This was achieved through the provision of a teamwork skills and behavior rubric (Table 1). Students completed their self- and intra-team peer assessment based on this rubric, thereby encouraging them to observe and consider the behavior of self and others in a collaborative environment. An example of the rubric, provided in Table 2, shows how the first skill listed in each category of personal, interpersonal, and technical skills, associated with

18

Peer and Collaborative Assessment

363

Table 1 A summary of the broad range of skills that peer and collaborative assessment can support Personal skills Evaluative judgement Critical thinking Personal responsibility Ethics Receiving constructive feedback Using feedback

Interpersonal skills Listening Empathy Respect Collaboration Providing constructive feedback Interpersonal communication

Technical skills Time management Use of technology Written communication Verbal communication

Table 2 An example of a four-level rubric used to support student self- and intra-team peer assessment of teamwork skills Achievement levels

Industry-ready Criteria team member Transferrable employability skills Evaluative Provided judgement thoughtful and (personal constructive skill) feedback regarding the quality of the team project as it progressed, to ensure the project met all requirements. Ensured that feedback was useful, achievable, and supported the development of skills Listening Listened and skills allowed others to (interpersonal share ideas. skill) Encouraged the whole team to listen and share Time Punctual to all management team meetings. (technical Took the lead to skill) ensure meetings occurred when everyone could attend or that minutes/video were taken to be shared with the team

Valued team member (minimum standard)

Underperforming team member

Not a team member

Provided thoughtful and constructive feedback regarding the quality of the team project as it progressed, to ensure the project met all requirements

Feedback was provided; however it was often not constructive or useful

Did not provide feedback

Listened and allowed others to share ideas

Sometimes listened and sometimes allowed others to share ideas

Dominated communications and made it difficult for others to share

Punctual to all team meetings

Sometimes late to Did not attend meetings meetings

364

T. Gunning et al.

collaborating with others (Table 1), was designed to provide four clearly defined levels of achievement that aligned to well met, met, partially met, and not met (Rust 2002). This style of rubric was chosen to divert the student focus away from marks and grades and to draw attention to the learning outcomes associated with working with others. The level of achievement descriptors was specifically chosen to re-enforce the link between the skills demonstrated and employability. This style of assessment is also authentic to the world of work, where employees need to demonstrate achievement against performance goals (Schultz et al. 2021). As the strategy was based on the development of the generic transferrable employability skills underpinning collaboration, the criteria for these rubrics were designed at the faculty level for use across programs. Multiple criteria were designed with incremental differences between year levels, to enable teaching teams to choose criteria for their students and to scaffold learning from the first to final years of a program.

Academic Preparedness and Programmatic Assessment This TBA/PCA strategy was honed through an iterative process of feedback and change over a 5-year period. This strategy began by collaborating with early adopter academics (Conlan et al. 2019) before targeting specific subjects to fulfil a programmatic assessment approach (Gunning et al. 2022). The goal was to design at least one instance of the strategy, at each year level of a student’s program, to scaffold their skills from the first to final year. To ensure academic confidence and consistent use of the strategy across programs, professional development was provided to incoming academics on a one-to-one basis. Topics included the explanation of the strategy and underlying pedagogy, how to use the online tool, and how to support students through the self- and intra-team peer assessment. Professional development was further supported through the provision of a series of generic resources, for teaching teams to use to support their students. These included instruction videos, infographics of the strategy, preparatory tasks to develop team trust and team agreements, and before-and-after collaboration reflection tasks to identify learning. This project highlighted that preparedness of academics and teaching teams, to implement PCA, cannot be over-emphasized for the virtual university.

Student Preparedness This strategy recognized the importance of preparing students (Li et al. 2020) for intra-team peer assessment and emphasized that it must be a design priority for the implementation of PCA in the virtual university. Teaching teams need to foster a learning environment that is inclusive and safe for students to ask questions, seek help and feedback from each other, and make mistakes as part of their growth and development. This can be a stark contrast for some learners who prefer to work autonomously, have only experienced independent learning and competitive classroom environments, or have concerns about offending peers with constructive

18

Peer and Collaborative Assessment

365

feedback. Teaching teams must emphasize that PCA is a challenging but fruitful reciprocal learning activity to improve performance of all learners while also targeting the development of teamwork and interpersonal skills. To help teaching teams set the climate for PCA, an introductory video was created at a faculty level to ensure a consistent message across programs. The video highlighted the challenges of TBA for students and teaching teams and justified the need for self- and intra-team peer assessment. It explained how the strategy would be used to engage students and give them a voice during the assessment process. To support student awareness of the value of developing the skills that underpin collaboration, explicit links were made between the collaborative assessment task, graduate learning outcomes, and employability. To prepare students for the giving and receiving of feedback, introductory strategies and examples were provided, highlighting the importance of honesty to support learning. To address psychological safety of students, the expectations of the university were stipulated, using the student code of conduct, and students were encouraged to seek support if required.

Technology The technology that supported this project was designed, trialed, and iteratively developed as a collaboration between the STEM-based project team and the education technology vendor, FeedbackFruits. This collaboration was initiated and subsequently supported by the Office of the Deputy Vice-Chancellor (Education), in recognition of the limited options in the education technology market to support selfand intra-team peer assessment at the time. To support the development of a community of practice, students were also introduced and encouraged to use the collaborative tool, Microsoft Teams. This enabled synchronous and asynchronous communication with their teams and learning cohort. In conclusion, the viability of this PCA strategy for the virtual university was demonstrated in 2020, as the COVID-19 pandemic required all teaching and learning to move online quickly, at scale. This strategy not only continued in its entirety as designed, but it also provided an important learning bridge to connect learners to their peers across the globe, through a challenging time.

Macro-level View of Peer and Collaborative Assessment Within the Virtual University This section explores how successful implementation of peer and collaborative assessment within the virtual university can be supported at an institutional level. Drawing on the experience of Deakin University, a case study is presented that focuses on how a centrally based project supported the implementation of PCA, by addressing both the technological challenges and the macro-level institutional limitations.

366

T. Gunning et al.

Deakin University is a large Australian institution which has had a long-standing history of being and becoming a premium virtual university, since its birth in 1974, as a distance education provider. While there are several ways to define a virtual university, we follow the definition: “An institution which is involved as a direct provider of learning opportunities to students and is using information and communication technologies to deliver its programs and courses and provide tuition support” (Ryan et al. 2000). The following case study presents how Deakin University, as a virtual university, investigated and then promoted self- and peer assessment as a strategy for online learning and how such an endeavor proved to be vital in the move to establish itself as a virtual university. This section explores three key themes: practice, platforms, and policy.

The Self- and Peer Assessment Project: 2015–Present In 2015, a university-wide assessment tool project was initiated by the Office of the Deputy Vice-Chancellor (Education). The aims of the project were to investigate the current practices of self- and peer assessment within the university and promote sound pedagogical design and digital delivery of innovative assessment approaches across the university. In consultation with all faculties, the current state of self- and peer assessment practice and requirements for online delivery were captured. Analysis of the results revealed a wide variation in the practice of self- and peer assessment across the faculties, with its use and popularity differing between disciplines. The project then turned its attention to the education technology market to identify a suitable platform that would enable our current practices in self- and peer assessment and allow room for future refinement and improvement of practice while meeting the pedagogical, technological, and institutional obligations of the university. At that time, the development of educational technology to support selfand peer assessment was immature, with various tools meeting some but not all the project requirements. After engaging with numerous vendors in the initial years of the project, several tools were chosen for the pilot. These tools were used sparingly across faculties during the pilot phase. The practice of peer and collaborative assessment, across multiple project-based learning subjects, emerged within the science-based faculty. To support this innovation, the university engaged with the education technology vendor FeedbackFruits, who collaborated directly with the faculty to develop tools to facilitate their users. To support the scalability of the toolset, the resulting technology was integrated into the learning management system (LMS), thereby involving a diverse range of technical support, teaching teams, and stakeholders across the university. The resulting extensive dialogue increased awareness of the use of digitally supported peer and collaborative assessment, as an essential element of the assessment portfolio within the university.

18

Peer and Collaborative Assessment

367

Key Elements of a Strategic Virtual University for Peer and Collaborative Assessment To support the effective implementation of PCA as a component of assessment in the virtual university, three key elements must be addressed: practice, platforms, and policy.

Practice The practice of PCA must be underpinned by pedagogical philosophy that articulates why and how PCA should be implemented to support learning. Understanding the existing, wide-ranging types of PCA is a critical first step, as this is not a new pedagogy but rather has existed for some decades in various forms across diverse subject areas, particularly in practice-based disciplines. Reimagining practice is therefore vital in ensuring that new ways of designing and delivering PCA, particularly through online technology, can be developed and communicated to support continuous improvement in teaching practice. One of the successful aspects of the institutional-level case study presented here was demonstrated through the faculty-level case study presented above. The STEM-based, academic research and teaching team designed and successfully implemented an innovative online form of PCA, in combination with a team-based assessment, to address a serious faculty-wide assessment challenge. The provision of an online solution facilitated change and improved teaching practice across the faculty and beyond.

Platforms Education technology platforms have the potential to drive and nudge certain types of PCA practice. To be successful, platforms must provide positive user experiences for both teachers and students, support ease of use, engagement, ease of marking, and focus on the learning process of PCA. Importantly, any technology-supported assessment endeavors need to be scalable. In the case of peer and collaborative assessments, the formation and dynamic adjustment of group memberships is a vital requirement. As technology is driven by demand, it is anticipated that technology to support PCA will become more readily available, as student-centered and active learning approaches move from alternative to mainstream. Several technology platforms already offer functionality for PCA; any list is likely to soon become outdated. As such, the virtual university will need to assess what technology is currently available to ensure it is fit for purpose. That is, the technology provides an ease of navigation for students and teaching teams while also providing them with the functionality to support and analyze PCA, with minimum administrative burden (Tai and Adachi 2020).

368

T. Gunning et al.

Policy Given that PCA is still considered alternative assessment, or a minority form of assessment, consideration of policy that enables, advocates, or inhibits the practices of PCA needs to be given. As Adachi et al. (Adachi et al. 2018b) have revealed through their empirical research, teachers perceive expertise of students as an issue in peer assessment. This results in PCA often being designed for formative rather than summative assessments. This teacher perception raises the question, “How can universities promote the valuable learning opportunities afforded by using PCA, to develop future-ready graduates with twenty-first-century skills?” Policy articulates universities’ aspirational principles and rules that guide the best practice and prohibit inappropriate practices. A set of PCA policy therefore needs to recognize the need for room for interpretation in local contexts for innovative practices while also restricting practice that does not align with university policy.

Opportunities for Innovative Practice and Scholarship for Peer and Collaborative Assessment Within the Virtual University We are not able to move from the present time to a fully instantiated future virtual university, without addressing the limitations of current understanding and practice and mapping out what opportunities there are to advance our understanding and enactment of PCA. We advocate for a joint practice and research agenda, since without practice, there will be little to research, and without research (in its broadest sense), practice is unlikely to be refined and developed in a way that allows for productive advances toward the future of education. Systematic ways of thinking, designing, and communicating about PCA are important to promote conversations among practitioners and researchers. There are several typologies and frameworks already in existence to support the design of peer assessment activities, which might also work for collaborative assessment (Adachi et al. 2018a; Gielen et al. 2011; Panadero et al. 2023). This highlights the importance of considering the role of technology in PCA, which is highly relevant within the virtual university. Beyond the details of the assessment, we suggest some broader principles which could be considered to achieve the goals of PCA set forth in this chapter, i.e., preparing students for the world beyond the university, as ethical and socially responsible citizens who are committed to lifelong learning. These principles are guided by contemporary directions in education research and phrased as questions to remain open to new and evolving possibilities: 1. How can students take a partnership role in developing PCA? This aligns with moves to integrate “Students as Partners” practices within higher education (Bovill and Felten 2016), where students and staff contribute on equal terms to the development and implementation of teaching and learning activities.

18

Peer and Collaborative Assessment

369

2. What are the roles of stakeholders beyond immediate students and staff in PCA? This extends concepts of programmatic assessment and employability, to also take in the perspectives, goals, and needs of educators whom students might subsequently interact with – industry, employers, and community – within the development of PCA. 3. How does PCA contribute to students’ development beyond the immediate task? This asks us to consider how, through its potential for learning, PCA might support students’ future practices, even without a specific assessment, in alignment with the concept of sustainable assessment (Boud and Soler 2016). Applying development, implementation, iteration, and reflection, we can continue to build evidence for PCA in the virtual university through interwoven research projects. While there are many aspects of PCA which could be considered from several perspectives, there are some substantive areas which would be of most utility at the outset. These reflect calls already made in the fields of PCA at present but extend to virtual university considerations. Firstly, factors influencing acceptability of PCA for both students and staff should be explored. Aspects of this might include: • The workload for both students and staff in undertaking assessment and how this is spread over time and space • Types of interactions between students that add value and how these interactions can be mediated • Topics that are most amenable to PCA in virtual settings • How student seniority (e.g., both within a program – from the first to third year – and between programs or degrees) impacts on the content and format of PCA • How industry bodies view PCA and the expectations of what types of activities they expect graduates to be capable of Secondly, empirical research and evaluation of the outcomes of PCA are required to establish its value and purpose. This might include: • How graduate capabilities such as teamwork and critical thinking are developed, as well as a more domain-specific application of capabilities such as evaluative judgement and feedback literacy • How certain design components of PCA, such as rubrics, exemplars, and [a] synchronous discussion, contributes to outcomes Cross-sectional and pre-post studies might be helpful to illustrate the feasibility and acceptability of PCA. Longitudinal studies are also needed to understand how PCA leads to student development and post-graduation outcomes. This would require following students throughout their degree and beyond. Research methods to approach this range of work are likely to be varied, and thus multi-, inter-, and transdisciplinary perspectives will be helpful to draw upon and increase the quality of the research. Approaching the research from explicit onto-epistemological and

370

T. Gunning et al.

theoretical stances will also add rigor to research designs and assist in the acceptance of educational research across disciplines. Since markers of research quality may differ across paradigms, researchers are encouraged to consider what these differences may be, in the process of designing and enacting research. From a psychological and quantitative perspective, developing and using validated scales are important, while qualitative phenomenographical work to understand student experiences would be more concerned with trustworthiness, as represented through credibility, transferability, dependability, and confirmability (Shenton 2004). The focus of research might also vary greatly, from the micro-level of student interactions to macro-level considerations of policy and systems. Both human and technological factors, and the interactions between them, will need to be investigated to determine which methods, from analytics, to experience sampling, to observations, interviews, and focus groups, might be warranted. The opportunities and possibilities are almost endless, but what is crucial in all approaches to research is ensuring quality and rigor in the process. To advance PCA across all these research methods, involving multiple stakeholders will be helpful to consider multiple perspectives. In particular, participatory approaches can respond to the call for the intertwining of research and practice, where iteration can take place and lead to more effective outcomes.

Conclusion and Future Directions In this chapter we have presented case studies to illustrate how a virtual university designed and implemented PCA, drawing on emerging and existing technologies to support students to connect authentically within digital spaces, could be implemented. The world is becoming ever more digital, and what occurs in the virtual university needs to respond to future developments, ensuring technology used is current and fit for purpose. Considering the need for authentic assessment and practices, the ways in which PCA is applied also needs to replicate how workforces are becoming digital. Within this, technology choice and capability may need to be developed. As educators, we further need to ensure that the focus and process of PCA match the ways in which we are and will be working, placing a priority on the preparedness of students to successfully engage with PCA. This is an important step to recognize in order to counterbalance the criticism of students as novices in the disciplinary fields, engaging with PCA for superficial learning. Looking toward the future, we imagine that PCA will become a commonplace assessment form within universities. Before this happens, it is important to consider how institutions might collectively agree upon practices, learning outcomes, and minimum standards, such that everyone has a shared understanding of PCA. This might be especially important in situations where PCA is required for external accreditation but also allows students the possibility to gain recognition of their abilities across the university experiences. Furthermore, shared standards and understandings of PCA may better allow graduates to develop, identify, and express their

18

Peer and Collaborative Assessment

371

transferrable skills and ensure they can contribute as ethical and responsible members of a future world.

Cross-References ▶ Aligning the Vision for Technology-Enhanced Learning with a Master Plan, Policies, and Procedures ▶ Authenticity, Originality, and Beating the Cheats ▶ Making Online Assessment Active and Authentic ▶ Preparing Students for the Future of Work and the Role of the Virtual ▶ Using Institutional Data to Drive Quality, Improvement, and Innovation

References Adachi, C., J. Tai, and P. Dawson. 2018a. A framework for designing, implementing, communicating and researching peer assessment. Higher Education Research & Development 37 (3): 453–467. https://doi.org/10.1080/07294360.2017.1405913. Adachi, C., J.H.-M. Tai, and P. Dawson. 2018b. Academics’ perceptions of the benefits and challenges of self and peer assessment in higher education. Assessment & Evaluation in Higher Education 43 (2): 294–306. https://doi.org/10.1080/02602938.2017.1339775. Bearman, M., P. Dawson, S. Bennett, M. Hall, E. Molloy, D. Boud, and G. Joughin. 2017. How university teachers design assessments: A cross-disciplinary study. Higher Education 74 (1): 49–64. https://doi.org/10.1007/s10734-016-0027-7. Boud, D., and N. Falchikov. 1989. Quantitative studies of student self-assessment in higher education: a critical analysis of findings. https://doi.org/10.1007/BF00138746. ———. 2006. Aligning assessment with long-term learning. Assessment & Evaluation in Higher Education 31 (4): 399–413. https://doi.org/10.1080/02602930600679050. Boud, D., and R. Soler. 2016. Sustainable assessment revisited. Assessment & Evaluation in Higher Education 41 (3): 400–413. https://doi.org/10.1080/02602938.2015.1018133. Boud, D., R. Cohen, and J. Sampson. 1999. Peer learning and assessment. Assessment & Evaluation in Higher Education 24 (4): 413–426. https://doi.org/10.1080/0260293990240405. Bovill, C., and P. Felten. 2016. Cultivating student–staff partnerships through research and practice. International Journal for Academic Development 21 (1): 1–3. https://doi.org/10.1080/ 1360144X.2016.1124965. Carless, D., and D. Boud. 2018. The development of student feedback literacy: Enabling uptake of feedback. Assessment & Evaluation in Higher Education 43 (8): 1315–1325. https://doi.org/10. 1080/02602938.2018.1463354. Chong, S.W. 2021. Reconsidering student feedback literacy from an ecological perspective. Assessment & Evaluation in Higher Education 46 (1): 92–104. https://doi.org/10.1080/ 02602938.2020.1730765. Conlan, X.A., M.L. Harvey, T.K. Gunning, and A. Durdle. 2019. Forensic undergraduate cohort; job readiness curricula. Australian Journal of Forensic Sciences 51 (sup1): S243–S246. https:// doi.org/10.1080/00450618.2019.1568550. Dawson, P. 2020. Defending assessment security in a digital world: Preventing e-cheating and supporting academic integrity in higher education. London: Routledge. https://doi.org/10.4324/ 9780429324178.

372

T. Gunning et al.

Falchikov, N. 2007. The place of peers in learning and assessment. In Rethinking assessment in higher education: Learning for the longer term, London: Routledge, 128–143. https://doi.org/ 10.4324/9780203964309. FeedbackFruits GME. 2023. Setting up group member evaluation. https://help.feedbackfruits.com/ en/articles/2080613-setting-up-group-member-evaluation Freeman, S., S.L. Eddy, M. McDonough, M.K. Smith, N. Okoroafor, H. Jordt, and M.P. Wenderoth. 2014. Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences 111 (23): 8410. https://doi.org/10.1073/pnas. 1319030111. Gielen, S., F. Dochy, and P. Onghena. 2011. An inventory of peer assessment diversity. Assessment & Evaluation in Higher Education 36 (2): 137–155. https://doi.org/10.1080/ 02602930903221444. Goldfinch, J., and R. Raeside. 1990. Development of a peer assessment technique for obtaining individual marks on a group project. Assessment & Evaluation in Higher Education 15 (3): 210–231. https://doi.org/10.1080/0260293900150304. Gravett, K. 2020. Feedback literacies as sociomaterial practice. Critical Studies in Education: 1–14. https://doi.org/10.1080/17508487.2020.1747099. Gunning, T.K., X.A. Conlan, P.K. Collins, A. Bellgrove, K. Antlej, A.P.A. Cardilini, and C.L. Fraser. 2022. Who engaged in the team-based assessment? Leveraging EdTech for a self and intra-team peer-assessment solution to free-riding. International Journal of Educational Technology in Higher Education 19 (1). https://doi.org/10.1186/s41239-022-00340-y. Hall, D., and S. Buzwell. 2012. The problem of free-riding in group projects: Looking beyond social loafing as reason for non-contribution. Active Learning in Higher Education 14 (1): 37–49. https://doi.org/10.1177/1469787412467123. Jorre de St Jorre, T., and B. Oliver. 2018. Want students to engage? Contextualise graduate learning outcomes and assess for employability. Higher Education Research & Development 37 (1): 44–57. https://doi.org/10.1080/07294360.2017.1339183. Lave, J., and E. Wenger. 1991. Situated learning: Legitimate peripheral participation. New York: Cambridge University Press. Lejk, M., M. Wyvill, and S. Farrow. 1996. A survey of methods of deriving individual grades from group assessments. Assessment & Evaluation in Higher Education 21 (3): 267–280. https://doi. org/10.1080/0260293960210306. Li, H., R. Tywoniw, Y. Xiong, C.V. Hunter, and X. Guo. 2020. Does peer assessment promote student learning? A meta-analysis. Assessment and Evaluation in Higher Education 45 (2): 193–211. https://doi.org/10.1080/02602938.2019.1620679. Meijer, H., R. Hoekstra, J. Brouwer, and J.-W. Strijbos. 2020. Unfolding collaborative learning assessment literacy: A reflection on current assessment methods in higher education. Assessment & Evaluation in Higher Education 45 (8): 1222–1240. https://doi.org/10.1080/02602938.2020. 1729696. Molloy, E., D. Boud, and M. Henderson. 2020. Developing a learning-centred framework for feedback literacy. Assessment & Evaluation in Higher Education 45 (4): 527–540. https://doi. org/10.1080/02602938.2019.1667955. Oliver, B. 2015. Redefining graduate employability and work-integrated learning: Proposals for effective higher education in disrupted economies. Journal of Teaching and Learning for Graduate Employability 6 (1): 56–65. https://doi.org/10.21153/jtlge2015vol6no1art573. Panadero, E. 2016. Is it safe? Social, interpersonal, and human effects of peer assessment: A review and future directions. In Handbook of human and social conditions in assessment, ed. G.T.L. Brown and L.R. Harris, 247–266. New York: Routledge. Panadero, E., M. Alqassab, J. Fernández Ruiz, and J.C. Ocampo. 2023. A systematic review on peer assessment: Intrapersonal and interpersonal factors. Assessment & Evaluation in Higher Education: 1–23. https://doi.org/10.1080/02602938.2023.2164884. Rust, C. 2002. The impact of assessment on student learning: How can the research literature practically help to inform the development of departmental assessment strategies and

18

Peer and Collaborative Assessment

373

learner-centred assessment practices? Active Learning in Higher Education 3 (2): 145–158. https://doi.org/10.1177/1469787402003002004. Ryan, S., Scott, B., Freeman, H., & Patel, D. (2000). The virtual university: The internet and resource-based learning. Open and distance learning series. London: Kogan Page. Schultz, M., K. Young, T.K. Gunning, and M.L. Harvey. 2021. Defining and measuring authentic assessment: A case study in the context of tertiary science. Assessment & Evaluation in Higher Education: 1–18. https://doi.org/10.1080/02602938.2021.1887811. Schuwirth, L.W.T., and C.P.M. Van der Vleuten. 2011. Programmatic assessment: From assessment of learning to assessment for learning. Medical Teacher 33 (6): 478–485. https://doi.org/10. 3109/0142159X.2011.565828. Schuwirth, L.W.T., and C.P.M. van der Vleuten. 2019. How ‘testing’ has become ‘programmatic assessment for learning’. Health Professions Education 5 (3): 177–184. https://doi.org/10.1016/ j.hpe.2018.06.005. Shenton, A.K. 2004. Strategies for ensuring trustworthiness in qualitative research projects. Education for Information 22: 63–75. https://doi.org/10.3233/EFI-2004-22201. Sridharan, B., and D. Boud. 2019. The effects of peer judgements on teamwork and self-assessment ability in collaborative group work. Assessment and Evaluation in Higher Education 44 (6): 894–909. https://doi.org/10.1080/02602938.2018.1545898. Strijbos, J.W. 2016. Assessment of collaborative learning. In Handbook of social and human conditions in assessment, ed. G.T.L. Brown and L. Harris, 302–318. Abingdon: Routledge. Sutton, P. 2012. Conceptualizing feedback literacy: Knowing, being, and acting. Innovations in Education and Teaching International 49 (1): 31–40. https://doi.org/10.1080/14703297.2012. 647781. Tai, J. 2018. Evaluative judgement and peer assessment: Promoting a beneficial reciprocal relationship. Open oceans: learning without borders. In Proceedings ASCILITE 2018, Geelong, 516–521. Tai, J., and C. Adachi. 2019. The transformative role of self- and peer-assessment in developing critical thinkers. In Innovative assessment in higher education: A handbook for academic practice, ed. C. Bryan and K. Clegg, 64–74. London: Routledge. https://doi.org/10.4324/ 9780429506857. ———. 2020. The future of self and peer assessment: Are technology or people the key? In Re-imagining university assessment in a digital world. The enabling power of assessment, ed. M. Bearman, P. Dawson, R. Ajjawi, J. Tai, and D. Boud, 213–227. Cham: Springer. https://doi.org/10.1007/978-3-030-41956-1_15. Tai, J., R. Ajjawi, D. Boud, P. Dawson, and E. Panadero. 2018. Developing evaluative judgement: Enabling students to make decisions about the quality of work. Higher Education 76 (3): 467–481. https://doi.org/10.1007/s10734-017-0220-3. Tai, J., C. Contessotto, J. McBurnie, K. Nicola-Richmond, and P. Brown. 2021. The reciprocal nature of assessment-as-learning and feedback literacy: Case studies from higher education in Australia. In Assessment as learning: maximising opportunities for student learning and achievement, ed. Z. Yan and L. Yang. London: Routledge. Van Der Vleuten, C.P.M., L.W.T. Schuwirth, E.W. Driessen, M.J.B. Govaerts, and S. Heeneman. 2015. Twelve tips for programmatic assessment. Medical Teacher 37 (7): 641–646. https://doi. org/10.3109/0142159X.2014.973388. Winstone, N.E., R.A. Nash, M. Parker, and J. Rowntree. 2017. Supporting learners’ agentic engagement with feedback: A systematic review and a taxonomy of recipience processes. Educational Psychologist 52 (1): 17–37. https://doi.org/10.1080/00461520.2016.1207538. Zhou, J., Y. Zheng, and J.H.-M. Tai. 2020. Grudges and gratitude: The social-affective impacts of peer assessment. Assessment & Evaluation in Higher Education 45 (3): 345–358. https://doi. org/10.1080/02602938.2019.1643449.

Preparing Students for the Future of Work and the Role of the Virtual

19

Bonnie Amelia Dean , Matthew Campbell , Courtney Ann Shalavin , and Michelle J. Eady

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Technology, Work, and Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Sociomaterial Framing of Technology and Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Technology and WIL Typology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Virtual WIL Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Online Industry Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Online/Remote Placements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Online Simulation and Virtual Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digital Service Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gamification and Artificial Intelligence (AI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Considerations for Virtual Modes of WIL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning for Dynamic Work Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

376 377 378 381 382 382 383 384 385 385 386 388 389 389

Abstract

Higher education plays a significant role contributing to a skilled and prosperous economy and labor workforce through preparing graduates for the future of work. Given the recent significant shift to technology-supported workplaces and flexi-

B. A. Dean (*) Learning, Teaching and Curriculum, University of Wollongong, Wollongong, NSW, Australia e-mail: [email protected] M. Campbell University of Queensland, Brisbane, QLD, Australia e-mail: [email protected] C. A. Shalavin · M. J. Eady School of Education, University of Wollongong, Wollongong, NSW, Australia e-mail: [email protected]; [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_19

375

376

B. A. Dean et al.

ble work arrangements, higher education providers must consider effective ways to align pedagogical experiences with these conditions. This chapter explores the changing nature of work and the demands this is creating for practice-based learning. Through the lens of sociomateriality, the chapter frames technology as both constituted by, and constitutive of, education, pedagogical, and work practices. Given this entanglement of technology, learning, and work, the chapter explores how virtual models of work-integrated learning (WIL) can provide a bridge between the virtual university and the reality of the workplace. It highlights emerging pedagogical approaches, such as virtual internships, digital service learning, and online placements, that enable students to engage with experiential learning through WIL in the virtual university. The chapter reflects on how these models can enhance graduate employability and successful transition to a dynamic world of work. Keywords

Work-integrated learning · Cooperative education · Technology-enhanced learning · Sociomateriality

Introduction A goal of higher education is to support future economic growth through the development of highly skilled, flexible, and digitally literate graduates who can succeed in an increasingly competitive global marketplace. Ways of working have been evolving as part of the emergence of the postindustrial economy and the “third-wave” information society (Toffler 1981), which is seeing a continuing change in the nature of work and expectations of graduates. The “new economy” is demanding skills in creativity, communication, digital literacies, team work, and entrepreneurship for most graduates in ways that were previously isolated to a select few (Smith et al. 2019). Work-integrated learning (WIL) is a pedagogical approach adopted by educators to enable students to apply discipline knowledge and learn employability skills, within a real or simulated environment and supported by an industry or community expert (Patrick et al. 2009). Such practical educative experiences that originated through arrangements where students spent sustained time in the workplace with industry have evolved over the last decade to include a range of innovative and nonplacement forms of WIL (Dean et al. 2020). Technology plays a facilitative role in WIL for the administrative and pedagogical functions of education (Schuster and Glavas 2017). Yet, in the last several years, new and innovative WIL practices have emerged from entangled technological and pedagogical innovations. In these new WIL practices, pedagogy, technology, and education are mutually constituted; they are enacted together within the practice

19

Preparing Students for the Future of Work and the Role of the Virtual

377

itself. That is, technology has moved beyond serving administrative or organizational functions, to a space where technology and pedagogy are entwined and performed within (Gibson 2001), and as, the WIL experience itself. This reimagining of WIL has significant implications for shaping the role of technology as a tool for the virtual university to enhance graduate employability. However, what remains to be explored is how these innovative models of WIL can be leveraged for the purpose of preparing students for a digitally enabled world of work. The aim of this chapter is to explore innovative, virtual models of WIL and how they can be utilized by the virtual university to prepare students for an increasingly virtual world. The chapter commences with an exploration of the impact of technological advancement, recent social changes in how work and higher education is experienced, and how this is shaping the provision of higher education. Through the lens of sociomateriality and the work of Orlikowski (2007), shifts in technology are explored to describe how technology, work, and education are co-constructed in practice. Next, the chapter outlines innovative models of virtual WIL including online placements and industry projects, digital service learning, gamification, online simulation, virtual reality, and artificial intelligence. The chapter concludes with considerations for how the constitutive entanglement of technology, learning, and pedagogy can enhance graduate employability and support successful transition to a dynamic world of work.

Technology, Work, and Education The inception of the fourth industrial revolution (Industry 4.0) has significant implications for the nature of work. An increasing uptake of automation, connectivity, and artificial intelligence across workspaces, while initially forecast as having negative economic impact, is now considered more broadly as an opportunity for economic development (Ra et al. 2019). Automation, for example, was originally projected as detrimental to loss of jobs. However, studies now show that automation is less likely to displace workers, but certain tasks instead (Arntz et al. 2016). As a result of technology uptake in workspaces, workers may face transitions as jobs decline in one industry where technology is adopted, but rise in other industries (Autor and Salomons 2017), particularly where tasks are more difficult to automate (PWC 2016). As Industry 4.0 prioritizes certain roles, it has also reorganized demands for certain skills sets. Studies show that jobs requiring nonroutine cognitive tasks are growing faster than those with manual tasks (ADB 2018). This is demonstrated in Australia, where national data reports skills shortages in professional roles such as service or Internet technology (IT) management, accounting and auditing, engineering, and medicine (Australian Government 2021). Although focus on automation of routine work supplants some activities, it also opens possibilities for occupations that concentrate on higher-order cognition and skills (Ra et al. 2019).

378

B. A. Dean et al.

Tertiary institutions play a social, ethical, and economic role in preparing individuals for a prosperous labor market (Macklin 2020). According to Macklin (2020), there needs to be closer alignment between the needs of students, communities, industry, and the economy, with what education providers offer to learners. There is a need for educational institutions to focus on higher-order skills that will better equip learners for work. Ra et al. (2019) argue that this includes three core notions: first, the development of foundational cognitive skills including critical thinking, analyzing, and problem-solving, which are crucial for labor market outcomes. Second, “soft” skills, also known as employability skills, such as self-awareness, motivation, teamwork, adaptability, and flexibility, crucial for working within Industry 4.0. Finally, they propose that the workforce demands workers have greater “learnability” – willingness to learn, unlearn, and relearn. Awareness of these emerging workplace trends and skills marks the importance of higher education in its role to prepare students for the future world of work (Bayerlein et al. 2021a). One way higher education providers address closing the gap between learning and working is by focusing on graduate employability. Employability refers to a range of skills, career competencies, professional aptitudes, and understandings that students develop during their studies that help them to reflect, plan, and action learning and work postgraduation (Dacre Pool and Sewell 2007). Studies show that students who develop career management competencies and participate in work experiences during their degree demonstrate higher levels of perceived employability and work readiness (Jackson and Wilton 2017; Jackson and Bridgstock 2021). Participating in WIL has positive outcomes on a graduates’ employability, not only for placement-based approaches but for a range of virtual work opportunities as well (Jackson and Bridgstock 2021). For the virtual university, understood as a technology-enabled and enhanced network of learners and learning (Barjis 2003; Abdoli Sejzi et al. 2012; Tejedor et al. 2020), supporting graduate employability will encompass a range of strategies and activities that foreground technology and innovation in a range of ways. Exploring how technology can meaningfully be integrated into education and how this impacts on WIL pedagogies is explored in the next section.

A Sociomaterial Framing of Technology and Education Inherent in previous discussions of the role of technology in education, constructs have largely been framed as technology being a solution, or tool, in response to educational issues and challenges (Johri 2011). For example, enhancing remote learning opportunities can be viewed as a product of the greater use of online learning platforms and digital conferencing, where technology use is viewed as pragmatic and learning as merely supported by technology (Henderson et al. 2017). Further, the efficient management of education, in particular WIL, is delivered through a suite of technology solutions, such as administrative and communication tools. However, it is suggested in this chapter, in relation to the full realization of the virtual university, that there is benefit in moving beyond the lens of

19

Preparing Students for the Future of Work and the Role of the Virtual

379

“technology as a solution” to seeing technology as both constituted by, and constitutive of, education and pedagogical practices. Drawing on the work of Orlikowski (2007), the argument is made that technology and the experience of work are intertwined through a connection of the social experience of the workplace and the material intersections of technology and practice. Such an argument considers that work has both a social and material dimension, which coexist and shape each other. This, therefore, places demands on the human actor, within a virtual university context, to understand and experience these multiple dimensions of practice and work, constituted within a learning context which itself is reshaped through intersections of technology and being. As Orlikowski (2007) contends, technology becomes constitutive of work and learning, in the same way that work and learning are constitutive of technology, or a notion of constitutive entanglement. The challenge inherent in the virtual university is that previously accepted and constituted roles of education and technology no longer necessarily hold true. This applies even more to the connection between work and learning through pedagogical models such as WIL, the focus of this chapter. In comparison to classroom-oriented learning models, WIL challenges models of learning to encompass explicit connections between work, experience, practice, and learning. In this paradigm, learning is considered to occur within and be connected with activity, and is shared embodiment of knowing how to do something (Schatzki 2001). Conceptions of WIL often position the workplace and the university as being two separate and distinct places of being. This is easily done when the workplace and university are also physically separate, “bricks and mortar,” spaces. However, the advent of a virtual university, in which physical boundaries dissolve and learning permeates into a range of spaces and places of being, facilitated by technology, what constitutes the workplace and university is no longer clear. For example, the emergence of the virtual internship in which students undertake experience and learning through online engagement with “real” professional settings, such as managing a project online across multiple countries, allows for the integration of “just-in-time” learning and instruction simultaneously with the experience of practice and work. In this example, neither the workplace nor the university can be easily defined, separated, or bounded, including separation of identities as “workplace supervisior” or “lecturer” where these roles become entwined and co-productive. Instead, learning and practice, the university and workplace, are blended and entangled. Past arguments between “formal” and “informal” learning, or classroom-based and practice-based education, are challenged where the boundaries around particular forms of learning are no longer (if they ever were) clear. Learning in and through practice is no longer about connecting separately constituted physical spaces, but is instead an entanglement of virtual spaces created by, and through, experiences of technology. The complexity of work, learning, and the university is further illuminated through the growth of educational technology providers. As a new business model for the virtual university, online program providers have become suppliers of education, through university uptake, to capitalize on the enabling features of online management systems, on-demand digital learning, data analytics, and market or

380

B. A. Dean et al.

labor analysis. It is in relation to this pivot to online providers that Williamson (2021, p. 63) offers the following insight: HE marketization is actively being accomplished through a complex sociotechnical arrangement of market devices including platforms, as well as the numbers and charts, human and nonhuman agents, machine learning algorithms, visualizations and infographics, market valuations, reports and discourses that all support the construction, maintenance and diffusion of those platforms.

Higher education has become interlaced with the performativity of technology through the growing global industry of educational technologies (Komljenovic 2021). Online platforms are now central to university governance and how teaching and learning is being organized, managed, and evaluated (Komljenovic 2021; Teräs et al. 2020; Williamson 2021). Technology platforms demand novel technological expertise from human and nonhuman capacities including large datasets, calculative equipment, and broad understandings of labor market needs (Williamson 2016). Furthermore, universities are adopting learning modules and experiences developed by third-party providers as part of their learning offerings. Through external providers, platforms are being integrated across diverse disciplines to facilitate the teaching and learning experience. In a recent article, Williamson (2021) explores the example of Pearson©, a key “edu-business” and also a platform-based business that can be customized and leveraged for the delivery of higher education. Specifically, in WIL contexts, technological or “WIL edubusiness” systems are implemented within singular programs or across institutions for the provision of organizing WIL experiences between stakeholders. Consider digital businesses such as InPlace©, Sonia©, or Practera© that market their services to tertiary administrators to deliver and manage experiential learning programs. These systems are able to organize and report on vast amounts of data to facilitate and audit communication, assessment, and procedures between industry, community, university, and student stakeholders. Being a combination of pedagogical and administrative functions, the capacity of these systems are more enabling than many bespoke manual WIL management processes, so the attraction and ease of an online provider is enticing. In response to advances in technologies, data analytics and metrics, and other evaluation techniques, the contemporary higher education sector is experiencing processes of marketization, privatizing, and consumerization in order to remain competitive (Williamson 2021). Students are increasingly subjected to educational processes with companies outside their institution, which shape the way teaching and learning are delivered and managed. This socioeconomic interconnection of digital technology providers and education is known as “platform capitalism” and is a key “edu-business” in the global education industry (Williamson 2021). This has implications for the virtual univeristy, as it impacts the way students experience learning. In an increasingly conflated virtual space of work and learning boundaries, external providers and platforms can potentially be incredibly useful for managing large student cohorts. In the WIL space, for example, external, third-party providers

19

Preparing Students for the Future of Work and the Role of the Virtual

381

offer solutions to sourcing and managing diverse virtual industry projects and placements. Other technological enablers, but not necessarily platform providers, have also made it possible for students to engage in a range of opportunities to apply their discipline knowledge and skills in a virtual practice-based environment. This chapter now turns to examine these different approaches of digitally enabled WIL to prepare students of the virtual university for the future world of work.

Technology and WIL Typology Approaches adopting technology for WIL are growing (Bayerlein et al. 2021b). In conceptualizing electronic WIL (eWIL), Schuster and Glavas (2017, p. 65) define eWIL as the “use of technology to support the administrative processes or students of WIL and/or deliver entirely online or blended WIL experiences.” These functions are further classified into four core categories in their eWIL typology: 1. Technology supported: low technology involvement to support information/ administrative processes (e.g., use of spreadsheets in managing student placements) 2. Technology facilitated: high technology involvement to support students during preparation for, during, and assessing after WIL (e.g., use of virtual workshops to provide guidance to students throughout placement) 3. Technology blended: low technology involvement to deliver WIL experiences that combine online and offline activities (e.g., online discussion boards to support student reflections and sharing of experiences) 4. Technology based: high technology involvement in which all stakeholders (students, educators, and industry partners) are technologically mediated (e.g., shared and virtual coaching platforms). When Schuster and Glavas (2017) developed this typology , they noted that the majority of WIL models landed within the first two categories (see above), utilizing technology to support and facilitate WIL. Over the last few years, however, this claim can no longer be supported, given the uptake of digital WIL experiences. Further, what is missing from this typology is the extent to which technology is employed for WIL as an enabler of a specific kind of work. The trends discussed earlier in the Industry 4.0 section suggest that Schuster and Glavas (2017) may have overlooked a category. Instead, we propose three primary uses of technology and WIL. First, we agree that technology can be used for administrative purposes. Here, an educator may use technology as a platform for storing resources or communicating with students, for sharing information on assessments, or for showcasing assessments, for example, ePortfolios. In this first category, we have collapsed Schuster and Glavas’ (2017) categories of technology supported and technology facilitated, to highlight that for both, technology involves administrative of educational processes and information. Second, we highlight practices where technology changes the way WIL occurs. For

382

B. A. Dean et al.

example, an internship or project that may have otherwise taken place within a school, hospital, or business is now facilitated wholly online in so far as the students communicate and conduct work without a physical relocation for work. This category conflates Schuster and Glavas’ (2017) third and fourth categories, technology blended and technology based, to highlight how learning, communication, and work use technology as a substitute for being physically present. Our third category breaks away from the typology above to propose the mutual constitution of learning, technology, and work through WIL. Here, digitally enabled work shapes how WIL is designed, where technology is present in specific skills or jobs that impact on the pedagogical choices for virtual WIL models. In these WIL experiences, students engage with technology as both the enabler of pedagogy and as the work experience. Examples may include: students engaging in telehealth consultations in an industry where telehealth is rapidly expanding; virtual internships in a global business where employees are dispersed around the world; or virtual simulations in engineering where engineers use virtual simulations to test designs and products. Within these experiences, both the workplace (e.g., work supervisor) and the university (e.g., learning facilitator) can be simultaneously present, shaping both the practice and learning experience of the student. Ultimately, the learning outcomes for WIL will drive an educator’s use of technology and inform the choice of WIL model. If the virtual university is serious about developing skills and competencies to prepare learners for Industry 4.0, then in certain spaces and disciplines virtual WIL models will be designed with the third category in mind. However, there will also be spaces where technology in WIL will play a more facilitative or administrative function. Once an educator is clear on their purpose for WIL, or what they want their students to learn, they next need to consider how they want students to learn. The following section highlights several established and emerging virtual models of WIL for an educator within the virtual university.

Virtual WIL Examples Core to the experience of eWIL, or virtual WIL, is the centrality of authentic learning experiences, integrated with technological platforms, in which students are afforded opportunities to build new relationships and extend the breadth of their experience (Glavas and Schuster 2020). The following examples aim to highlight how the ideas of a virtual WIL experience may be evident in higher education pedagogies.

Online Industry Projects Online industry projects occur when students undertake resolving an authentic, reallife issue presented by an industry partner, which is overseen by an educator at the higher education institution (Kaye et al. 2018). Students are able to problem-solve, research, and analyze to solve these issues and are assessed accordingly. Students

19

Preparing Students for the Future of Work and the Role of the Virtual

383

communicate with industry partners, educators, and their team through online communication technologies to be virtually present rather than in a physical workplace (Patrick et al. 2009). In some cases, projects may also include an on-campus or face-to-face component, such as workshops, field trips to the organization, or networking sessions. Online industry projects are founded on problem-based learning which provides students with practice-related problems to apply knowledge and to resolve a situation (Jackson and Meek 2020). It can offer students effective ways of enhancing employability as students often work in teams to negotiate, research, and communicate possible solutions. In an online project for business students, Rook and colleagues (2020) outline how they used videoconferencing technology (Zoom©) for students to consult industry partners. Elsewhere, Winchester-Seeto and Piggott (2020) use a learning platform and online collaboration technology (Moodle Collaborate Ultra) for interdisciplinary teams to communicate and work together on projects provided by partners in public, private, and community sectors. Jackson and Meek (2020) describe how online industry projects can provide an equitable learning experience for students, enabling all students access to industry mentorship and avoiding the dissimilarities of individual placement-based experiences. However, they warn that third-party providers or platforms can charge fees, per student, which can become costly. Additionally, students may not gain the same exposure to organizational culture, teamwork, and conversation that a placement format provides. This may limit access to networking and career development learning.

Online/Remote Placements Online WIL includes remote, virtual, and synchronous experiences where the workplace experience is happening live but is accessed from a distance. Wood et al. (2020, p. 333) name this category remote WIL and define it as a “WIL experience focused on the student completing authentic, relevant actual tasks for an organization through a remote connection to the workplace/community.” The medical education field provides rich examples of online or remote placements, particularly through telehealth. Salter et el. (2020) describe how videoconferencing technology was employed in physiotherapy and mental health nursing placements for students to conduct client consultations. They also highlight how online placements have been expanded into primary school contexts, with speech pathology students participating in class from home (at a distance) for four weeks and then at school (in situ) for three weeks afterward. Similarly, Hodges and Martin (2020) describe how a campus-based prescription clinic for exercise science students was translated into an online placement experience, requiring students to deliver fitness assessments and exercise programs to their clients online. For these students, the platforms selected varied but included Zoom©, Microsoft Teams©, Google Meet©, or Facebook Live©. Hodges and Martin (2020) claim that this online

384

B. A. Dean et al.

placement, despite its challenges, allowed students to develop skills such as resilience and flexibility as well as enhance their confidence in communicating with clients. Salter et al. (2020), and Hodges and Martin’s (2020) cases were designed as online solutions to physical placements in response to the move to online delivery due to COVID-19. During 2020, there was rapid uptake of this model as instructors grappled with maintaining students’ experiences of real work within the limitations of working from home (Dean and Campbell 2020). Before 2020, however, there are limited examples of online placements. Most of these outline various forms of telehealth for education in science, medicine, and health disciplines, while others are in teacher education, for example, preservice teachers teaching K-12 students through online education (Eady et al. 2021; Luo et al. 2017). These studies of online placement models articulate the selection of online placements to coincide with the digitization of work practice and the benefits this presents by allowing students to develop work and technological skills.

Online Simulation and Virtual Reality Online WIL simulation is the construction of a scenario or work environment based on real-life events, to engage students in problem-solving and practice in a safe environment (Rasalam and Bandaranaike 2020). It is designed and facilitated by a university educator with input from industry partners (Zegwaard et al. 2020). Learners are actors within these simulated environments that afford a low-risk, low-pressure learning opportunity that all students experience. It can allow students to practice, make mistakes, or take risks without consequences, and reflect on their learning in this controlled environment (Zegwaard et al. 2020). Simulations can range from smaller in-class (virtual or physical) role-plays through to experiences that evolve throughout the semester. In accounting education, Bayerlein (2020) employs online simulations to produce cognitive, skills-based, and affective learning outcomes by engaging students in a semester-long simulation of an accounting workplace. Through the online learning management platform (Moodle©), students are exposed to real taxation and financial reporting scenarios with progressive problems that require application of skills and knowledge. Over the last few years though, simulations have evolved with the uptake of technologies that enable students to immerse themselves into fictional workspaces through virtual reality (VR). VR environments can provide unique learning experiences for students as they engage with virtual people, processes, and places and apply knowledge within these contexts. In health care, VR has been adopted to standardize training experiences, and engage students with simulated patients and human interaction (Robinson et al. 2018). Also in health sciences, Pal et al. (2020) outline how VR simulation videos assist medical students to develop nontechnical skills such as leadership, situational awareness, teamwork, and decision-making. Delivered by experienced facilitators, Pal et al.’s (2020) project included 360 cameras in a clinical environment that proposed a certain scenario, and would

19

Preparing Students for the Future of Work and the Role of the Virtual

385

be interrupted at key points to enable discussion and debriefing. Although there is a small body of work evaluating the effectiveness of VR for student employability development, it is an emerging but growing form of WIL.

Digital Service Learning Service learning is an experiential learning approach that integrates discipline knowledge with community service, which results in mutual outcomes for students and society (Deeley 2015). Service learning includes reflection on real-world experiences to develop knowledge and understanding of citizenship, community, and social justice (Deeley 2015). Digital service learning is a form of service learning; however, it is completed through technology and creates opportunities for service learning in the digital arena (Waldner et al. 2012). Digital service learning additionally encompasses digital citizenship through developing students’ appropriate and responsible conduct online, and an awareness of online information (Shah et al. 2018). Digital service learning is only a recent development in WIL models. It has been applied to business programs that teach sustainability in cross-disciplinary groups. In these groups, students engage in real-world activities for the benefit of society and to develop critical thinking and intention to act sustainably in business (Perkiss et al. 2020). Digital service learning has also been employed in a software engineering course to bring students closer to realistic work environments (Nascimento et al. 2018). In their engineering program, students contribute to an open-source project, where the partners and consumers are the global community who have open access to the software and its features. In both examples, students learn discipline, technological and employability skills as well as an increased awareness of civic responsibility. Although this model has had slow uptake across the WIL community, it offers great potential for the virtual university to collaborate with not-for-profit partners, global organizations, and to teach digital citizenship.

Gamification and Artificial Intelligence (AI) Gamification has been a major focal area in research that aims to increase engagement of student learning through online activities in a game-based style (Lavouè et al. 2019). In relation to WIL, this has been shown to be effective in digital WIL experiences and can increase a student’s motivation to partake in the activity (Lavouè et al. 2019). Digital gaming has been in higher education for over two decades and is said to increase students’ “twenty-first-century skills” as well as align with the next generation of jobs (McClary et al. 2012). While gaming is used in higher education to provide personalized experiences for motivating students, in WIL contexts, there are very few examples where gaming has engaged students online in work-like scenarios. Of those that are available in the literature, gaming has been in nursing education, where students have shown

386

B. A. Dean et al.

enjoyment for gaming technology to simulate clinical environments and contextualize theory (Reed 2020). Despite this limited adoption, there is vast potential for gaming and WIL in the virtual university, to design learning experiences to teach and apply complex concepts and skills. This limitation is similarly extended to WIL and AI, where, to date, there is little in the literature specifically relating to how AI is employed for developing students’ employability skills or preparedness for the workplace. Yet, there are projections claiming a growing role of AI in teaching and learning in higher education (Popenici and Kerr 2017). This claim reflects advances in AI to open new possibilities for teaching and learning, and imagines a future where AI will be part of the fabric of universities (Popenici and Kerr 2017). Therefore, over the next few years, we could see more emerging models of virtual or online WIL that integrate more AI technologies.

Considerations for Virtual Modes of WIL Virtual WIL has helped to develop students, institutions, and industry and there is evidence of many successful outcomes for both students and practitioners in virtual, digital, and technologically enhanced WIL settings. There are cases where studies have shown benefits, including a cost-benefit, for the institution and the industry (Morrison-Smith & Ruiz 2020). Virtual WIL is also time saving, in that it allows students to have WIL experiences without having to travel back and forth (Vriens et al. 2010) to a physical work setting. Virtual modes of WIL provide students with WIL opportunities from a distance in cases where students live far from the physical location of the industry. Through online WIL opportunities students become proficient with information and communication technology (ICT) and this familiarity in itself is a benefit of virtual modes of WIL (Morrison-Smith & Ruiz 2020). There are some striking opportunities for growth for all parties involved, for the industry partner, the university, and the student, when delivering virtual modes of WIL. Recently, emerging models of online WIL have been identified by small-tomedium enterprises, as approaches that would enable greater connection, flexibility, and access to undergraduate talent (Bieler 2021). There is a consistent moving forward for everyone to grow when building familiarity, creating content, and engaging in online WIL. For example, Rasalam and Bandaranaike (2020) looked at a virtual WIL experience using simulations to allow students to conduct consultations with voluntary simulated patients. They noted that it assisted with the delivery of subject content; in other words, the universities were able to create meaningful WIL experiences in partnership with industry and also assess the students’ growth and progress, and adjust content if needed. One example of how they assessed students’ growth is through the SAMR (substitution, augmentation, modification, and redefinition) model to support and inform practice. The authors note, “Using the SAMR model, educators can effectively scaffold the necessary skills to take students through the stages of technology integration and adoption, helping them become creators of their own knowledge” (Rasalam and Bandaranaike

19

Preparing Students for the Future of Work and the Role of the Virtual

387

2020, p. 574). Learning through online WIL experiences also helps students to build resilience in the workplace. Adaptability is a key disposition of career-ready graduates, and these experiences immersed students firsthand in the ever-changing workplace. Thus, students can begin to understand the diverse workplace environments and prepare for success in the workplace. While there are many identifiable benefits of virtual WIL placements, there are also challenges and barriers that each stakeholder can face. The literature posits that simulations and online modes of WIL offer an alternative solution to in-person WIL (Wood et al. 2020). However, this can pose a challenge, as Wood et al. (2020) explain: “for simulated WIL to meet the definition of WIL, there must be active engagement between the three stakeholders, however, the proximity of the external stakeholder will likely be low and perhaps limited to mentoring.” Therefore, although the experience is virtual, there is still a challenge of the institution connecting with the industry partner and for the student to be mentored closely. Perhaps more common challenges include computer accessibility, Internet, and connectivity issues, as well as lack of confidence and therefore motivation to learn (Gros and García-Peñalvo 2016). There are barriers in the fact that many industry partners may not know how to create an online project, and a lack of opportunities to “train the trainer” can find the industry partner frustrated and the higher education academic overworked. As educators, it is pivotal to utilize support services and ICT specialists, and to prepare students for online WIL readiness. Ultimately, this will improve virtual WIL activities that result in the students gaining employability skills, industry partners seeing the benefits to their business, and academic coordinators providing quality learning experience in online WIL. Taking these benefits and challenges into consideration, there are a number of design features to keep in mind when educators of the virtual university wish to enhance students’ critical thinking and career readiness. Several years after developing their typology, Glavas and Schuster (2020) extended their research by deriving four eWIL design principles. They espouse that eWIL needs to focus on promoting copresence and opportunities for relationship building, and, ideally, programs should be developed through a codesign process with undergraduate and postgraduate students. They highlight four key factors for designing an eWIL experience: 1. Provide an authentic eWIL experience (learning activities, assessment, and technology) 2. Carefully select and integrate technological platforms employed to support or deliver eWIL 3. Develop effective administrative processes to support eWIL 4. Promote copresence and relationship building When designing online WIL activities, Bowen (2020) argues that educators need to be explicit in the tasks and instructions of how to proceed with a task. Students need to become proactive in asking questions to clarify expectations and confirm the pace and quality of the work to meet standards. Bowen (2020) also suggests that

388

B. A. Dean et al.

students practice communicating through videoconferencing and reflect on their professionalism to improve communication. Taken together, Schuster and Glavas’s (2017) eWIL typology and eWIL design principles (Glavas and Schuster 2020) are helpful for curriculum design, while Bowen’s (2020) insights are useful for supporting students working through virtual WIL activities. For the virtual university, these suggestions need to be considered by educators when designing experiences that prepare students for the future of work and promote real-world experiences. However, reflecting on the typology proposed by the authors here, there is opportunity to adapt these virtual WIL models for complex workspaces where work and learning are entangled in virtual practice. It is this latter category that will grow in future years, as workplaces evolve and WIL pedagogies accommodate resulting changes.

Learning for Dynamic Work Environments Evident across the previous discussion of models of virtual WIL is the constitutive entanglement of technology, practice, and learning expressed through new models of working and connecting with practice. For example, the medical student engaged in remote “placements” has their experience of the patient, interactions with colleagues, and connections with peers moderated through their utilization of various technologies. Instead of being able to physically touch the patient and see immediate responses inherent in the “real” human interaction, technology has reshaped the very notion of valued practice and the experiences required to be successful; that is, technology has reconstituted the very essence of work and therefore the experience of WIL. With the movement to the virtual space, the materiality of the work of the medical student/professional has changed. This evolution of work is not confined to the experience of medical students but is being experienced across a myriad of contexts and professions, particularly since the ubiquity of technology and advancement of remote working is accelerating. The accelerating change in the nature of work through the constitutive entanglement with technology is demanding new skills and learning experiences for students. The demands of Industry 4.0, and emerging new forms of work, requires adaptable, flexible, and agentic learners, with new workers more able to respond to increasing uncertainty. As academic and pedagogical work transforms in response to the virtual university, there are challenges for success in education, and employability. Evident in the interface between the university and work is the constitutive entanglement of technology and practice. Adoption, and adaptation, of technology and the virtualization of work are driving new ways of understanding valued practice. Concurrently, emergent practice is also shaping the role and purpose of technology within constructs of work. This is evident in the previously described digital service learning, where the historical practice of service learning (with a history traced at least back to Dewey’s constructs of experiential learning in the early twentieth century) is being transformed through the use and engagement with technology. Similarly, the experience of service learning is driving new ways of working with

19

Preparing Students for the Future of Work and the Role of the Virtual

389

technology and expectations around how technology is used to both support learning as well as community and social development. It is not possible, nor desirable, to disentangle the constitutive interrelationships between technology and practice. These demands of the virtual university and entanglement of technology, work, and learning require new models of WIL in which digitally enabled work shapes how WIL is designed, and where technology, present in specific skills or jobs, informs and shapes the pedagogical choices for virtual WIL models.

Cross-References ▶ Emerging, Emergent, and Emerged Approaches to Mixed Reality in Learning and Teaching ▶ Innovation and the Role of Emerging Technologies

References Abdoli Sejzi, A., B. Aris, and N. Yahaya. 2012. The phenomenon of virtual university in new age: Trends and Changes. Procedia - Social and Behavioral Sciences 56 (2012): 565–572. https:// doi.org/10.1016/j.sbspro.2012.09.689. Arntz, M., T. Gregory, and U. Zierahn. 2016. The risk of automation for jobs in OECD Countries: A comparative analysis (OECD Social, Employment and Migration Working Paper No. 189). OECD Publishing, Paris, France. Asian Development Bank. 2018. Asian development outlook (ADO) 2018: How technology affects jobs. Asian Development Bank, Manila, Philippines. Australian Government. 2021. National skills commission: Skills priority list. Australian Government. Autor, D., and A. Salomons. 2017. Does productivity growth threaten employment? Paper prepared for the ECB Forum on Central Banking, Sintra, Portugal. Barjis, J. 2003. An overview of virtual university studies: Issues, concepts, trends. In F. Albalooshi (Ed.), Virtual Education: Cases in Learning & Teaching Technologies: 1–20. IGI Global. https:// doi.org/10.4018/978-1-93177-739-1.ch001 Bayerlein, L. 2020. The impact of prior work-experience on students learning outcomes in simulated internships. Journal of University Teaching & Learning Practice. 17 (4): 1–17. Bayerlein, L., B.A. Dean, S. Perkiss, and D. Jeske. 2021a. Using technology platforms for workintegrated learning. In Advances in research, theory and practice in work-integrated learning, ed. S. Ferns, A. Rowe, and K.E. Zegwaard, 239–248. Routledge. Bayerlein, L., M.T. Hora, B.A. Dean, and S. Perkiss. 2021b. Developing skills in higher education for post-pandemic work. Labour & Industry: A Journal of the Social and Economic Relations of Work. https://doi.org/10.1080/10301763.2021.1966292. Bieler, A. 2021. WIL in a post COVID-19 world. The Conference Board of Canada. Bowen, T. 2020. Work-integrated learning placements and remote working: Experiential learning online. International Journal of Work-Integrated Learning 21 (4): 377–386. https://www.ijwil. org/files/IJWIL_21_4_377_386.pdf. Dacre Pool, L., and P. Sewell. 2007. The key to employability: Developing a practical model of graduate employability. Education + Training. 49 (4): 277–289. Dean, B.A., and M. Campbell. 2020. Reshaping work-integrated learning in a post-COVID-19 world of work. International Journal of Work-Integrated Learning 21 (4): 356–364.

390

B. A. Dean et al.

Dean, B.A., M.J. Eady, and V. Yanamandram. 2020. Advancing non-placement work-integrated learning across the degree. Journal of University Teaching & Learning Practice 17 (4): 1–4. Deeley, S.J. 2015. Critical perspectives on service-learning in higher education. London: Palgrave Macmillan. Eady, M.J., C.A. Green, and H. Capocchiano. 2021. Shifting the delivery but keeping the focus: A reflection on ensuring quality teacher preparation during a pandemic. Education Sciences 11 (8): 401. https://doi.org/10.3390/educsci11080401. Gibson, I. 2001. At the intersection of technology and pedagogy: Considering styles of learning and teaching. Journal of Information Techology for Teacher Education 10 (1–2): 37–61. https://doi. org/10.1080/14759390100200102. Glavas, L., and C. Schuster. 2020. Design principles for electronic work integrated learning (eWIL). Internet & Higher Education 47: 1–8. https://doi.org/10.1016/j.heduc.2020.100760. Gros, B., and García-Peñalvo, F. J. 2016. Future trends in the design strategies and technological affordances of e-learning. In M. Spector, B. B. Lockee, and M. D. Childress (Eds.), Learning, Design, and Technology. An International Compendium of Theory, Research, Practice, and Policy (pp. 1–23). Switzerland: Springer International Publishing. https://doi.org/10.1007/9783-319-17727-4_67-1 Henderson, M., N. Selwyn, and R. Aston. 2017. What works and why? Student perceptions of ‘useful’ digital technology in university teaching and learning. Studies in Higher Education 42 (8): 1567–1579. https://doi.org/10.1080/03075079.2015.1007946. Hodges, L., and A. Martin. 2020. Non-placement WIL: The case of an exercise prescription clinic. Journal of University Teaching & Learning Practice. 17 (4): 1–10. Jackson, D., and R. Bridgstock. 2021. What actually works to enhance graduate employability? The relative value of curricular, co-curricular, and extra-curricular learning and paid work. Higher Education 81 (4): 723–739. https://doi.org/10.1007/s10734-020-00570-x. Jackson, D., and S. Meek. 2020. Embedding work-integrated learning into accounting education: The state of play and pathways to future implementation. Accounting Education. 30 (1): 63–85. https://doi.org/10.1080/09639284.2020.1794917. Jackson, D., and N. Wilton. 2017. Perceived employability among undergraduates and the importance of career self-management, work experience and individual characteristics. Higher Education Research & Development 36 (4): 747–762. https://doi.org/10.1080/07294360.2016. 1229270. Johri, A. 2011. The socio-materiality of learning practices and implications for the field of learning technology. Research in Learning Technology 19. https://doi.org/10.3402/rlt.v19i3.17110. Kaye, J., S. Ferns, L. Russell, and J. Smith. 2018. Expanding work integrated learning (WIL) possibilities: enhancing student employability through innovative WIL models. Australian Technology Network (ATN), Melbourne, Australia. Komljenovic, J. 2021. The rise of education rentiers: Digital platforms, digital data and rents. Learning, Media and Technology. https://doi.org/10.1080/17439884.2021.1891422. Lavoué, E., Monterrat, B., Desmarais, M. and George, S. 2019. Adaptive gamification for learning environments. IEEE Transactions on Learning Technologies, 12 (1), 16–28. https://doi.org/10. 1109/TLT.2018.2823710 Luo, T., L. Hibbard, T. Franklin, and D.R. Moore. 2017. Preparing teacher candidates for virtual field placements via an exposure to K-12 online teaching. Journal of Information Technology Education: Research. 16 (10): 1–14. Macklin, J. 2020. Future skills for Victoria, driving collaboration and innovation in post-secondary education and training. Melbourne: Victorian Government. McClary, K.L., A. Orr, P.M. Frey, R.P. Dolan, V. Vassileva, and A. McVay. 2012. A literature review of gaming in education. Pearson. Morrison-Smith, S., and Ruiz, J. (2020). Challenges and barriers in virtual teams: a literature review. SN Applied Sciences 2, 1–33. Nascimento, D.M.C., C.F.G. Chavez, and R.A. Bittencourt. 2018. The adoption of open source projects in engineering education: A real software development experience. In Proceedings in IEEE Frontiers in Education Conference (FIE), 1–9.

19

Preparing Students for the Future of Work and the Role of the Virtual

391

Orlikowski, W. 2007. Sociomaterial practices: Exploring technology at work. Organization Studies 28 (9): 1435–1448. https://doi.org/10.1177/0170840607081138. Pal, S., R. Benson, P. Duvall, and V. Taylor-Jones. 2020. Do innovative immersive virtual reality simulation vidoes have a role to play in teaching non-technical skills and increasing prepareness for clinical placements for medical students? MedEdPublish. https://doi.org/10.15694/mep. 2020.000164.1. Patrick, C.-J., D. Peach, C. Pocknee, F. Webb, M. Fletcher, and G. Pretto. 2009. The WIL report: A national scoping study. Queensland University of Technology. https://eprints.qut.edu.au/44065/. Perkiss, S., S. Anastasiadis, L. Bayerlein, B. Dean, H. Jun, P. Acosta, M. Alejandra Gonzalez-Perez, A. Wersun, and B. Gibbons. 2020. Advancing sustainability education in business studies through digital service learning. American Business Review 23 (2): 276–292. Popenici, S., and S. Kerr. 2017. Exploring the impact of artificial intellegence on teaching and learning in higher education. Research and Practice in Techology Enhanced Learning 12 (22): 1–13. Price Waterhouse Coopers (PWC). 2016. They say they want a revolution: Total retail 2016. Retrieved from https://www.pwc.ru/en/publications/totalretail-2016.html, Crossref. Ra, S., U. Shrestha, S. Khatiwada, S. Won Yoon, and K. Kwon. 2019. The rise of technology and impact on skills. International Journal of Training Research 17 (1): 26–40. Rasalam, R., and Bandaranaike, S. (2020) Virtual WIL clinics in medicine: Overcoming the COVID-19 challenge. International Journal of Work-Integrated Learning 21 (5): 573–585. Reed, J.M. 2020. Gaming in nursing education: Recent trends and future paths. Journal of Nurse Education 59 (7): 375–381. https://doi.org/10.3928/01484834-20200617-04. PMID: 32598006. Robinson, K.E., P.J. Allen, M. Quial, and J. Beilby. 2018. Virtual patient clinical placements improve student communication competence. Interactive Learning Environments 28 (6): 795–805. https://doi.org/10.1080/10494820.2018.1552869. Salter, C., Oates, R. K., Swanson, C., and Bourke, L. 2020. Working remotely: Innovative allied health placements in response to COVID-19. International Journal of Work-Integrated Learning 21 (5): 587–600. https://eric.ed.gov/?id=EJ1271186 Schatzki, T. R. (2001). Practice mind-ed orders. The Practice Turn in Contemporary Theory. T.R. Schatzki, K.K. Cetina and E. von Savigny. London, Routledge. Schuster, L., and C. Glavas. 2017. Exploring the dimensions of electronic work integrated learning (eWIL). Eduactional Research Review 21 (2): 55–66. Shah, R.W., J.S. Troester, R. Brooke, L. Gatti, S.L. Thomas, and J. Masterson. 2018. Fostering eABCD: Asset-based community development in digital service-learning. Journal of Higher Education Outreach and Engagement 22 (2): 189–221. Smith, J., S. Ferns, J. Kay, L. Russell, M. Marcus, D. Wache, and E. Nielsen. 2019. The EDGE report: Enabling and evidencing enterprise and entrepreneurship through work integrated learning. Brisbane: Australian Technology Network. Snith, S., K. Maund, T. Hilaire, T. Gajendran, J. Lyeham, and S. Geale. 2020. Enhancing discipline skills using a virtual environment built with gaming technology. International Journal of WorkIntegrated Learning 21 (3): 193–209. Tejedor, S., L. Cervi, F. Tusa, and A. Parola. 2020. Education in times of pandemic: Reflections of students and teachers on virtual university education in Spain, Italy, and Ecuador. Revista Latina de Comunicación Social 78: 1–21. Teräs, M., J. Suoranta, H. Teräs, and M. Curcher. 2020. Post-Covid-19 education and education technology ‘solutionism’: A seller’s market. Postdigital Science and Education 2 (2): 863–878. https://doi.org/10.1007/s42438-020-00164-x. Toffler, A. 1981. The third wave. Toronto: Bantam Books. Vriens, M., Op de Beeck, I., De Gruyter, J., and Van Petegem, W. 2010. Virtual placements: Improving the international work experience of students. In EDULEARN 2010, 2nd International Conference on Education and New Learning Technologies (pp.1175–1183). International Academy of Technology, Education and Development.

392

B. A. Dean et al.

Waldner, L.S., McGorry, S.Y., and Widener, M.C. (2012). Eservice-learning: The evolution of service-learning to engage a growing online student population. Journal of Higher Education Outreach and Engagement 16 (2): 123. Williamson, B. 2016. Digital methodologies of education governance: Pearson plc and the remediation of methods. European Educational Research Journal 15 (1): 34–53. ———. 2021. Making markets through digital platforms: Pearson, edu-business, and the (e)valuation of higher education. Critical Studies in Education 62 (1): 50–66. https://doi.org/10. 1080/17508487.2020.1737556. Winchester-Seeto, T., and L. Piggott. 2020. Workplace or workforce: What are we preparing students for? Journal of University Teaching & Learning Practice 17 (4): 1–6. Winchester-Seeto, T., and L. Piggott. 2020. Projects of consequence: Interdisciplinary WIL projects designed to meet the needs of partners and students. Journal of University Teaching & Learning Practice 17 (4). https://ro.uow.edu.au/jutlp/vol17/iss4/9 Wood, Y.I., Zegwaard, K.E., and Fox-Turnbull, W. 2020. Conventional, remote, virtual and simulated work-integrated learning: A metaanalysis of existing practice. International Journal of Work-Integrated Learning 21(4): 331–354. Zegwaard, K., T.J. Pretti, and A.D. Rowe. 2020. Responding to an international crisis: The adaptability of the practice of work-integrated learning. International Journal of WorkIntegrated Learning 21 (4): 317–330.

Authenticity, Originality, and Beating the Cheats

20

Sheona Thomson, Alexander Amigud, and Henk Huijser

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Academic Misconduct as a Wicked Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The State of Affairs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Assessment and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Originality and Plagiarism in an Historical Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Originality and Collaboration in Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Authenticity: Trust Model in a Complex System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Contributions of Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Computers and Originality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Toward an Integrated Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Role of the Instructor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Integrating the Patchwork into a Coherent a Holistic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

394 394 396 400 401 402 403 404 404 406 407 409 409 410 410

Abstract

The chapter argues that ensuring academic integrity is vital to higher education, but it can be challenging to implement effectively. Academic staff may struggle due to unclear policies and a lack of support for the related administrative workload. Additionally, faculty members must balance their personal and insti-

S. Thomson (*) · H. Huijser Learning and Teaching Unit and Faculty of Engineering, Queensland University of Technology, Brisbane, QLD, Australia e-mail: [email protected]; [email protected] A. Amigud Tecnológico de Monterrey, Monterrey, Mexico e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_20

393

394

S. Thomson et al.

tutional obligations, which can lead to inconsistent application of policy. However, a holistic approach that considers various aspects of academic integrity is necessary to develop a comprehensive solution. This approach involves creating a learning community, developing authentic and meaningful assessment, and adopting the best verification and authentication technologies. By prioritizing student engagement and building relationships of trust, virtual universities can foster a culture of academic integrity that benefits both students and faculty. Keywords

Academic integrity · Academic misconduct · Authentic assessment · Assessment · Wicked problem · Policy

Introduction The previous chapters in this section have explored how to create an online learning environment distinguished by active, authentic, and collaborative approaches to assessment. The underpinning concern with assessment is that it is produced with integrity. Arguably, concerns about the integrity of assessment in online learning have been stoked by the perceived experience of student cheating during remote teaching in response to COVID-19, particularly in translating in-person invigilated examinations to online, “take-home” exams. Most recently, such concerns have been exacerbated by the emergence of generative algorithms and the potential impact of machine learning (ML) and artificial intelligence (AI) more broadly. Authentic assessment is often cited as a potential defense against academic integrity problems such as plagiarism and contract cheating (Amigud and Dawson 2020; Dawson 2020; Press et al. 2023). However, the concept has recently been critiqued as overly focused on the world of work rather than learning itself. McArthur (2023) suggests, for example, that “we move from simply focusing on the authentic task to considering why that task matters” (p. 85). This recognizes the importance of context in assessment design and focuses on the aims and objectives of assessment. However, the concerns around academic integrity will not disappear, and these concerns are amplified in digital environments, including in the virtual university. Given the long-standing debates about academic integrity and, by association, academic misconduct, it could be called a wicked problem.

Academic Misconduct as a Wicked Problem Academic misconduct has multiple intersecting causes and must be addressed in ways that respect its complexity. (Rettinger 2020)

20

Authenticity, Originality, and Beating the Cheats

395

The notion of academic integrity is not a single concept. Instead, it is complex and invites various opinions and assumptions about ethical behavior and institutional credibility. Academic integrity can be considered a “wicked problem” (Rittel and Webber 1973). Wicked problems are described as “a class of social system problems which are ill-formulated, where the information is confusing, where there are many clients and decision-makers with conflicting values, and where the ramifications in the whole system are thoroughly confusing” (Churchman 1967, p. B-141). The concept of wicked problems can be applied to debates about academic integrity, which seem to be characterized by unclear positions and conflicting perspectives among stakeholders. This often leads to a focus on detecting and punishing misconduct which can create an “arms race” between technologies of production and technologies of detection. While detection technologies may have their place in the overall assessment landscape, they will never be able to “solve” the academic misconduct problem. Indeed, wicked problems require broader and more holistic approaches because of many unknowns. This means that assessment design in the virtual university – where anxiety around (digital) academic misconduct may be amplified – should carefully consider the larger aims and objectives of the assessment to combat this issue. A holistic approach would help mitigate the complexity of an issue which is sometimes oversimplified as “plagiarism and cheating are wrong” when it is impossible to enforce this belief en masse. Acting holistically means recognizing the impossibility of a faultlessly integral and ethical system and working with the implications of that. Furthermore, it requires us to look at the academic misconduct in a historical context to establish which factors have been a constant in this debate and which are new. As Fishman (2015) has noted, our modern-day understanding has been woven from various often contradictory threads of belief and purpose. The intertwining of religion, morality, and education, stemming from the earliest manifestation of the university, has persisted in some form despite the predominant secularity of contemporary institutions. Most institutions express their mission in terms of values. In the contemporary university, endeavors typically have an ethical framing and a presupposition of trustworthiness and integrity. Since the widening of access to higher education, the situation has become even more complicated, with increasingly diverse student cohorts entering universities, including many who are the first in their family to attend a higher education institution. As Fishman (2015) explains, “One facet of this challenge has been meeting the needs both of students who arrive without a clear expectation of what is expected of them in terms of integrity and the needs of institutions whose reputations, relevance, and very survival depend on maintaining high ethical standards with respect to teaching, credentials, and scholarship.” When it comes to assessing a student’s academic behavior, what is considered acceptable or unacceptable can depend on the specific context and individual perspectives. The idea of integrity is relative, meaning it can be interpreted differently by different people. It’s basis in ethical or moral principles, which are subject to interpretation and cultural norms, are at the heart of this subjectivity. In the academic

396

S. Thomson et al.

process, everyone involved may encounter a situation where they must choose between fulfilling institutional obligations and prioritizing their own personal interests or beliefs. Decisions about academic integrity can seem subjective and influenced more by subjective experiences rather than objective evidence (Amigud and Pell 2021), leading to variations in opinions among different stakeholders. For instance, while some faculty believe in following strict rules, hierarchies, and procedures, others prefer a more flexible, principle-based approach that allows individual discretion. Correspondingly, integrity can be considered a zero-sum game by some but not by others. Typically, students and staff members are not actively involved in policy development, and policies can be difficult to understand. Policies often fail to clearly explain why each rule is essential and cannot be replaced with a different one. While students are increasingly encouraged to learn collaboratively, they are also expected to maintain a certain distance from one another and avoid collaboration that could be perceived as collusion, cheating, or laziness, especially when boundaries are not clearly defined (Higbee et al. 2011; Crook and Nixon 2019). “Academic integrity” thus becomes a catch-all term and is often synonymous with the “Pandora’s box” of plagiarism, inferring its opposite “academic misconduct” in the process. Instead of explaining what academic integrity is, we often describe what it is not, and we tend to focus on punishing students as a disincentive to misconduct. When students fail to uphold academic integrity, we refer to their actions as academic dishonesty and academic misconduct. However, it is crucial to ask whether we are providing students with enough information about what it means to work with academic integrity, including the nuances involved. Are we exploring the gray areas and encouraging students to understand how academic integrity can be demonstrated in their work?

The State of Affairs Distance learning has been with us for over 50 years (The Open University 2021). Throughout this time, open and virtual universities have filled a gap in responding to the needs of identifiable cohorts of students long unmet by traditional colleges and universities. In addition, online and virtual universities create new learning opportunities: they increase accessibility and offer greater convenience and flexibility. However, the distributed learning model may challenge academic integrity if not approached critically. For example, we have seen how the COVID-19 pandemic brought academic integrity in higher education into focus like no other time due to unpreparedness and lack of clearly defined strategies. Many struggled with translating in-person activities to a distance mode, only to experience a perceived “explosion” of cheating, making many wary of online teaching and learning. Studies into integrity breaches arising from the translation from on-campus to remote learning

20

Authenticity, Originality, and Beating the Cheats

397

quickly appeared, reinforcing a perception that cheating was rife (Lancaster and Cotarlan 2021; Subin 2021). However, others have also noted that despite the growing concern about prevalence, “it’s unclear how much cheating increased” (Dey 2021). Another aspect of “wickedness” of this problem is that it is difficult to objectively research. For example, up to 90% of academic cheating studies are based on self-reported data, which we know may be subject to social desirability bias, where participants may be reluctant to report negative behavior (Winrow 2016). Pressures building up within the higher education system over decades, combined with the sudden lurch to remote delivery, created a perfect storm for this perceived explosion in student cheating and collusion, with an emphasis on perceived. We lack firm evidence to corroborate alarmist statements about a “cheating pandemic.” Academic institutions were already struggling to detect student assessments outsourced to essay mills and homework services (Eaton 2021; Lancaster 2020). If there was a spike in academic misconduct cases due to the pandemic pivot online, the cause should not be uncritically attributed to remote learning but rather to a lack of understanding and failure to implement adequate foundational academic integrity strategies and assessment security tactics (Dawson 2021). Rettinger and Bertram-Gallant (2022) remind us that the remote teaching experienced by many during the COVID pandemic was not intentionally designed as online learning. Ni Fhloinn and Fitzmaurice described remote learning during COVID lockdown as “emergency remote teaching” (2021). Nevertheless, since the pandemic forced many more academics to at least think about online teaching and learning design, it raises the question of what can be learned from this period of enforced experimentation and how we can use this learning to reframe approaches moving forward in all higher education settings. For instance, what are the most effective practices to maximize the security and integrity of student assessment outcomes? Is it possible to ensure the security and integrity of learning assessment with traditional teaching, learning, and assessment approaches? And what is the potential impact of artificial intelligence and machine learning in this respect? Several potential questions arise. For example, what proportion of student assessment should be conducted independently, and how much collaboration is permitted? How much original thought is expected, and how is it measured? Why does exam invigilation persist as the perceived “gold standard” for maintaining the integrity of assessments? Does it result in better learning outcomes, knowledge creation, and retention than an open-book test? What are the specific integrity concerns in the context of a virtual university? The most significant perceptual hurdle to maintaining academic integrity in a virtual university would be the concern that if a student is not present before an instructor in a physical setting, it is more difficult to feel assured they are completing their work. However, it is important to note that even in traditional campus-based education, students are not always present for every step of the learning process, and many aspects of their assignment production are not observed. Research has shown

398

S. Thomson et al.

that traditional institutions have also struggled with maintaining the integrity of the learning process and addressing plagiarism, assignment outsourcing, collusion, exam cheating, and exam impersonation (Franklyn-Stokes and Newstead 1995; Hardy 1982; Nuss 1984; Kroll 1988). There is a common assumption that learning in virtual environments is inferior, less secure, and prone to integrity violations, but this is yet to be empirically examined. It is important to note that the physical co-location of learners and instructors and in-person invigilation can create a false sense of assurance and security (Dawson 2021). As one famous magician pointed out, “what the eyes see and the ears hear, the mind believes,” but when it comes to exam invigilation, this may not always be true (Bretag et al. 2019; Harper et al. 2019). Despite decades of discourse about academic integrity, little progress has been made in effectively addressing issues like plagiarism and collusion. One reason for this may be that the literature on academic integrity tends to focus on specific aspects of the problem, such as the prevalence of misconduct (McCabe and Trevino 1996; Curtis and Tremayne 2019; Ewing et al. 2019), different manifestations of academic misconduct (Siddhpura and Siddhpura 2020; Mason et al. 2019), or constructing correlations between individual characteristics and reported cheating experience (Curtis et al. 2022; Farooq and Sultana 2021). The literature on academic integrity often fails to account for the broader societal context and the conflicting values and obligations that may be at play. As a result, the issue is not always approached in a practical matter that considers the underlying causes of academic misconduct, although there are some exceptions where this approach is taken (Dawson 2021). Often proposed solutions to breaches of academic policy involved training and punishment to reform “bad apple” students who are assumed to lack an understanding of academic rules (Powell 2003; Akeley Spear and Miller 2012; Curtis et al. 2013). However, this oversimplifies the problem as competing student obligations such as work, and family is not considered (Lambert 2019). Students may have various compelling reasons for working around the rules, and moral training alone is often not enough to prevent misconduct. Instead, institutions may supplement moral training with cheating minimization strategies, ranging from honor pledges (Mundy et al. 2020) to sophisticated artificial intelligence systems remotely policing examinee behaviors (Zawacki-Richter et al. 2019). Despite little empirical evidence of the efficacy of methods for preventing and detecting breaches of integrity, practitioners are not dissuaded from promoting them as the best practices. The interpretation of what constitutes academic misconduct can be problematic because some behaviors that are considered unacceptable in academic integrity policies, such as collaboration and outsourcing, are valued and even required outside the academia (Ferguson et al. 2021). Collaboration, project management, and hiring external contractors are skills that employers look for, but they are discouraged in academic settings because assessments are often focused on individual learning outcomes rather than more workplace-like group efforts. This emphasis on

20

Authenticity, Originality, and Beating the Cheats

399

individual deliverables over the learning process can lead to students prioritizing perfect outcomes over the messiness of the learning experience. One may argue that the level of assurance cannot be increased without changes to the assessment process, adding logistical and financial burdens to the shoulders of academic administrators, faculty, and students. The cost of ensuring academic integrity process is rarely discussed in relation to the balance between security and convenience, accessibility, affordability, and other factors. While students are often the focus of academic integrity discussions, they are not the only stakeholders in the educational process and do not pose the greatest threat to integrity. However, the overwhelming amount of research on academic integrity tends to focus on students as the main culprits of misconduct looking for an “easy” way out (McCabe et al. 1999; Bretag et al. 2019; Lancaster and Cotarlan 2021). This narrow focus ignores the influence and actions of other players such as society, parents, peers, researchers, administrators, policymakers, grant issuers, publishers, editors, peer reviewers, ghost writers, and commercial service providers. Future research should take a broader perspective and examine the roles and influence of these stakeholders in the integrity process, as academic integrity issues are certainly not confined to students alone. For example, academic misconduct in research is a significant issue that is usually only detected after publication by “watchdog” groups like Retraction Watch (Christensen Hughes and Eaton 2022). Another facet of the problem is that academic integrity research often promotes the idea of crisis by tallying various indicators that continue to highlight and perpetuate the problem, rather than unpick it (Harper et al. 2019; Bretag et al. 2019; Lancaster and Cotarlan 2021) – they count how many students are requesting academic help rather than how to empower them to learn on their own. The true extent of the problem remains unknown, given the clandestine nature of “cheating.” Much of the research relies on self-reports and perceptions rather than the actual number of transactions (Nuss 1984; Kroll 1988; Cronan 2018; Bretag et al. 2019; Awdry 2021; Curtis et al. 2022; Kwong et al. 2010; Ford and Hughes 2012). Not all students are going to take a shortcut, nor will all academic staff be willing to take a stand for academic integrity. The problem is that there is little explanation of the “why”; we do not understand why within the same learning environment some will take a shortcut, while some will not. It is naive to assume that making everyone aware of the problem will somehow spark conscientiousness and unquestioned commitment to truth, quality, rigor, accountability, and justice. So, it may not just be about raising awareness of academic dishonesty as a problem, but the larger challenge is to develop a more holistic approach that aims to develop learning communities with a keen sense of shared values and integrity and which would be “self-policing.” An additional question is whether the development of such learning communities would require a different approach in the virtual university. The challenge is exacerbated by what one may call a definitional crisis. For example, plagiarism is an umbrella term that describes all sorts of academic transgressions, yet it is only one form of academic dishonesty. Similarly, the act of

400

S. Thomson et al.

outsourcing academic work, paying for it, and submitting it for credit is often used to describe a wide range of collaborative behaviors – from peer groups to parents helping children with homework assignments, regardless of whether there was a contract and reward.

Assessment and Evaluation The assessment process is essential because it validates learners’ knowledge, provides learners with valuable feedback, and measures learning gains. It also provides necessary data to evaluate academic programs and faculty performance. Academic integrity concerns and confirming the learner’s identity present a challenge for educators in the virtual university (Baggio and Beldarrain 2011) due to its two-tiered nature. Without an effective process for mapping student identities to their work, there is no assurance that the credential awarded has been authentically earned. Integrity of academic assessments is predicated on a mechanism for identifying students and a process for mapping student identities to their learning outcomes (Amigud et al. 2018). Some universities have placed the onus on the instructor to ensure that learners submit their own work (Colwell and Jenks 2005; Barnes and Paris 2013), thus further confirming the identity of the learner and establishing authenticity of the work and of the learning experience. Authentic learning involves activities and assessments that require the use of real-world skills to show proficiency in certain learning objectives (Herrington and Herrington 2006), which potentially creates a requirement for instructors to directly observe such skills in an authentic setting. This may create an additional challenge in the virtual university where this process will be conducted online. Furthermore, for many (less authentic) assessment tasks, instructors may rely on both learning technologies and human invigilators to enhance the security of assessments, i.e., to confirm that a student’s identity matches their work. However, due to logistical challenges and costs associated with continuous assessment academic integrity strategies, universities tend to either validate identity (username and passwords, student IDs) or authorship (non-duplicate content) but rarely both. Exam proctoring and behavioral biometrics are the strategies that provide a two-tiered validation of identity and authorship of the student-produced work (Amigud et al. 2018), but not all institutions are convinced of their value, partly because of the cost involved and partly because of potential discomfort around the use of surveillance instruments in learning environments (Selwyn et al. 2021). Assessment security may affect convenience and accessibility, negating some of the benefits of anytime and anyplace learning in the virtual university, as Coghlan et al. (2021) have noted: Autonomy might be restricted by online proctoring in several ways. For example, it may require students to avoid doing things they can often do in traditional exams, such as muttering to themselves, looking to the side, and going to the bathroom – lest they raise automated red flags about suspicious behavior. Some students may simply prefer not to be

20

Authenticity, Originality, and Beating the Cheats

401

invigilated by AI or by online human proctors or to have their images and personal data collected and viewed. (p. 1596)

Originality and Plagiarism in an Historical Context Plagiarism education should account for plagiarism in the historical emergence of academic culture as a quasi-legal system with its different genres and academic norms, ethics, and procedures that govern the acceptability or non-acceptability of various practices of academic writing. Plagiarism should be understood as part of a cultural evolution of text production that generates legal, ethical, and pedagogical problems where “plagiarism detection services, under the aegis of pedagogical reform and the promise of technological progress, serve to regulate student writing and reading practices” (Marsh, 2007, p. 7). Students, especially those at the graduate level, need a critical awareness of the “publish or perish” culture that only measures performance through citation analysis in journals with the highest impact factor, without due regard for how academic writing culture technologically and socially reconstructs the scholar, the researcher, and the graduate student. In addition, existing scholars need to be aware of different forms of plagiarism and how academic norms have changed over the last few decades. Lang (2013) has described the “fuzzy territory” of interpretations of “original” work. The productive question for scholars at any stage is: “How do I produce my own work in this discipline, and why does it matter that I produce my own work?” (p. 194). Educators should explain and exemplify this rationale clearly to students in the contexts of disciplines, acknowledging that expectations vary based on the field of endeavor, which makes it challenging. As Fishman (2015) notes: Finding ways to address, if not resolve, some of the tensions between conflicting concepts such as homage, originality, mashups, aggregation, social authorship, and artistic quotation are challenges likely to persist for some time. (p. 9)

Artifactual and stylistic originality, as hallmarks of authorship, are notions that can be traced back to as early as the Hellenistic period. An Alexandrian scholar, Aristophanes of Byzantium, was eager to identify plagiarism, dispute the originality of the works presented during a poetry competition, and present the original texts as evidence (Love 2002; McGill 2012). The nineteenth century saw some early attempts to quantify writing style. In 1851, English logician Augustus de Morgan proposed using word length to delineate stylistic differences. Around the same period, a physicist named Thomas Mendenhall (1887) analyzed the works of Bacon, Marlowe, and Shakespeare using similar markers. The interest in establishing a distinct set of textual features to distinguish one author from another accurately has continued to the present day in the disciplines of stylometry and forensic linguistics. Such technologies can be usefully applied in the academic context where the outputs of the scholarly endeavor are embodied in papers, assignments, and presentations. Some are developing writing-style recognition

402

S. Thomson et al.

software in digital contexts to ensure originality (Brocardo et al. 2019), making it relevant to the virtual university. Furthermore, originality, in the context of learning, can be equated with students’ ability to master expected competency and complete work independently. Education and training presuppose that students graduate from courses, programs, and academic institutions with skills, competencies, and abilities greater than they possessed upon enrolment. Educational institutions of all sizes and types are proverbial foundries that refine and transform the “material” of learners to deliver value to society. To this end, they aim to develop active citizens, train a labor force to meet the skill requirements of societies and nations, and develop enlightened and wellrounded individuals (Ferguson et al. 2017). The capability of original thinking can be considered a quality or performance indicator of the “refining” process, which validates the learning. There are, however, numerous exceptions to this principle. For example, fields concerned with methods and techniques for imitation may blur interpretation such that “right” becomes “wrong” and “wrong” becomes “acceptable” under some circumstances. The boilerplate language of legal documents, articulation in music, and the stylistic elements of different art genres are somebody else’s inventions that need to be preserved and reused, often without attribution. “Imitation and invention exist on a genre-defined continuum” (Bawarshi 2008, p. 80). The mastery of content in these cases, and by extension integrity, is not demonstrated through originality but through the ability to fit the template and to accurately imitate or appropriate words, styles, and ideas (Amigud and Pell 2021).

Originality and Collaboration in Context When I am asking students to write their original interpretation of works of literature we are studying, I assume that they will derive those interpretations on their own, without collaborating with their peers. Joe Hoyle by contrast expects and encourages his students to get together before class and discuss the questions and problems in his course, perhaps because he well knows how collaboration will play an important role in their careers as accountants. (Lang 2013, p. 192)

The realities of technology and the contemporary workplace seem to encourage and celebrate sharing and collaboration. Similarly, academic institutions aim to foster collaborative environments where ideas are shared and questions asked. Yet, many academic assessments expect individuals to work alone in isolation. When boundaries between group and individual learning are not clearly set, students may misread and misinterpret encouragement to learn from peers. The line between collusion and collaboration is often blurred leading to confusion. Crook and Nixon (2019) argue therefore that “A priority should be that the integrity conditions should not, as far as possible, contradict well-established patterns of social interaction, programme design, or those practices of private study that otherwise support effective and genuine progress.”

20

Authenticity, Originality, and Beating the Cheats

403

A study by Prescott (2016) has corroborated this emphasis on choice and collaboration as providing a valuable framework for helping students build defenses against unintentional plagiarism in their study behaviors. O’Connell (2016) aligned project premise and teaching aims with good academic practice in collaborative writing activities, demonstrating how online work in wikis could make writing “visible” and create self-awareness with more effective self-monitoring with engaging resources. This approach recognizes that writing is a developmental and iterative process and not simply a product.

Authenticity: Trust Model in a Complex System A core challenge with administration of academic integrity is that each learning activity is typically guided by its own set of implicit rules and expectations. Although a universal policy is easier to write and publish, it is unlikely to be effective due to the possibility of misinterpretation and misapplication. At the same time, it may create confusion. Depending on the task, being a skillful “copycat” may be as important and rewarding as being original. This inconsistency presupposes logistical implications at every level of the institutional academic integrity process. A one-size-fits-all integrity policy that values originality at the expense of other factors seems ineffective and uninformed (Moriarty and Wilson 2022). An overly dogmatic definition of academic integrity will inevitably be challenged. Context is an important consideration. What is the intelligent alternative to this approach in the context of the virtual university, which potentially creates even more diverse contexts? Institutional policy loses its authority and power when it does not recognize the realities of people’s experiences and fails to provide practical guidance. For example, two American and Canadian faculty surveys indicate that some practitioners perceive their institutional policies as inequitable or unfair (Coalter et al., 2007; MacLeod and Eaton 2020). In this situation, we see a backlash against the policy position, where the discretionary powers of instructors, holding personal notions of originality, authenticity, fairness, and integrity, often prevail (Amigud and Pell 2021). Institutions employ various strategies but often on an ad hoc basis and based on an atomized view of integrity – pedagogical, legal, and technical, rather than a holistic approach (Chankova 2020). The academic integrity process can be organized into three steps: communicate the requirements, monitor the activities to detect anomalies, and enforce the rules. Academic practitioners may employ several strategies to communicate rules and expectations, validate the veracity of the student-produced content, and ensure that the academic standards are upheld (Sefcik et al. 2020). These include academic integrity tutorials, honor codes and pledges, instructional design approaches, and invigilation techniques that control the examination environment. Punitive sanctions issued by pseudo-legal tribunals are used as a last resort to excommunicate the violators, while re-education remains a preferred approach. The objective is to ensure that students have met the learning outcomes, and there are many ways to reach that goal.

404

S. Thomson et al.

The Contributions of Technology Technologies play a big part in maintaining integrity standards, and of course, they play a significant role in the virtual university. They are used to limit access to external resources and monitor students’ private learning environments, traditionally outside the reach of instructors and teaching assistants. With advances in artificial intelligence, academic integrity technologies can validate student identities and map them to the work students produce. “Smart” surveillance applications promise to outperform human proctors without the overhead costs of hiring one (Arnò et al. 2021; Jia and He 2021). An academic integrity process is only as effective as the strategies it employs. Emphasizing communication but neglecting to detect cases of misconduct and showing reluctance to enforce the rules is unlikely to yield reliable results. All three steps (communication, detection, and enforcement) in the academic integrity process must be aligned and fine-tuned to address the peculiarity of the learning environment and student needs (Amigud and Pell 2021). The main issue is that the process is predicated on arbitrary assumptions about the nature of cheating. Although much of the literature proposes solutions to the problem of plagiarism, contract cheating, and collusion, the literature is silent on how effective any proposed strategies are or the implications for teaching and learning. For example, are signed integrity pledges and remote proctoring technologies as effective as plagiarism detection software coupled with question randomization? What topics should be covered in academic integrity tutorials, and how frequently should they be delivered to be meaningful for students? Despite the plethora of guidance from academic integrity associations and quality assurance agencies, we have not yet reached a consensus on what integrity is and how to think about it. Mainstream conferences do not usually conclude with setting measurable targets. While we know this problem exists, we do not seem to have any means to assess it accurately. Part of the problem is that academic integrity forums tend to be open to anyone willing to tell a story despite a lack of objective supporting evidence. It would be beneficial to identify thought and practice leaders who had managed to rid their campuses of pervasive essay mills and plagiarists and to learn from their experience in an evidence-based manner. Instead, academic integrity practices seem steeped in inconsistency and confusion. Unfortunately, if a policy is not perceived as just and fair, educators may devise and implement ad hoc approaches without a solid evidence base.

Computers and Originality Much modern-day writing is facilitated by technology. Authors rely on word processors, spell checkers, typing assistants, reference managers, summarization tools, and plagiarism checkers to streamline the production of essays, articles, reports, presentations, and proposals. There is an ongoing trend toward automating content creation, which impacts the traditional notions of authorship, authenticity,

20

Authenticity, Originality, and Beating the Cheats

405

and originality. As new methods and techniques emerge, authors have more opportunities to experiment, imitate, synthesize, and outsource parts of their work to technology. These technological advances have become essential tools for authors, and their increasing sophistication is shaping the writing process and consequently the experience of readers, end users, and consumers of ideas. The development of each innovative technology, from the fountain pen to the typewriter and from the typewriter to the computer, has recreated the relationship between authors and their works. The tasks performed by contemporary authors look quite different from those performed by their predecessors only a brief time ago. However, we tend to hold authors to the same standards of originality and authenticity, without considering the significant impact that technology has on the creative process. Writing a book in 2021 (Aalho 2023; Dzieza 2022) is much easier than it was in 2001 or in 1921. One technology that has been particularly disruptive for the authorship process is machine learning. Autoregressive language models, as described by Brown et al. (2020) can produce impressive results. These algorithms can be learned from examples to generate original content, such as natural language, computer code, musical notation, or visual art – tasks that were previously thought to be uniquely human. The widespread availability of these technologies has significant implications for societal institutions that rely on the assumption of authenticity, originality, and creativity. For example, in academic institutions, generative algorithms make it difficult to identify the human contribution to student assessments like essays, discussion forums, and computer science projects. Also, the competitive culture of academic publishing is an ideal market for tools that automate content production, but there are few safeguards to mitigate abuse. Currently, algorithms can create course outlines and diverse written outputs, such as journal papers, grant proposals, and policy documents. Despite arguments not to panic (McMurtrie 2022), it is essential to acknowledge that changes at the institutional level take time and societal institutions have a longer evolution cycle. Existing tools like plagiarism checkers and stylometric analysis tools (Amigud et al. 2018; Heather 2010) cannot guarantee that the artifacts were produced by those claiming authorship credit or responsibility. A remarkable aspect of language models is that they can be trained on your own writing style and subsequently will generate new material in that style. It will take years for educational institutions to adjust student assessment practices to prioritize oral presentations and authentic assessments over take-home assignments and even longer to screen research products. Forcing students to write on command, while being monitored, either by in-person invigilation, or remote keystroke logging, would undermine the authenticity and creativity of the writing process. Maintaining control over remote learning environments has proven to be challenging (Harwell 2020; Ives and Cazan 2023), and modern technologies only add to this difficulty. The ability to generate pages on demand is a highly attractive feature that some academics are already discussing in the context of meeting academic objectives. Citation cartels (Fister Jr et al. 2016; Haley 2017; Perez et al. 2019), p-hacking (Head et al. 2015), and self-plagiarism (Andreescu 2013) are the maladies of the past. The future holds new challenges for academic integrity.

406

S. Thomson et al.

Some argue that new technological advancements can be used to enhance the authorship process and serve as teaching aids (Amigud 2019). However, over time, the role of the author could change from being the original content creator to being a technology user responsible for generating content. In his 1968 essay “The Death of the Author,” Roland Barthes (2001) argued that although authors give their texts voices, it is the reader who interprets them. In other words, the author is “dead” because the meaning of the text lies in the mind of the reader. Now, five decades later, the author is “dead” once again, but this time it is because voices in texts are no longer the product of the author’s cognitive efforts but instead a result of statistical computations. The alienation of the authors from their work can potentially amplify certain ideas, of those who control the technology. Language has an influence on the perception of reality (Edelman 1985; Whorf 2012). Allowing language models to contribute to the discourse without having a solid understanding of their capabilities or a way to contain them is a risky move prone to unintended consequences.

Toward an Integrated Approach Some have argued that approaches to academic integrity are better seen as a continuum where at one end, one takes a firm stand for integrity, including clear consequences for non-compliance, and at the other, one takes a more developmental approach where integrity is seen as part of the learning process, within which making “mistakes” is integral and therefore accommodated. In between, a whole array of strategies aims to reconcile the two extremes (Amigud and Pell 2021). The latter approach is valuable because it acknowledges that integrity during the learning process is not black and white but rather a nuanced concept that can be neither fully maintained nor completely forfeited. It recognizes that it can be difficult to discern a learner’s true intent as they may merely be going through the motions without genuine commitment to the task. Although it is important for everyone to prioritize integrity, students may not face immediate consequences for failing to do so. This could create a false impression that they can choose to stop trying to maintain integrity whenever they feel like it, either based on their own discretion or because of their teachers’ attitudes, as Peters and Cadieux (2019) have suggested. According to Hillman et al. (2020), instructors have a large responsibility for fostering integrity in their online classes. If all instructors fulfil this obligation, students have a clear idea of what academic integrity is about and conversely what constitutes academic misconduct. But what does this look like, and how does the wider institutional context and other stakeholders build out support for instructors? This question is important because promoting integrity involves creating and nurturing an academic learning community. This, in turn, is related to helping students develop their identities as learners and as members of that academic community. In

20

Authenticity, Originality, and Beating the Cheats

407

an ever-changing digital context, such as the virtual university environment, building consensus on what constitutes academic integrity is a crucial aspect of the process. Even if a developmental learning approach is used, policy must still be appropriate, consistent, and adequate to reflect firstly what integrity means at various stages of the student learning journey. Additionally, policies must ensure the security of assessment at each level. For example, if academic integrity is taught consistently in the first 2 years of study as part of an educative approach, expectations for compliance in the third and fourth years may be higher. This means that assessment should be designed differently in later years, with clear detection methods and explicit consequences for academic misconduct. In the virtual university, this may be aided using detection technologies and other measures, such as randomization of questions. However, despite the stricter approach in later years, building relational trust and maintaining a helping mindset is still necessary. This includes fostering openness, providing timely support for students, clear communication, transparent purpose, and understandable and achievable grading criteria. Importantly, this relational trust must be developed gradually over time.

The Role of the Instructor Academic staff often struggle with enforcing institutional standards, due to unclear policy and a reported lack of support with the related administrative workload. This difficulty with implementation can lead to a perception that standards are ineffective (MacLeod and Eaton 2020). Frontline staff are expected to provide the first line of defense against policy violations. However, it is not objectively clear how willing they are to fully embrace institutional policies. This lack of understanding can make it challenging to effectively implement and enforce policies. Upholding academic integrity and fulfilling the duty of fidelity can come at a cost, such as potentially conflicting with personal commitments like family obligations (Drago et al. 2006) or duty to oneself. It takes considerable time and effort to “investigate” academic misconduct, especially at scale in large classes. Furthermore, the notion that “one key to recognizing cheating or plagiarism is to become familiar with a student’s writing style” (Barnes and Paris 2013, p. 4) is not practical outside of the thesis supervision scenario. Yet, according to Colwell and Jenks (2005), it is the responsibility of every online instructor to ensure that a course provides a credible assessment of students’ knowledge. Additionally, Dawson and Sutherland-Smith (2018) note that instructors have some capacity to identify instances of misconduct on a smaller scale. However, this strategy is unlikely to work effectively in large cohorts, regardless of the mode of instruction. Just because there is no evidence of academic misconduct does not mean that the observation strategies used by traditional, in-person institutions are more effective or superior to those employed in remote settings. Therefore, the effectiveness of academic integrity strategies, both

408

S. Thomson et al.

in-person and remote, is still not well-understood and requires further exploration (Amigud and Pell 2021; de Maio et al. 2020). Strategies that rely on instructors to enforce academic integrity in their courses and programs have a significant limitation in that some instructors may be reluctant to get involved in these matters. This may be due to competing obligations, lack of resources, fear of conflict or legal action, or perceptions of inconsistent or unfair institutional policy (de Maio et al. 2020; Macleod and Eaton 2020). Faculty members must balance their personal and institutional obligations and may use their discretion to grant exemptions, potentially leading to inconsistent application of integrity policy (Amigud and Pell 2021). Therefore, ensuring academic integrity is not only an administrative, student, or technical issue but also a faculty issue, which requires a more comprehensive approach. Narrowly focusing research into specific areas such as contract cheating or policy is unlikely to lead to viable solutions. A holistic approach that considers various aspects of academic integrity, including the limitations and conflicting stakeholder interests, is necessary. This is particularly important if academic integrity is to be integrated into a developmental learning approach in the initial stages. The weakest link in the institutional integrity process is academic staff, particularly when their discretion to act according to their own judgement is sanctioned or if they do not have the perceived time or resources to investigate an academic integrity breach. Although institutional initiatives assume that faculty members will uphold institutional values, evidence suggests that this is not always the case, resulting in a range of subjective policy interpretations concurrently, creating confusion and inconsistencies in policy application across the institution (Amigud and Pell 2021; Pell and Amigud 2022). Striepe et al. (2023) found evidence that while educator approaches to academic integrity were influenced by institutional mandates, other factors such as the educator’s personal philosophies and consideration of student backgrounds were impactful. The issue of inconsistency around academic integrity and misconduct in universities is exacerbated by what Macdonald (2017) has calls the “understandable reluctance of universities to prosecute students who are also paying customers” (p. 8). While higher education has become transactional in many respects, the result of potential inconsistency around academic integrity and misconduct is also related to the educational approach discussed earlier in this chapter. To ensure academic integrity, an approach involves giving students many opportunities to understand what they need to do to be successful with their assessment and why this is important. This is especially important for international students or students who are not learning in their first language. Creating a learning community where learning is instructors can get to know their students and develop a learning relationship is essential. Getting to know your students is a crucial step toward authentication, which is a critical issue in online learning environments and thus in the virtual university. Developing authentic and meaningful assessment is part of the same process, as are online standards, or what we might call “digital citizenship” in this context (Buchholz et al. 2020).

20

Authenticity, Originality, and Beating the Cheats

409

Integrating the Patchwork into a Coherent a Holistic Approach To achieve academic integrity, a holistic approach is necessary because no single solution will work for all situations. Academic integrity is complex and depends on many factors, so achieving it is a continuous process involving different elements. The idea of authentic assessment is often proposed to prevent academic misconduct. However, there is no unambiguous evidence to support this claim (Press et al. 2023). Despite this, designing assessments that are contextualized and personalized is considered a good learning design practice and is part of a developmental approach discussed earlier. A goal of such assessments is to engage students in their learning, encouraging them to care about their assessment, rather than seeing it as an arbitrary obstacle to overcome. As there is no guarantee that authentic assessment will prevent academic misconduct, it is also prudent to adopt the best verification and authentication technologies, especially in the digital spaces of the virtual university. Developing relationships of trust with students is also important, which involves being explicit about requirements and expectations, including those related to academic integrity. These expectations should be framed in a disciplinary context and be progressively changing according to the students’ level of study. The learning and assessment approach should be developmental where students can make mistakes and receive constructive feedback in a low-stakes environment. As a critical component of a holistic approach, technology can be used to facilitate and reinforce agreed expectations, where agreement becomes a basis for trust between student and educator. Technology can be used as in assessment for both production and compliance. With regard to production, this could involve asking students to post an introductory video at the start of each course in the virtual university to get to know each other, using virtual clinics, video-based submission, live video presentations, and oral exams (e.g., via Zoom). The use of multi-factor authentication and proctoring software are examples of technology-reinforced compliance approaches.

Conclusion At a fundamental level, the act of students receiving credit for work they did not do diminishes the value of degree credentials and undermines trust in higher education. Within academia, when students or faculty misrepresent their abilities and efforts through dishonest or unfair practices, it erodes the integrity of the entire system (Wong et al. 2016). This problem can be made worse by a sense of mistrust that may arise in such an atmosphere. The development of trust is crucial to building a learning community and fostering student identity. This, in turn, is therefore central to the holistic approach we have outlined in this chapter toward academic integrity.

410

S. Thomson et al.

Cross-References ▶ Artificial Intelligence and Evolution of the Virtual University ▶ Making Online Assessment Active and Authentic ▶ Transparency in Governing Technology Enhanced Learning

References Aalho, J. 2023, January 16. I wrote a book with GPT-3 AI in 24 hours – And got it published. The startup. https://medium.com/swlh/i-wrote-a-book-with-gpt-3-ai-in-24-hours-and-got-itpublished-93cf3c96f120 Akeley Spear, J., and A.N. Miller. 2012. The effects of instructor fear appeals and moral appeals on cheating-related attitudes and behavior of university students. Ethics & Behavior 22 (3): 196–207. https://doi.org/10.1080/10508422.2012.659603. Amigud, A., and Phillip Dawson. 2020. “The law and the outlaw: is legal prohibition a viable solution to the contract cheating problem?” Assessment & Evaluation in Higher Education 45 (1): 98–108. https://doi.org/10.1080/02602938.2019.1612851. Amigud, A. 2019. Post-traditional learning analytics: How data and information technology transform learning environment. In Emerging trends in learning analytics, 13–25. Brill Sense. Amigud, A., and D.J. Pell. 2021. When academic integrity rules should not apply: A survey of academic staff. Assessment & Evaluation in Higher Education 46 (6): 928–942. https://doi.org/ 10.1080/02602938.2020.1826900. Amigud, A., J. Arnedo-Moreno, T. Daradoumis, and A.-E. Guerrero-Roldan. 2018. An integrative review of security and integrity strategies in an academic environment: Current understanding and emerging perspectives. Computers & Security 76: 50. Andreescu, L. 2013. Self-plagiarism in academic publishing: The anatomy of a misnomer. Science and Engineering Ethics 19: 775–797. Arnò, S., A. Galassi, M. Tommasi, A. Saggino, and P. Vittorini. 2021. State-of-the-art of commercial proctoring systems and their use in academic online exams. International Journal of Distance Education Technologies (IJDET) 19 (2): 55–76. https://doi.org/10.4018/IJDET. 20210401.oa3. Awdry, R. 2021. “Assignment outsourcing: moving beyond contract cheating.” Assessment & Evaluation in Higher Education 46 (2): 220–35. https://doi.org/10.1080/02602938.2020. 1765311. Baggio, B., and Y. Beldarrain. 2011. Anonymity and learning in digitally mediated communications: Authenticity and trust in cyber education. IGI Global. Barnes, C., and B.L. Paris 2013. An analysis of academic integrity techniques used in online courses at a southern university. In Northwest decision sciences institute annual meeting proceedings. Barthes, R. 2001. The death of the author. Contributions in Philosophy 83: 3–8. Bawarshi, A. 2008. Genres as forms of in(ter)vention. In Originality, imitation, and plagiarism, ed. C. Eisner and M. Vicinus, 79–89. University of Michigan Press. https://doi.org/10.2307/j. ctv65sxk1.10. Bretag, T., R. Harper, M. Burton, C. Ellis, P. Newton, P. Rozenberg, S. Saddiqui, and K. van Haeringen. 2019. Contract cheating: A survey of Australian university students. Studies in Higher Education 44 (11): 1837–1856. https://doi.org/10.1080/03075079.2018.1462788. Brocardo, M.L., I. Traore, and I. Woungang. 2019. Continuous authentication using writing style. In Biometric-based physical and cybersecurity systems, ed. M.S. Obaidat, I. Traore, and I. Woungang, 211–232. Springer. https://doi.org/10.1007/978-3-319-98734-7_8.

20

Authenticity, Originality, and Beating the Cheats

411

Brown, T., B. Mann, N. Ryder, M. Subbiah, J.D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, and A. Askell. 2020. Language models are few-shot learners. In Advances in neural information processing systems, vol. 33, 1877–1901. Buchholz, B.A., J. DeHart, and G. Moorman. 2020. Digital citizenship during a global pandemic: Moving beyond digital literacy. Journal of Adolescent & Adult Literacy 64 (1): 11–17. https:// doi.org/10.1002/jaal.1076. Chankova, M. 2020. Teaching academic integrity: The missing link. Journal of Academic Ethics 18 (2): 155–173. https://doi.org/10.1007/s10805-019-09356-y. Christensen Hughes, J., and S.E. Eaton. 2022. Academic misconduct in higher education: Beyond student cheating. In Academic integrity in Canada, ed. S.E. Eaton and J. Christensen Hughes, 81–102. Springer. Churchman. 1967. Free for all. Management Science 14 (4): B-141–B-146. https://pubsonline. informs.org/doi/pdf/10.1287/mnsc.14.4.B141. Coalter, T., L., Chi, and W. Tekle. 2007. “Factors that influence faculty actions: a study on faculty responses to academic dishonesty.” International Journal for the Scholarship of Teaching and Learning 1 (1). https://doi.org/10.20429/ijsotl.2007.010112. Coghlan, S., T. Miller, and J. Paterson. 2021. Good proctor or “big brother”? Ethics of online exam supervision technologies. Philosophy & Technology 34: 1581–1606. https://doi.org/10.1007/ s13347-021-00476-1. Colwell, J., and C. Jenks 2005. Student ethics in online courses. In Proceedings frontiers in education 35th annual conference, T2D-17–T2D-19. https://doi.org/10.1109/FIE.2005. 1611897. Crook, C., and E. Nixon. 2019. The social anatomy of ‘collusion’. British Educational Research Journal 45 (2): 388–406. https://doi.org/10.1002/berj.3504. Curtis, G.J., and K. Tremayne. 2019. Is plagiarism really on the rise? Results from four 5-yearly surveys. Studies in Higher Education 46 (9): 1816–1826. https://doi.org/10.1080/03075079. 2019.1707792. Curtis, G.J., B. Gouldthorp, E.F. Thomas, G.M. O’Brien, and H.M. Correia. 2013. Online academic-integrity mastery training may improve students’ awareness of, and attitudes toward, plagiarism. Psychology Learning & Teaching 12 (3): 282–289. https://doi.org/10.2304/plat. 2013.12.3.282. Curtis, G.J., J. Clare, E. Vieira, E. Selby, and P.K. Jonason. 2022. Predicting contract cheating intentions: Dark personality traits, attitudes, norms, and anticipated guilt and shame. Personality and Individual Differences 185: 111277. https://doi.org/10.1016/j.paid.2021.111277. Dawson, P. 2020. Defending assessment security in a digital world: Preventing e-cheating and supporting academic integrity in higher education. Routledge. ———. 2021. Strategies for using online invigilated exams: TEQSA Online learning good practice. TEQSA. https://tlu.cit.ie/contentFiles/files/strategies-for-using-online-invigilatedexams.pdf Dawson, P., and W. Sutherland-Smith. 2018. Can markers detect contract cheating? Results from a pilot study. Assessment & Evaluation in Higher Education 43 (2): 286–293. https://doi.org/10. 1080/02602938.2017.1336746. de Maio, C., K. Dixon, and S. Yeo. 2020. Responding to student plagiarism in Western Australian universities: The disconnect between policy and academic staff. Journal of Higher Education Policy and Management 42 (1): 102–116. https://doi.org/10.1080/1360080X.2019.1662927. Dey, S. 2021. “Reports of cheating at colleges soar during the pandemic.” NPR, August 27, 2021, sec. Education. https://www.npr.org/2021/08/27/1031255390/reports-of-cheating-at-collegessoar-during-the-pandemic. Dzieza, J. 2022, December 24. How Kindle novelists are using ChatGPT. The Verge. https://www. theverge.com/23520625/chatgpt-openai-amazon-kindle-novel Eaton, S.E. 2021. Plagiarism in higher education: Tackling tough topics in academic integrity. ABC-CLIO.

412

S. Thomson et al.

Edelman, M. 1985. Political language and political reality. PS: Political Science & Politics 18 (1): 10–19. Ewing, H., K. Mathieson, A. Anast, and T. Roehling. 2019. Student and faculty perceptions of plagiarism in health sciences education. Journal of Further and Higher Education 43 (1): 79–88. https://doi.org/10.1080/0309877X.2017.1356913. Farooq, R., and A. Sultana. 2021. Measuring students’ attitudes toward plagiarism. Ethics & Behavior: 1–15. https://doi.org/10.1080/10508422.2020.1860766. Ferguson, R., S. Barzilai, D. Ben-Zvi, C.A. Chinn, C. Herodotou, Y. Hod, Y. Kali, A. KukulskaHulme, H. Kupermintz, P. McAndrew, B. Rienties, O. Sagy, E. Scanlon, M. Sharples, M. Weller, & D. Whitelock. (2017). Innovating pedagogy 2017: Open University Innovation Report 6. Milton Keynes: The Open University, UK. Ferguson, C., M. Toye, S.E. Eaton, and S. Boisvert 2021. Understanding student experiences with contract cheating and other outsourcing behaviours: Research project brief. https://prism. ucalgary.ca/bitstream/handle/1880/113326/ContractCheatingProjectBriefFINAL.pdf. Fishman, T. 2015. Academic integrity as an educational concept, concern and movement in US institutions of higher learning. In Handbook of academic integrity, ed. T. Bretag. Springer. https://doi.org/10.1007/978-981-287-079-7_1-2. Fister, I., Jr., I. Fister, and M. Perc. 2016. Toward the discovery of citation cartels in citation networks. Frontiers in Physics 4: 49. Ford, P. J., and C. Hughes. 2012. “Academic integrity and plagiarism: perceptions and experience of staff and students in a school of dentistry: a situational analysis of staff and student perspectives.” European Journal of Dental Education 16 (1): e180–86. https://doi.org/10. 1111/j.1600-0579.2011.00695.x. Franklyn-Stokes, A., and S.E. Newstead. 1995. Undergraduate cheating: Who does what and why? Studies in Higher Education 20 (2): 159–172. https://doi.org/10.1080/03075079512331381673. Haley, M.R. 2017. On the inauspicious incentives of the scholar-level h-index: An economist’s take on collusive and coercive citation. Applied Economics Letters 24 (2): 85–89. https://doi.org/10. 1080/13504851.2016.1164812. Hardy, R.J. 1982. Preventing academic dishonesty: Some important tips for political science professors. Teaching Political Science 9 (2): 68–77. https://doi.org/10.1080/00922013.1982. 9942904. Harper, R., T. Bretag, C. Ellis, P. Newton, P. Rozenberg, S. Saddiqui, and K. van Haeringen. 2019. Contract cheating: A survey of Australian university staff. Studies in Higher Education 44 (11): 1857–1873. https://doi.org/10.1080/03075079.2018.146278. Harwell, D. 2020, April 1. Mass school closures in the wake of the coronavirus are driving a new wave of student surveillance. Washington Post. https://www.washingtonpost.com/technology/ 2020/04/01/online-proctoring-college-exams-coronavirus/ Head, M.L., L. Holman, R. Lanfear, A.T. Kahn, and M.D. Jennions. 2015. The extent and consequences of p-hacking in science. PLoS Biology 13 (3): e1002106, Article 3. Heather, J. 2010. Turnitoff: Identifying and fixing a hole in current plagiarism detection software. Assessment & Evaluation in Higher Education 35 (6): 647, Article 6. Herrington, T., and H. Jan, eds. 2006. Authentic Learning Environments in Higher Education. Hershey, PA: IGI Global. https://doi.org/10.4018/978-1-59140-594-8. Higbee, J.L., L.S. Jennifer, and S. Thomas. 2011. “Student perspectives on behaviors that constitute cheating”. Contemporary Issues in Education Research (CIER) 4 (10):1–8. https://doi.org/10. 19030/cier.v4i10.5968. Hillman, D., R. Schudy, and A. Temkin. 2020. Best practices for administering online programs. 1st ed. Routledge. https://doi.org/10.4324/9780429329081. Hoshiar, M., J. Dunlap, J. Li, and J.N. Friedel. 2014. Examining the effectiveness of student authentication and authenticity in online learning at community colleges. Community College Journal of Research and Practice 38 (4): 337–345. https://doi.org/10.1080/10668926.2012. 755649.

20

Authenticity, Originality, and Beating the Cheats

413

Ives, B., and A.-M. Cazan. 2023. Did the COVID-19 pandemic lead to an increase in academic misconduct in higher education? Higher Education: 1–19. https://doi.org/10.1007/s10734-02300996-z Jia, J., and H. Yunfan. 2021. “The design, implementation and pilot application of an intelligent online proctoring system for online exams.” Interactive Technology and Smart Education 19 (1): 112–20. https://doi.org/10.1108/ITSE-12-2020-0246. Kroll, B.M. 1988. How college freshmen view plagiarism. Written Communication 5 (2): 203–221. Kwong, T., N.G. Hing-Man, M. Kai-Pan, and W. Eva. 2010. “Students’ and faculty’s perception of academic integrity in hong kong.” Campus-Wide Information Systems 27 (5): 341–55. https:// doi.org/10.1108/10650741011087766. Lambert, S.R. 2019. Six critical dimensions: A model for widening participation in open, online and blended programs. Australasian Journal of Educational Technology 35 (6): 161–182. https:// doi.org/10.14742/ajet.5683. Lancaster, T. 2020. Academic discipline integration by contract cheating services and essay mills. Journal of Academic Ethics 18 (2): 115–127. https://doi.org/10.1007/s10805-019-09357-x. Lancaster, T., and C. Cotarlan. 2021. Contract cheating by STEM students through a file sharing website: A Covid-19 pandemic perspective. International Journal for Educational Integrity 17 (1): 3. https://doi.org/10.1007/s40979-021-00070-0. Lang, J.M. 2013. Cheating lessons: Learning from dishonesty. Harvard University Press. Love, H. 2002. Attributing authorship: An introduction. Cambridge University Press. Macdonald, S. 2017. Of essay mills. http://www.stuartmacdonald.uk.com/recent-publications/ofessay-mills/ MacLeod, P.D., and S.E. Eaton. 2020. The paradox of faculty attitudes toward student violations of academic integrity. Journal of Academic Ethics 18 (4): 347–362. https://doi.org/10.1007/ s10805-020-09363-4. Marsh, B. 2007. Plagiarism: alchemy and remedy in higher education. Ithaca: State University of New York Press. Mason, T., Gavrilovska, A., & Joyner, D. A. (2019). Collaboration versus cheating: Reducing code plagiarism in an online MS computer science program. In Proceedings of the 50th ACM technical symposium on computer science education, Minneapolis. https://doi-org.ezp01. library.qut.edu.au/10.1145/3287324.3287443 McArthur, J. 2023. Rethinking authentic assessment: Work, well-being, and society. Higher Education: 85–101. https://doi.org/10.1007/s10734-022-00822-y. McCabe, and L.K. Trevino. 1996. What we know about cheating in college longitudinal trends and recent developments. Change (New Rochelle, N.Y.) 28 (1): 28–33. https://doi.org/10.1080/ 00091383.1996.10544253. McCabe, D.L., L.K. Trevino, and K.D. Butterfield. 1999. Academic integrity in honor code and non-honor code environments. The Journal of Higher Education 70 (2): 211–234. https://doi. org/10.1080/00221546.1999.11780762. McGill, S. 2012. Plagiarism in latin literature. Cambridge: Cambridge University Press. https://doi. org/10.1017/CBO9781139088350. McMurtrie, B. 2022, December 13. Teaching experts are worried about ChatGPT, but not for the reasons you think. The Chronicle of Higher Education. https://www.chronicle.com/article/aiand-the-future-of-undergraduate-writing Moriarty, C., and B. Wilson. 2022. Justice and consistency in academic integrity: Philosophical and practical considerations in policy making. Journal of College and Character 23 (1): 21–31. https://doi.org/10.1080/2194587X.2021.2017971. Mundy, T., K. Murray, K. Tubridy, and J. Littrich. 2020. The role of a law student pledge in shaping positive professional and ethical identities: A case study from Australia. International Journal of the Legal Profession 27 (3): 317–337. https://doi.org/10.1080/09695958.2020.1749638. Ní Fhloinn, E., and O. Fitzmaurice. 2021. Challenges and opportunities: Experiences of mathematics lecturers engaged in emergency remote teaching during the COVID-19 pandemic. Mathematics 9 (18): 2303.

414

S. Thomson et al.

Nuss, E.M. 1984. Academic integrity: Comparing faculty and student attitudes. Improving College and University Teaching 32 (3): 140–144. O’Connell, J. 2016. Networked participatory online learning design and challenges for academic integrity in higher education. International Journal for Educational Integrity 12 (1): 1–15. https://doi.org/10.1007/s40979-016-0009-7. Pell, D.J., and A. Amigud. 2022. The higher education dilemma: The views of faculty on integrity, organizational culture, and duty of fidelity. Journal of Academic Ethics: 1–21. https://doi.org/10. 1007/s10805-022-09445-5. Perez, O., J. Bar-Ilan, R. Cohen, and N. Schreiber. 2019. The network of law reviews: Citation cartels, scientific communities, and journal rankings. The Modern Law Review 82 (2): 240–268. https://doi.org/10.1111/1468-2230.12405. Peters, M., and A. Cadieux. 2019. Are Canadian professors teaching the skills and knowledge students need to prevent plagiarism? International Journal for Educational Integrity 15: 1–16. https://doi.org/10.1007/s40979-019-0047-z. Polčáková, P. 2021. “Guy curtis: contract cheating is four times more common than we thought.” Universitas, Magazín Vysokých Škol. November 4, 2021. https://www.universitas.cz/en/people/ 7588-a-bigger-number-of-students-have-their-work-written-by-someone-else-guy-curtis-isinvestigating-why Powell, B.V. 2003. The limits of integrity or why cabinets have locks. Fordham Law Review 72 (2): 311–332. Prescott, Lynda. 2016. “Using Collaboration to Foster Academic Integrity.” Open learning: the journal of open, distance and e-learning 31 (2): 152–62. https://doi.org/10.1080/02680513. 2016.1169162. Press, N., K. Ashford-Rowe, and H. Huijser 2023, Forthcoming. Authentic assessment: Opportunities and challenges. In Research handbook on innovations in assessment and feedback in higher education: Implications for learning and teaching, ed. C. Evans and M.J. Waring. Edward Elgar Publishing. Rettinger, D. (2020). “Wiley’s wicked summer camp – when good students make bad decisions: the psychology of why students cheat.” https://wiley.brightcovegallery.com/accounting-webinars? videoId¼6171340468001. Rettinger, D.A., and T.B. Gallant, eds. 2022. Cheating academic integrity: Lessons from 30 years of research. Wiley. Rittel, H.W.J., and M.M., Webber (1973). “Dilemmas in a general theory of planning.” Policy Sciences 4 (2): 155–169. Robert D., L. Carol., Colbeck, Kai Dawn Stauffer, Amy Pirretti, Kurt Burkum, Jennifer Fazioli, Gabriela Lazzaro and Tara Habasevich. 2006. “The avoidance of bias against caregiving.” American Behavioral Scientist 49 (9): 1222–1247. https://doi.org/10.1177/0002764206286387. Sefcik, L., M. Striepe, and J. Yorke. 2020. Mapping the landscape of academic integrity education programs: What approaches are effective? Assessment & Evaluation in Higher Education 45 (1): 30–43. https://doi.org/10.1080/02602938.2019.1604942. Selwyn, N., C. O’Neill, G. Smith, M. Andrejevic, and X. Gu. 2021. A necessary evil? The rise of online exam proctoring in Australian universities. Media International Australia 186 (1): 149–164. https://doi.org/10.1177/1329878X211005862. Siddhpura, A., and M. Siddhpura 2020. Plagiarism, contract cheating and other academic misconducts in online engineering education: Analysis, detection and prevention strategies. In 2020 IEEE international conference on teaching, assessment, and learning for engineering (TALE). Striepe, M., L. Sefcik, and S. Thomson. 2023. Understanding academic integrity education: Case studies from two Australian universities. Journal of Academic Ethics 21: 1–17. https://doi.org/ 10.1007/s10805-021-09429-x. Subin, S. 2021. “How college students learned new ways to cheat during pandemic remote schooling.” CNBC. March 21, 2021. https://www.cnbc.com/2021/03/21/how-college-stu dents-learned-new-ways-to-cheat-during-covid-.html.

20

Authenticity, Originality, and Beating the Cheats

415

The Open University. n.d. About the open university. https://www.open.ac.uk/about/main/. Whorf, B.L. 2012. Language, thought, and reality: Selected writings of Benjamin lee Whorf. MIT press. Winrow, B. 2016. Do perceptions of the utility of ethics affect academic cheating? Journal of Accounting Education 37: 1–12. https://doi.org/10.1016/j.jaccedu.2016.07.001. Wong, S.S.H., Stephen Wee Hun Lim, and Kathleen M. Quinlan. 2016. “Integrity in and beyond contemporary higher education: what does it mean to university students?” Frontiers in Psychology 7. https://www.frontiersin.org/articles/10.3389/fpsyg.2016.01094. Zawacki-Richter, O., V.I. Marín, M. Bond, and F. Gouverneur. 2019. Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education 16 (1): 1–27. https://doi.org/10.1186/ s41239-019-0171-0.

Part VII The Role Openness Plays in the Virtual University

Open Educational Practice as an Enabler for Virtual Universities

21

Carina Bossu and Darren Ellis

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Policy Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Capacity Building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Open Pedagogy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social Justice and Inclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Affordances and Key Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Recommendations for Virtual Universities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Recommendation 1: Adopt a Policy of Openness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Recommendation 2: Create Institutional Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Recommendation 3: Invest in People and Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Recommendation 4: Listen to Learners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Recommendation 5: Move South to North . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

420 421 423 424 425 426 427 427 427 428 428 429 429 429 429

Abstract

This chapter explores a number of initiatives and research on open educational practices (OEP) to demonstrate their central role as enablers and catalysts for innovation and change in higher education, including for virtual universities. Key enablers such as open pedagogy, capacity building, policy development, and social justice and inclusion are covered. The chapter then makes recommenda-

C. Bossu (*) Institute of Educational Technology, The Open University, Milton Keynes, UK e-mail: [email protected] D. Ellis Centre for Air Transport Management, Cranfield University, Cranfield, UK e-mail: darren.ellis@cranfield.ac.uk © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_21

419

420

C. Bossu and D. Ellis

tions for OEP implementation in the virtual university context and ends with some concluding thoughts. Key recommendations are predicated on both top-down and bottom-up approaches, along with engagement from not only educators but also learners, the need to adopt a wider view beyond westerncentric and English language dominance in OEP, and the importance of encouraging active participation from those in the global south as well as the global north. Ultimately, OEP and the open educational resources (OER) movement, more generally, represent core enablers for virtual universities. However, their potential and affordances cannot be more fully realized unless a holistic and multidirectional approach is adopted across all levels – institutional, national, and international. If carefully and thoughtfully harnessed and implemented, the promise of OEP for virtual universities is substantial. In this manner, higher education can then be more accessible to more people in more places and contexts. Keywords

Open educational practices (OEP) · Open educational resources (OEP) · Virtual universities · Open policy · Open pedagogy · Social justice · Inclusion

Introduction The concept of open educational practices (OEP) is a shift in thinking and a development from the open educational resources (OER) movement. It is particularly relevant when linked with the practical pedagogical and philosophical applications of a virtual university. Current literature offers a range of definitions for OER and for OEP. For the purpose of this chapter, we have adopted the OER definition developed by UNESCO (2019), which states: Open Educational Resources (OER) are learning, teaching and research materials in any format and medium that reside in the public domain or are under copyright that have been released under an open license, that permit no-cost access, re-use, re-purpose, adaptation and redistribution by others.

Meanwhile, the growing diversity of OER initiatives, coupled with a better understanding of the limitations of adopting open content without open practices, have influenced the development of OEP (Bossu and Stagg 2018; Cronin 2017; Camilleri and Ehlers 2011). Some of the key principles of OEP include: • Engagement among all stakeholders in the OER process (authors, users, managers, and policy makers) • Support to guide creation and use of OER, and technologies to assist storage and dissemination • An understanding of the context in which OEP are adopted and implemented (Open Educational Quality Initiative 2011)

21

Open Educational Practice as an Enabler for Virtual Universities

421

Cronin (2017) incorporated these principles into a helpful definition of OEP, which situates OEP as “collaborative practices that include the creation, use, and reuse of OER, as well as pedagogical practices employing participatory technologies and social networks for interaction, peer-learning, knowledge creation, and empowerment of learners” (p. 10). OEP, including OER, have the potential to increase access to education to those excluded from mainstream education, including minority groups, adult learners, single mothers, and so forth. In addition, OEP can enable innovation and contextualization of learning and teaching and as a result enhance student learning experiences and promote lifelong learning. Some of these OEP concepts can easily overlap with the principles underpinning virtual universities, such as to provide flexible formal and/or informal learning opportunities to learners who would not be able to participate in traditional education (e.g., attend classes on a physical campus). Virtual universities also try to reach learners that tend to be excluded from mainstream higher education, including minority groups, incarcerated learners, and so on. OEP have already impacted education at all levels around the world. In higher education more specifically, they have benefited learners and educators and influenced the way educational institutions design curriculum and approach their strategic plans, policies, and business models. It has brought equity and access back to the discussion, including debates on how wealthier nations could assist less advantaged ones to increase access to free and open education (Willems and Bossu 2012). OEP approaches and initiatives are increasing exponentially around the world. These initiatives have been the catalyst for innovation in learning and teaching and the enabler for policy development, social justice, and inclusion. Despite the fact that OEP have the potential to positively “affect all aspects of higher education,” they have not yet reached mainstream education (Weller 2014, p. 2). There are still many practitioners and institutions that are not aware of OEP. For those who are aware, some remain reluctant to recognize the potential of OEP to enhance learning and teaching in higher education due mostly to ingrained misperceptions surrounding low quality and reliability. This chapter explores some of these initiatives and the research-based evidence on OEP as enablers and catalysts for innovation and change in higher education, including online and distance education. It discusses key enablers such as open pedagogy, capacity building, policy development, and social justice and inclusion. The chapter then makes recommendations for OEP implementation in the virtual university context and ends with some concluding thoughts.

Policy Development One of the enablers of OEP has been strategic policy development at institutional, national, and international levels. At an international level, developments such as the Cape Town Open Education Declaration (Cape Town Declaration 2007), UNESCO Paris OER Declaration (UNESCO 2012), and most recently the UNESCO

422

C. Bossu and D. Ellis

Recommendations on OER (UNESCO 2019) have all encouraged many governments and institutions to consider such policies, as these international agreements ultimately recognize the need for national-level frameworks or regulations for open practices. At a national government level, policies that support open practices can ensure that resources created with public funding are openly licensed and made accessible to the public; this includes not only educational resources, but also documents created by government officials. Furthermore, major global research funding bodies have also encouraged grantees to release data and publish journal articles and other resources under an open license (Educause 2018). This ensures that information is accessible to all interested parties, and that the impact of such funded projects is maximized. There are many examples of such policies. Some of them can be found in the Open Education Policy Hub (OE Policy Hub 2023). Meanwhile, institutions around the world have adopted a variety of open policies. Some have changed their intellectual property (IP) policy to move away from the traditional model where institutions retain the rights of educational content produced by individuals, to a model where individual employees can choose to publish and share content they develop more freely through the use of open licenses. An example of this is The University of Cape Town, South Africa, which has developed an IP policy that encourages practitioners to license educational resources under the Creative Commons Licenses with the aim to share knowledge and create OER (Cronin 2019). Some institutions have invested in resourcing through building capacity and expertise, which will be discussed in a later section of this chapter. While others have offered small internal grants to encourage practitioners to engage with OEP, one example of such an initiative is the Open Grants Program from the University of Southern Queensland, Australia. The program started in 2015, and since then it has funded numerous projects to support staff in the creation of open textbooks, and other OEP-related initiatives such as to enhance assessment and feedback, to create teaching strategies to enhance students’ experience, and to support early career practitioners (Stagg and Partridge 2019). Other institutions have embedded OEP into their recognition and award policies in an attempt to encourage developments in this area. One example of this is the TU Delft Strategic Framework 2018–2024 from the Delft University of Technology, the Netherlands, which explicitly states that the institution will provide recognition of “engagement with Open Science and Open Education” (TU Delft 2018, p. 36). An important element of institutional OEP adoption is the institutional guidelines for OER and OEP. These guidelines can pave the way for how an institution view such developments, including what can be done, how, by whom, what support and recognition are in place, and which pedagogical approaches should be considered. Many institutions which encourage their staff to adopt OEP would have these guidelines; they are many and varied and found on institutional websites, particularly within learning and teaching centers and libraries. It can be seen from the discussion above that policy development, and adopting an international, national, or institutional level outlook, can lay the foundations for

21

Open Educational Practice as an Enabler for Virtual Universities

423

OEP development in many ways through a range of strategies that can support open practices. It can also be seen that most policy approaches presented here are top-down. However, it cannot be ignored that many OEP initiatives are actually bottom up. A number of practitioners engage with free content (but not necessarily open licensed), available on the Internet and share them with their communities of practice, to find solutions to improve their teaching and to help their students to learn. They build their own capacity, share their success and failures, and create pedagogies to underpin their innovations with the ultimate aim to better support their students. Some of these innovations end up being published formally or informally through social media (twitter and blogs), but many do not – occurring instead organically – they simply reflect good teaching practice. But are these practices considered open practices? Others have already raised this question and argued that a purist approach can prevent OEP from reaching its full potential (Lambert 2018; Mishra 2017). We are going to revisit this issue later in the chapter. The next section explores investing in building capacity and expertise in OEP, briefly touched on above.

Capacity Building Meaningful transformation and change in higher education often unfold slowly and can attract many skeptics along the way. This is also true for some virtual universities. Staff professional development and capacity-building can be enablers of change and influential instruments to empower academic staff to embrace and participate in change (Healey et al. 2013; Smyth 2003). Building capacity in OEP can help to raise staff awareness and understanding of its potential to enhance learning and teaching, and as a result create communities of practice and opportunities for innovation (Bossu and Fountain 2015). Common opportunities for capacity building in OEP among universities are in the form of workshops, webinars, and one-on-one consultancies (Bossu 2016). These approaches are familiar to practitioners as they are adopted across both traditional and virtual universities. Also familiar to practitioners is the way these activities tend to be delivered, face-to-face, blended, or online, with a strong emphasis on content delivery and minimum interaction with learners. Even though short capacity-building activities can be helpful to introduce academics to the concept of OEP, and raise awareness and understanding of them, they should ideally be linked to a wider OEP-institutional strategy. A one-off workshop about OEP is not only unlikely to encourage a deep level of understanding and knowledge, but perhaps more impactfully, it is not likely to promote long-lasting adoption and change in approaches to learning and teaching. Generally speaking, capacity building programs for educators should be engaging, meaningful, contextualized, and practical and promote reflection on practice to enable deep and lasting transformation in a practitioner’s teaching and learning approaches through OEP (Karunanayaka and Naidu 2020). It must be remembered in this context that educators tend to bring to teaching their previous professional

424

C. Bossu and D. Ellis

experiences and beliefs, and are not blank slates, or necessarily aware or open to meaningful change (Salmon 2004; Webb 2003). Building practitioners’ capacity in OEP and OER has also been recognized by some international organizations as being key to the future progress and evolution of OEP. Organizations such as the Commonwealth of Learning (CoL), UNESCO, International Council for Open and Distance Education (ICDE), Open Education Consortium, and many others have invested in the creation of resources, programs, and activities to develop and support educators and their institutions to adopt open content and practice. As a result, a substantial number of such resources can be found freely available on the Internet. One could ask, have these efforts and investment paid off? Using website analytics, it is possible to track numbers of views, downloads, registrations, and citations of these resources, but measuring the impact of capacity building requires a range of evidence, including sustained engagement with OEP (Nascimbeni and Burgos 2019). Research has shown that if capacity building for OEP is purposefully designed, meaningful, and contextualized, it can be transformative and create positive shifts in practice toward openness, sharing of knowledge, and can also increase learner engagement (Karunanayaka and Naidu 2020).

Open Pedagogy For some time now, OER and OEP have both been recognized as catalysts for innovation in learning and teaching. The possibilities are many and varied, from student cocreation of content, to networked and collaborative learning, to adoption of open technologies and licenses, through to new pedagogical approaches whereby others can reuse, remix, and redistribute content and practices. Although considered a branch of OEP (Mishra 2017), open pedagogy is actually a term that has been in place since the late 1970s (Morgan 2016), but has now reemerged to meet the increasing need to explore flexible pedagogical approaches to support OEP developments. DeRosa and Jhangiani (2017) provide a useful definition of open pedagogy, which enables this flexibly. They suggest that “open pedagogy, is a site of praxis, a place where theories about learning, teaching, technology, and social justice enter into a conversation with each other and inform the development of educational practices and structures” (DeRosa and Jhangiani 2017, p. 7). Open pedagogy appears closely related to more recent pedagogical theories such as cognitive and social constructivism, and connectivism, as it includes co-creation and sharing of knowledge, learning design approaches that better meet students’ needs, and the use of collaborative technologies. However, as the name suggests open pedagogy embraces elements of openness and harnesses existing open technologies and the abundance of free resources available online (Baran and AlZoubi 2020). In order to help educators to become open practitioners, Hegarty (2015) developed eight attributes of open pedagogy:

21

1. 2. 3. 4. 5. 6. 7. 8.

Open Educational Practice as an Enabler for Virtual Universities

425

Use of participatory technologies People, openness, and trust Innovation and creativity Sharing ideas and resources Connected community Learner-generated Reflective practice Peer review (p. 5)

Hegarty cautioned that although these attributes seem to stand alone, they overlap with each other. She goes on to explain: “It is impossible to discuss participatory technologies without mentioning innovation, trust, sharing, collaboration, connectedness, peer interaction and review, learner contributions, or reflective practice” (Hegarty 2015, p. 10).

Social Justice and Inclusion It might seem a little redundant to specifically discuss social justice and inclusion while writing a chapter about open educational practices, which, similar to virtual universities, have as their central agenda to increase access to quality and affordable education, and to create flexible learning opportunities for learners from all walks of life. For example, OER are available in a range of formats and granularity, from learning objects to full courses. However, the large majority of open resources are only available in English, which limits access and reach for many learners around the world. There have been some important developments aimed at creating databases with resources in several languages and/or translated OER such as the Centre for Open Educational Resources and Languages Learning, Open Educational Resources (OER) in less used languages (LangOER project), and OER for Specific Languages, but these efforts cannot be compared to the large number of existing resources available in English. This is problematic because in many developing countries English is not spoken nor studied. Restricting the use of OER to those who arguably need them the most, and in the process going against one of the core principles of OEP and OER which is to increase access to education, generates significant ethical questions and concerns. Likewise, a western-centric view is more likely to be embedded in English language OEP and OER, raising additional concerns around the downplaying or ignoring of voices from elsewhere, although unintentional to begin with. It is not only lack of language diversity in OER; a study by Lambert (2018) revealed that references to social justice and inclusion in a selection of publications about openness were scarce. Earlier publications tended to have a more inclusive focus, while later work on openness moved to technological and access issues – implying, but not explicitly addressing, social justice. Social justice should underpin openness discourse (Lambert 2018).

426

C. Bossu and D. Ellis

The principle of openness as defined by purists is that technologies and content need to be openly licensed. Social justice advocates argue against this concept, as it limits access and development of educational materials making it harder for educators and learners to embrace openness unless all the boxes are ticked (Mishra 2017). To be fully inclusive OEP should also embrace collegiality, sharing of resources and open practices, and the willingness to be open not only because OER are used, but also because they make a difference.

Affordances and Key Recommendations In this chapter some key enablers of OEP were discussed. Some of them overlap in many ways, and together they can form a set of strategies for institutions, including virtual universities, to promote and support the adoption of OEP. The affordances of these enablers are not only possible, but also some of them could be easily embedded in the core philosophy, mission, and business of virtual universities. For example, OEP policies can be weaved into current policies through updates and review processes, and during the development of new institutional strategic frameworks. Scott (2013) suggests that universities, including virtual universities, can use their IP policy review processes as an opportunity to engage their employees in discussion about open licensing options and adoption of OEP. This will, in turn, raise awareness and inform university policy and guidelines. Institutions should consider conducting a consultation process to identify motivation and strategic direction, establish employee expectations, and identify required actions (Scott 2013). Another strategy to be considered in order to encourage institutional adoption of OEP is resourcing. As discussed above, investment in building capacity and creating opportunities for staff to engage with OEP through small grants, for example, can raise awareness and enable a change in culture toward openness. In addition, institutions need to: • Provide technical support to their staff, so they can make informed decisions regarding OEP • Create incentives for educators, such as recognition and awards for those involved in OEP activities within their institutions or when working collaboratively with other institutions • Create a culture of openness, which includes open collaboration, open learning design strategies, and open academic (encourage publication in open access journals) (Camilleri and Ehlers 2011), among other strategies Previous discussions also revealed that the affordances of capacity building in OEP to virtual universities are clear, with some already engaging their staff with at least a few concepts related to OEP (DeVries 2019). Additionally, issues “regarding quality control, whether or not to support translation and localisation of resources, how to facilitate access for students with disabilities, and technical issues” need to be

21

Open Educational Practice as an Enabler for Virtual Universities

427

analyzed when adopting OEP (Bossu and Tynan 2011). This would in turn support the affordances of open pedagogies and social justice, which tend to also be aligned with the mission and vision of most virtual universities. However, as suggested by Lambert (2018) social justice must be explicit and part of the open discourse and OEP development.

Key Recommendations for Virtual Universities Virtual universities have a great deal to gain from embedding OEP into their core values, policies, and practices. With this in mind, the following key recommendations aim to encourage institutions, management, and educators to address the following specific matters, together with their implementation, in order to ensure progress is made in this space. This way, aspirations, conceptualizations, and theory have a better chance of being transformed into practice and of becoming a tangible and entrenched reality.

Recommendation 1: Adopt a Policy of Openness Virtual universities need to devise and propagate institutional-level policies with OEP at their core. Top-down leadership has an important role to play at virtual universities in adopting and embedding OEP into institutional cultures and practices. Without effective high-level policies to begin with, the task of generating wider and deeper engagement with OEP is considerably more challenging. A start can be made by: • Reviewing existing institutional policies, contracts, and grant conditions to decide what changes may be required • Ensuring policies clearly state who owns employee-generated content developed by administrators, academic staff members, students, and others associated with the university • Updating performance review and academic promotion policies to recognize OEP engagement

Recommendation 2: Create Institutional Guidelines Virtual universities should create institutional guidelines covering OEP which radiate from their overarching policies. This way, strategic-level OEP policies can be transformed into departmental specific operational policies and practices which establish themselves more deeply throughout the institution and its functional architecture. As mentioned before, there is no shortage of resources and examples of such guidelines available on the web. However, a start can be made by looking at resources such as the Guidelines on the development of open educational resources

428

C. Bossu and D. Ellis

policies (Miao et al. 2019), developed in collaboration between UNESCO and the Commonwealth of Learning, the Open Education Policies: Guidelines for Co-creation (Atenas et al. 2020), and the Policy Approaches to Open Education – Case Studies from 28 EU Member States (OpenEdu Policies) (Inamorato dos Santos et al. 2017). Some of these guidelines could also inform Recommendation 1 above and assist university policy makers and senior leaders in the development of open policies. However, it is important to understand that there is not a one-size-fits-all approach to open policies and guidelines. Stakeholders must consider the organizational culture, current staff pressures and workloads, technological infrastructure, and financial resources at each individual institution.

Recommendation 3: Invest in People and Research Virtual universities ought to also look beyond policies and guidelines and focus considerable attention on understanding the resources needed (financial and personnel) to effectively implement OEP. One of them is to create professional development opportunities to not only build individual capacity, but also concurrently drive institutional-level capacity-building as well. A sustainable long-term approach to capacity-building is required whereby opportunities are made available for staff to regularly and meaningfully engage with OEP and which also empower them to adopt life-long interaction with, ongoing reflection on, and regular use of OEP in their situational context. Awards, workshops, short courses, and training sessions, along with other continuing professional development (CPD) opportunities and programs like a formal peer review of teaching requirement, can help to ensure that the needs of people are front and center and ongoing at an institution. Alongside professional development, virtual universities should provide adequate and targeted funding and resourcing to encourage and equip their employees in such a way that bottom-up ideas, projects, and research investigating and incorporating OEP have the means to come to fruition. In this manner, individual educators can drive OEP developments via organically achieved research outputs, webinars, symposiums, in-house conferences, and collaborative projects, among others.

Recommendation 4: Listen to Learners Virtual universities must acknowledge that learners are also central in learning and teaching, including with OEP. The co-creation and sharing of knowledge need to not only cut across internal institutional boundaries, but likewise also move freely among educators and learners. Connections, reflections, participation, and open pedagogies should be centered on the learner to move beyond simply greater learner engagement, to then produce learner-generated OEP and learner empowerment. A starting point to engage learners with OEP could be to better understand their digital literacy skills and create opportunities for learners to learn about OER in practice and through collaboration within and outside the institution.

21

Open Educational Practice as an Enabler for Virtual Universities

429

Recommendation 5: Move South to North Virtual universities should encourage a flow of ideas, concepts, and innovative practice related to OEP from the global south to the global north. Non-English language-generated OER, OEP, and open research from the global south ought to be translated into English and disseminated widely in the global north, rather than a predominant emphasis on a reverse directional flow. Collaborative projects and connections need to be explicitly pursued with institutions in the global south for those virtual universities located in the global north, while those based in the global south should establish direct links north. For instance, a list of strategic institutional relationships could be maintained, and funding and resources allocated, so that OEP-related teaching, research, and learning opportunities can be formed between such institutions, with a proportionate level of staff and learner mobility (online and physical) linked to the underpinning knowledge flows, thus making social justice part of the practice and discourse of virtual universities.

Conclusion and Future Directions This chapter and its associated key recommendations are provided as a way to continue and further encourage the conversation on OEP as an enabler for virtual universities. The ideas, examples, and suggestions provided here are by no means exhaustive or immovable. In fact, their evolution and possible deletion over time, due to being superseded, are to be expected. The chapter here hopes to present a flexible and inclusive view on the topic, not a prescriptive and rigid approach. In this sense, the significance of this chapter is to situate OEP as a positive and proactive enabler for virtual universities, therefore literally opening a world of possibilities for these institutions to better realize the promise that they bring to higher education.

Cross-References ▶ Academic Engagement in Pedagogic Transformation ▶ Models of Professional Development for Technology-Enhanced Learning in the Virtual University ▶ The Affordances of Openness for the Virtual University ▶ The Virtual University in Practice

References Atenas, J., L. Havemann, J. Neumann, and C. Stefanelli. 2020. Open Education Policies: Guidelines for co-creation. Zenodo. https://doi.org/10.5281/zenodo.4281363. Baran, E., and D. AlZoubi. 2020. Affordances, challenges, and impact of open pedagogy: Examining students’ voices. Distance Education 41 (2): 230–244. https://doi.org/10.1080/01587919. 2020.1757409.

430

C. Bossu and D. Ellis

Bossu, C. 2016. Open educational practices in Australia. In Open educational resources: Policy, costs and transformation, ed. F. Miao, S. Mishra, and R. McGreal, 13–25. Paris/Burnaby: UNESCO/Commonwealth of Learning. Bossu, C., and W. Fountain. 2015. Capacity-building in open education: An Australian approach. Open Praxis 7 (2). https://doi.org/10.5944/openpraxis.7.2.197. Bossu, C., and A. Stagg. 2018. The potential role of open educational practice policy in transforming Australian higher education. Open Praxis 10 (2): 13. https://doi.org/10.5944/ openpraxis.10.2.835. Bossu, C., and B. Tynan. 2011. OERs: New media on the learning landscape. On the Horizon 19 (4): 259–267. https://doi.org/10.1108/10748121111179385. Camilleri, A., and U. Ehlers. 2011. Mainstreaming open educational practices: Recommendations for policy. In The OPAL Consortium, ed. Opal Consortium. Cape Town Declaration. 2007. Cape town open education declaration. Retrieved from https:// www.capetowndeclaration.org/cpt10/#about Cronin, C. 2017. Openness and praxis: Exploring the use of open educational practices in higher education. The International Review of Research in Open and Distributed Learning 18 (5). https://doi.org/10.19173/irrodl.v18i5.3096. Cronin, C. 2019. Open education: Design and policy considerations. In Rethinking pedagogy for a digital age: Principles and practices of design, ed. R.S.H. Beetham, 3rd ed., 149–163. London: Routledge. DeRosa, R., and R. Jhangiani. 2017. Open pedagogy. In A guide to making open textbooks with students, ed. E. Mays. The Rebus Community for Open Textbook Creation. DeVries, I.J. 2019. Open universities and open educational practices: A content analysis of open university websites. The International Review of Research in Open and Distributed Learning 20 (4). https://doi.org/10.19173/irrodl.v20i4.4215. Educause. 2018. 7 Things you should know about Open Education: policies. Retrieved from https:// library.educause.edu/-/media/files/library/2018/8/eli7159.pdf Healey, M., M. Bradford, C. Roberts, and Y. Knight. 2013. Collaborative discipline-based curriculum change: Applying change academy processes at department level. International Journal for Academic Development 18 (1): 31–44. https://doi.org/10.1080/1360144x.2011.628394. Hegarty, B. 2015. Attributes of open pedagogy: A model for using open educational resources. Educational Technology 55: 3–13. Retrieved from https://www.researchgate.net/profile/ Bronwyn-Hegarty/publication/281286900_Attributes_of_Open_Pedagogy_A_Model_for_ Using_Open_Educational_Resources/links/55dfa74908aede0b572b916b/Attributes-of-OpenPedagogy-A-Model-for-Using-Open-Educational-Resources.pdf Inamorato dos Santos, A., F. Nascimbeni, P. Bacsich, J. Atenas, S. Aceto, D. Burgos, and Y. Punie. 2017. Policy approaches to open education–case studies from 28 EU member states (OpenEdu Policies). EUR 28776 EN. https://doi.org/10.2760/283135. Karunanayaka, S.P., and S. Naidu. 2020. Ascertaining impacts of capacity building in open educational practices. Distance Education 41 (2): 279–302. https://doi.org/10.1080/01587919. 2020.1757406. Lambert, S.R. 2018. Changing our (dis)course: A distinctive social justice aligned definition of open education. Journal of Learning for Development 5(3). Retrieved from https://jl4d.org/ index.php/ejl4d/article/view/290 Miao, F., S. Mishra, D. Orr, and B. Janssen. 2019. Guidelines on the development of open educational resources policies. Paris: UNESCO. Mishra, S. 2017. Open educational resources: Removing barriers from within. Distance Education 38 (3): 369–380. https://doi.org/10.1080/01587919.2017.1369350. Morgan, T. 2016. Open pedagogy and a very brief history of the concept. EdTech in the Wild blog. Retrieved from https://edtechbooks.org/wild/open_pedagogy Nascimbeni, F., and D. Burgos. 2019. Unveiling the relationship between the use of open educational resources and the adoption of open teaching practices in higher education. Sustainability 11 (20): 5637.

21

Open Educational Practice as an Enabler for Virtual Universities

431

OE Policy Hub. 2023. Open Education Policy Hub: shaping open futures in education together. Retrieved from https://www.oepolicyhub.org/ Open Educational Quality Initiative. 2011. Beyond OER: shifting focus to open educational practices: Open Education Quality Initiative (OPAL). Salmon, G. 2004. E-moderating in higher education. In Distance learning and university effectiveness: Changing educational paradigms for online learning, ed. M. Khosrow-Pour, J. Travers, A. Appicello, and M. Rossi, 55–77. Hershey: Idea Group. Scott, B. 2013. Supporting OER engagement at Australian Universities: An overview of the intellectual property rights, copyright and policy considerations for OER. Retrieved from https://rune.une.edu.au/web/retrieve/aaeeb1b6-4e35-47ad-8627-278271a83b60 Smyth, R. 2003. Concepts of change: Enhancing the practice of academic staff development in higher education. The International Journal of Academic Development 8 (1–2): 51–60. Stagg, A., and H. Partridge. 2019. Facilitating open access to information: A community approach to open education and open textbooks. Proceedings of the Association for Information Science and Technology 56 (1): 477–480. https://doi.org/10.1002/pra2.76. TU Delft. 2018. Impact for a better society: TU Delft Strategic Framework 2018–2024. Retrieved from https://d2k0ddhflgrk1i.cloudfront.net/TUDelft/Over_TU_Delft/Strategie/Towards%20a% 20new%20strategy/TU%20Delft%20Strategic%20Framework%202018-2024%20%28EN% 29.pdf UNESCO. 2012. 2012 Paris OER declaration. World Open Educational Resources (OER) congress. Paris: UNESCO. Retrieved from http://www.unesco.org/new/fileadmin/MULTIMEDIA/ HQ/CI/CI/pdf/Events/Paris%20OER%20Declaration_01.pdf UNESCO. 2019. Recommendation on Open Educational Resources (OER). Retrieved from http:// portal.unesco.org/en/ev.php-URL_ID¼49556&URL_DO¼DO_TOPIC&URL_SECTION¼201. html Webb, G. 2003. Management of academic development. In Planning and management in distance education, ed. S. Panda, 87–97. London: Kogan Page. Weller, M. 2014. Battle for open: How openness won and why it doesn’t feel like victory. London: Ubiquity Press. Willems, J., and C. Bossu. 2012. Equity considerations for open educational resources in the glocalization of education. Distance Education 33 (2): 185–199. https://doi.org/10.1080/ 01587919.2012.692051.

The Affordances of Openness for the Virtual University

22

Sanjaya Mishra

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Technology, Openness, and Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Affordances of “Openness” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Openness Framework for Virtual Universities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Entry Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Study Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Time of Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Curricular Flexibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pedagogical Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Technology Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Assessment Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Recognition of Credentials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cost of Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Recommendations for Virtual Universities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

434 435 436 438 438 439 439 439 440 440 441 441 442 442 443 444 444 444

Abstract

The chapter explores the affordances of “openness” for virtual universities and elaborates a framework for rethinking openness in higher education. It critically examines the appropriateness of open technologies, open educational resources (OER), and open educational practices (OEP) to facilitate/ support teaching and learning in the Virtual University, and to provide new opportunities for universities to promote lifelong learning. The framework discusses ten dimensions of openness to support equity and inclusion, and access to quality educational S. Mishra (*) Commonwealth of Learning, Burnaby, BC, Canada © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_22

433

434

S. Mishra

opportunities globally, to support the United Nations Sustainable Development Goal 4 – ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. In many cases, this calls for a paradigm shift in how higher education might benefit from adopting open principles of fairness, flexibility, and freedom. Keywords

Openness · Virtual University · Affordances

Introduction Open education as a philosophy of teaching and learning has been in existence well before the establishment of the first open university in 1969. Its importance has been reemphasized recently due to the unprecedented closure of educational institutions due to the COVID-19 pandemic. Before the COVID-19 crisis, the current higher education system has been inaccessible to many, especially in low and middleincome countries (UNESCO 2020; World Bank 2021). The pandemic exacerbated this existing crisis manifold ways, creating a massive surge in demand for online learning (Gallagher and Palmer 2020; ICEF Monitor 2020). In 2020 alone, Coursera, a massive open online course platform, demonstrated a 430% increase in users, reaching up to 69 million enrollments (Vandenbosch 2020). Distance and online learning, as we know it today, has progressed a long way. Correspondence education started in the eighteenth century (Holmberg 1986), leading to the establishment of the first distance teaching university (i.e., University of South Africa in 1946), followed by the Open University, United Kingdom (OUUK) in 1969. According to Lord Crowther, the founding chancellor of OUUK, openness relates to being open to people, places, methods, and ideas (Perry 1976). While the concept of “open” brought a paradigm shift to thinking about access to higher education, technology played an instrumental role in promoting the use of distance education to facilitate that openness. As such, Keegan (1996) presented an analytical definition of distance education, which involves the following: (i) separation of teacher and learner, (ii) presence of an institution to provide accreditation of learning, (iii) use of mixed media courseware, (iv) two-way communication between student and teacher, (v) possibility of face-to-face meetings of learners, and (vi) industrial process of operation. The use of information and communication technologies and virtualization of teaching and learning experience at a distance using both synchronous and asynchronous technologies have enabled increased flexibility for both the teachers and students. While technology-enabled learning provides affordances for anytime and anywhere learning, the concept of “openness” enables open, virtual, and digital universities to become sustainable and resilient. This chapter explores the affordances of “openness” for the Virtual University and presents a framework for rethinking openness more generally in higher education. The focus is to offer lessons learned from research and practices to make a case

22

The Affordances of Openness for the Virtual University

435

for increased “openness” in teaching and learning in higher education and provide new opportunities for universities to promote lifelong learning and create new business opportunities for sustainable higher education. This is in line with the United Nations Sustainable Development Goal 4 that looks to “ensure inclusive and equitable quality education and promote lifelong learning opportunities for all” (United Nations 2015). The proposed framework and associated lessons will also help virtual universities systematically use the affordances of “openness” and face future crisis, such as this latest pandemic, and help institutions establish more robust and resilient systems.

Technology, Openness, and Learning Learning is a process. It is emancipatory and enlightening. Therefore, learning is associated with the ability to question the status quo, investigate, and search for the “truth” about ourselves and the society in the world – including both living and nonliving systems. As such, Delor’s commission report emphasized four pillars of education (UNESCO 1996): learning to know, learning to do, learning to live together, and learning to be. Education, therefore, always requires openness, which was not necessarily possible throughout the history of human civilization. It has been a process primarily controlled by the state for the benefit of their subjects. As a result, the power structure in the society gets automatically transferred into the education system and the teaching-learning processes. The design created a web of rigidities and merit-based elitist processes making education accessible only to a few privileged by economic and social status. Higher education today is mainly behind “paywalls” – a mechanism restricting access to teaching and research ecosystem. These paywalls increase student debt and hamper scientific research due to many universities’ lack of access to peerreviewed research information. The emergence of correspondence education provided an alternative to access the privileged system, albeit initially perceived as a “secondclass” system. However, the establishment of the OUUK had a significant effect on the broader spread of openness in education, leading to the establishment of many other open universities and the use of distance education to increase flexible access to higher education at a lower cost. Open education, as such, is a philosophical construct that refers to policies and practices which allow entry to learning with no or minimum barriers to age, gender, or time constraints. In practice, openness is about open entry, learning anywhere, anytime, and the freedom to choose courses (Kember 2007). Therefore, an education system can embrace openness to different degrees of affordances, as discussed in the next section. Distance education (DE), as a system, practices openness to a large extent. Most DE systems, though, have been trying to copy the conventional system of education, which is the dominant system influencing the accreditation and quality assurance systems around the world. As per the definition provided by Keegan, distance education is a system of education that uses mixed media approach (covering the use of a range of technical media, from print to digital). Thus, technology plays a significant role in teaching and learning, facilitating openness and learning in

436

S. Mishra

distance education. Taylor (2001) critically analyzed distance teaching systems using technology and categorized these into five generations: (I) Correspondence model, II) Multimedia model, III) Tele-learning model, (IV) Flexible Learning model, and V) Intelligent Flexible Learning model. While the printed book was used in the correspondence model, the intelligent flexible learning model is based on the Internet and auto-response system, which is becoming more and more relevant due to the use of big data and artificial intelligence in teaching and learning (Kabudi et al. 2021; Ouyang and Jiao 2021). The system focuses beyond technology and prioritizes interaction in the teaching and learning process, which is about how the curriculum is organized and delivered. Designing learning content, using a range of low- and high-technology solutions to provide interactive learning environments, received a boost with the emergence of Web 2.0, facilitating teachers and students to share resources and knowledge (Brake 2013). Such sharing practices led to the coining of open educational resources (OER) by UNESCO in 2002 (UNESCO 2002). The field has also seen many similar developments, such as open-source software and open access (OA) to scientific information, primarily among librarians. The OA movement has emerged as a response to the paywall for accessing scientific information controlled by private publishing houses. A common thread in these areas is the challenge of navigating copyright laws in the context of budgetary constraints to provide open education. OER presented an opportunity for teachers to use, adopt/adapt, revise, and remix existing copyrighted materials released with an open license in their context. Late Fred Mulder (2015) from the Netherlands called for an ecosystem approach to open education. He highlighted that a truly open education enterprise should focus on open teaching, open learning services, open educational resources, openness to learners’ needs, and openness to their employability. The use of OER in teaching and learning is referred to as open educational practice (OEP) or open pedagogy. Hegarty (2015) presented a model of eight interconnected attributes of open pedagogy: participatory technologies; people, openness, and trust; innovation and creativity; sharing ideas and resources; connected community; learner-generated; reflective practice; and peer review. The field of openness in education is growing. Broadly this has three dimensions: (i) open access, which is inclusive and equal access to educational opportunities without barriers such as entry qualifications and ability to pay, (ii) open learning which is the ability to study and learn at anytime, anywhere, and at any pace, and (iii) open scholarship which comprises releasing educational resources under an open license that permits no-cost access, use, adaptation, and redistribution by others (Naidu 2016).

Affordances of “Openness” A recent systematic review of the literature identified two main categories of technology use in education during crisis which are relevant to the Virtual University: (i) Internet-based technologies and (ii) non-Internet broadcast technology, such

22

The Affordances of Openness for the Virtual University

437

as radio and television (Crompton et al. 2021). While the teaching strategies used communication, delivery systems, student readiness, partnerships, promoting students learning and engagement, and resources, the types of digital technologies used were conferencing tools, free distance learning resources, social media, online learning management systems, and other specific applications/platforms. So, what are the affordances of technology use in education? Naidu (2009) identifies three critical attributes of technology affordances to help facilitate teaching and learning: information storage and retrieval, communication and collaboration, and engagement and interaction. Likewise, what are the affordances of openness? According to Gibson (1979), affordances of an environment relate to what it offers/provides, either for good or ill. They provide the opportunity for a particular kind of behavior or action. Ontological studies of affordances have typically assumed that affordances are properties of the environment (Stoffregen 2000; Turvey 1992) with significance to the user in the context. Reed (1996) argued that affordances are resources in the environment that might be exploitable. Chemero (2003) argued that affordances are neither properties nor resources; they are relations between the abilities of individuals and features of a particular context/situation. So, applying the theory of affordances in the field of ecology, we look at open education as an idea/concept/ practice in an environment that provides affordances in the context of the abilities of the people using it. In the context of technology-enabled learning in higher education institutions, openness provides the following affordances discussed in the next section: (i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x)

Entry requirements Study location Time of learning Curricular flexibility Pedagogical approach Technology use Learning resources Assessment approach Recognition of credentials Cost of education

Beyond the affordances of openness in teaching and learning, openness is also a recognized personality trait (Antinori et al. 2017), identified by personality psychologists, that relates to “openness to experience” (DeYoung 2015). People who demonstrate open mindsets are often motivated to explore the world, seek new possibilities (McCrae and Costa Jr 1997), and are inquisitive and creative (Kaufman et al. 2016). Thus, we encourage readers to engage an open mindset when considering the possibilities, opportunities, and affordances that open education offers learners in our global community. In the next section, we discuss openness in the context of technology-enabled learning and virtual universities to propose a framework that institutions can adopt to offer sustainable education in times of national and international crisis and beyond.

438

S. Mishra

Openness Framework for Virtual Universities The ten dimensions of openness are presented here as a two-ended continuum with contrasting values at either end. Though situations are rarely dichotomous in the real world, they are more complex, and therefore, we have made nine equal interval points between both ends (see Fig. 1). The range enables us to position the status of an institution at a particular point in the continuum. Also, it is imperative to highlight that while the individual dimensions are essential, they are just a subset of the whole. They are independent but are interrelated to enable the complete framework to be used as a coherent score of openness and help compare practices across institutions and departments.

Entry Requirements

While tertiary education is an aspiration for more young people, there is a lack of places to accommodate everyone and provide access to higher education. As such, higher education institutions deploy selective measures to recruit learners, creating barriers to educational opportunities. For example, in Nigeria, only about 26% of those seeking admission to higher education are accepted in universities (Kazeem 2017). In principle, entry requirements could range from exclusive entry (as in conventional tertiary institutions) to open entry, like many open universities. Research shows older female learners (Colvin 2013) and people from rural areas (Lasselle 2016) face more challenges in accessing higher education. Open entry enables institutions to increase access and create a new revenue stream. By creating flexible entry provisions by creating part-time, evening classes and online learning opportunities, educational

Fig. 1 Hypothetical application of openness framework

22

The Affordances of Openness for the Virtual University

439

institutions could improve their commitment to openness by not enforcing rigid entry requirements. Flexible entry requirements also create enabling environments for selfdirected learning and put the responsibility of learning on the student.

Study Location

Learning space could range from anywhere learning to a fixed location. Some universities having multiple locations provide options to learners and may fall in between. The growth of distance and online education has enabled learning from anywhere without making it a precondition to join a particular cohort in person. During COVID-19, the study location played a crucial role in providing the openness to continue teaching and learning. Learners can interact with teachers and peers and access courses online from anywhere, making online learning a preferred option (Du et al. 2019). Online education can attract learners on the job and from out of province and national territory. Educational institutes also embrace online education to prevent attrition of their traditional face-to-face students (Maddux and Johnson 2014).

Time of Learning

Older people with family and social responsibilities often have many demands on their time (Daniel 2016). Open learning provides flexibility to study for people with family responsibilities, including those who have a day job and cannot attend college to learn further. Time to study is related to how the teaching is structured. Does it require a specific schedule, or can learners complete study asynchronously? When online education provisions use more synchronous sessions, it will lead to less openness.

Curricular Flexibility

440

S. Mishra

The curricular options available to the learner are vital to provide flexibility and openness (Kember 2007). University courses are often combined to earn credits for a program. The level of flexibility provided for learners to decide what courses to study and earn credit for a program is key to curricular flexibility. Completing a graduate program with a mix of courses is an option available to learners in many open universities. However, some programs may not have options, for example, short diploma or certificate program for which there could be professional standards. Curricular flexibility presents more logistic issues for administrators to organize teaching and learning on the campus. Still, it is worth considering as learners perceive that they learn better and are more satisfied when they choose their schedule and course type rather than being assigned to courses without options or input (Rhoads 2019).

Pedagogical Approach

The pedagogical approach adopted in courses could vary due to the subject and the teacher’s comfort level to adopt innovations. Teachers’ pedagogic practice is dependent on their thinking or attitudes about what they do in the classroom and what they see as the outcome of their practice (Westbrook et al. 2013). Collaborative learning strategies help significantly improve students’ learning achievement and self-efficacy in flipped learning classes (Zheng et al. 2020). The “lecture only” model in practice must change to adopt deliberate planning and design of instructions for student learning. An institute can become highly collaborative when most of its teaching departments adopt collaborative teaching methods, and students and teachers collaborate/engage in constructing meaning and learning. Such collaborative environments focus on “the creation, use, and reuse of OER, as well as pedagogical practices employing participatory technologies and social networks for interaction, peerlearning, knowledge creation, and empowerment of learners” (Cronin 2017, p. 18).

Technology Use

While technology used in teaching and learning has to be pedagogically suitable and relevant to learning objectives, often the choice of such technologies are taken at an administrative level. For example, the use of a particular learning management

22

The Affordances of Openness for the Virtual University

441

system or a conferencing system would be a decision that often individual teachers would not have to make while teaching their courses. However, technology could influence the flexibility of a course in terms of customizing the software for specific needs and moving to student-centered learning (Lakhan and Jhunjhunwala 2008). Use of open-source tools to provide access to teaching and learning may help equity and inclusion as learners need not buy costly proprietary tools. Many institutions buy proprietary software for the students, the cost of which in turn are passed on to the students as tuition fees leading to the high cost of education.

Learning Resources

The cost of learning materials has reached epic proportions. Between 1998 and 2016, college textbook costs in the USA increased 181% compared to an increase of 48% of the consumer price index (Perry 2016). In Canada, due to the prohibitive cost of textbooks, 54% of students in the province of British Columbia study without at least one of their required textbooks, while 27% take fewer courses and 17% drop courses (Jhangiani and Jhangiani 2017). In Malaysia, 76.4% of the learners do not buy textbooks due to high costs (COL 2017). The use of openly licensed resources in teaching and learning is a critical flexible option, as these materials save student costs and the cost of material production for institutions. The open textbook project in British Columbia, Canada, has saved over 26 million CAD since 2012 (BC Campus n.d.). The use of OER in Antigua State College helped students save ECD 947 per year, and their achievement increased by 5.5% (Emarge Ed Consultants 2017). From an institutional perspective, adopting openly licensed material could substantially reduce the time for material development, thereby making it more cost-effective (Butcher and Hoosen 2012).

Assessment Approach

Higher education institutions focus on assessment as the key indicator for quality. It is the bedrock of trust of society in education. However, assessment has become more rigid where institutions decide when and how the evaluation should be held. There are also challenges related to authentication of student work, e-cheating, and contract cheating (Ali and Alhassan 2021; Lancaster and Cotarlan 2021). There is no

442

S. Mishra

flexibility given to learners to choose from among different ways of assessment (Wood and Smith 1999). The use of flexibility/openness in assessment formats supports core agendas in higher education such as accessibility and promoting autonomous learners, as alternative assessment approaches can lead to student-led pedagogy and increase student engagement (Irwin and Hepplestone 2012). Institutions can provide multiple pathways to assess student learning by adopting innovative methods and microcredentials (Rossiter and Tynan 2019). The National Institute of Open Schooling (India) adopts an on-demand examination system to allow learners to assess when they would like (Prasad 2008).

Recognition of Credentials

A student receives a credential at the end of a course of study, which is expected to be recognized everywhere. However, due to variations in national systems of education and accreditation processes, qualifications are not recognized globally. At times, qualifications are not recognized even within a country, making it difficult for learners to gain employment or start practice as independent professionals. As the world has become a global village, recognition of degree/ credential elsewhere without difficulty is a key component of flexibility. The European Credit Transfer System provides a framework for developing courses and programs approved within Europe. Similarly, UNESCO has adopted the “Global Convention on the Recognition of Qualifications concerning Higher Education” in 2019 to facilitate the global mobility of learners for education and employment (UNESCO 2019). It is the responsibility of education and training providers to offer courses and programs that are globally recognized. Provisions to permit accumulating credentials for receiving recognized degrees and fostering an enabling environment for lifelong learning (Brown et al. 2021) would enhance the openness of higher education.

Cost of Education

22

The Affordances of Openness for the Virtual University

443

High and low costs of education can be a relative concept in different economies. A report by Economist Intelligence Unit (2016) indicates that by 2030 the cost of a 4-year degree would be 75% of a person’s income in Australia. The cost would be above 250% in India and 500% in Turkey. As we have discussed before, the use of open-source technologies and OER may be the way forward. For this dimension of openness, at one end, the cost is free (as may be the case in Germany) while at the other end cost is high as in the USA where student loan is over one trillion USD (Friedman 2021) with average loan amount around USD 39,000. For educational institutions, courses/certificates can never be totally free of cost. However, calculating the cost as high or low can be done considering how much of the total delivery cost is subsidized. The more subsidy a course receives, the less cost it is to the learners. Any educational institution can apply the openness framework presented in this chapter. It could be more significant to virtual universities that aspire to promote equity, increase access, and lower cost while improving quality. As indicated before, the dichotomous focus on the ten dimensions has nine equal intervals giving flexibility for the institutions to self-assess their commitment and practice. Thus, each dimension can be scored from 1 to 10 with an overall score between 10 and 100. In Fig. 1, the application of the openness framework to a hypothetical institution, which scored 57. Using a range of high to low openness, this hypothetical institution could be considered to have “very good openness.” At the institutional level, such an analysis needs to be performed, including a wide range of stakeholders to arrive at a score that would reflect the nearest possible scenario for the organization and help develop strategies to become more open.

Recommendations for Virtual Universities Based on the ten dimensions of openness, the following recommendations would help build a resilient system to face future pandemics: • Adopt a flexible entry policy for anyone interested in pursuing a course. • Create a new revenue model for sustainable higher education. • Offer programs in multimodal pathways to enable learners to study from any location without being fixed to one system. A course may be offered in-person, online, and in blended mode to help the learner choose the most suitable option. • Reduce the need for synchronous meetings (either physical or online) to the minimum necessary to meet the learning outcomes. • Provide a variety of courses to choose from, and help the learners design their curriculum. • Build the capacities of teachers to adopt collaborative learning strategies online. • Invest in and use technology tools that are open source and reduce the cost of access to learners. • Embrace the use of open educational resources and open textbooks by adopting a policy for OER and curating relevant open textbooks.

444

S. Mishra

• Adopt a more flexible approach to assessment, providing how and when the learner may provide evidence of learning. • Offer courses in a modular and stackable manner to accumulate credentials within a lifelong learning framework.

Conclusion Considering the need to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all by 2030 as the mandate for achieving sustainable development goals, it is the responsibility of governments and educational institutions to adopt practices that could help achieve the same. As discussed, openness provides a robust framework to rethink education and training in tertiary education. Adopting the ten dimensions of openness would enable virtual universities and other higher education institutions to build resilient and future proof systems that are centers of sustainable academic and management practices. Openness fosters fairness, flexibility, and freedom. Fairness focuses on equity and inclusion – making learning accessible to all; flexibility offers opportunities – anytime, anywhere, and any media; and freedom ensures sharing learning resources, collaboration, and cocreating knowledge for development (Mishra 2020).

Cross-References ▶ Open Educational Practice as an Enabler for Virtual Universities

References Ali, H.I.H., and A. Alhassan. 2021. Fighting contract cheating and ghostwriting in higher education: Moving towards a multidimensional approach. Cogent Education 8 (1): 1885837. https:// doi.org/10.1080/2331186X.2021.1885837. Antinori, A., O.L. Carter, and L.D. Smillie. 2017. Seeing it both ways: Openness to experience and binocular rivalry suppression. Journal of Research in Personality 68: 15–22. https://doi.org/10. 1016/j.jrp.2017.03.005. BC Campus. n.d. Open textbook stats. https://open.bccampus.ca/advocate-for-open-education/ open-textbook-stats/ Brake, D. 2013. Are we all online content creators now? Web 2.0 and digital divides. Journal of Computer-Mediated Communication 19: 591–609. http://onlinelibrary.wiley.com/doi/10.1111/ jcc4.12042/abstract. Brown, M., M.N.G. Mhichil, E. Beirne, and C. Mac Lochlainn. 2021. The global micro-credential landscape: Charting a new credential ecology for lifelong learning. Journal of Learning for Development 8 (2): 228–254. https://jl4d.org/index.php/ejl4d/article/view/525. Butcher, N., and S. Hoosen. 2012. Exploring the business case for open educational resources. Commonwealth of Learning. http://oasis.col.org/handle/11599/57 Chemero, A. 2003. An outline of a theory of affordances. Ecological Psychology 15 (2): 181–195. COL. 2017. Towards national policy guidelines on open educational resources in Malaysia. Commonwealth of Learning. http://oasis.col.org/handle/11599/2739

22

The Affordances of Openness for the Virtual University

445

Colvin, B.B. 2013. Where is Merlin when I need him? The barriers to higher education are still in place: Recent re-entry experience. New Horizons in Adult Education and Human Resource Development 25 (2): 19–32. Crompton, H., D. Burke, K. Jordan, and S.W.G. Wilson. 2021. Learning with technology during emergencies: A systematic review of K-12 education. British Journal of Educational Technology 52 (4): 1554–1575. Cronin, C. 2017. Openness and praxis: Exploring the use of open educational practices in higher education. The International Review of Research in Open and Distributed Learning 18 (5): 1–18. https://doi.org/10.19173/irrodl.v18i5.3096. Daniel, J. 2016. Making sense of flexibility as a defining element of online learning. https:// teachonline.ca/fr/node/85357 DeYoung, C.G. 2015. Openness/intellect: A dimension of personality reflecting cognitive exploration. In APA handbook of personality and social psychology, Vol. 4. Personality processes and individual differences, ed. M. Mikulincer, P.R. Shaver, M.L. Cooper, and R.J. Larsen, 369–399. American Psychological Association. https://doi.org/10.1037/14343-017. Du, X., M. Zhang, B.E. Shelton, and J.L. Hung. 2019. Learning anytime, anywhere: A spatiotemporal analysis for online learning. Interactive Learning Environments: 1–15. https://doi.org/ 10.1080/10494820.2019.1633546. Emarge Ed Consultants. 2017. Impact of open textbook adoption at Antigua State College. Commonwealth of Learning. http://oasis.col.org/handle/11599/2750 Friedman, Z. 2021. Student loan debt statistics in 2021: A record $1.7 Trillion. https://www.forbes. com/sites/zackfriedman/2021/02/20/student-loan-debt-statistics-in-2021-a-record-17-trillion/? sh¼5abc46a41431 Gallagher, S., and J. Palmer. 2020. The pandemic pushed universities online. The change was long overdue. Harvard Business Review. https://hbr.org/2020/09/the-pandemic-pushed-universitiesonline-the-change-was-long-overdue Gibson, J.J. 1979. The ecological approach to visual perception. Boston: Houghton Mifflin. Hegarty, B. 2015. Attributes of open pedagogy: A model for using open educational resources. Educational Technology: 3–13. Holmberg, B. 1986. A discipline of distance education. Journal of Distance Education 1 (1): 25–40. ICEF Monitor. 2020. Demand for online learning and alternative credentials surges during COVID19. https://monitor.icef.com/2020/08/demand-for-online-learning-and-alternative-credentialssurges-during-covid-19/ Irwin, B., and S. Hepplestone. 2012. Examining increased flexibility in assessment formats. Assessment & Evaluation in Higher Education 37 (7): 773–785. https://doi.org/10.1080/ 02602938.2011.573842. Jhangiani, R., and S. Jhangiani. 2017. Investigating the perceptions, use, and impact of open textbooks: A survey of post-secondary students in British Columbia. The International Review of Research in Open and Distance Learning 18 (4). https://doi.org/10.19173/irrodl.v18i4.3012. Kabudi, T., I. Pappas, and D.H. Olsen. 2021. AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence 2: 100017. https:// doi.org/10.1016/j.caeai.2021.100017. Kaufman, S.B., L.C. Quilty, R.G. Grazioplene, J.B. Hirsh, J.R. Gray, J.B. Peterson, and C.G. DeYoung. 2016. Openness to experience and intellect differentially predict creative achievement in the arts and sciences. Journal of Personality 84 (2): 248–258. Kazeem, Y. 2017. Only one in four Nigerians applying to university will get a spot. https://qz.com/ africa/915618/only-one-in-four-nigerians-applying-to-university-will-get-a-spot/ Keegan, D. 1996. Foundations of distance education. 3rd ed. London: Routledge. Kember, D. 2007. Reconsidering open and distance learning in the developing world: Meeting students’ learning needs. Routledge. Lakhan, S.E., and K. Jhunjhunwala. 2008. Open source software in education. Educause Quarterly 31(2): 32–40. https://er.educause.edu/articles/2008/5/open-source-software-in-education

446

S. Mishra

Lancaster, T., and C. Cotarlan. 2021. Contract cheating by STEM students through a file sharing website: A Covid-19 pandemic perspective. International Journal for Educational Integrity 17 (1): 1–16. https://doi.org/10.1007/s40979-021-00070-0. Lasselle, L. 2016. Barriers to higher education entry – A Scottish rural perspective, special edition: Widening access to higher education in Scotland. Scottish Educational Review 48 (1): 78–88. Maddux, C.D., and L.M. Johnson. 2014. Anytime, anywhere learning and the critical need for expert, anytime, anywhere technical support. Computers in the Schools 31 (31): 117–118. McCrae, R.R., and P.T. Costa Jr. 1997. Conceptions and correlates of openness to experience. In Handbook of personality psychology, ed. R.H.J. Briggs, 825–847. Academic. Mishra, S. 2020. Understanding open education. https://www.col.org/news/col-blog/understandingopen-education Mulder, F. 2015. Open (ing up) education for all. . . boosted by MOOCs? In MOOCs and open education around the world, ed. C.J. Bonk, M.M. Lee, T.C. Reeves, and T.H. Reynolds, xvii–xxvii. Routledge. Naidu, S. 2009. Pedagogical affordances of technology. In E-learning, ed. S. Mishra, 4–13. Indira Gandhi National Open University. ———. 2016. The case for open educational practice. Distance Education 37 (1): 1–3. https://doi. org/10.1080/01587919.2016.1157010. Ouyang, F., and P. Jiao. 2021. Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence 2: 100020. https://doi.org/10.1016/j.caeai.2021.100020. Perry, W. 1976. Open University: A personal account of the first vice-chancellor. The Open University Press. Perry, M.J. 2016. Chart of the day: The astronomical rise in college textbook prices vs. consumer prices and recreational books. https://www.aei.org/carpe-diem/chart-of-the-day-the-astronomi cal-rise-in-college-textbook-prices-vs-consumer-prices-and-recreational-books/ Prasad, S.K. 2008. Examination on-demand: Implementation strategies in open schooling system. Paper presented at the Pan-Commonwealth Forum on Open Learning (PCF 5), London, 13–17 July 2008. http://oasis.col.org/handle/11599/3168 Reed, E.S. 1996. Encountering the world. Oxford University Press. Rhoads, D. 2019. Increasing flexibility, satisfaction, and efficiency using the hybrid flexible approach. In Hybrid-flexible course design, ed. B.J. Beatty. EdTech Books. https:// edtechbooks.org/hyflex/increasing_flexibility Rossiter, D., and B. Tynan. 2019. Designing and implementing micro-credentials: A guide for practitioners. Commonwealth of Learning. http://oasis.col.org/handle/11599/3279 Stoffregen, T. 2000. Affordances and events. Ecological Psychology 12: 1–28. Taylor, J.C. 2001. Fifth generation distance education. Instructional Science and Technology 4(1): 1–14. https://eprints.usq.edu.au/136/ Turvey, M. 1992. Affordances and prospective control: An outline of the ontology. Ecological Psychology 4: 173–187. UNESCO. 1996. Learning: the treasure within. UNESCO. ———. 2002. Forum on the impact of Open Courseware for higher education in developing countries: Final report. UNESCO. http://unesdoc.unesco.org/images/0012/001285/128515e.pdf ———. 2019. Global convention on the recognition of qualifications concerning higher education 2019. UNESCO. http://portal.unesco.org/en/ev.php-URL_ID¼49557&URL_DO¼DO_ TOPIC&URL_SECTION¼201.html ———. 2020. Towards universal access to higher education: international trends. UNESCO. https://globaleducationforum.org/wp-content/uploads/2021/10/DOC-11-Towards-universalaccess-to-higher-education-international-trends.pdf United Nations. 2015. Transforming our world: the 2030 agenda for sustainable development. https://www.unfpa.org/sites/default/files/resource-pdf/Resolution_A_RES_70_1_EN.pdf Vandenbosch, B. 2020. Most popular courses of 2020: A year of mental health, contract tracing, and job-relevant skills. https://blog.coursera.org/most-popular-courses-of-2020-a-year-of-mentalhealth-contract-tracing-and-job-relevant-skills/

22

The Affordances of Openness for the Virtual University

447

Westbrook, J., N. Durrani, R. Brown, D. Orr, J. Pryor, J. Boddy, and F. Salvi. 2013. Pedagogy, curriculum, teaching practices and teacher education in developing countries. Department for International Development. https://eppi.ioe.ac.uk/cms/Portals/0/PDF%20reviews%20and% 20summaries/Pedagogy%202013%20Westbrook%20report.pdf?ver¼2014-04-24-121331-867 Wood, L., and G. Smith. 1999. Flexible assessment. Paper presented at The Challenge of Diversity: The Delta 99 Symposium on Undergraduate Mathematics, November 23, in Queensland, Australia. World Bank. 2021. Higher education: Context. https://www.worldbank.org/en/topic/ tertiaryeducation#1 Zheng, X., T.E. Johnson, and C. Zhou. 2020. A pilot study examining the impact of collaborative mind mapping strategy in a flipped classroom: Learning achievement, self-efficacy, motivation, and students’ acceptance. Educational Technology Research and Development 68 (6): 3527–3545. https://doi.org/10.1007/s11423-020-09868-0.

Part VIII New and Alternate Forms of Credentialing

Micro-credentialing Models and Practice

23

Ratna Selvaratnam

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Student Demographic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Global Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Australasia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Success Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

452 453 454 456 459 460 461 463 463

Abstract

Micro-credentials can range from MOOCs and short courses to alternative credentials that can also be complementary to an award. While they can be offered in person, micro-credentialing lends itself very well to virtual environments due to its flexibility and portability. This is important as the student demographic for these courses tends to be the lifelong learning target market. Lifelong learners are likely to be working in jobs and need flexible modes of study, with onsite education generally a challenge due to time and travel constraints. Options a virtual university can provide are desirable to this growing cohort. With the right value proposition and support, a global student cohort is also achievable especially with travel restrictions that have recently come into place with the COVID-19 pandemic. While the potential for micro-credentials as successful offerings in a virtual university is nascent, it is not clear what success metrics can be used to ensure relevancy and remain contemporary. This chapter will examine microcredential models and practice and suggests strategies that can contribute to some R. Selvaratnam (*) Centre for Learning and Teaching, Edith Cowan University, Joondalup, Australia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_23

451

452

R. Selvaratnam

of these approaches. These strategies are grounded in the student experience and success of the lifelong and global learner demographic. There will be a particular emphasis in Australasia as the author has conducted surveys on the state of microcredentialing in the region on behalf of the Australasian Council on Open, Distance and e-Learning (ACODE). The author also suggests evidence-based measures of success that can be used to implement and sustain a successful approach to microcredentials particularly in a virtual university. Keywords

Micro-credentials · Lifelong learning · Virtual university

Introduction This chapter examines micro-credential models and practice from a global perspective and suggests strategies that can contribute to some of these approaches. Some of the more mature models stem from Europe. This is likely due to national cooperation and bilateral ties which already exist in multiple political, economic, and social spheres. The literature available is also predominantly North American and European. Hence, the analysis that follows with regard to good practice strategies to adopt may not be universal but will go a long way in informing this still new area of micro-credentials. These strategies are grounded in the student experience and success and lifelong and global learner demographic. The chapter concludes with some recommendations for adoption drawn from similarities and approaches to different models of micro-credentialing globally. The definition of a micro-credential is generally accepted to be a certification of assessed learning which complements formal qualification within or outside of the qualification (Oliver 2019). Micro-credentials range from Massive Open Online Courses (MOOCs) and short courses to alternative credentials that can also be complementary to an award. This is not new to the market anymore though meaningful credentialing is still a maturing field. While they can be offered in person, micro-credentialing lends itself very well to virtual environments due to its flexibility and portability. Virtual infrastructures can offer and track such credentials more accurately, with various data points to capture completion and competencies from learning management systems to customize student information systems. As per the premise of this book, micro-credentials have the ability to provide an equivalence of experience in the online space, making possible the success of the virtual university. The increasing options of technology enhanced learning applications, such as credentialing platforms and delivery modes, make establishing a fully virtual university, which offers a suite of award and non-award programs consisting of microcredentials and degrees, an attractive value proposition. The interest in, and proliferation of, micro-credentials has been heightened by the COVID-19 pandemic. While the current continuing uncertainty of the pandemic has no doubt made a significant impact on all sectors of society, of note to this audience

23

Micro-credentialing Models and Practice

453

is the impact on higher education. The pivot to emergency remote teaching (Hodges et al. 2020) when campuses were shut to students and staff has driven home the need for rethinking delivery and modality in higher education. To provide this, there is a growing exploration of hybrid and flexible delivery modes. One such example is the HyFlex model (Columbia CTL 2021) to cater for diverse cohorts of students and different ways they need to access education, both on and off campuses and synchronous and asynchronous. Governments have intervened in varying degrees to provide support for the continuity of education for its citizens. Governments in countries like Australia have articulated COVID-19 economic recovery paths which includes the provision of short online courses in high-demand areas (Department of Education, Skills and Employment 2020) to skill up and reskill citizens for employment. A review of the Australian Qualifications Framework (AQF) (Noonan 2019) was recently conducted and found it needed to be more responsive to contemporary needs in the industry. The recommendation is to broaden guidelines for credit recognition across AQF qualifications and to define and provide for the recognition of micro-credentials. This chapter will further detail how Australian universities have embraced this as part of an approach to micro-credentialing as an example of practice and implementation of strategies to do so successfully in a young field. As student experience is key to any successful learning program, we will now explore the student profile for micro-credentials.

Student Demographic There are various types of learners who take up micro-credentials, one of the reasons being it meets the needs of diverse age learners (Fedock et al. 2016). A distinct cohort are lifelong learners, many of whom are looking to reskill either of their own accord or due to other incentives such as discounted fees or government support for priority national employment skills to focus on certain industries. This is accelerated due to economic recovery pathways within the current pandemic. Lifelong learning can be conceptualized as a connected ecosystem to even include larger national imperatives. In the current disruption to learning, it is an opportunity to ride the wave of uncertainty by equipping the learner as best as possible, in many cases reskilling to meet the current demand. Student access and equity can avail of these new offerings through infrastructure that is fit for purpose (Kift 2021). These courses in turn service the lifelong learning target market while ideally keeping abreast of employer demands which are the largest motivator for lifelong learners, i.e., job or work relevancy. Lifelong learners are likely to be working in jobs and need flexible modes of study, with onsite education generally a challenge due to time and travel constraints. The inbuilt option for flexibility a virtual university can give is desirable to this growing cohort. Overall, what is important for an employee is flexibility of time and opportunity to improve skills (Nic Giolla Mhichíl et al. 2020). When learning is driven by employers, they need to recognize that employees see priorities between work deliverables and commitment to lifelong learning as possibly stressful to resolve. Micro-credentials need

454

R. Selvaratnam

to complement the commitment to work. The employer-employee nexus is a key driver for micro-credentials demand. European Centre for the Development of Vocational Training (CEDEFOP) (2021) reported that in the specific context of remote online work, micro-credentials positively influenced job prospects for a third of employees. This scenario is predicated on the relatively impersonal relationships between service providers, client, and employee. The engagement with these credentials is perceived as a signal of the overall trustworthiness and experience of job candidates in working relationships that are relatively impersonal. Overall, employees recognize micro-credentials help them keep abreast of developments in their skill areas especially since commitment to continuous professional development that is credentialed is significant for promotions and, in many cases, staying in the job. Hence, employees are not always looking to stack credentials into larger awards or qualifications. The Organisation for Economic Co-operation and Devlopment (OECD) conducted a large-scale global survey on adult skills, garnering important data on participation in non-formal education and training (Kato et al. 2020). Important insights into learner motivation in engaging with alternative credentials emerged. It is not just the acquisition of skills and knowledge that is important to the learner but also its verification, which is seen as ensuring credibility. The data shows the profile of lifelong learners tended to be those who have completed higher education and generally have a higher level of skills. They, and those with higher literacy skills, are subsequently more likely to participate in non-formal education and training than those without higher education. Another set of lifelong learners are those who earn at least an average wage and work in larger companies. Concordantly, employers are encouraging of employees pursuing non-traditional education. Some employers like Google and Microsoft offer their own internal certifications to ascertain whether the employee now has the skills and knowledge needed for the job they are in (Busteed 2021). When employers invest in non-traditional education for employees, especially with the preference for fully online for flexibility, it weakens the demand for traditional degrees. In addition to lifelong learners, even high school graduates are beginning to question the value of going to university. Higher education costs are getting more expensive so many are looking for alternative ways to secure and progress in jobs (Horton 2020). They are also not limiting their prospects to local education, but micro-credentialing is an opportunity to look further abroad.

Global Profiles With the right value proposition and support, a global student cohort is also achievable especially with travel restrictions that have recently come into place with the COVID-19 pandemic. Horton (2020) identified global trends that enable the growth of micro-credentials. One of these is the demand for quality, and for developing nations, this usually means foreign, university degrees. Their local providers are usually perceived as not ready to meet the demand for quality education

23

Micro-credentialing Models and Practice

455

which meets international standards. The other trend that enables the growth of micro-credentials is “digital transformation” that industries are investing in. The skills required for workers in these industries lend themselves well to flexible microcredentials such as in the areas of information technology. Geography is not pertinent for such study, just access to a computer and the Internet. To facilitate learning across borders, Chakroun and Keevy (2018) propose what they refer to as World Reference Levels (WRLs). Countries are increasingly updating their qualifications frameworks to accommodate the rise of micro-credentials with an example of one of the first being the New Zealand Qualifications Agency (2018). The WRL will help create a space to discuss the recognition of qualifications. What would be key is to also develop a common language to recognize such learning internationally. The engagement with the ecosystem of micro- and digital credentials has the involvement of global players which shows progress toward international mobility and portability of credentials. While global reach is proliferating for learners across borders, so are various national systems attempting to recognize and include micro-credentials in their higher education qualifications frameworks. Southeast Asia, Malaysia, and Singapore are good examples. The Malaysian Qualifications Authority (2020) has incrementally developed its approach to micro-credentials over the last 3 years. It has provided guidance for higher education providers both to develop and unbundle existing awards to offer quality micro-credentials within the ambit of the higher education framework. It considers the learner profile as non-traditional. Interestingly, it offers voluntary quality assessments to help these institutions differentiate themselves in the higher education market as quality microcredential providers. In Singapore, the government has introduced the SkillsFuture Series alongside free credentials to focus on what it considers eight priority areas for the country (Bhunia 2018). These emerging skill areas are to further support lifelong learning directions from the government. To that end, universities have introduced various programs such as extending enrolment to 20 years and offering additional free courses, and sometimes credits, during enrolment. Changes can also be seen in Africa and Latin America. While it requires a dedicated data gathering of where universities are at, there are some snapshots that are useful. Ghasia et al. (2019) have studied the effort in micro-credentials in Tanzania and make key recommendations for a successful way forward at a national level. Their recommendations include the creation of a micro-credentials ecosystem, formulation and coherence of strategies and policies to support this, and importantly the necessary infrastructure and the people who can support it. Latin America’s approach is young and bold to address what appears to be a “bottleneck in the education pipeline” (Penarredonda 2019). While about half of the young people are enrolled in tertiary education, there are much smaller numbers in universities. One of the biggest hindrances is access which is not equal. The main reason is due to lower socioeconomic demographics of half of the population. Lower cost and access to micro-credentials can change this. The right model can resolve issues of access.

456

R. Selvaratnam

Models While the intersection of demand from lifelong learners, employer requirements, governmental recognition, and global mobility of micro-credentials has been discussed, how they all come together to form a coherent model for delivery is the focus of this section. A scan of the literature shows there is no single way of doing this, nor are there many models in practice that clearly capture the end-to-end process of policy, provision, and consumption of micro-credentialing. Governments are recognizing the importance of upskilling and reskilling citizens for employment. Such an effort will reflect positively in national economic indicators. To achieve this end, governments are beginning to provide national-level incentives and enablers to support micro-credentialing. In Singapore, the Ministry of Education (2021) has announced its two-track higher education providers’ system will participate in the nation’s micro-credentialing agenda. The Institutes of Higher Learning (IHLs) will offer free Continuing Education and Training (CET) modules to all students graduating in 2021. Autonomous universities (AU), which consist of the more prestigious universities in the country, will allow graduates to continue to access free CET modules. These modules are free, and many can be stacked to form certifications. In Europe, the Erasmus+ program funds European Consortium of Innovative Universities (ECIU) to scope three types of micro-credentials (Brown et al. 2021). They are certifications for learning within and outside regular learning paths and non-formal/informal learning experiences. The technical work includes exploring solutions to facilitate learning across borders using the Europass and other interoperable digital platforms. The European conversation is relatively more mature in the microcredentialing space largely due to the portability of credentials across borders which is already available in the present practice for macro-awards. There is, however, an acknowledgment that assessment (Beirne and Nic Giolla Mhichíl 2020) is still an area that needs robust work. It is important for trust, recognition, and quality assurance. Fedock et al. (2016) posit that micro-credentials afford opportunities for the use of alternative assessments. Engaging in different types of learning meets the needs of diverse learners. The documented outcomes of these assessments become recognition signals for educational accomplishments and workplace efficiency. Hence, the work carried out by the European Commission is still grappling with ways to position micro-credentialing in the current European credential ecology. An attempt at a macro model was evident in UNESCO’s (2018) work which analyzed what worked in a high-level digital ecosystem for credentialing. It identifies storage, networks, information links, badging, and blockchain as architectures for consideration against scope, mobility, and security. This model can be used as a broader guide for planning for micro-credentials along with guiding principles at a more local level. The State University of New York (SUNY 2021) is an example of an institution that has developed guiding principles for their micro-credential policy. They touch on academic quality, consistency, market needs, industry partnerships, flexibility, portability, and stackability.

23

Micro-credentialing Models and Practice

457

These larger models and considerations flow on to more specific models on the delivery and stakeholder inclusions. Selvaratnam and Sankey (2021) have attempted to bring the different players and technology platform considerations together as depicted in Fig. 1. At the center of the practice is the student engaging with the virtual learning environment (VLE), the basis of learning and assessment. The learner profile sits within the student management system (SMS) where identity is assured and credits or certificates are assigned. These credentials are meaningful when they can be public-facing, and there are a variety of Credentialing Management Systems (CMS), such as Badgr, that can do this. Professional Representation Systems (PRS), such as LinkedIn, will allow employers to make hiring decisions through consumption of public-facing credentials. Oliver (2019) proposes a model where the nexus between learners, providers, and employers is the provision of both degree qualifications and micro-credentials. However, she also proposes that the provision of these micro-credentials takes on a partnership model between universities and third-party providers including industry partners. This partnership is likely to be both public and private. The clear advantage here is that the relevance for the micro-credential is established early in the planning, taking into account the needs of the employer. If the partnership is with industry, then the credentials are a representation of what is needed in the employment market with specific learning benchmarked against currency of employment needs. Her model also charts a distinct role for the employer in the ecosystem as the

Fig. 1 Micro-credentials and stakeholder engagement with relevant technologies (Selvaratnam and Sankey 2021)

458

R. Selvaratnam

source of not only the market demand but in many instances offering credentials themselves. Here, the employer provides the micro-credentials or in-house certifications, and the employee learns and earns from the employer. Relevance of such a micro-credential to job performance is then very clear. However, the journey of the learner is not only limited to the employer but can span the lifetime of the employee. Yet, Rossiter and Tynan (2019) suggest the learner very much has agency. As the learner is now also an earner, they can stack credentials as they go along their lifelong learning journey from various providers including their employers. The value of the university is still important in this public-private partnership model to provide micro-credentials. While employers and other third-party providers can offer micro-credentials independently, partnering with universities is advantageous in that it brings pedagogical expertise and quality assurance processes, which complement the immediate relevance and direct workplace experience that employers can provide. JISC and Emerge Education (2020) depict the strengths and value of the employer university partnership in Table 1. The value of the partnership is evident in terms of student acquisition, course design, course delivery, and student success. Blending in learning and career choice right from the beginning through relevant courses and currency of knowledge is the proposed win. This model provides the opportunity to facilitate experiential learning through projects and partnerships which gets the learners work-ready while studying. While it seems clear there are real benefits in offering micro-credentials through public-provider partnerships with employers, some argue that the burden on the learner needs consideration. There may be a shifting of the training burden to the employee rather than the prevalent practice of employers providing on-the-job training. Brown and Souto-Otero (2020) outline the concern that the cost of internal training then shifts from the employer to the individual. The learner does not always have informed agency to choose the right micro-credentials for their needs. Additionally, if there is no clarity in the structure of learners earning relevant credentials for employment, the learner or employee is now left to guess what would be relevant Table 1 Value of the employer-university partnership model in providing micro-credentials (JISC and Emerge Education 2020)

23

Micro-credentialing Models and Practice

459

for employment (Wheelahan and Moodie 2021). However whether this is different to the earning of other awards is arguable. The partnership model for delivery can take several shapes. It can be provideremployer partnerships, but it can also be provider-provider partnerships. There are many examples of provider-employer partnerships. One of these is the model of trusted educational providers partnering with Skillnet Networks (Nic Giolla Mhichíl et al. 2020). It has begun piloting models of co-design, co-delivery, and co-development within and across sectors. It both promotes and funds these initiatives not just with potential employers and learners that are members but also with professional bodies that bring a strong element of quality assurance. An example of a provider-provider partnership is the University of Leeds partnership with FutureLearn, an established provider of MOOCs (Contact North 2018). The model bundles a selection of short programs from FutureLearn in a coherent manner to map to a single qualification. MOOCs are generally free or low-cost, which presents a low-risk entry into higher education studies for registrants. On-campus students also have the opportunity to take MOOCs as credit-bearing electives. This provides flexibility and reduces the administrative burden on the university. FutureLearn is not the only third-party provider in the market which universities can consider partnering with. Other providers such as Coursera and edX provide different types of micro-credentials ranging from professional certificates to MicroMasters (Shah 2020). Some non-university providers build a clear niche in the market themselves, pitching unique industry linkages and direct relevance to employability. Udacity’s (2021) slogan “Get Real. Employability Skills” pitches directly to the learner and employers through their trademarked “nano-degrees.” Their offerings are fully online and provide what they position as four key valueadds, i.e., real-world projects from industry experts, technical mentor support, career services, and a flexible learning program. Udacity positions its online offering as bridging the “gap between learning and career goals,” possibly hinting at traditional higher education institutions’ deficiency in this space. However there also needs to be a consideration of how the learner figures in the provider partnerships.

Australasia Using Australasia as an example, we will now consider the impact on the learner. The Australasian landscape in micro-credentials is still maturing, as with many other regions. Two important surveys were recently conducted on the state of microcredentialing in Australasia on behalf of the Australasian Council on Open, Distance and e-Learning (ACODE). Selvaratnam and Sankey’s findings in 2019 indicated most Australasian higher education institutions are already working to varying degrees on micro-credentialing or are at least planning to do so. The trend for initial offerings seems to be targeted at short courses and postgraduate programs. These courses are ripe for initial conversion to micro-credentials by nature of their shorter structures. An area for higher education institutions to work on would be producing policies that govern this work in their institutions and to formalize micro-

460

R. Selvaratnam

credentialing. The authors’ follow-up survey in 2020 shows the encouraging growth in initiatives across all micro-credentialing efforts including policy, platform, and delivery. There is also an indication that business-to-business, business-to-government, and provider partnership models are being explored. The general growth in effort is likely precipitated by the COVID-19 pandemic. The related government initiatives in incentivizing an increase for providers to offer short courses and the introduction of undergraduate certificates are also likely to have contributed. It is clear that if not already well-underway, institutions are beginning to work on microcredentialing strategies. The authors (Selvaratnam and Sankey 2020) note that the data indicates higher education institutions also need to rethink their operating structures. Current operating structures quite often are obstacles to design the offering of microcredentials. Across the sector, governments are considering different funding models for micro-credentials as the old models do not fit. The current learner profile shows they are less likely to have time on their hands as they are workers looking for additional training while in their current employment or looking to move to new roles. Being time-poor and having to pay upfront for microcredentials is a concern for many learners. There is now an argument for the Australian government to provide financial assistance for the study of microcredentials, just like they do for awards (Zhou 2021). This financial incentive improves the uptake of micro-credentials. The Business Council of Australia (2018) recently recommended a rethink of the provider-centered system to one which is leaner- and employer-centered enabled by relevant infrastructure. The ease of navigation between employers and individuals is underpinned by three requirements. Firstly, there needs to be a system that allows individuals to identify their strengths and career paths. Secondly, a funding model needs to be conceptualized to allow for access to learning from the right provider. The third requirement is the ability for individuals to produce a record of skills and knowledge development. The Australian government has recently promised AUD $4.3 million to build and run a central platform for micro-credentials which is envisioned to help students identify educational opportunities. This platform will act as a marketplace to provide a nationally consistent space to “compare course outcomes, duration, mode of delivery and credit point value” (Tehan and Cash 2020). The various efforts to encourage and accelerate the growth of micro-credentials also need an approach to evaluate the success of the strategies.

Success Measures With micro-credentials being such a new field in the higher education landscape, it is likely too early to establish success measures that have been robustly tested in the market, especially if the target is employability outcomes. Research into the relationship between qualifications and employment shows this is not easily defined, but it does indicate a rethink of the role of general credentials in the employment market

23

Micro-credentialing Models and Practice

461

(Brown and Souto-Otero 2020). There is limited literature to show evidence-based measures of success that can be used to implement and sustain a successful approach to micro-credentials particularly in a virtual university. Success measures are further problematic when there is no clear or common roadmap or framework for the delivery of micro-credentials within nations and across borders. Such a framework needs to be created in the first instance, and in the case of more mature credentialing efforts in certain regions, these frameworks need to be tested and clearly establish meaningful partnerships between different stakeholders and the enabling technologies to connect the stakeholders and the micro-credentials. There is also a word of caution that there needs to be further research in credentials awarded by employers (Gallagher 2021). These are not regulated the same way higher education institutions are which are generally subject to quality assurance processes established by national governments. Hence, employer-awarded credentials are again not commonly recognized among stakeholders. Rossiter and Tynan (2019) propose general success metrics that include impact, cost-benefit analysis, strategic alignment to larger goals, and sustainability across the components of the ecosystem, measuring product quality and efficiency. They also emphasize that the relationships within these models need to reflect successful interactions between learner and stakeholder experience and industry and employer acceptance. We can apply these success metrics within four umbrella considerations gleaned from preceding discussions, namely, that of student support including financial and learning success, portability both within and across borders, public and private partnerships provisioning credentials, and provider and employer cooperation, and finally provisioning of micro-credentials across multiple technology platforms especially relevant to virtual universities. See Fig. 2 for a summary of the proposed success metrics. The used case that could work in the model captured in Fig. 2 could, for example, be the lifelong learner student demographic who would benefit from establishing a global profile while thriving locally within the Australasian context in terms of employability success. This model addresses the ever-evolving success metrics that are not directly measured in other models as discussed previously. These success metrics would clearly be applicable in a virtual university to inform the constant design and delivery iteration of both award and non-award programs to ensure relevancy and to remain contemporary.

Conclusion and Future Directions This chapter has sought to outline the contemporary considerations in the microcredentialing landscape with high relevance to a virtual university model. While this is an exciting field, it is also only just developing, albeit quite rapidly. The learner profile looking for micro-credentials is generally someone already in or seeking to enter the job market, ideally with the opportunity to have reached across borders. Employer partnerships with providers, or as providers themselves, ensure the success of the learner mapping the learning to relevant employment skills.

462

R. Selvaratnam

Student support

Success metrics Impact Cost-benefit Plaorms

Strategic alignment Sustainability

Portability

Quality Stakeholder experience Industry acceptance

Partnerships

Fig. 2 Proposed success metrics of micro-credentials against key criteria

Governments are also stepping in with the realization that national employability metrics can be improved with micro-credentials offering (re)skilling opportunities for positive employment outcomes. This is evidenced by several national incentives for providers to speed up the diversification of their offerings. This chapter has proposed a simple approach to measure the success of micro-credentials. Universities can use these success metrics in planning to offer micro-credentials in a virtual university either for the first time or as part of a continuous improvement cycle. However, it is recommended that more research be done to establish robust success measures to ensure the sustainability of the micro-credentialing initiatives both by traditional and non-traditional providers for the longer-term assurance of quality and relevance. The learner success is at the center of any model for microcredentialing policy, provision, and consumption; hence the learner’s agency is key for the larger growth and success of any micro-credentialing effort. Additionally, the rapidly evolving technological landscape, especially accelerated by the global pandemic necessitating agile reskilling, makes any single static model obsolete very quickly. This results in learner success also needing clearer measurements and development to remain contemporary.

23

Micro-credentialing Models and Practice

463

Cross-References ▶ Innovation and the Role of Emerging Technologies ▶ Preparing Students for the Future of Work and the Role of the Virtual ▶ The Opportunities and Challenges in the Portability and Authentication of Microcredentials and Short Courses in a Post-COVID Landscape

References Beirne, E., M. Nic Giolla Mhichíl, and M. Brown. 2020. Micro-credentials: An evolving ecosystem. Dublin City University. https://www.skillnetireland.ie/wp-content/uploads/2020/08/MicroCredentials-An-Evolving-Ecosystem-Insights-paper.pdf Bhunia, P. 2018. Singapore government aims to develop lifelong learners in preparation for a dynamic future. Open Gov, March 7. https://opengovasia.com/singapore-government-aims-todevelop-lifelong-learners-in-preparation-for-dynamic-future/ Brown, P., and M. Souto-Otero. 2020. The end of the credential society? An analysis of the relationship between education and the labour market using big data. Journal of Education Policy 35 (1): 95–118. https://doi.org/10.1080/02680939.2018.1549752. Brown, M., M. Nic Giolla Mhicil, C. Mac Lochlain, H. Pirkkalainen, and O. Wessels. 2021. Supporting the micro-credentials movement, ECIU white paper on micro-credentials. ECIU University. https://doi.org/10.5281/zenodo.4438507. Business Council of Australia. 2018. Future-proof: Australia’s future post-secondary education and skills system. https://www.voced.edu.au/content/ngv:80598 Busteed, B. 2021. Pandemic to permanent. Forbes, May 2. https://www.forbes.com/sites/ brandonbusteed/2021/05/02/pandemic-to-permanent-11-lasting-changes-to-higher-education/amp/ CEDEFOP. 2021. Microcredentials: Are they here to stay? CEDEFOP: European Centre for the Development of Vocational Training. https://www.cedefop.europa.eu/en/news-and-press/news/ microcredentials-are-they-here-stay Chakroun, B., and J. Keevy. 2018. Digital credentialing: Implications for the recognition of learning across borders. https://unesdoc.unesco.org/ark:/48223/pf0000264428 Columbia CTL. 2021. Hybrid/hyflex teaching and learning. Columbia University. https://ctl. columbia.edu/resources-and-technology/teaching-with-technology/teaching-online/hyflex/ Contact North. 2018. Projects with multiple uses: Micro-credentials for undergraduate and graduate students and support courses for online and on-campus graduate students at the University of Leeds, England. TeachOnline.ca. https://teachonline.ca/pockets-innovation/international/projectsmultiple-uses-micro-credentials-undergraduate-and-graduate-students-and-support-courses Department of Education Skills and Employment. 2020. Short, online courses available. https:// www.dese.gov.au/news/short-online-course-available Fedock, B., M. Kebritchi, R. Sanders, and A. Holland. 2016. Digital badges and micro-credentials: Digital age classroom practices, design strategies, and issues. In Foundation of digital badges and micro-credentials, ed. D. Ifenthaler, N. Bellin-Mularski, and D.K. Mah. Springer, Switzerland. https://doi.org/10.1007/978-3-319-15425-1_15. Gallagher, S. 2021. More employers are awarding credentials. Is a parallel higher education system emerging? Ed Surge, March 25. https://www.edsurge.com/news/2021-03-25-more-employersare-awarding-credentials-is-a-parallel-higher-education-system-emerging? Ghasia, M.A., H.J. Machumu, and E. DeSmet. 2019. Micro-credentials in higher education institutions: An exploratory study of its place in Tanzania. International Journal of Education and Development using Information and Communication Technology 15 (1): 233–244. https:// files.eric.ed.gov/fulltext/EJ1214271.pdf. Hodges, C., S. Moore, B. Lockee, T. Trust, & A. Bond. 2020. The difference between emergency remote teaching and online learning. Educause Review, March 27. https://er.educause.edu/ articles/2020/3/the-difference-between-emergency-remote-teaching-and-online-learning

464

R. Selvaratnam

Horton, A.P. 2020. Could micro-credentials compete with traditional degrees? The Future of Work, BBC, February 17. https://www.bbc.com/worklife/article/20200212-could-micro-credentialscompete-with-traditional-degrees JISC and Emerge Education. 2020. The future of employer-university collaboration- a vision for 2030. November 26. https://www.jisc.ac.uk/reports/the-future-of-employer-university-collaboration Kato, S., V. Galán-Muros, and T. Weko. 2020. The emergence of alternative credentials, OECD education working papers: No. 216. OECD Publishing, Paris. https://doi.org/10.1787/b741f39e-en. Kift, S. 2021. Foreword. Journal of Teaching and Learning for Graduate Employability 12 (1): i–v. https://doi.org/10.21153/jtlge2021vol12no1art1015. Malaysian Qualifications Authority. 2020. Guidelines to good practices: Micro-credentials. https:// www2.mqa.gov.my/qad/v2/garispanduan/2020/GGP%20Micro-credentials%20July% 202020.pdf New Zealand Qualifications Authority. 2018. Micro-credentials. https://www.nzqa.govt.nz/ providers-partners/approval-accreditation-and-registration/micro-credentials/ Nic Giolla Mhichíl, M., M. Brown, E. Beirne, and C. Mac Lochlainn. 2020. A micro-credential roadmap: Currency, cohesion and consistency. Dublin City University, Dublin. https://www. skillnetireland.ie/wp-content/uploads/2021/03/A-Micro-Credential-Roadmap-Currency-Cohe sion-and-Consistency.pdf. Noonan, P. 2019. Review of the Australian qualifications framework. https://docs-edu.govcms.gov. au/system/files/doc/other/aqf_review_2019_0.pdf Oliver, B. 2019. Making micro-credentials work for learners, employers and providers. Deakin University. http://dteach.deakin.edu.au/2019/08/02/microcredentials/. Penarredonda, J.L. 2019. The man who wants to make university degrees obsolete. Worklife 101, BBC, August 17. https://www.bbc.com/worklife/article/20190814-the-man-who-wantsto-make-university-degrees-obsolete Rossiter, D., and B. Tynan. 2019. Designing and implementing micro-credentials: A guide for practitioners. Commonwealth of learning knowledge series. http://oasis.col.org/handle/11599/3279 Selvaratnam, R, and M. Sankey. 2019. Micro-credentialing as a sustainable way forward for universities in Australia: Perceptions of the landscape. (ACODE 80 whitepaper). https:// www.researchgate.net/publication/337884817_Micro-credentialing_as_a_sustainable_way_for ward_for_universities_in_Australia_Perceptions_of_the_landscape ———. 2020. Survey of micro-credentialing practice in Australasian 2020. ACODE whitepaper. https://www.acode.edu.au/pluginfile.php/8411/mod_resource/content/1/ACODE_MicroCreds_ Whitepaper_2020.pdf Selvaratnam, R.M., and M. Sankey. 2021. An integrative literature review of the implementation of micro-credentials in higher education: Implications for practice in Australasia. Journal of Teaching and Learning for Graduate Employability 12 (1): 1–17. https://doi.org/10.21153/ jtlge2021vol12no1art942. Shah, D. 2020. Massive list of MOOC-based microcredentials. The Report, May 16. https://www. classcentral.com/report/list-of-mooc-based-microcredentials/ SUNY. 2021. Micro-credentials at the State University of New York. https://system.suny.edu/ academic-affairs/microcredentials/ Tehan, D., and M. Cash. 2020. Marketplace for online microcredentials. Ministers’ Media Centre, June 22. https://ministers.dese.gov.au/tehan/marketplace-online-microcredentials Udacity. 2021. Nano-degrees. https://www.udacity.com/nanodegree UNESCO. 2018. Digital credentialing report. https://unesdoc.unesco.org/ark:/48223/ pf0000264428 Wheelahan, L., and G. Moodie. 2021. Analysing micro-credentials in higher education: A Bernsteinian analysis. Journal of Curriculum Studies. 53 (2): 212–228. https://doi.org/10. 1080/00220272.2021.1887358. Zhou, N. 2021. Australian universities push for HECS-style loan system for non-degree short courses. The Guardian, May 7. https://www.theguardian.com/australia-news/2021/may/07/ australian-universities-push-for-hecs-style-loan-system-for-non-degree-short-courses

The Opportunities and Challenges in the Portability and Authentication of Micro-credentials and Short Courses in a Post-COVID Landscape Rachel Fitzgerald

24

and Henk Huijser

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contextualizing Micro-Ccredentials and Short Courses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Opportunities for Micro-credentials and Short Courses for the Virtual University . . . . . . . . . . . . Risks of Micro-credentials and Short Courses for the Virtual University . . . . . . . . . . . . . . . . . . . . . What’s Next for Micro-credentials in a Virtual University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

466 467 470 472 474 474 475 475

Abstract

This chapter considers the affordances of micro-credentials and short courses in the virtual higher education ecosystem. We explore the differences between micro-credentials and short courses and consider their value to learners and employers and the opportunities they present for the virtual university. We evaluate the general risks that short courses and micro-credentials present and what we perceive to be the risks of an ad hoc approach to engaging with short courses for skills development. We propose a strategic and coherent approach for the virtual university that enables collaborative and innovative approaches to higher education delivery. We believe that this approach could transform both the degree experience and lifelong learning opportunities, thus enabling the virtual university to maximize its position as an agile service model of flexible education, delivering the key skills that will be required in the workplace of tomorrow. R. Fitzgerald (*) The Faculty of Business, Economics and Law, University of Queensland, Brisbane, QLD, Australia e-mail: [email protected] H. Huijser Learning and Teaching Unit, Queensland University of Technology, Brisbane, Australia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_24

465

466

R. Fitzgerald and H. Huijser

Keywords

Micro-credentials · Short courses · Accreditation · Skills · Careers · Risk · Upskilling

Introduction Micro-credentials (which can also be referred to as short form credentials) and short courses) can offer students a timely and flexible way to explore subjects of interest. Unlike a higher education degree program, micro-credentials are courses that are self-contained and assessed units; short courses are similar, though normally without formal assessment. Both can offer learners certification and/or badged opportunities of learning, which sit well in digital portfolios (see Fig. 1). During the COVID-19 pandemic, skills-based, online courses and Massive Open Online Courses (MOOCs) have seen considerable growth from those seeking to upskill and gain new knowledge worldwide (Impey and Formanek 2021). While this growth can be attributed to low cost and ease of access, the value of short course qualifications for lifelong learning cannot be ignored. In the context of higher education and the virtual university, micro-credentials offer opportunities to widen participation, offer specific skills development, and develop a path to true flexible learning. These courses can enable learning opportunities for all learners, regardless of access and resources, and suit those who may be unable to participate in a traditional degree program. The UNESCO views micro-credentials and short courses as an opportunity to “help bridge the exacerbating gap in learning opportunities and outcomes in the world, including the digital divide” (UNESCO 2021). Indeed, it should be noted that huge surges in enrolment to short courses came from countries with lower mean wealth, during the pandemic (Impey and Formanek 2021). Short courses and micro-credentials can also add value to traditional degree programs, enabling learners to demonstrate specific competences and knowledge

Fig. 1 Open badges: new opportunities to recognize and validate achievements digitally (UNESCO 2020)

24

The Opportunities and Challenges in the Portability and Authentication. . .

467

to future employers and others (European Commission 2020; Oliver 2019). Microcredentials and short courses further offer opportunities to address global shortages of workplace skills (McGreal and Olcott 2022). Learners (and their employers) may even prefer engaging with short and targeted options instead of a full-degree program, yet this can create other challenges as limited duration courses do not enable some of the richer elements of a degree-based learning experience. Richer elements are often the holistic capabilities that are highly valued but often forgotten when we reduce learning to pure skills and outcomes. These elements may include the ability to critically reflect, to solve complex problems, and to engage in leadership, collaboration, creative thinking, and much more. Such holistic capabilities are also considered to be the key skills of the future workforce (World Economic Forum 2021, p. 12). These tacit and sometimes explicit skills and knowledges are usually developed over time in a carefully crafted, scaffolded degree (Carey 2015). In this chapter, we explore how the virtual university could navigate the risks and maximize the opportunities micro-credentials and short courses present, to deliver flexible, yet complex, scaffolded learning with embedded holistic capabilities.

Contextualizing Micro-Ccredentials and Short Courses There are many definitions of micro-credentials worldwide, and while globally there is not yet an agreed-upon, shared definition, for the purposes of this chapter, we use the definition accepted by the Australian National Microcredentials Framework (Department of Education, Skills and Employment 2021): A microcredential is a certification of assessed learning or competency, with a minimum volume of learning of one hour and less than an AQF award qualification, that is additional, alternate, complementary to or a component part of an AQF award qualification. (p. 9)

This is similar to the European definition (European Commission 2020) whereby: A micro-credential is a proof of the learning outcomes that a learner has acquired following a short learning experience. These learning outcomes have been assessed against transparent standards.

Both micro-credentials and short courses are focused, short and vocational, and in this chapter, in line with the Australian National Microcredentials Framework, we therefore also make assessment the distinction between the two. Micro-credentials have specific learning outcomes that are assessed as per former definitions and therefore can be measured, which enables them to be stand-alone or part of a formal qualification (Brown et al. 2021; Cirlan and Loukkola 2020; Oliver 2019). Short courses offer skills development without formal assessment, and therefore the outcomes are less measurable. Either type of course can offer proof of learning such as digital badges and/or digital and paper certificates (Fig. 1) which can be used to build a digital portfolio of skills that aligns to modern recruitment practices, for example,

468

R. Fitzgerald and H. Huijser

LinkedIn, where employers and recruiters can connect directly with people with the right skills, and their badges represent achievements (Stone 2015). Often, these courses attest to specific knowledge or skills and competencies (Brown et al. 2021; Cirlan and Loukkola 2020), and while they do not have to be virtual to be labeled a short course, they often are. These courses present an innovative and flexible format that focuses on specific areas or skills and opens opportunities for flexible modularity, as well as fitting with trends of stackable credentials and the unbundling of education (Huijser et al. 2020). Although we make a distinction between short courses and micro-credentials, many providers do not, and this can cause confusion for both learners and employers. Just as higher and further education providers are developing their own micro-credentials and short courses, so too is the private sector on a large scale. There are many private and global providers of short courses such as FutureLearn, 2 U (formerly edX), Coursera, Google, Udemy, and LinkedIn Learning. Private providers offer a vast range of short courses, open to all and accessible online and delivered on global platforms, where users can crowd-share opinions on their value. The nomenclature around these courses can cause confusion regarding accreditation and alignment to frameworks. For example, FutureLearn, a global short course provider, describe their micro-credentials as professional credentials designed to build in-demand career skills (FutureLearn 2021). Additionally, there are a range of different titles that also represent short courses (see Table 1). This gives some insight into the broad and ever-changing array of options available to learners. Despite confusion about the definition of a micro-credential and short courses, and the vast array of different courses on offer, there is consistency about their focus on key skills for employment. Private providers have an agility to create focused, needs-driven curriculum, and this means they can quickly pivot to topics that are perceived to be needed now in the workplace. While there is generally limited research into the impact of short courses and micro-credentials in relation to career development (Boud and Jorre de St Jorre 2021; Healy 2021; Gauthier 2020), this does not appear to impact their overall popularity and growth. Short courses of all types continue to be touted as a means for learners to create a portfolio of “skills” that would be appealing to future employers or for career enhancement (Perna 2021). It is popular in general media to suggest that micro learning is the solution to education more generally and to suggest that this model will become the preferred Table 1 Class central list of short courses available (Shah 2021) Micro-credentials on the market today Platform Micro-credentials Coursera Specialization, MasterTrack, Professional Certificate edX XSeries, MicroBachelors, MicroMasters, Professional Certificate, Professional Education Udacity Nanodegree FutureLearn Program, ExpertTrack, Micro-credentials Kadenze Program

24

The Opportunities and Challenges in the Portability and Authentication. . .

469

option for the industry to support workforce development. This usually accompanies a general implication that the traditional university model is outdated and that short courses are a disruptive threat to the university model which we explore later in this chapter. Indeed, the competitive landscape of courses has raised many questions about what universities should focus on and what is in demand (Croucher and Locke 2020). However, there is room to co-exist well, as short courses and microcredentials can both deliver “in-demand” skills at the right time and can “fill the gap between academic programmes and the skills required by the labour market” (Cirlan and Loukkola 2020, p. 15). This makes them quite beneficial to a learning ecosystem and a huge advantage in a carefully constructed portfolio of learning. It is also the key reason that we should not ignore the opportunity short courses and micro-credentials present in the design of higher education into the future, including the virtual university. We often look for unique propositions and added value when designing our programs, and we suggest this is the opportunity to enhance and add value to the core curriculum and to offer opportunities for lifelong learning for alumni. The development of formal frameworks and a focus on the opportunity for formal credit-bearing short courses will enable us to develop roadmaps for learners that enable them to personalize their learning and add value to their degree program. However, universities are not alone in showing a keen interest in this space. Apart from earlier-mentioned general big players like Coursera and FutureLearn, an increasing array of much smaller and much more specialized providers is springing up, which are focusing on skills needed in specific industries. One example in the Australian context is ACS (Australian Computer Society), which offers micro-credentials in specific topics related to cyber security: information security, compliance and assurance, intrusion detection, and identity and access (ACS n.d.). Another example, also in the Australian context, is AFTA (Australian Federation of Travel Agents), but interestingly the micro-credentials they offer are not industry-specific: “they are generic but invaluable skills – common and relevant to all travel businesses regardless of size and include supervisory, managerial and business skills” (AFTA n.d.). This suggests that this is very much an emerging space, where the micro-credentials offered in the latter example may be relevant to the travel industry but could also be offered by other providers, including the virtual university. By contrast, the skills addressed by the micro-credentials on offer in the ACS example are highly specialized and current, so larger institutions (including universities) may struggle to develop the agility and relevance needed in that industry. Because of the increasing demand for relevant and current skills development, and the perception that universities may not be best-placed to respond to this demand, there is also an increase in smaller, independent higher education providers wanting to carve out their niche in this market. This is reflected in Peter Hendy’s (CEO of Independent Higher Education Australia) identified opportunity for independent higher education providers “to re-energise Australian higher education, tackle skills shortages, and drive economic growth” (IHEA 2022). It is thus important for the virtual university to establish which slice of the micro-credentials pie it is best-placed to occupy, recognizing that this is a highly dynamic and rapidly developing space.

470

R. Fitzgerald and H. Huijser

Opportunities for Micro-credentials and Short Courses for the Virtual University Micro-credentials and short courses are often seen as personalized, low-cost opportunities that enable students to demonstrate specific competencies and skills. They mostly sit outside the formal qualification frameworks of traditional university models; however, more recently, momentum has been building to recognize micro-credentials as formal qualifications. In Australia, this has resulted in the recently published Australian National Microcredentials Framework, which followed the New Zealand Qualifications Authority (NZQA) and the EMC Micro Credential Framework (EMC). These frameworks formalize the quality and value of micro-credentials and aim to enable alignment and recognition for these awards across institutions. As Oliver (2019) has noted, it is essential to have clear definitions to develop trust among agencies, which is a significant step forward in developing clarity and sensemaking in terms of the value of short courses, and it will create transparency about how credentials can be “stackable” and how they can be embedded into formal degree programs (Kato et al. 2020). There is a widespread appeal in the concept of developing “stackable” qualifications, where credits add up to a final overall qualification. Deakin University (2021) describes this as their “skills for now, credit for later” model, as it offers a flexibility not often seen in formal higher education qualifications. Frameworks and common accreditation will also serve to clarify the difference between quality-assured creditbearing short courses and “others,” due to the assessment requirement. The inclusion of assessment assures the learner and the higher education awarding bodies that specific learning outcomes have been met (Boud and Jorre de St Jorre 2021). This affords the virtual university opportunity in the design of courses and programs for the future learner. Offering short, accredited courses that are stackable offers maximum flexibility for traditional and non-traditional learners and opportunities to follow different study pathways toward the same awards. This could essentially be considered a “build your own degree, in your own time” approach, which is not dissimilar to the original innovative model pioneered by the UK Open University (UKOU), albeit with even more flexibility, and driven by online learning. Indeed, the UKOU was an original founding partner of FutureLearn, which pioneered the early concept of using open and free MOOCs as an introduction to programs of formal study in higher education, a marketing concept that still has a considerable value. FutureLearn has since become one of the leading providers of micro-credentials and short courses globally; co-owned by SEEK Careers, their key values are built around transforming access to education with university and other partners. Beyond creating short courses and micro-credentials for delivery and MOOCs for marketing, partnership is the key opportunity that should be considered by the virtual university. Collaborative partnerships, particularly partnerships with corporate and industry providers afford opportunities to connect formal and informal learning (Oliver 2019). There is an opportunity here for the virtual university to consider how best to leverage the global reach and reduced costs of working with partners, who offer a vast array of courses that can be accredited. Many private course

24

The Opportunities and Challenges in the Portability and Authentication. . .

471

providers see accredited short courses as an opportunity to offer learner flexibility and broaden opportunities (Richter 2018), and they see university partnerships as a benefit for all learners (Coursera 2021; EdX 2021; FutureLearn 2021). Universities are slowly exploring opportunities to enhance programs with cutting-edge training such as artificial intelligence, digital marketing, and python programming (Coursera 2021). Credit is not always required, and some universities are already exploring options where they offer unassessed short course opportunities for students to create digital credentials beyond the traditional transcript (McGreal and Olcott 2022). Additionally, private providers collate learning analytics data which could provide insights into how learners learn, thereby building digital capability for the virtual university. As much of the short course/micro-credentials world is digital, it could be said that the virtual university is in a good position to both maximize new frameworks and opportunities for collaboration. Formal accreditation also affords opportunities for universities to partner with accrediting bodies that have oversight over professional programs in a variety of disciplines (e.g., education, business, healthcare, and engineering). For programs in these disciplines, micro-credentials may be able to fulfill areas that require evidence of specific workplace competencies or that enable employees to “upgrade” professionally. This is critical in a world where the need to upskill with technological solutions becomes ever more prevalent. It is often suggested that employers are increasingly interested in what employees can do and less in what degree they have (Guri-Rosenblit 2019, p. 190); indeed, it has been reported that learning on the job is a preferable option for those already employed (Gallagher 2019). This sense that micro-credentials offer an opportunity to enable the workforce to develop professional competencies enables the industry to retrain staff on the one hand and universities to offer pathways to learning that may not otherwise have been considered by employees on the other. Oliver (2019) suggests that micro-credentials offer opportunities to those unable to commit to full-time education and who are in need of accessible learning, and if they align to formal qualifications, it makes them extremely attractive. Additionally, it is suggested that authentic learning (learning on the job) is preferable. For industry, in-house training could be acknowledged and given credit using this model, to enable employees to go beyond the basics in their training (Oliver 2019). These are enabling models that bring the best of all worlds together for the learner. It offers an opportunity to develop a traditional transcript of learning and a portfolio of digital skills and competencies that complement a core degree. We suggest that this is a perfect time for higher education, and the virtual university in particular, to consider collaboration with private providers. Micro-credentials and short courses offer us a space for a partnership model between universities and thirdparty providers, including accrediting bodies, industry partners, and private providers (Huijser and Fitzgerald 2020; Oliver 2019). Developing strategies and roadmaps to work long term with such partners affords opportunities to develop engaging real-world curriculum that offers learner opportunities to develop both the graduate skills that come from engaging with well-designed, scaffolded degree learning that delivers the deep skills needed in the world today (e.g., analytical

472

R. Fitzgerald and H. Huijser

thinking, complex problem-solving, and creativity) as well as a portfolio of skills that can be used for marketing oneself in the job market using, for example, Digital Certificates on LinkedIn that demonstrate skills in, for example, the use of “R” or “Python.” Brown et al. (2021) suggest that this enables higher education to partner with the industry to “harness digital learning models beyond the pandemic” (p. 228). Additionally, working with external partners to design such curriculum will help create opportunities that work for both participants and industries seeking to reskill and upskill their workforce. Furthermore, short courses are a useful recruitment instrument (Resei et al. 2019), and using them as stackable credits (microcredentials) or as value-added content (unaccredited short course skills development) can add to the desirability of the program.

Risks of Micro-credentials and Short Courses for the Virtual University There are a range of barriers to the inclusion of short courses and micro-credentials in a university model that go beyond the apparent apathy of universities to embrace the concept (McGreal and Olcott 2022). A lack of understanding at a leadership level can be a major factor, as this generally removes the incentives to make it work. Without a top-down drive, it is unlikely that faculty will be on board and flexible study will be an additional model beyond business as usual, rather than a strategic direction (McGreal and Olcott 2022). For this to work, the virtual university needs to embrace the concept of offering a flexible learning ecosystem to support lifelong engagement. By embracing this model, the virtual university could offer flexible pathways for learners and enable employers to recognize learning in a way that speaks to them about capabilities and quality. While we make assumptions about the expectations of employers and employees regarding agile and flexible learning, there is currently limited research about the experience of employers and employees with microcredentials and about the impact of digital badging and credentialing in the modern workplace (Raish and Rimland 2016; Ralston 2021). At present, it is widely accepted that employers do not consider alternative credentials substitutes for conventional higher education qualifications (Cirlan and Loukkola 2020); therefore, developing ways to bundle learning in a way that fits with accepted models of credit is an opportunity that should be explored. Short courses and micro-credentials are being promoted as an economic and workforce skills solution on a global scale, and the narrative that they are an alternative to a traditional qualification is becoming a common refrain in political and media circles. Ralston (2021) suggests that microcredentials have become popular because there is increasingly less value attached to a university degree and that they are reducing university curriculum to vocational training. It can be said that micro-credential and short course design, in terms of subject, availability, and structure, does fit the gig economy model by implying that learning is only about what is needed in the labor market at the time it is needed (Wheelahan and Moodie 2021); however, we suggest that as with many other

24

The Opportunities and Challenges in the Portability and Authentication. . .

473

technological disruptors over the last 20 years, short courses and micro-credentials have the potential to disrupt traditional higher education models in a way that should not be underestimated. In response, we suggest that the virtual university should embrace flexible models that incorporate skills development but in a way that leverages the opportunities that such flexibility will offer. For now, the field is confusing, with access to multiple and often disjointed courses of study. Online micro-credentials often feature digital certification and badges, which can be shared via social recruitment tools like LinkedIn; however, this can also demonstrate a haphazard and inconsistent approach to skills development that may negate the positives of flexible learning (Gauthier 2020). Researchers also find short courses and micro-credentials a difficult landscape to navigate and understand, largely due to marked differences in areas such as quality, duration, content, and price (Pickard 2018). However, it is reported that employers generally view MOOCs positively and that they find a relevant skill portfolio easier to understand than a transcript of knowledge, skills, and competencies (Cirlan and Loukkola 2020). More sensemaking is needed to ascertain what employers truly expect from short courses and microcredentials and to gauge understanding of the differences and the benefits of both (Nic Giolla Mhichíl et al. 2020), particularly in the current climate where learning is a good response to the disruption of the ongoing pandemic and other Industry 4.0 trends (Kift 2021). At the moment, the idea of “unbundling and re-combining” is “messy” (McGreal and Olcott 2022); however, this is an opportunity for the virtual university to purposefully design pathways and support models to create stackable credentials that enable the learner to navigate their learning in a way that is understandable both to the learner and to employers. Healy (2021) notes that many who study in this way may not be able to express the value of the skills they accumulate in a context that supports a career strategy. This makes it critically important to consider a framework strategy (Hall-Ellis 2016; Healy 2021) and to develop a “lifelong learning ecosystem” (Kift 2020). Otherwise, we risk fragmented models of education with incoherent qualifications (Austin et al. 2021), where learners can often end up paying more than if they studied credit-based programs (Pickard 2018). As we develop more clarity about what short courses and micro-credentials are and how they can be used in higher education and the virtual university, we are keenly aware that it is still very much early days for these new models of learning. The disruptive impact that this will have on higher education is not yet known, yet we firmly believe that the genie is out of the bottle and to ignore the wider range of learning opportunities available to learners globally is a move toward obsolescence. We acknowledge the many concerns about the inclusion of micro-credentials in university models, especially with regard to being overly driven by vocational need (Ralston 2021). We also know that the traditional university model is risk-averse, and it will take a courageous step forward to fully engage in partnership with employers and private providers in a way we envisage the development of a fully flexible learning experience. Embracing digital portfolios and blockchain models may offer the virtual university more opportunities to recognize credentials earned outside core learning pathways and to provide oversight of creditable value of previous learning, thereby assuring employers of quality. New flexible models and

474

R. Fitzgerald and H. Huijser

pathways will be essential to the success of future partnerships and stackable learning and perhaps to the future of post-compulsory education, with the move to formally defining standards the key to removing many barriers (Oliver 2019).

What’s Next for Micro-credentials in a Virtual University We suggest that the virtual university should embed short courses and micro-credentials as part of an overall framework for lifelong learning. We propose that the virtual university should actively seek partnerships with others, including partners in industry, partnering with professional bodies and other learning providers to develop quality frameworks for learning that deliver contemporary skills for learners but that are also suitable for upskilling or reskilling of the workforce. This will enable the virtual university to deliver flexible, personalized learning that develops learners’ holistic capabilities over a longer period of engagement. We posit that partnering with others will allow a richer and more contemporary education design that is both accessible, suits lifelong learning, and combines the best of all worlds. Developing true partnerships that involve co-designing and co-developing micro-credentials will also ensure a reliable and quality approach that delivers what industry (or rather, industries) need during disruptive times (Nic Giolla Mhichíl et al. 2020). We see much value for the virtual university in developing accredited skills-based micro-credentials that conform to the national standards, delivered by a range of partners, and we urge the virtual university to be attentive and ready to adapt new technological advances in storing and collating the digital outcomes of our lifelong education experiences. Developing learning frameworks and acknowledging the wider learning ecosystem will foster the development of clarity around qualification and pathways and assure learners of the quality of their prior experiences, which in turn enables learners to choose appropriate learning journeys. While we cannot guarantee career success or employment, we know that this model will reduce confusion and empower learners to develop the knowledge to choose appropriative pathways and create a narrative around the value of all their education experiences (Healy 2021). COVID-19 has reminded us of the uncertainty that we face regarding skills development for the future and of the relevance of virtual and online education to lifelong learning. The pandemic has further served to accelerate the need to deliver agile strategies to support learners, to acknowledge the broad and capable competition that all universities face, and to consider the horizon and plan how we engage with partners and our future learners to enable the use of micro-credentials and short courses to upskill and reskill the workforce of the future while at the same time assuring quality and holistic development.

Conclusion and Future Directions The aim of this chapter was to put forward an informed position that the virtual university is in an ideal position to build a learning ecosystem that recognizes a variety of learning including short courses and micro-credentials. Most future

24

The Opportunities and Challenges in the Portability and Authentication. . .

475

education models will need to acknowledge micro-credentials and short courses as part of a wider framework of collaboration and partnership, and the incoming national micro-credential frameworks will make strategizing with both learners and industry much more consistent. Online micro-credentials will continue to grow in popularity, and the virtual university is in a position to lead on how to assure quality and cohesion to guide learners on how micro-credentials add value to their learning journey. While it may not suit every university to engage with flexible models, we believe that the virtual university must do so to be able to compete in a global context and to best serve the learners that a virtual university attracts, which is a potentially large population. Higher education-level short courses and microcredentials attract educated, professionally skilled, and employed learners and offer equitable opportunities and pathways that may otherwise not be available for all learners. This presents an opportunity for the virtual university to set out its role in the myriad of education opportunities and play a key role in connecting the dots to provide the quality that will assure learners and their employers and assist them in unbundling and recombining courses (Huijser et al. 2020) and creating a holistic approach to skills development within a purposeful framework.

Cross-References ▶ Micro-credentialing Models and Practice

References ACS. n.d. ACS microcredentials. https://www.acs.org.au/professionalrecognition/microcredentialshome.html AFTA. n.d. AFTA micro-credential program. https://www.afta.com.au/micro-credentials Austin, K., S. Kift., and N. Zacharias. 2021. Student equity network. STARS Conference 2021 Networks, Online, 21–25 June 2021, viewed 14 July 2021, https://www.ncsehe.edu.au/event/ stars-2021-online/ Boud, D., and T. Jorre de St Jorre. 2021. The move to micro -credentials exposes the deficiencies of existing credentials. Journal of Teaching and Learning for Graduate Employability 12 (1): 18–20. https://ojs.deakin.edu.au/index.php/jtlge/article/view/1023/1018. Brown, M., M.N. Giolla Mhichil, E. Beirne, and C. Mac Lochlainn. 2021. The global microcredential landscape: Charting a new credential ecology for lifelong learning. Journal of Learning Development 8 (2). https://jl4d.org/index.php/ejl4d. Carey, K. 2015. Here’s what will truly change higher education: online degrees that are seen as official. New York Times, 5 March. https://www.nytimes.com/2015/03/08/upshot/true-reformin-higher-education-when-online-degrees-are-seen-as-official.html Cirlan, E., and T. Loukkola. 2020. European project MICROBOL – Micro-credentials linked to the Bologna Key Commitments. https://eua.eu/downloads/publications/microbol%20desk% 20research%20report.pdf Coursera Inc. 2021. The campus guide to modernizing and scaling your curriculum. Coursera for Campus Report. https://www.coursera.org/campus/resources/ebooks/gs-scaling-yourcurriculum

476

R. Fitzgerald and H. Huijser

Croucher, G., and W. Locke 2020. A post-coronavirus pandemic world: Some possible trends and their implications for Australian higher education [Discussion paper]. University of Melbourne. https://melbourne-cshe.unimelb.edu.au/__data/assets/pdf_file/0010/3371941/a-post-coronavi rus-world-for-higher-education_final.pdf Deakin University. 2021. Stackable short courses at Deakin. https://www.deakin.edu.au/study/finda-course/short-courses/stackable-short-courses Department of Education Skills and Employment. 2021. National Microcredentials Framework. https:// www.dese.gov.au/higher-educationpublications/resources/national-microcredentialsframework? utm_source¼sendgrid.com&utm_medium¼email&utm_campaign¼website EdX. 2021. More about partnering. https://www.edx.org/schools-partners?hs-referral¼navigationmenu-link#membership European Commission. 2020. A European approach to micro-credentials: Final report. https://doi. org/10.2766/50302. Future Learn. 2021 Microcredentials and programs. https://www.futurelearn.com/programs Gallagher, S. 2019. Peak human potential: Preparing Australia’s workforce for the digital future. Swinburne University of Technology, Melbourne. https://apo.org.au/node/241456 Gauthier, Thomas. 2020. The value of microcredentials: The employer’s perspective. The Journal of Competency-Based Education 5 (2). https://doi.org/10.1002/cbe2.1209. Guri-Rosenblit, S. 2019. Open universities: Innovative past, challenging present, and prospective future. The International Review of Research in Open and Distance Learning 20 (4): 179–194. https://doi.org/10.19173/irrodl.v20i4.4034. Hall-Ellis, Sylvia D. 2016. Stackable micro-credentials – A framework for the future. The Bottom Line 29 (4): 233–236. https://doi.org/10.1108/BL-02-2016-0006. Healy, M. 2021. Microcredential learners need quality careers and employability support. Journal of Teaching and Learning for Graduate Employability 12 (1). https://doi.org/10.21153/ jtlge2021vol12no1art1071. Huijser and Fitzgerald (2020). Managing expectations and developing trust: An evaluation of a public-private partnership. Australasian Journal of Educational Technology, 36(5), 58–70. https://doi.org/10.14742/ajet.6368 Huijser, H., R. Fitzgerald, and G. Salmon. 2020. Partnerships for scaled online learning and the unbundling of the traditional university. Australasian Journal of Educational Technology 36 (5): 1–4. https://doi.org/10.14742/ajet.6664. IHEA. 2022. 2022 IHEA federal election platform. https://ihea.edu.au/wp-content/uploads/2022/ 05/IHEA-2022-Federal-Election-Platform.pdf Impey, C., and M. Formanek. 2021. MOOCS and 100 days of COVID: Enrolment surges in massive open online astronomy classes during the coronavirus pandemic. Social Sciences & Humanities Open 4 (1). https://doi.org/10.1016/j.ssaho.2021.100177. Kato, S., V. Galán-Muros, and T. Weko. 2020. The emergence of alternative credentials. www.oecd. org/edu/workingpapers Kift, S. 2021. Foreword: Future work and learning in a disrupted world: “The best chance for all.”. Journal of Teaching and Learning for Graduate Employability 12 (1): i–v. https://doi.org/10. 21153/jtlge2021vol12no1art1015. McGreal, R., and D. Olcott. 2022. A strategic reset: Micro-credentials for higher education leaders. Smart Learning Environments 9 (1): 9. https://doi.org/10.1186/S40561-022-00190-1. Nic Giolla Mhichíl, M., M. Brown, E. Beirne, and C. Mac Lochlainn. 2020. A micro-credential roadmap: Currency, cohesion and consistency. Dublin City University, Ireland. Oliver, B. 2019. Making microcredentials work. Deakin University. https://dteach.deakin.edu.au/ wp-content/uploads/sites/103/2019/08/Making-micro-credentials-work-Oliver-Deakin-2019full-report.pdf Perna, M. 2021. Small but mighty: Why micro-credentials are huge for the future of work. Forbes. https://www.forbes.com/sites/markcperna/2021/10/05/small-but-mighty-why-micro-creden tials-are-huge-for-the-future-of-work/?sh¼72b7d8e0302b

24

The Opportunities and Challenges in the Portability and Authentication. . .

477

Pickard, L. 2018. Analysis of 450 MOOC-Based Microcredentials Reveals Many Options But Little Consistency, https://www.classcentral.com/report/moocs-microcredentials-analysis-2018/ Raish, V., and E. Rimland. 2016. Employer perceptions of critical information literacy skills and digital badges. College and Research Libraries 77 (1): 87–113. https://doi.org/10.5860/crl.77. 1.87. Ralston, S. 2021. Higher education’s microcredentialing craze: A postdigital-Deweyan critique. Postdigital Science and Education 3: 83–101. https://doi.org/10.1007/s42438-020-00121-8. Resei, C., C. Friedl, T. Staubitz, and T. Rohloff. 2019. Micro-credentials in EU and Global. Corship EU. https://www.corship.eu/wp-content/uploads/2019/07/Corship-R1.1c_micro-credentials.pdf Richter, C. 2018. Credentials and the future of education: Key insights from the Administrators track at the 2018 coursera partners conference blog. https://blog.coursera.org/credentialsfuture-education-key-insights-administrator-track-2018-coursera-partners-conference/ Shah, D. 2021. Massive list of MOOC-based micro-credentials. The report. https://www. classcentral.com/report/list-of-mooc-based-microcredentials/ Stone, E. 2015. Digital badges: A hot career booster. Strategic Finance 96 (12). https://link.gale. com/apps/doc/A418226835/AONE?u¼anon~5c540589&sid¼googleScholar&xid¼d9f69be0. UNESCO. 2020. Open badges: new opportunities to recognize and validate achievements digitally. UNESCO Institute for Information Technologies in Education. https://iite.unesco.org/ highlights/open-badges-new-opportunities-to-recognize-and-validate-achievements-digitally/ UNESCO. 2021. Defining micro-credentials: Opportunities and challenges in shaping the educational landscape. https://www.unesco.org/en/articles/defining-micro-credentials-opportunitiesand-challenges-shaping-educationallandscape? Wheelahan, L., and G. Moodie. 2021. Gig qualifications for the gig economy: Micro-credentials and the ‘hungry mile’. Higher Education. https://doi.org/10.1007/s10734-021-00742-3. World Economic Forum. 2021. Upskilling for shared prosperity. Insight report in conjunction with PWC. https://www.pwc.com/gx/en/issues/upskilling/shared-prosperity/upskilling_for_shared_ prosperity_final.pdf

Part IX Gamification, Adaptive and Conditional Learning

Developing and Quantifying Intrinsically Motivating Instruction: Models and Principles of Gameful Design, Adaptive and Online Experiential Learning

25

Kevin Bell

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . How the Pandemic Pulled the Future Forward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . It’s SIMPLE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Principle 1 – Gameful Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case 1 – The University of New Hampshire – EconJourney . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case 2 – The Threat of Terrorism and Crime – Penn State University . . . . . . . . . . . . . . . . . . . . . . . . Principle 2 – Online Experiential Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case 3 – Northeastern University’s Online Experiential Learning (OEL) . . . . . . . . . . . . . . . . . . . . . Academic Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Personal/Social Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Northeastern’s OEL Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Principle 3 – Personalized/Adaptive Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case 4 – Western Sydney University – Adaptive Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Feedback from Academics and Students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Intrinsic Motivators – A SIMPLE Means of Tying them Together . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion and Future Direction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

482 483 485 486 487 488 490 490 492 492 493 494 495 496 496 497 498 498

Abstract

Through presentation of three key principles and four case studies, this chapter presents exemplars and metrics for digital course content that will intrinsically motivate and engage students. It illustrates how emergence from the COVID-19 experience provides an almost unrivalled opportunity to enhance the learning experience in the virtual university, making best use of both digital/technical and

K. Bell (*) AWS World Wide Public Service, Sydney, NSW, Australia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_25

481

482

K. Bell

human/behavioral advances. The SIMPLE matrix, presented at the end of the chapter, builds on the work of Czsikszentmihalyi (Flow), Karl Kapp (Gamification), and the work of referenced practitioners, illustrating how these principles can be applied and quantified across a range of courses and disciplines. Keywords

Intrinsic motivation · Instructional design · Gameful design · Adaptive learning · Personalized learning · Experiential learning · Online learning · Blended learning · Flipped learning · Technology enhanced learning · Pedagogy · Andragogy

Introduction Since the dawn of this millennium, many institutions have sought to diversify revenue streams by launching online initiatives. A secondary driver for institutions moving online was the desire to provide flexibility and an alternative for on-campus students who, despite having enrolled in traditional programs, were not attending lectures in person. One report, titled: “Why bother if the students don’t?” (The impact of declining student attendance at lecture on law teacher wellbeing), recorded student lecture attendance at 38% of enrolments across a sample of 16 units (Offer et al. 2019). At Western Sydney University where I worked between 2016 and 2019, in some of the larger lectures (enrolment of 100–200), attendance of between 10% and 20% by the third or fourth week of class was not atypical. The straight recording or streaming versions of hard-to-sit-through lectures was never likely to engage learners, though it did provide another reason not to go to the physical classroom. Studies have shown that mandated, passive lecture observation is a demotivator to on-going study (Selvi 2010). There is a school of thought that this is a consequence of modern distractions damaging brains and dropping attention spans to seconds. A longitudinal Microsoft study claimed that human attention span had declined by 25% between 2000 and 2015 – beaten out in focus duration by the humble Goldfish (Microsoft Canada 2015). Other, more serious studies (Benjamin Jr. 2002; Wankat 2002; McKeachie 2013) have the typical student’s attention span at about 10–15 min, while most university classes still last 50–90 min. At least one analyst points out the clear ambiguity in the “short-span” narrative, stating: Contrary to popular belief, students don‘t have short attention spans. They can focus for hours on a single project. But it has to feel relevant and meaningful to them and they need to have the time and the space to accomplish it. It‘s not easy in a world of school bells and curriculum maps. However, it‘s something we should strive for. We should draw students into the deeper, slower work of creativity — because when that happens, learning feels like magic. (Subramanian 2018, p. 3)

25

Developing and Quantifying Intrinsically Motivating Instruction:. . .

483

As modern users assimilate new technologies and modalities of content, they do jump between apps, but nonetheless love a regular binge. The 2020 Edison “Infinite Dial” study showed that Australians listen to an average of six podcast episodes a week (around 4.3 h) with 93% of listeners staying tuned for “all, or the majority” of each episode. On average in 2020, Netflix viewers watched 3.2 h of streaming video per day. Attention spans may indeed be getting shorter as people deal with numerous technologies and distractions, but evidence (Benjamin Jr. 2002; Wankat 2002; McKeachie et al. 2013) suggests that content that is highly relevant or entertaining will always hold attention. Sustained involvement with engaging content is a welcome refuge from people’s busy lives. In a world of rapid-fire e-mails, text messages, tweets, and app notifications, immersive content can feel like appreciated respite and even a luxury. While there are always inspirational outliers, and many outstanding lecturers, most institutions’ Digital Futures strategy documents looking ahead to 2020–2025, are stressing that moving beyond the lecture was overdue. Yet, the full capabilities of online and digital-blended delivery have not been leveraged. For most, including almost all prestige institutions, online education seems destined to serve only as an alternative or minor, supplemental revenue stream. Putting a lecture online still means just that: capturing without amending, the lecture experience. This means high overheads – capital (equipment), storage (servers), and operational / handling – especially if transcriptions have to be developed. The Tragedy of the horizon (Carney 2015) for Higher Education is that obligatory or mandated (real) change has seemed far away enough to ignore and/or procrastinate for the last couple of decades. Few vice chancellors or presidents were going to stake all, or risk a vote of no confidence by insisting on radical change. Then COVID-19 arrived.

How the Pandemic Pulled the Future Forward COVID-19 has represented a time of challenge, of crisis, and also a time of opportunity, of possibility and, most importantly, a time for conversations. COVID has forced all institutions to “give it a go” to see what could be done in terms of distant and remote learning. For once, there was no obvious source of blame, mixed motives, or political subterfuge. At the same time, there was a compliant audience with the compulsion to access content and services online, given the dearth of other options. Rapid transitions to online saw traditional content formats uploaded to institutional Learning Management Systems – for many academics their first excursion into this world. Gaps and directions were supported by Zoom sessions, with initial indulgence for so many, “you’re on mute”-s and “tech” failures that were

484

K. Bell

predominantly due to user unfamiliarity. The sense of novelty, short-termism, and being “all in this together,” meant that special effort was made to get by and persist. After initial teething issues, most participants (instructors and learners) realised that the technology, their bandwidth, and the content presented could keep them connected to a unit, module, or course, necessity proving the mother of invention once again. In this manner, most participants “made it.” While the challenge of a primarily text-based medium (and materials) made the situation extra challenging for non-firstlanguage speakers of the instructional tongue, domestic students (many house-bound and/or locked down) had time and inclination to stay on track and make the best of it. Instructors, in most cases, at least anecdotally, managed to maintain essential class functionality and, with the tolerance of students for whom the experience was novel and, hopefully, to be short-lived. Some were presumably not unhappy at not having to commute and mingle with, potentially COVID –carrying passengers, particularly in the early / pre-vaccine days. As time wore on, with the boon of COVID vaccines, many institutions have been addressing “back to school” in a post-COVID world. The pandemic has been referenced in many studies (Odgers Berndston 2020; CapGemini 2021; PWC 2021) as pulling the future forward. Many academics and administrators had read about the pending “death of the lecture” (Allain 2017) for the last decade or so, but had generally continued on their way. Many institutions including my own (Western Sydney University) had already invested in new flipped learning facilities, which even pre-COVID, anticipated a modality obligating delivery of content to students outside of the classroom with soft(er) debate / dialogue / interactive activities in person. Others have put those two and two’s together, with the assumption that having made the COVID “flip,” the sustained / whole of curriculum “flipped” is but a small step further. The tolerance and appreciation on all sides of pan(dem)ic “pivoting” is unlikely to be something that can be relied upon far into the future. Academics need to discover sustainable means of delivering support to students who choose, or who have no option but, to stay online, along with those who choose to return to in-person classes. Administrators need tools and processes to track students who might flex in and out of modalities, while students will need to see clear benefits to attending in-person sessions as they look to engage with well-architected, perhaps even intrinsically motivating, content and activities. This chapter explores formats and concepts that have the potential to accentuate this intrinsic motivation, thereby supporting student interaction with materials, activities, and concepts. Starting with examples illustrating the principles of Gameful Design and experiential learning (online), the chapter then moves to a review of a technology being touted as a clear step forward in student engagement through personalization, before presenting a rubric that can be used to review all types of learning objects and experiences for a quantifiable “likelihood of increasing time on task” metric. The SIMPLE matrix (Bell 2018) provides a means of evaluating formats and learning experiences for their propensity to engender motivation, encompassing flow concepts (Csikszentmihalyi 1990), and the work of a range of theorists in related spaces.

25

Developing and Quantifying Intrinsically Motivating Instruction:. . .

485

It’s SIMPLE The Student Intrinsic Motivation in Personal Learning Environments (SIMPLE) matrix was developed as a means of reviewing and quantifying the likelihood that a developed course or resource will engage a student group through implementation of evidence-based intrinsic motivators. It supports the principle of flipping delivery with (traditional) content and foundational course elements presented outside of the classroom with the intent of using class time, or synchronous time in fully online classes, for processing and reflecting on the presented concepts. This format does obligate learner engagement with materials in their home or at least outside of the classroom, and it aligns motivators, gleaned from study of gamification, flow, and contemporary/emergent pedagogies (Bell 2014). Engagement with and through intrinsically motivating online content and experiences prepares students to show up for live/ synchronous or face-to-face time, ready to practice the application and processing of complex nuance and interpretation. Instructors in the “live” sessions are encouraged to set up groupwork, roleplays, case studies, and scenarios where the studied principles are applied or practiced in authentic situations. Students are encouraged to go beyond memorisation or regurgitation of facts reflecting on why these acts are relevant, and how they can be critically applied to fluid life/work experiences. This practice of soft, or twenty-first century, skills will prepare graduates for employability in an AI/ algorithm-driven Fourth Industrial Revolution world (Schwab 2015) (Fig. 1).

Fig. 1 The Student Intrinsic Motivation in Personal Learning Environments (SIMPLE) Matrix Bell (2014)

486

K. Bell

In the near future, when mundane, repetitive tasks have been automated, analysis and critical thinking skills will be increasingly marketable. The ability to critique, then make a case, and defend a position while working across diverse teams and cultures, will be highly sought-after graduate qualities (University of Adelaide Division of Academic and Student engagement 2021). With content presented and received in a “compulsive” digital environment, and with students’ choice to dip in and out or to binge study, student centricity will cease to be mere lip service. Team dynamics with just-in-time support, collaboration, sense of progression, and even a soupçon of competition can provide a heady mix of motivation. Instructors will be liberated to use face time for active learning, and have their charges practice and reflect on new concepts and wicked challenges in the very spaces where they used to sit passively in over-long, theoretical lectures.

Principle 1 – Gameful Design Engagement is an essential precursor of student success in face-to-face and online classes. Chen, Gonyea and Kuh (2008) have noted that engagement is positively linked to a number of desired outcomes, including high grades, student satisfaction, and perseverance. The general conclusion from the literature is that engagement is a complicated blend of active and collaborative learning, participation in challenging academic activities, communication between teachers and students (and between students), and involvement in enriching educational experiences and communities (Allain 2017; Chickering and Gamson 1987; Clark 2005). Initial explorations of Gameful Design in the “serious” world were – in military “war games” – used for centuries to train personnel without loss of life. These games were not intended to be frivolous or fun. They were grounded in authenticity, by definition requiring realism, but also embodying elements we now recognize as components of Gameful Design. The developed rationale articulated by academics, such as Kapp (2012) and Schell (2008), was that elements intrinsic to games could be factored into materials development to improve student-learning outcomes. Materials structured with game principles embedded, even if hidden and not based in narrative, could enable students to work with big ideas contextually, as well as symbolically, so they learn how to apply abstract ideas in qualitative and meaningful ways. Engagement taken to its limits has the potential to engender flow – the point at which engagement makes effort feel compelling and achievement feasible. Csikszentmihalyi (1990, p. 4) describes eight components as critical in engendering flow: (i) The task at hand must be achievable – the learner must believe that they can achieve it with some degree of effort (ii) The task must obligate focus or concentration (iii) The task must have clear goals (iv) There must be feedback, both immediate and continual

25

Developing and Quantifying Intrinsically Motivating Instruction:. . .

(v) (vi) (vii) (viii)

487

The participant should feel a sense of effortless involvement with Control over actions, with all of the above leading to: Concern for ‘self’ disappearing Loss of sense of time

The language used in these components lends itself to experiences beyond gaming. Engaging academic experiences, including those that might potentially engender flow, can certainly exert the required elements. Key when implementing these kinds of motivators is that they should be an integral part of the activity. Extraneous or extrinsic motivators have the benefit of novelty and can provide a short-term spike of interest but this is almost never sustained. In the gaming world, this kind of extrinsic ‘gloss” that does not fundamentally amend the underlying experience is referred to as chocolate-covered broccoli (Hopkins and Roberts 2015). Intrinsic motivators are woven into the fabric of the activity/content, and they provide engagement and positivity as a consequence of the experience rather than as an appended or disjointed “badge” or (extrinsic) reward. The following case, studied as part of my doctoral studies at the University of Pennsylvania in 2014, provides an example where many of the motivators are implemented directly into the learning experience, providing support to the content delivery with mnemonic devices akin to those discussed in work on Memory Palaces or Methods of Loci dating back to Cicero and the ancient Greeks (Eikeseth 2020, pp. 87–97).

Case 1 – The University of New Hampshire – EconJourney Professor Neil Niman holds the belief that learning best takes place when it is part of a co-created process. Students do not like to be told what to do. They are looking for assistance in reaching goals that they establish along a journey that takes them where they would like to go. They need the freedom to explore, a variety of pathways to choose from, and the tools needed to help them succeed. The model encourages student participants to develop their own narratives to personalize the key elements that they need to remember. Nyman has quoted Shavitz and Bedford (2012) to provide more context on this perspective: “If you read between the lines, you’ll discover that the entire Facebook platform is organized around the generation and amplification of stories” (Nyman 2013) (Fig. 2). The course was developed with two parallel lines – one explaining the concepts and definitional language, the other developing the story, encouraging student-led contextualizing. The gaming develops around both the journey and the sharing of stories within the group through discussion forums. In these forums, students compare stories and characters, voting for best, most creative, etc. Through this format, both social interaction and a degree of competitiveness are built into the model. The hyper-personalized context ultimately serves as a mnemonic tool to help

488

K. Bell

Fig. 2 Screenshot from Neil Niman’s EconJourney course

students weave economic concepts into a framework that they can recall when needed. Niman reflected that it became more meaningful when the examples were selfgenerated by the students and then compared within the class. The value emerges from this co-created process that has the user learning more about themselves and their friends while enhancing feelings of belonging within a community of peers. He concluded that the most effective motivator is not some sort of points or badging system, but rather the fact that students are posting their work to their “community” and that their peers are going to rate it and provide feedback. “The use of literature to teach economics or illustrate economic principles is not a new concept” (Niman 2013). The need for a personally resonant narrative is elaborated upon by Niman who referenced Hawtrey (2007): “It is about empowering students to identify pertinent content in order to create their own stories that are both relevant and meaningful for them” (Hawtrey 2007, pp. 143–152). Many students seem to be more interested in managing risk than achieving success. Whether they are trying to prevent losing points on an exam, looking foolish answering a question in class, or selecting easy rather than difficult courses to take, it is more about averting losses than gaining success. The EconJourney process is designed to build students up so that that they are more willing to take a risk and try to learn something new. It is about replacing fear with achievement.

Case 2 – The Threat of Terrorism and Crime – Penn State University This was the basis of another case study I researched as part of my doctoral program at the University of Pennsylvania (2014) (Fig. 3). Fred Aebli is an instructor at Penn State. Prior to that he was a US Marine and a security officer. He still harbors the ambition to be an astronaut “1 day.”

25

Developing and Quantifying Intrinsically Motivating Instruction:. . .

489

Fig. 3 Screenshots from Fred Aebli’s Terrorism and Security course

His class project begins much like the game of Cluedo (known as “Clue” in the United States): he seals the details of a fictional terror plot — the what, where, who, and when — in a large envelope, where it remains sealed and untouched until the end of the term. His students are then split into teams, assigned national defense credentials (FBI, Scotland Yard, etc.) and tasked with uncovering the details of the plot. After each reading assignment, the students take a quiz. If a team’s collective average score is high enough, they are given access to that week’s batch of intelligence, including police reports, emails, satellite images, phone conversations, and government intelligence. Aebli uses the Canvas learning management system to release the intelligence to each team once they have high enough scores. Finally, at the end of the semester and after all the intelligence has been released, each team is given the chance to submit their guesses. If they guess correctly, they are awarded additional points on their final grades. Aebli says he hoped that by rewarding students with clues to help them solve a terror plot, they would read more and perform better on the quizzes. He had initially seen gamification work first-hand in his former role as a Marine. “Turning anything into a game automatically makes it more engaging,” said Aebli. “You definitely see it in the military. You see engagement go up when you train marines in realistic scenarios, so I thought, why wouldn’t it work for my students? Why should they have to learn solely from listening to lectures and reading textbooks?” Aebli’s project gives students the chance to not just read about how to counter a terrorist attack, but actually practice doing it. Getting a high enough quiz score to get access to the intelligence batches was only the beginning — making sense of them wasn’t always easy. “I want my students to come out of the course more engaged in current events and with a better idea of what’s going on in the world,” Aebli said. “Sometimes students come in with assumptions and stereotypes about what terrorism is, and I want them to be more informed and able to think critically about things.” Since Aebli had done this project for a few semesters, his students seemed much more engaged and feedback was positive, along with engagement and grades going up. “I think splitting the students into teams also helped,” Aebli said. “I saw some positive

490

K. Bell

peer pressuring happening, where if a team’s score wasn’t high enough, the students tried to raise their individual scores to raise the team average.” One of the students commented: The project made it seem like we were a real team of FBI agents trying to save the public from a terrorist attack, which I enjoyed. I liked how the groups consisted of 4 to 5 members so that the work can be divided up equally. I also liked how the readings we had to do in order to take the quizzes contained material that was helpful and related to the project. I enjoyed looking through all the Intel and trying to formulate an idea of what is going on. It was fun in the sense that it made you think about how things fit together almost like a puzzle and it constantly made you think on your toes with all the twists and turns. It was a great way to motivate the team to perform well and making attempts to defeat the other team.

Aebli felt that releasing the intel batches after the completion and average score of quizzes was an interesting way of bringing the groups together. It emphasized that supporting and encouraging others to complete their work was essential for the group to advance further in the project. The project, he felt, advanced students’ analytical abilities and critical thinking skills. The feedback he received was uniformly positive with many students stating that they wished other courses followed this modality and format.

Principle 2 – Online Experiential Learning In its simplest form, experiential learning means learning from experience or learning by doing. Experiential education first immerses learners in an experience and then encourages reflection about the experience to develop new skills, new attitudes, or new ways of thinking. (Lewis and Williams 1994, p. 5)

Experiential learning hits many of the intrinsic motivators listed in the SIMPLE matrix. Typically, work provides more autonomy than formal study (student has control) and most work environments encourage cooperation and teamwork. A sense of competition can be apparent in terms of gaining market share or being first to market while most positive work experiences will involve a sense of progression and/or levelling up with opportunities for progression and promotion.

Case 3 – Northeastern University’s Online Experiential Learning (OEL) The OEL team was created with the challenge of approximating online/remote student outcomes that students at Northeastern traditionally gain from their “CoOp” professional internships. Ninty-five percent of students at Northeastern (all but some part time/alternative pathways students) have an experiential co-op experience lasting at least 3 months.

25

Developing and Quantifying Intrinsically Motivating Instruction:. . .

491

A 2016 third party research study of employers indicated that these students were statistically significantly better prepared across a large number of skills and attributes compared to students who had not had this kind of experiential learning. The challenges of the model are: it adds a full year’s tuition and living expenses in one of the most expensive regions in the country, and it is not scalable or transferrable to other regions (where employer-partner relations have not been established). Experiential learning is the process of learning through experience, or learning through reflection on doing – as distinguished from rote or didactic learning where the learner plays a comparatively passive role. The general concept of learning through experience dates back to around 350 BCE and Aristotle who wrote “for the things we have to learn before we can do them, we learn by doing them” (Aristotle, The Nicomachean Ethics). Opportunities in a student’s field of interest can provide valuable experiential learning, which contributes significantly to the student’s overall understanding of the real-world environment. The modern theory of experiential learning was promulgated in the 1970s by David Kolb (1984), building on the legacies of John Dewey, Kurt Lewin, and Jean Piaget, stated that in order to gain genuine knowledge from an experience, the learner must have four abilities: • • • •

The learner must be willing to be actively involved in the experience. The learner must be able to reflect on the experience. The learner must possess and use analytical skills to conceptualize the experience. The learner must possess decision-making and problem-solving skills in order to use the new ideas gained from the experience.

Represented diagrammatically: The model is grounded in a constructivist and development perspective of learning. Experience plays a key role in learning; however, it is only one phase in Kolb’s experiential learning cycle (Fig. 4). Fig. 4 Kolb’s theory of experiential learning. (Image by Kirk 2007)

Concrete Experience

Reflective Observation

Active Experimentation

Abstract Conceptualization

492

K. Bell

Phases of Experiential Learning (i) Concrete Experience – Students actively engage in an experience. (ii) Reflective Observation – Students reflect on the experience, identifying any connections, inconsistencies, or alignment between the experience and their prior knowledge. (iii) Conceptual Thinking/Abstraction – Through reflection, students generate new understandings/ideas or modify their existing conceptualization of an idea/ concept in order to draw conclusions and make hypotheses (iv) Active Experimentation – Students plan and test their conclusions/hypotheses by applying their knowledge to new experiences. (Kolb 1984, p. 38) Literature coalesces in this area around both academic and social/personal outcomes that can be delivered through experiential learning:

Academic Outcomes • Increases in students’ content knowledge and skills. • Statistically higher outcomes in application of coursework to everyday life than comparable students not engaged in experiential learning. • Improved higher-order thinking skills – an ability to demonstrate greater complexities of understanding. • Statistically significant increases in ability to analyze increasingly complex problems. • Significant increases in students’ critical-thinking abilities.

Personal/Social Outcomes • Increases students’ self-esteem. • Enhances students’ sense of self-efficacy and empowerment. • Increased students’ likelihood to engage in prosocial behaviors and decreases students’ likelihood to engage in at-risk behaviors. • Provides a positive effect on students’ motivation for learning. While experiential learning has traditionally been thought of as an obligate in-person experience, many of the elements – reflection, abstraction, critical thinking – can be documented and processed in a quiet, even online environment. Through the opportunity of emergent technology and simulations, elements of training can be made available anytime and anywhere, across multiple devices, with work done both in the classroom and away from the instructor. Students learn by doing, failing, observing (videos, graphics, audio, etc.) and then practicing/re-applying. Applications of experiential learning; online experiential learning can provide authenticity and impact for the virtual university experience. A degree of variability and uncertainty can be introduced in course content to better approximate real-world challenges. Students can pace their learning based on

25

Developing and Quantifying Intrinsically Motivating Instruction:. . .

493

the skills they learned previously (using a modular approach). E-Learning systems can be developed to support monitoring, tracking, and provision of human or automated feedback utilizing bite-sized learning that tracks progression, provides feedback, and incentivizes completion. Students’ intrinsic motivation can be ramped up by varying levels of challenge, a sense of progression, colleague cooperation or competition, and where applicable, a complex/authentic narrative (Bell 2014). These elements were built into the model that the Online Experiential Learning team launched at Northeastern.

Northeastern’s OEL Model Utilizing an Articulate Storyline (LMS-agnostic) build, we developed a selfcontained, navigable model with consistent design features and clear signposting. We leveraged the industry connections endemic to Northeastern – founded and connected to industry since 1898: • Connecting experts at Regional Centers with faculty in Boston (research and curriculum) • Soliciting ideas for experiential activities and modules • Developing learning outcomes and industry-supported competencies We met the requisite experiential elements in the following manner: Concrete Experience – Students actively engage in an experience An example project was the development of a “Green” thermostat – providing students with the experience of defining a system and analyzing mathematical statements to evaluate whether a product could be redesigned and marketed as green technology. Authentic data was provided, teams were established to collaborate, with the instructor acting as more of a “consultant” who was available to offer advice and nudges, without lecture or prescribed content. Reflective Observation – Students reflect on the experience, identifying any connections, inconsistencies, or alignment between the experience and their prior knowledge. Conceptual Thinking/Abstraction – Through reflection, students generate new understandings/ideas or modify their existing conceptualization of an idea/ concept in order to draw conclusions and make hypotheses. Active Experimentation – Students plan and test their conclusions/hypotheses by applying their knowledge to new experiences Students were provided with new opportunities to apply their knowledge to new problems. Northeastern as a selective school (accepting 19% of applicants) has relatively few issues with graduation rates – 84.5% of first time, full-time in 2018–2019. The impact of the deliverables therefore was anecdotal rather than quantifiable. Students

494

K. Bell

reported greater interest and satisfaction, while instructors were impressed with the student engagement and commitment, along with their cultivation of skills that were commensurate with those expected in traditional Co-Op experiences. Six years on from my team’s development work, it is of interest to note that when COVID hit, Northeastern was immediately able to go fully remote with productive learning experiences that engaged and sustained students. This again speaks well to the potential for Online Experiential Learning and its key tenets as potential pillars of an effective virtual university. Other institutions have started exploring Online Experiential Learning. In 2020, the Australian Collaborative Education Network (ACEN) commissioned a review by academics from Deakin, RMIT, and the University of Tasmania. Their term for the same project is Online Work Integrated Learning (WIL) – they defined Online WIL as “a collaboration between the employer and the university to engage students in a work-related learning experience as a part of their study.” Projects that ACEN outlines as potentially forming the basis of Online WIL include: • Consultancies in which the student receives a brief from the organization outlining defined scope and deliverables. Students research, problem-solve, and communicate findings to the host organization via an oral online presentation (recorded or live) and/or a written report. The host organization may provide feedback to students. • Simulated workplaces offer students scenario-based learning or problem-based learning using an authentically work-related online simulation. Students navigate the simulated workplace environment to respond to the problem or scenario posed in the learning activity and assessment. Industry partners can be involved through Q&A with students and/or the provision of industry intelligence or feedback to students, either on an individual or team basis. • Entrepreneurial projects whereby students are invited to create their own digital job, product, or project. Potentially undertaking market research or developing a business plan to create their own company or developing a product (e.g., an app). Ideally, partnered with an online mentor from the sector or industry (ACEN, 2020).

Principle 3 – Personalized/Adaptive Learning Personalized learning refers to instruction in which the pace of learning and the instructional approach are optimized for the needs of each learner. Learning objectives, instructional approaches, and instructional content (and its sequencing) may all vary based on learner needs. In addition, learning activities are meaningful and relevant to learners, driven by their interests, and often self-initiated. Adaptive Learning more specifically calls out methodology driven by computer algorithms and artificial intelligence (in some systems) to deliver tailored content to address specific needs and knowledge gaps.

25

Developing and Quantifying Intrinsically Motivating Instruction:. . .

495

Simple adaptive learning experiences can be developed in basic LMSs, as Fred Aebli did in the Terrorism and Security course, by using adaptive release and/or gateway quizzes. More complex software providers can increase sophistication up to, and including, full game or full experiential mode, whereby each individual action produces a tailored, individual reaction. The critical aspect of any personalized system is the ability to provide tailored, appropriate feedback that keeps nudging the learner toward higher-level understanding. The work of Vygotsky (1978) is often referenced by proponents with his key principle – the “Zone of Proximal Development” (ZPD) central. ZPD provides contextual feedback that moves learners gently to their next level, whatever that is, without jumping too far ahead or making them go through redundant, busy-work. Commercial platforms abound. Foundational knowledge and fundamental elements requiring either comprehension or memorization can be well supported with automatically generated (proximal development) nudges, but the interaction with human – peer and/or instructor – needs to be central and provides the connectivity and motivation that inspires learners. As noted earlier, immediate, corrective feedback is a key motivator for students. Gameful Design packages a set of motivators, as does Experiential Learning. Adaptive learning systems should be analyzed for the add-ons they provide to augment human efforts. To review across these modalities and concepts, a ‘simple’ rubric is of value.

Case 4 – Western Sydney University – Adaptive Learning In 2017–2019 Western Sydney University worked with Cogbooks (a UK-based Adaptive Learning Platform provider) to test out adaptive/personalized learning in non-credit bearing offerings. The Western Sydney Digital Futures team ran two pilots in 2015 to trial the adaptive platform’s impact and efficacy: • Readiness Module: Digital, Professional, and Cultural Literacies, developed in partnership with the Library and School of Nursing and Midwifery. • Literacy and Numeracy modules: Preparation for LANTITE testing, working with the School of Education. Adaptive personalized learning technology was used to replace traditional textbooks and eLearning with a learning experience that adjusts to the individual student learner. Adaptive learning systems fall into a number of categories, ranging from simple to sophisticated: • • • •

Repetition-based: Optimize the repetition of activities to maximize retention Practice-based: Optimize a sequence of practice exercises to suit a student’s level Feedback-based: Provide feedback or support when a student needs extra help Path-based: Continuously optimizes the path of learning activities followed by a student

496

K. Bell

The path-based approach involves elements of the other three but has the benefit that it can draw from a whole network of learning activities to find the optimum item for a student at any moment in time. The other methods restrict the range of support provided to a student and usually are limited to hard-wired responses, reducing the likelihood of success. Path-based systems: • Use a range of decision-making methods so that courses work out of the box for the first student and then improve over time, through machine-learning. • Offer a complete solution that includes intuitive authoring, instructor dashboards, LMS integration, gradebook integration, and data analytics tools • Address a number of areas of intrinsic motivation in the student experience.

Feedback from Academics and Students The Literacy and Numeracy modules have been well received by School of Education academics. One concluded: Cogbook’s Literacy and Numeracy modules are well designed to support Initial Teacher Education students to develop, consolidate, and enhance their proficiencies in Literacy and Numeracy. The platform allows for guided learning across areas where student teachers struggle with Numeracy or Literacy concepts. It does allow students to self-assess their own ability and choose to go deeper to further content. The adaptive platform provided real time feedback to students. It allows us at the School of Education to track students’ progression rates and diagnose their areas for further academic and professional development. Students responded well to the online, independent, and asynchronous mode and like the self-pacing options. These modules have improved the School of Education strategy to support the numeracy and literacy needs of pre-service teachers and have assisted in more strategic and effective LANTITE preparation. (Carroll 2018)

Preliminary results of the pilot study, while limited by the small sample and timeframe, indicated that student perceptions and experiences of engaging with the online literacy and numeracy modules positively influenced their self-efficacy in these domains (as measured in terms of frequency of engagement and satisfactory completion of literacy and numeracy tasks). Interview data indicated that students found the online modules relevant, useful, scaffolded, and customized to their specific needs and study pattern preferences. Importantly, students determined that their self-efficacy with undertaking the task items aligned to LANTITE was high on the scales used in the survey.

Intrinsic Motivators – A SIMPLE Means of Tying them Together The SIMPLE matrix presented earlier provides a means of assessing learning environments and activities to evaluate the likelihood that they will engage students and increase time on task. At the higher end of scoring, activities may reach Csikszentmihalyi’s Flow but, more importantly, activities that are otherwise non-gamified and/or not experiential can still be assessed and, with some guidance,

25

Developing and Quantifying Intrinsically Motivating Instruction:. . .

497

incrementally improved to accentuate even just one or two elements, with corresponding student interest enhancement. Using the matrix as a rubric allocates 3 points to elements ranked as Signature Elements of a build, down to zero if an element or aspect is absent or not considered. A case such as Aebli’s Terrorism course would score highly in the Rules category, in the Conflict/Cooperation/Competition category, as it has clear goals (uncovering the plot) and a strong narrative (also authentic) with a lot of student control over their actions and strategies to reach a solution. Niman’s EconJourney course scored well for Cooperation (less competition) and Narrative, but overall came in less highly rated (for propensity to engage).

A longitudinal study looking for correlation between the SIMPLE score and measured student connectivity – frequency of logins, or other activity within the LMS – would support engagement theories. The tool does demonstrate that courses or learning activities do not need to be gamified/not-gamified, in a binary sense. Many disciplines such as medicine and law can provide their own authentic “storyline” with realistic case studies or scenarios, negating the need for any peripheral narrative.

Conclusion and Future Direction Future research should focus on the correlation between SIMPLE evaluations and subsequent student engagement. Potential additions or substitutions for criteria should be considered and the potential of new/emergent technology options,

498

K. Bell

reviewed. Any Virtual University offerings can be tracked and incrementally improved by focusing on these motivators, many of which are addressed by real work, authentic and experiential, even online experiential learning experiences. Artificial Intelligence (AI) driven by Machine Learning with Natural Language Processing (NLP) will drive new means of accentuating motivators. Many online games, dating back to PacMan (the first commercial game to use AI), use AI to generate competition at an appropriate skill level to challenge participants “just enough” – akin to a Vygotski – an Zone of Proximal Development (Vygotsky 1978). ChatBots have been implemented in a number of courses where their feedback (on basic elements of course/subject comprehension) has been rated as the best advisor by participants unaware that they had been interacting with an algorithm. Obviously caution will have to be exercised in all of these developments, but the notion that an individual academic as the sole source of content delivery, support, advice, counselling, instructional design, architecture, and coordination of a thousand other moving parts is at best questionable. Skilled instructional design, (some) automated feedback, and intrinsically motivating materials that will encourage engagement and preparation prior to dynamic, challenging, even fun in-class sessions seems an attainable goal. Given the challenges and intense losses of the COVID experience, comfort may be gleaned in that it forced an expedited, if clumsy, embrace of new technologies and systems that, when more reflectively applied, could end up enhancing the teaching and learning experience in a manner that was (still) likely decades away without the pandemic. Hope springs eternal.

Cross-References ▶ The 3C Merry-Go-Round: Constructivism, Cognitivism, Connectivism, Etc. ▶ The Role of Adaptive Learning Technologies and Conditional Learning

References Allain, R. 2017. The traditional lecture is dead. I would know—I’m a professor. Wired. https:// internet.psych.wisc.edu/wp-content/uploads/532-Master/532-UnitPages/Unit-03/Allain_ WiRED_2017.pdf Australian Collaborative Education Network. 2020. Online work integrated learning placements and projects guide. https://acen.edu.au/ Bell, K. 2014. Online 3.0 – The rise of the gamer educator: The potential role of gamification on online education. ProQuest Dissertations Publishing. ———. 2018. Game on!: Gamification, gameful design and the rise of the gamer educator. Baltimore: Johns Hopkins Press. Benjamin, L.T., Jr. 2002. Lecturing. In The teaching of psychology: Essays in honor of Wilbert J McKeachie and Charles L. Brewer, ed. S.F. Davis and W. Buskist, 57–67. New Jersey, USA: Lawrence Erlbaum Associates.

25

Developing and Quantifying Intrinsically Motivating Instruction:. . .

499

CapGemini. 2021. Fast forward to the future. https://www.capgemini.com/au-en/research/fastforward-to-the-future/ Carney, M. 2015. Breaking the tragedy of the horizon: Climate change and financial stability speech by Mr Mark Carney, Governor of the Bank of England and Chairman of the Financial Stability Board, at Lloyd’s of London, London, 29 September 2015. Carroll, K. and E. MacKenzie. 2018. Preliminary report into the effectiveness of the literacy and numeracy online modules and support for LANTITE for School of Education Master of teaching students (Western Sydney University report). Chen, P.-S., R. Gonyea, and G. Kuh. 2008. Learning at a distance. Journal of Online Education 4 (3) https://www.learntechlib.org/p/104252/. Chickering, A.W., and Z.F. Gamson. 1987. Seven principles for good practice in undergraduate education. AAHE Bulletin 39 (7): 3–7. https://files.eric.ed.gov/fulltext/ED282491.pdf. Cicekci, M.A., and F. Sadik. 2019. Teachers’ and students’ opinions about students’ attention problems during the lesson. Journal of Education and Learning 8 (6): 15–30. https://doi.org/10. 5539/jel.v8n6p15. Clark, R. 2005. Instructional strategies. In What works in distance learning guidelines, ed. H.F. O’Neill, 43–61. North Carolina, USA: Information Age Publishing. Csikszentmihalyi, M. 1990. Flow: The psychology of optimal experience. New York, USA: Harper. Edison Research. 2020. The infinite dial 2020. https://www.edisonresearch.com/?p¼9516 Eikeseth, U. 2020. Finding places: The search for the brain’s GPS. (Transl. L. Moffatt). World Scientific. https://doi.org/10.1142/11735 Hawtrey, K. 2007. Using experiential learning techniques. Journal of Economic Education 38 (2): 143–152. https://doi.org/10.3200/JECE.38.2.143-152. Hopkins, I., and D. Roberts. 2015. ‘Chocolate-covered broccoli’? Games and the teaching of literature. Changing English (Studies in Culture and Education) 22 (2): 222–236. https://doi. org/10.1080/1358684X.2015.1022508. Kapp, K.M. 2012. The gamification of learning and instruction: Game based methods and strategies for training and education. New Jersey, USA: Pfeiffer/ASTD. Kirk, K. 2007. Experience-based environmental projects. https://serc.carleton.edu/introgeo/ enviroprojects/index.html. Kolb, D.A. 1984. Experiential learning: Experience as the source of learning and development. Englewood Cliffs: Prentice Hall. Lewis, L.H., and C.W. Williams. 1994. Experiential learning: Past and present. New Directions for Adult and Continuing Education 62: 5–16. Microsoft Canada (Consumer insights). 2015. Attention spans. https://prc.za.com/2016/11/ attention-spans-report-microsoft-2015/ Niman, N. 2013. The allure of games: Toward an updated theory of the leisure class. Journal of Games and Culture 8 (1): 26–42. https://doi.org/10.1177/1555412013478685. Odgers Berndtston (2020). Coronavirus has brought the future forward by accelerating the uptake of digital technologies. https://www.odgersberndtson.com/en-de/insights/coronavirus-hasbrought-the-future-forward-by-accelerating-the-uptake-of-digital-technologies Offer, K., M. Wesson, F. McGaughey, N. Skead, and L. Elphick. 2019. Why bother if the students don’t?' the impact of declining student attendance at lectures on law teacher wellbeing. In Wellness for law: Making wellness core business, ed. J. Marychurch and A. Sifris. LexisNexis. https://ssrn.com/abstract¼3491487. PWC. 2021. Pulling the future forward. https://www.capgemini.com/au-en/research/fast-forwardto-the-future/ Savitz, E., and J. Tedford. 2012. Facebook timeline for brands: It’s about storytelling. Forbes. www. forbes.com/sites/ciocentral/2012/02/29/facebook-timeline-for- brands-its-about-storytelling. Schell, J. 2008. The art of game design: A book of lenses. Burlington: Morgan Kaufmann. Schwab, K. 2017. The fourth industrial revolution. New York: Penguin Books Limited. Selvi, K. 2010. Motivating factors in online courses. Procedia: Social and Behavioural Sciences 2 (2): 819–824. https://doi.org/10.1016/j.sbspro.2010.03.110.

500

K. Bell

Subramanian, K.R. 2018. Myth and mystery of shrinking attention span. International Journal of Trend in Research and Development 5 (3) http://www.ijtrd.com/papers/IJTRD16531.pdf. Svinicki, M.D., and W.J. McKeachie. 2013. McKeachie’s teaching tips: Strategies, research and theory for college and university teachers. Boston: Houghton-Mifflin. University of Adelaide Division of Academic and Student engagement. n.d.. Graduate attributes and learning outcomes. https://www.adelaide.edu.au/learning/strategic-projects/graduate-attri butes-and-learning-outcomes Vygotsky, Lev. 1978. Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard Press. Wankat, P.C. 2002. The effective efficient professor: Scholarship and service. Boston: Allyn and Bacon.

The Role of Adaptive Learning Technologies and Conditional Learning

26

Kate Thompson, Anna Charisse Farr, Thom Saunders, and Gavin Winter

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Features to Inform the Design of the Adaptive Learning System . . . . . . . . . . . . . . . . . . . . . Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proposed Bayesian Network to Describe the Model of System of Learning and Teaching . . . Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Undergraduate Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Postgraduate Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Core Features of the Virtual University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

502 503 505 506 508 510 513 513 515 518 519 520 520

Abstract

In a Virtual University, adaptive learning systems can provide a model for learners, teaching staff, and administrators to be supported with the challenges associated with virtual learning and the creation of a personalized learning experience. In this chapter we describe recent research about the design and implementation of adaptive learning systems and propose the development of a Bayesian network (BN) as the preferred methodology. Using a design framework K. Thompson (*) · A. C. Farr School of Teacher Education and Leadership, Queensland University of Technology, Brisbane, QLD, Australia e-mail: [email protected]; [email protected] T. Saunders · G. Winter Visualization and Interaction Solutions for Engagement and Research (VISER) Group, Queensland University of Technology, Brisbane, QLD, Australia e-mail: [email protected]; [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_26

501

502

K. Thompson et al.

to structure the BN, we have created a Virtual University BN. We then provide two conceptual scenarios (undergraduate and postgraduate) to demonstrate how the adaptive learning system could support students at our imagined Virtual University. This proposed model for the use of adaptive learning technologies relies on infrastructure currently used in sectors such as digital marketing and communications and social networking. The authors discuss the risks associated with bias and the complexity of the underlying models involved. However, there are many benefits in terms of teaching, student experience, institutional efficiencies, and, if designed appropriately, for contributions to understanding about learning in higher education itself. Keywords

Higher education · Adaptive learning technology · Adaptive learning system · Personalized learning experience · Bayesian network · Conditional probability · Design for learning · Assessment · Conceptual scenario

Introduction In a Virtual University, adaptive learning technologies would link data about learners and learning to pedagogical decision-making in the design of higher education courses as well as during student learning. While significant research has demonstrated the effectiveness of different types of adaptive measures on learning (Martin et al. 2020), the studies are based on individual courses, units, or topics. In this chapter, we provide an example of how a university-wide approach to adaptive learning technologies could be implemented. First, we provide a brief overview of adaptive learning technologies, including AI techniques for creating a model for adaptive educational technologies (Colchester et al. 2017). We discuss the potential barriers to implementation in the Virtual University, such as the lack of research of the design, implementation, or effectiveness of adaptive learning technologies in subject areas outside of engineering/computing, languages, or mathematics (Xie et al. 2019) or the technology adoption preferences of students (Shawky and Badawi 2018). We also examine key design features from the existing systems (Khosravi et al. 2020) and acknowledge the need for robust models of learners and learning (Aleven et al. 2016). We then propose the use of a Bayesian network (BN) to describe the proposed model of the Virtual University’s system of learning and teaching. BNs have been used in combination with adaptive learning systems previously (How and Hung 2019). Building on Essa’s (2016) outline of nextgeneration adaptive learning systems, and connecting with inquiry approaches to design for learning in higher education (Alhadad and Thompson 2017), we demonstrate, using conceptual scenarios, how adaptive learning technologies can provide support for data-driven decision-making in the design of learning situations as well as during the implementation of these designs, allowing the assessment of student progress to inform feedback, learner paths, content, as well as the development of students’ meta-cognitive skills such as self-regulated learning.

26

The Role of Adaptive Learning Technologies and Conditional Learning

503

Background Learning technologies are adaptive when they are designed to enable changes to be made to some elements of a learning design, in response to some measure (or measures) connected to the individual student in order to personalize their opportunities for learning. In work so far, this has been related to the presentation of the content and based on the record of interaction (Rosen et al. 2018). Other authors have suggested that adaptions can be related to sequence and assessment (Morze et al. 2021) or to support and presentation (Mavroudi et al. 2018). It is distinctly different from using background information (such as demographics or test scores) about a person to suggest particular content (Rosen et al. 2018). In the proposed system, some elements will be adaptable and some adaptive. An adaptable element is one over which the student has some control (Burgos et al. 2007), whereas for an adaptive element, the system makes these decisions for the learner (Tomé Klock et al. 2015). When content is adapted, it is done in relation to the way it is presented (e.g., videos vs. readings) and allows students to choose their own trajectories of learning according to preferences for time and format. An adaptive sequence facilitates an automated choice of content, difficulty level, and order of material based on what learners do – this is the most complex. Adaptive assessment means that each question is based on the way in which the previous question was answered (Morze et al. 2021). The system has to keep learning and to measure, at regular intervals, the knowledge level, progress, configuration of learning resources, tasks, and assessment (Morze et al. 2021). The ultimate aim of adaptive learning technologies is to personalize learning through the adaptation of the designable elements to the progress of each student, in real time, without the help of teachers (Morze et al. 2021; Peng et al. 2019). In the 2020 EDUCAUSE Horizon Report, they differentiate between adaptive technology (including digital platforms and applications), personalized learning, and adaptive learning (a form of personalized learning in which adaptive technologies play a role). In most cases, when adaptive learning technologies have been implemented, a blended form is a common approach to implementation. At Arizona State University, they found that this was needed to achieve the most in terms of student success (EDUCAUSE 2020). In the other examples provided in the Horizon report, they suggest that one result of this is that the role of the teacher can then shift to that of a coach who has access to all of the learning data. This would have an impact on the expectations related to data literacy for the role of educators. The report also explains that at some institutions, the kinds of exercises given to students in a particular unit are dependent on the overall degree in which they are enrolled (EDUCAUSE 2020). An adaptive learning system consists of an algorithm to identify a learner’s current state and a recommendation engine, based on characteristics of the learner and what other similar learners have done (Rosen et al. 2018). In this understanding of an adaptive learning system, it is important that the assumptions that are used to build the model of both the learners, as well as how recommendations are made, are both key factors in determining the success of the designed system. In many adaptive

504

K. Thompson et al.

learning technologies and systems, this is related to questions around the assessment used (Bergner et al. 2018). Adaptive learning in itself is not a new concept. In the 1950s, Skinner (1958) characterized teaching as the division of content into smaller parts that could then be personalized for students. As learning and teaching moved to become more technology-enabled, areas such as instructional design (Reigeluth 1999) and education design patterns (McAndrew et al. 2006) have also drawn on the idea that systems of teaching and frameworks of design could be used to adapt pedagogical approaches, materials, and social arrangements to best support the learners participating. The assumptions, though, about how to personalize learning and whether students are the best judge of how they learn continue. As technology and automation of learning management systems (LMSs) such as Moodle or approaches to teaching and learning, such as MOOCs, continue to develop, it is clear that the models on which we base these systems are key to their effectiveness (Wise and Shaffer 2015). Learning is a complex undertaking (Goodyear and Carvalho 2013), and a complex combination of theoretical approaches can be applied to understand and design for learning. If designed appropriately, the algorithms used in the adaptive learning system can make inferences about the state of a learner based on data that is otherwise hidden from an instructor (Bergner et al. 2018). Think, for example, about the order of resources accessed or whether a student attended a lecture face to face or online. These are not factored into the decision-making of tertiary educators in the current system and definitely not at scale. The importance, then, of the model and the way in which the data is determined to fit the model is extremely important as a methodological step (Bergner et al. 2018). Kabudi et al. (2021) suggest that there is little research on the implementation of adaptive learning systems. When they have been implemented, the systems have not helped students as intended. A review of the literature on the implementation of adaptive learning systems and personalized learning was used to identify the potential barriers to implementation in the Virtual University that we imagine for this chapter. These barriers are related to the appropriateness of content for online teaching and learning, the completeness of models of learning and access to data to inform these models, and the changing practice of instructors. In Xie et al.’s (2019) review of 70 research studies into technology-enhanced adaptive/personalized learning, of the areas of content addressed, most studies focused on engineering/computer, languages, mathematics, and science. There were no studies in arts/design, business/management, or health/medical/nursing. The authors suggested that the reason for this was that the researchers involved in these studies did not have a background in these subject areas. They also found that existing studies focus on the learning outcomes that are relatively straightforward to measure – such as learning achievements – rather than higher-order thinking skills or communication (Xie et al. 2019). The operationalization of an adaptive learning system is dependent on data (Adams Becker et al. 2017). A significant barrier to the implementation of adaptive learning systems is the availability of reliable and complex data about learning and learners. Gašević, Dawson, Rogers, and Gasevic (Gašević et al. 2016) describe the process of learning analytics as the generation of

26

The Role of Adaptive Learning Technologies and Conditional Learning

505

log data, recorded by a learning management system, with time-stamped events of some kind of learner activity. Patterns are then identified in the data, and the interpretation of the patterns is used to aid educational decision-making. Essa (2016) outlines the principal components necessary for a big data learning architecture: a real-time or streaming layer; a standard-based protocol for describing, capturing, and transmitting learning activities and events; short-term and long-term databases; a parallelized computation layer; and an output layer to support end-user visualizations or APIs. The practice of tertiary educators would also need a significant revision where this has to be implemented in higher education settings (McKenney and Mor 2015). Instructors would need a significant support to interacting with the data generated (Ghislandi and Raffaghelli 2015) and how to process, represent, analyze, and interpret that data for teaching and to support learning.

Bayesian Networks In this chapter we are suggesting a Bayesian network (BN) approach to provide a whole-of-university system to support learning. BNs are probabilistic graphical models used for reasoning under uncertainty (Pearl 1985). Using BNs as a methodology has several advantages. Firstly, they are a useful tool which can provide support for decision analysis and can collate, organize, and formalize information such as empirical data, model outputs, and expert knowledge about the issue of concern (Uusitalo 2007). BNs are also able to use each piece of available information, meaning that even when information is sparse, it can still be used (Farr et al. 2014). BNs are also able to combine different sources of knowledge because they are able to, in a mathematically coherent manner, incorporate data with different accuracies and from different sources, allowing the combination of data measured on different levels of accuracy to be undertaken (Marcot et al. 2001). Combining survey data, expert elicited data, and data from the literature to quantify the resulting BN is therefore possible (Farr et al. 2014). Once a BN is created, it can be used to reason about the situation it models. Inference in the Bayesian framework entails the computation of the probability distribution over all variables given what is available. When this is completed, a posterior probability distribution is associated with each variable. The resulting distribution reflects the influence of evidence. BNs allow for two kinds of reasoning: diagnostic and predictive. Diagnosis is a task of identifying the most likely causes given a set of observations. Prediction (or forecasting), by contrast, attempts to identify the most likely event occurrence given a set of observations. Diagnosis looks at the past and present to reason about the present, while prediction looks at the past and present to reason about the future (Millán et al. 2010). Bayesian networks have previously been used in limited ways to inform the design of systems concerned with learning. Conati (2002) used BNs to handle uncertainty around modeling tasks involving a high level of uncertainty, when

506

K. Thompson et al.

students can follow various lines of reasoning and are not required to show all their reasoning explicitly. Almond et al. (2009) used BNs to provide an intuitive framework for modeling content domains at a diagnostic level, and, by explicitly modeling the internal relationships in the tested domain, assessments become more powerful tools for teachers to make rapid, accurate instructional decisions based on what students know. Di Pietro et al. (2015) proposed the use of Bayesian networks for jointly monitoring internal and external performance of a master’s program of an Italian university in a holistic approach. A study conducted by Kotsiantis et al. (2003) can be considered as one of the pioneering studies in investigating the application of machine learning techniques, in this case Bayesian networks, for student drop-out prediction. Lacave et al.’s (2018) investigation used BNs to explore the drop-out rate in computer science degrees at the at the University of Castilla-La Mancha in Spain, which is similar to Eliasquevici et al.’s (2017) investigation into identifying which factors have a stronger influence on student retention in distance undergraduate courses at the Federal University of Pará. Sharabiani et al. (2014) used BNs to predict students’ grades in three major courses which most of the students take in their second semester, while Fernández et al. (2011) used BNs to analyze some of the performance indicators that are used to compute the amount of public funds received by the University of Almería in Spain. Ferreira et al. (2016) used a hybrid student model approach that combined ontologies and Bayesian networks to identify the knowledge of each student, based on their characteristics and behavior, while using an adaptive educational system. Experiments were performed with real student participants in a higher education course using an experimental prototype developed to verify the viability of the approach, which showed satisfactory results.

Key Features to Inform the Design of the Adaptive Learning System For an adaptive system to be successful at a university level, as for any other curriculum design, it must align with the assumptions about learning that inform the enactment by students during learning and the practice of teachers. In this we also draw on the Activity-Centred Analysis and Design (ACAD) framework, a design framework that allows the design to be iterated through the meso-level all the way to the task level – to provide the system with a way to make design decisions and to utilize it to better understand learners as well as learning and teaching. An understanding of the technology used to support learning in a way that students learn is necessary (Morze et al. 2021), as well as an understanding of the potential student pathways to different educational outcomes to ensure that students are supported based on goals rather than preferences. In this model, we assume that each individual is different in all aspects and is in a state of constant change (Peng et al. 2019). Learning can be difficult and challenging, and it involves vulnerability and uncertainty and often feels uncomfortable or unpleasant, as well as joyful. It is essential that an adaptive system provides opportunities for all of these to occur as

26

The Role of Adaptive Learning Technologies and Conditional Learning

507

well as support for students, including encouraging students to risk failure during their learning journey. Essa has outlined the seven characteristics that a next-generation adaptive learning system should contain (Essa 2016) and be cost-effective, accurate, efficient, scalable, flexible, generalizable, and transparent. Other authors have built on these, such as Smart Sparrow or Open edX. Ripple (Khosravi et al. 2020) was created for use with university students to recommend learning activities to students based on a measured knowledge state. In this system, students were able to take an active role in knowledge construction; however, incentives to engage were needed. They adopted an open learner model which further influenced engagement in positive and negative ways. Custodianship of data stayed with instructors, and ethical principles guided the use of student data for inquiry approaches to practice. In developing a Bayesian network, a list of nodes is created. We argue in this chapter that a conceptual framework of learning and teaching in the Virtual University is essential to creating an adaptive learning system to support personalized learning. In Shawky and Badawi’s (2018) study, they proposed that factors be classified according to personal, social, cognitive, structural, and environmental factors. They suggested an extensive approach to data collection to inform the system, including questionnaires, sensors, and interaction data. In this chapter, we draw on research that is based on design for learning in higher education. The design for learning can be seen as forward-oriented practice and is essential to considering the negotiation of values and practices in an educational setting (Ghislandi and Raffaghelli 2015). The design for learning needs to be considered as the continuous reframing of practice (Goodyear and Dimitriadis 2013), occurring at several stages of an intervention, and influences it in an iterative process (Ghislandi and Raffaghelli 2015). The Activity-Centred Analysis and Design (ACAD) framework (Carvalho and Goodyear 2014) includes four elements to consider during design time – the social, set, and epistemic can be designed (roles and rules, tools and digital and physical learning environment, processes of knowledge building, tasks). The fourth of these occurs during learn time – the co-configuration and co-creation of the learning environment or what actually happens. In considering a whole-of-university approach to the design of an adaptive learning system, a suggested conceptualization that also includes scale levels (macro, meso, micro) will be adopted (Yeoman and Wilson 2019). We have used these elements and structure to design the BN proposed in this chapter. Drawing on the literature on co-design (Penuel et al. 2007), the learning sciences (Goodyear et al. 2014; Sawyer 2005), design for learning (Carvalho and Goodyear 2014), and user experience design (Dimitriadis et al. 2021), the following elements were determined to be essential for inclusion in the Virtual University’s adaptive learning system: • Stakeholders are to be included in the original design and the continued re-design of the system. • Teachers are considered to be vital to the success of the adaptive learning system. • Learners must be able to connect with other learners.

508

K. Thompson et al.

• The system will work in different subject areas and for different types of tasks. • The user experience is essential to the success of the system – expertise will be required for this area of design, and the appropriate production value for the learning intention is essential. • Learners and instructors will be able to identify their assumptions about learning and understand how they are connected to the design of the adaptive learning system.

Methods A Bayesian network (BN) is a compact, expressive representation of uncertain relationships among variables of interest. These variables are represented as nodes and arcs show the direct dependencies or probabilistic correlation between the variables (Pearl 1985). For simplicity, the nodes in a BN are discrete variables to ensure ease of computation (Farr et al. 2014). The strengths of the dependencies are given by probability values. The directed acyclic graph (DAG), resulting after the construction of the BN, is quantified through a series of conditional probabilities based on data or information available about the problem (Jensen and Nielsen 2007) and defines a factorization of a joint probability distribution over the variables in the DAG. These directed links in the DAG represent the factorization. Pairs of connected nodes are referred to as parents and children, with directed edges flowing from parents to children. A conditional probability distribution is specified for each node given its parents (Pearl 1985). BNs are a useful, simple, and convenient modeling technique, since the conditional independencies implied by the graph and the corresponding simplification to the general multiplication rule are easy to compute. Each variable is conditionally independent of all other variables, given the variables that surround it (Pearl 1985). The general multiplication rule gives a joint probability distribution as the product of successive conditional probability distributions. So, for example, the multiplication rule for P(A, B, C, D, E, F, G) can be written as PðA, B, C, D, E, G, GÞ ¼ PðAÞ  PðBjAÞ  PðCjA, BÞ  PðDjA, B, CÞ  PðEjA, B, C, DÞ  PðFjA, B, C, D, EÞ  PðGjA, B, C, D, E, FÞ: While this recursive relationship can be written for any order of variables, the order that matches the conditional probability distributions, associated with the nodes of the BN permits, allows for considerable simplification due to the conditional independencies implied by the graph. If, for example, a simple BN, as shown in Fig. 1, was constructed, P(A, B, C, D, E, F, G) would simplify to PðA, B, C, D, E, F, GÞ ¼ PðAÞ  PðBjAÞ  PðCjAÞ  PðDjBÞ  PðEjBÞ  PðFjCÞ  PðGjCÞ:

26

The Role of Adaptive Learning Technologies and Conditional Learning

Fig. 1 Nodes represent variables of interest; arrows represent conditional probability dependencies. Nodes at the tail of an arrow are parents; the nodes at the end of an arrow are the children

509

A

C

B

D

E

F

G

Additionally, the marginal distributions can be simplified by taking the product of a node’s conditional probability distribution and its parent’s conditional probability distribution. If we were interested in only P(X4), this would become PðGÞ ¼ PðGjCÞPðCjAÞPðAÞ: So, BNs allow complex relationships between variables to be described in terms of a smaller subset of variables which can be read directly from the DAG. The construction of the BN model is a three-step process: conceptual model structure, defining the model states, and quantifying the model (Farr et al. 2014; Johnson et al. 2010). The conceptual model, which shows the important factors represented by nodes and the interactions between the nodes represented by directed arrows, is developed. Building the conceptual model requires the identification of variables within the domain that are relevant to the problem. It is important to focus only on the clearly relevant ones. This process is usually helped by asking the following questions (Millán et al. 2010): • What is the problem being modeled? • What are its possible causes? • Which other factors can make problems or causes happen or prevent them from happening? • Which evidence could be available to support causes, problems, or factors? A fine balance lies in what to include in the model. Including too much background knowledge makes a model less transparent. Simple models are easy to understand and evaluate. However, this does not mean one should sacrifice expressive power of the model by limiting the number of variables just for the sake of keeping things simple. A variable should only be included in the model if it provides pertinent information. If there are doubts, then the variable can usually be left out. For a BN composed of discrete nodes, each node is categorized into a small number of states. These states are chosen to be meaningful in the context of the problem as well as the node in which they are put. These states are generally discrete

510

K. Thompson et al.

values and must be mutually exclusive. The nodes and states are quantified by assigning probabilities to each state. The probabilities assigned are conditional on the states of the nodes that directly affect it, i.e., its parents. Finally, the quantification of the nodes can be undertaken using information from several sources including experimental data, simulation models, statistical or mathematical models, results from previous studies, and expert knowledge (Farr et al. 2014; Johnson et al. 2010).

Proposed Bayesian Network to Describe the Model of System of Learning and Teaching The proposed BN (Fig. 2) is composed of 52 nodes and 56 connections. Five internal nodes, “student,” “teacher practice,” “task,” “learner activity,” and “technology,” feed directly into the outcome node “academic standing.” The reason for this is that these factors are the ones that the authors believe directly impact a student’s academic standing and whether they progress to the next stage of their degree. These five nodes are impacted by other factors, which, along with their descriptions and states, are described in Table 1. The states for the factors were chosen so as to be as simple and binary as possible to ensure simplicity for this novel model of a system of learning and teaching. The proposed BN would use information from the student, their study environment, teaching staff background and philosophies, educational activity information, and technological information to inform the outcome node, “academic standing.” All the nodes in the network can be updated as new information regarding the student, their results, and progress is found. This updating allows the “academic standing” node to also be updated as time progresses. In this chapter we are presenting a conceptual BN. If this was undertaken for implementation, an elicitation process involving interviews and surveys of informed

Fig. 2 The Virtual University BN

26

The Role of Adaptive Learning Technologies and Conditional Learning

511

Table 1 Node descriptions and states Node Academic standing Student Academic background Bridging course IELTS Number of degrees Degree type Life skills Mature age Straight from high school Challenging measures First in family Extra-curricular activities Student connectedness Digital competence Social connectedness School demographics Permanent residence High school Socioeconomic status Technology User fluency Physical quality Ergonomics Desk infrastructure Location Connection quality Platform

Description The progression of a student into the next step of their study Student factors allow for successful academic standing Suitability of a student’s academic background

States Yes, no

Completion/requirement of a bridging course Completion/requirement of IELTS Completion of previous degrees

Yes, no Yes, no Yes, no

Type of degree Suitability of student’s life skills Is the student a mature age student? Did the student come straight from high school?

Undergraduate, postgraduate Suitable, unsuitable Yes, no Yes, no

Level of challenging measures faced by student

Low, high

Is the student a first-in-family student? Number of extra-curricular activities student is involved in Does the student have connections to other students in the degree? Level of digital competence by student

Yes, no Low, high

Yes, no Suitable, unsuitable

Yes, no Low, high

Does the student attend university or social events? Demographics of school attended by student

Yes, no

Distance of student’s permanent residence from university High school type attended by student SES status of student

Near, far

Technology allows for successful academic standing How fluent the user is with technology? Suitability of study environment Quality of student’s ergonomic set-up Quality of student’s desk infrastructure

Yes, no

Suitability of student’s study location Internet connection quality

Suitable, unsuitable Low, high

Suitability of student technology platform

Suitable, unsuitable

Low, high

Independent, state Low, high

Low, high Suitable, unsuitable Low, high Low, high

(continued)

512

K. Thompson et al.

Table 1 (continued) Node Platform access Platform utilization Devices Personal devices Device quality Learner activity Time on task Apps/tabs open

Description Accessibility to platform Suitability of platform utilization

States Suitable, unsuitable Suitable, unsuitable

Suitability of student devices Accessibility of personal device by student

Suitable, unsuitable Accessible, not accessible Suitable, unsuitable Yes, no

Quality of devices used by student Learning activity allows for successful academic standing Amount of time on task Number of apps/tabs open while completing task Student’s inclination to ask for assistance

Short, long Low, high

Help-seeking behavior Log-on times Task Group work Discipline

Time of day a student logs on to study Types of task for successful academic standing Group work required Study discipline undertaken by student

Learning activity

Activity undertaken by student

Teacher practice Pedagogical approaches Teacher expertise Student feedback University benchmarks Pass rates Teacher training Experience

Suitability of teacher practice Suitability of pedagogical approach

Office hours, after hours Suitable, unsuitable Yes, no Education, science, engineering, arts Lecture, workshop, practical Suitable, unsuitable Suitable, unsuitable

Teacher expertise level Teacher responds to student feedback Teacher satisfies university benchmarks

Low, high Yes, no Yes, no

Unit pass rates Suitability of teacher training Experience level of teacher

Low, high Suitable, unsuitable Experienced, inexperienced Yes, no Yes, no

Informed practice Online training

Teacher uses informed practice in teaching Experience in online teaching

Yes, no

players would need to occur in order to complete the probability tables that make up the initial BN. Additionally, literature searches for probabilities of relevance would also need to be undertaken. This process would bring together data from a number of sources, which is something that the BN modeling framework handles well. The structure of the Virtual University BN presented in Fig. 2 is informed by the ACAD framework. Elements of the design related to the set (the technology node related to academic standing), task, and social (connected to task as well as to the student node and teacher practice) are included in this network. There is acknowledgment of the importance of the individual student as well as teacher practice in

26

The Role of Adaptive Learning Technologies and Conditional Learning

513

academic standing. Finally, learner activity – what students do when learning – is also connected to academic standing. The nodes included are not exhaustive, but they do serve to demonstrate the complexity of the system of learning and teaching, and training a network such as this on existing data more nodes to the Virtual University BN would be expected to be added. The following two scenarios will be used to demonstrate how the BN might be used to inform the adaptive learning system in the Virtual University.

Scenarios Two scenarios will be described in a way that helps imagine how the adaptive system would be implemented in different university contexts. Central to both implementations discussed is the importance of training carried out by students that will help the system build a model of the learner. The measures of success will depend on the intended learning outcomes for each context; however, there should be some common, broader objectives against which the adaptive learning system will be measured. The first scenario focuses on an undergraduate student, studying fulltime, who has transitioned directly from high school to university. The second focuses on a mature-aged student, studying part time, who is coordinating study with work and family responsibilities.

Undergraduate Scenario Jake has graduated his final year of high school and is excited to be heading to university after the summer holidays. The last few weeks of school were hard work; keeping up with study, completing assignments, and keeping it together for the exams. He had to start working a part-time job to help with the household expenses after his dad lost his management job when the company moved their factory overseas. Jake’s experience through high school was generally a happy one. He achieved good grades, had a lot of friends, and was lucky to enjoy extra activities like band practice, but it got harder when finances were low at home. He was always expected to attend university and really wanted to study Computer Science and Design. He often enjoyed coding programs that created wild visual designs, was fascinated in the way mathematics appeared in nature, and wanted to learn all he could about why. He had good support from his teachers and parents, and some of his extended family worked in IT, giving him the confidence that a computer science-design degree was a good choice. The Virtual University was not like the universities his parents or even his sister had been to – VU was a Virtual University – a completely digital institution of thousands of students and as many courses of study on offer, all taken online, whether you’re at home, in your office, or in a cafe. Jake liked the idea of attending virtual classes. His home set-up would support this: laptop (though a bit old now),

514

K. Thompson et al.

camera, microphone, and headset. The Internet connection was problematic if more than one person in the house was accessing it. Once Jake was accepted into the VU, one of the first semester units was Introduction to Digital Media, which stated the requirements as “a laptop or PC sufficient for the latest creative design suite.” Knowing that his laptop was only just making it through the last few weeks of high school, Jake became anxious – he needed a new computer, and while the Virtual University would let you download the creative design suite, his laptop would not run it all sufficiently. One of the other units was Programming Environments, a unit which explored the new generation of programming and application design, and the requirements of this unit were a working computer and an Internet connection. During enrolment, Jake received the following instant message from the VU Technology Support group: Dear Jake, we understand your computer might not be up to spec for the courses you’ve enrolled in, follow this link to our Remote Workstation Facility to explore what it can do for you.

The Remote Workstation Facility provided access to a high-end computer that would allow him to continue to use his existing laptop and install the software he needed. The Virtual University was also a Virtual Computer. As shown in Fig. 3, Jake’s situation can be manually entered into the BN to make the model suitable for his situation. As Jake progresses through his degree, other nodes in the BN will be updated to reflect his higher education journey. For example, the change in his access to more suitable technology can be updated. As this occurs the final node, “academic standing” is also updated and provides information on whether Jake will likely continue with his degree. The social connections at Virtual University were challenging for Jake. There were numerous online meetings to introduce teaching staff and unit outlines and requirements. Jake missed the interactions between and during classes with peers, and he found sitting in his chair for several hours to be uncomfortable. Jake was also working part-time some evenings during the week and on the weekends to contribute to his household. He was able to watch recorded lectures and do the tasks at times that suited him. This put significant pressure on Jake’s time management, and as the semester continued, Jake began to fall behind. One afternoon, his instant messenger pinged – it was the Virtual University – and he tentatively answered. Hi, Jake, this is Julia from VU Student Life, how are you? I’m just dropping a line to introduce myself and the team. We’re all about making sure our new students are settling into VU life and make sure you know that you can call us any time if you need advice or support.

Julia was able to access the progress that Jake was making in all the units and that he had begun to fall behind. She offered him an adjusted timetable to reduce the intensity of classes. She could see that the tutorials in particular were a challenge due to Jake’s work schedule. She was able to adjust his timetable to provide him with

26

The Role of Adaptive Learning Technologies and Conditional Learning

515

Fig. 3 The student section of the Virtual University BN with nodes updated for Jake

a better way to participate in the classes. She could also see what time he logged on and offered him a schedule based on his preferred working times, for him to review and accept. Jake found that the adjusted workshop sessions were much better; he could focus and was getting assignments in on time. He even found that the Internet connection was faster since the times no longer clashed with other members of his family.

Postgraduate Scenario Matilda Henson is a scientist who is married, with two school-age children, from a wealthy family, and has no health or personal issues. She is a dedicated mother busy with balancing professional and family life. The children are now at an age where they are becoming more independent (homework support and extra-curricular activities), which has given Matilda the opportunity to continue her professional

516

K. Thompson et al.

development. Matilda’s science background means she has done extensive research into various postgraduate opportunities and their alignment with her professional ambitions. Matilda is highly organized and has identified a serious risk associated with her need for flexibility around availability and the needs of her family. Matilda completed her undergraduate Bachelor of Science with a Chemistry major 20 years ago; she was offered honors at the time but was unable to complete this due to family commitments. Matilda was a high-performing student in her schooling years and started her undergraduate degree directly out of school. Matilda married soon after the completion of her undergraduate degree. As part of her studies, Matilda completed a placement with PRW Pharmacology Consulting where she was then employed as a junior upon completion of her degree. As a junior she was able to work across the organization, building a broad skillset in pharmacology and discovering her interest in microscopy and the chemical analysis area of the business. Returning to the workforce after caring for children, Matilda was challenged by the technical changes and advances made while she was not working. She completed a short course to upgrade her understanding of equipment operation. Matilda applied to several pharmacology organizations, and eventually she successfully gained employment in a pharmacology quality assurance group. Unhappy in the role, Matilda is now highly motivated to upgrade her skills and secure a high-level role as a microscopy analysis and visualization specialist. She applied for the VU’s postgraduate course Advanced Microscopy Techniques and Analysis so she can achieve her professional ambition. The initial application process was straightforward as her previous academic records were all immediately available, as were details of the short course. Issues of fees, course structures, and equipment expectations were all clearly articulated. Responsive personal communications from the VU included the completion of several video interviews with her application assessment group, made up of both industry and academic members. Her interviews were friendly and casual, and the focus was on outcomes, natural abilities, and what Matilda wanted to achieve in the future. Within days of her application, Matilda was accepted into VU, receiving access to the VU systems which included a visualization of her proposed journey, based on other similar postgrad student journeys. At the center of the VU system was the collection of units for Matilda to complete and the relationships between each of the units in terms of expectations of prior knowledge. The VU system provided options for Matilda to complete her selection of units and showed her the associated professional outcomes. As seen in Fig. 4, Matilda’s student demographic details are different from the undergraduate scenario. This reflects her personalized journey with VU. Again, as she progresses through her degree, the other nodes and their probabilities will update, and the propagation of the probabilities in the BN will update the final “academic standing” node As Matilda’s time constraints were critical to her successful completion of the postgrad studies, VU was able to set expectations around the time requirements for

26

The Role of Adaptive Learning Technologies and Conditional Learning

517

Fig. 4 The student section of the Virtual University BN with nodes updated for Matilda

each unit. These were calculated from past students and the engagement and learning practices required to achieve different results. This allowed Matilda to manage her time with a realistic expectation of outcomes. As Matilda worked her way through the process of building a desired unit commencement and completion schedule, additional options based on the unit structure from past students helped support the process. Being able to see different professional outcomes that were achieved by past students helped Matilda select options for professional placement that were aligned with her overarching professional goals. Matilda was then able to integrate additional units suggested by VU that were aligned with her unit selection and identify alternative professional outcomes. After the selection of units, Matilda was able to define her preferences for the types of tasks, pedagogical approaches, and the method of assessment of the units. The VU has a specialist methodology to accommodate different types of learners based on a matrix of past individual academic background, current student progress,

518

K. Thompson et al.

and individual challenges and successes. The VU’s advanced learning system interweaves knowledge of successful student outcomes with a broad range of pedagogical approaches captured from lecturer’s presentations, approaches, and expertise to provide students with appropriate learning solutions. A feature of the VU that impressed Matilda was her ability to select from different methods of assessment which aligned with how she best performs. The dynamic process provided an accurate reflection of her knowledge captured from a realistic environment that reflects real-life situational challenges. Matilda selected project-based learning approaches with activity-tracked milestones. Matilda was able to start at the VU immediately, selecting from unit options and combinations of units that could play a part in the completion of her Advanced Microscopy Techniques and Analysis postgraduate course. As Matilda progressed through her postgraduate course, the system continued to provide feedback on possible challenges and offer opportunities for restructure via a fluid set of units around her desired outcomes and her availability to complete the course. Her student journey continued to morph and change while tracking her decisions and their impacts on outcomes. During her degree, Matilda was required to take part in regular sessions where real-world situations are simulated in Virtual Reality (VR). She is required to complete a series of task-based simulations using virtual versions of actual tools, apply knowledge to solve challenges, and illustrate techniques, all within a constrained timeframe that mirrors real challenges faced in the professional role. Her performance in the simulation was tracked and her results calculated from how well she was able to recall knowledge and her overall performance with each specific task. When Matilda successfully completed her postgraduate course, having achieved a high-level academic standing, she was able to secure a professional position as a Senior Analyst. With the support of the VU system she was able to secure a higherlevel role due to the inclusion of additional units proposed by the VU.

Core Features of the Virtual University In the imagined scenarios, both students were beneficiaries of VU’s methodology for benchmarking and ranking of individual student performance. The measurement of performance was initially a core challenge for the VU but the advanced tracking and analytics, unlocked by the latest technology and aligned with the VU systems, provided a solution for accurate assessment, benchmarking and ranking of individual students. The VU analytical platforms make extensive interpretations of his performance, including behavior online, at-home technical capabilities, and sustained interest through attendance, interactions with professors, and of course grades. At the completion of subjects, the Virtual University undertakes revision and predictive modeling of student experiences and creates “pathway options” that assist students to optimize or adapt their learning by offering alternative course enrolments that suit their strengths and tap into interest areas they may not yet be aware of. This is built

26

The Role of Adaptive Learning Technologies and Conditional Learning

519

on an iterative machine learning mechanism to continually improve how the VU can contribute to student success, and in turn, optimize how the University delivers courses and plans for the future. The visualization of this complex system of learning for students is fundamental to the success of the adaptive learning system. In this approach to personalized learning, the agency of students is important, as the system helps them learn about themselves as learners. The presentation of data about learning decisions will show students their central interests, as well as offer options and suggestions based on other data that has been collected. The interactive visualization can demonstrate the opportunities for further learning and future careers should certain unit combinations be selected. This means that if students are intrigued by a particular unit, they can trace back, and make changes that will enable units to become active. The final important feature of the Virtual University mentioned in both scenarios was the provision of access to technology that would facilitate learning. This included access to VR headsets, high-performance computing, microscopy laboratories, and digital fabrication equipment. Students were not expected to provide their own access to the essential learning infrastructure, and because every student is remote, the Virtual University was able to invest in processes that ensured learning was accessible for all students.

Limitations All models come with biases which is why it is important to be explicit about the underlying assumptions of a model. As mentioned, in order to test these assumptions, the model would be run with existing data and tested against known outcomes. Through further elicitation as well as testing, it is expected that the proposed model would become significantly more complex. One risk with testing the model against real data is the lack of standardized data and the many undocumented factors that are associated with traditional university education systems. A further risk is that the model is based on current research about how learning occurs in traditional universities, and the implementation of the VU may change that significantly. The VU would need to make a commitment to investing in ongoing research with a wide variety of stakeholders to understand the impact and to ensure that this could continue to be adapted based on outcomes. By using the ACAD framework to structure the BN, a design for learning approach can be implemented from mesoto micro-levels of the university, which would facilitate an ongoing, inquiry approach to practice. There are risks to the traditional approach to higher education with this proposal and significant questions around shifting skills and epistemological practices that may not be able to be supported in online learning environments. This is another reason to take a considered, inquiry approach to the implementation, in consultation with stakeholders. Adaptive learning technologies should be used to help practitioners and students, not replace them.

520

K. Thompson et al.

Conclusions There are significant implications for learning and teaching in higher education from this proposed adaptive technology that should be carefully considered. The technology needed to do this, and the changes to the way connections are made already exist in other sectors like digital marketing and communications and social networking; consider targeted ads through social media, for example. These are based on factors such as age, postcode, previous interest in particular products, purchasing history, time of year, and in some cases the content of text-based exchanges with friends. The risk to not considering rigorous, open, evidence-informed ways to approach the use of adaptive learning technologies in higher education is that the model will become a black box and priorities could be driven not by learning objectives but only by objectives related to gaining competitive advantage or financial performance. Shifts to academic practice would be considerable; however, the opportunities to better support students and gain greater understanding of learning in higher education contexts are potentially significant. In this chapter, we have demonstrated how adaptive learning technologies can provide support for data-driven decision-making in the design of learning situations as well as during the implementation of these designs, assessing student progress to inform feedback, learner paths, content, and the development of students’ metacognitive skills such as self-regulated learning. The use of a Bayesian network as the methodology for the analysis and creation of an adaptive learning system has shown an advantage for several reasons. It gave us flexibility in the way in which the conceptual model was constructed, building on the ACAD framework in terms of identifying elements of the design that can be changed. It has also meant that a variety of data sources could be used in the model and that these could come from the research as well as from system-generated data. Finally, it meant that the inherent uncertainty that is present in learning systems could be accounted for. In this imagined Virtual University, personalized learning, designed by and for each learner, data-driven decision-making, assessment of student progress to inform feedback, learner paths, and content were supported through the use of an adaptive learning system.

References Adams Becker, S., Cummins, M., Davis, A., Freeman, A., Hall Gliesinger, C., and Ananthanarayanan, V. 2017. NMC horizon report. https://www.nmc.org/nmc-horizon/ Aleven, V., E.A. McLaughlin, R.A. Glenn, and K.R. Koedinger. 2016. Instruction based on adaptive learning technologies. In Handbook of research on learning and instruction, ed. R.E. Mayer and P.A. Alexander, 522–559. Routledge. https://doi.org/10.4324/ 9781315736419-33. Alhadad, S.S.J., and K. Thompson. 2017. Understanding the mediating role of teacher inquiry when connecting learning analytics with design for learning. Interaction, Design, & Architecture(s) 33: 54–74.

26

The Role of Adaptive Learning Technologies and Conditional Learning

521

Almond, R.G., V.J. Shute, J.S. Underwood, and J.-D. Zapata-Rivera. 2009. Bayesian networks: A teacher’s view. International Journal of Approximate Reasoning 50 (3): 450–460. https://doi. org/10.1016/j.ijar.2008.04.011. Bergner, Y., G. Gray, and C. Lang. 2018. What does methodology mean for learning analytics? Journal of Learning Analytics 5 (2). https://doi.org/10.18608/jla.2018.52.1. Burgos, D., C. Tattersall, and R. Koper. 2007. How to represent adaptation in e-learning with IMS learning design. Interactive Learning Environments 15 (2): 161–170. https://doi.org/10.1080/ 10494820701343736. Carvalho, L., and P. Goodyear. 2014. The architecture of productive learning networks. Routledge. Colchester, K., H. Hagras, D. Alghazzawi, and G. Aldabbagh. 2017. A survey of artificial intelligence techniques employed for adaptive educational systems within e-learning platforms. Journal of Artificial Intelligence and Soft Computing Research 7 (1): 47–64. https://doi.org/10. 1515/jaiscr-2017-0004. Conati, C. 2002. Probabilistic assessment of user’s emotions in educational games. Applied Artificial Intelligence 16 (7–8): 555–575. https://doi.org/10.1080/08839510290030390. Di Pietro, L., R. Guglielmetti Mugion, F. Musella, M.F. Renzi, and P. Vicard. 2015. Reconciling internal and external performance in a holistic approach: A Bayesian network model in higher education. Expert Systems with Applications 42 (5): 2691–2702. https://doi.org/10.1016/j.eswa. 2014.11.019. Dimitriadis, Y., R. Martínez-Maldonado, and K. Wiley. 2021. Human-centered design principles for actionable learning analytics. In Research on e-learning and ICT in education, ed. T. Tsiatsos, S. Demetriadis, A. Mikropoulos, and V. Dagdilelis, 277–296. Springer International Publishing. https://doi.org/10.1007/978-3-030-64363-8_15. EDUCAUSE (Association). 2020. 2020 EDUCAUSE Horizon report: Teaching and learning edition. https://library.educause.edu/-/media/files/library/2020/3/2020_horizon_report_pdf.pdf Eliasquevici, M.K., M.C. da R. Seruffo, and S.N.F. Resque. 2017. Persistence in distance education: A study case using Bayesian network to understand retention. International Journal of Distance Education Technologies 15 (4): 61–78. https://doi.org/10.4018/IJDET.2017100104. Essa, A. 2016. A possible future for next generation adaptive learning systems. Smart Learning Environments 3 (16). https://doi.org/10.1186/s40561-016-0038-y. Farr, A.C., T. Kleinschmidt, S. Johnson, P.K.D.V. Yarlagadda, and K. Mengersen. 2014. Investigating effective wayfinding in airports: A Bayesian network approach. Transport 29 (1): 90–99. https://doi.org/10.3846/16484142.2014.898695. Fernández, A., M. Morales, C. Rodríguez, and A. Salmerón. 2011. A system for relevance analysis of performance indicators in higher education using Bayesian networks. Knowledge and Information Systems 27 (3): 327–344. https://doi.org/10.1007/s10115-010-0297-9. Ferreira, H.N.M., T. Brant-Ribeiro, R.D. Araujo, F.A. Dorca, and R.G. Cattelan. 2016. An automatic and dynamic student modeling approach for adaptive and intelligent educational systems using ontologies and Bayesian networks. In 2016 IEEE 28th international conference on tools with artificial intelligence (ICTAI), 738–745. https://doi.org/10.1109/ICTAI.2016.0116. Gašević, D., S. Dawson, T. Rogers, and D. Gasevic. 2016. Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education 28: 68–84. https://doi.org/10.1016/j.iheduc.2015.10.002. Ghislandi, P.M.M., and J.E. Raffaghelli. 2015. Forward-oriented designing for learning as a means to achieve educational quality: Forward-oriented design for quality education. British Journal of Educational Technology 46 (2): 280–299. https://doi.org/10.1111/bjet.12257. Goodyear, P., and L. Carvalho. 2013. The analysis of complex learning environments. In Rethinking pedagogy for a digital age: Designing for 21st century learning, ed. H. Beetham and R. Sharpe, 2nd ed. Routledge. Goodyear, P., and Y. Dimitriadis. 2013. In medias res: Reframing design for learning. Research in Learning Technology 21. https://doi.org/10.3402/rlt.v21i0.19909. Goodyear, P., C. Jones, and K. Thompson. 2014. Computer-supported collaborative learning: Instructional approaches, group processes and educational designs. In Handbook of research

522

K. Thompson et al.

on educational communications and technology, ed. J.M. Spector, M.D. Merrill, J. Elen, and M.J. Bishop, 439–451. New York: Springer. https://doi.org/10.1007/978-1-4614-3185-5_35. How, M.-L., and W.L.D. Hung. 2019. Educational stakeholders’ independent evaluation of an artificial intelligence-enabled adaptive learning system using Bayesian network predictive simulations. Education Sciences 9 (2): 110. https://doi.org/10.3390/educsci9020110. Jensen, F.V., and T.D. Nielsen. 2007. Bayesian networks and decision graphs. 2nd ed. Springer. Johnson, S., F. Fielding, G. Hamilton, and K. Mengersen. 2010. An integrated Bayesian network approach to Lyngbya majuscula bloom initiation. Marine Environmental Research 69 (1): 27–37. https://doi.org/10.1016/j.marenvres.2009.07.004. Kabudi, T., I. Pappas, and D.H. Olsen. 2021. AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence 2: 100017. https:// doi.org/10.1016/j.caeai.2021.100017. Khosravi, H., S. Sadiq, and D. Gasevic. 2020. Development and adoption of an adaptive learning system: Reflections and lessons learned. In Proceedings of the 51st ACM technical symposium on computer science education, ed. S. Heckman, P. Cutter, and A. Monge, 58–64. Association for Computing Machinery (ACM). https://doi.org/10.1145/3328778.3366900. Kotsiantis, S.B., C.J. Pierrakeas, and P.E. Pintelas. 2003. Preventing student dropout in distance learning using machine learning techniques. In Knowledge-based intelligent information and engineering systems, ed. V. Palade, R.J. Howlett, and L. Jain, vol. 2774, 267–274. Springer. https://doi.org/10.1007/978-3-540-45226-3_37. Lacave, C., A.I. Molina, and J.A. Cruz-Lemus. 2018. Learning analytics to identify dropout factors of computer science studies through Bayesian networks. Behaviour & Information Technology 37 (10–11): 993–1007. https://doi.org/10.1080/0144929X.2018.1485053. Marcot, B.G., R.S. Holthausen, M.G. Raphael, M.M. Rowland, and M.J. Wisdom. 2001. Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement. Forest Ecology and Management 153 (1–3): 29–42. https://doi.org/10.1016/S0378-1127(01)00452-2. Martin, F., Y. Chen, R.L. Moore, and C.D. Westine. 2020. Systematic review of adaptive learning research designs, context, strategies, and technologies from 2009 to 2018. Educational Technology Research and Development 68 (4): 1903–1929. https://doi.org/10.1007/s11423-02009793-2. Mavroudi, A., M. Giannakos, and J. Krogstie. 2018. Supporting adaptive learning pathways through the use of learning analytics: Developments, challenges and future opportunities. Interactive Learning Environments 26 (2): 206–220. https://doi.org/10.1080/10494820.2017. 1292531. McAndrew, P., P. Goodyear, and J. Dalziel. 2006. Patterns, designs and activities: Unifying descriptions of learning structures. International Journal of Learning Technology 2 (2/3): 216. https://doi.org/10.1504/IJLT.2006.010632. McKenney, S., and Y. Mor. 2015. Supporting teachers in data-informed educational design: Supporting teachers in data-informed design. British Journal of Educational Technology 46 (2): 265–279. https://doi.org/10.1111/bjet.12262. Millán, E., T. Loboda, and J.L. Pérez-de-la-Cruz. 2010. Bayesian networks for student model engineering. Computers & Education 55 (4): 1663–1683. https://doi.org/10.1016/j.compedu. 2010.07.010. Morze, N., L. Varchenko-Trotsenko, T. Terletska, and E. Smyrnova-Trybulska. 2021. Implementation of adaptive learning at higher education institutions by means of Moodle LMS. Journal of Physics: Conference Series 1840 (1): 012062. https://doi.org/10.1088/1742-6596/1840/1/ 012062. Pearl, J. 1985. Bayesian networks: A model of self-activated memory for evidential reasoning. In Proceedings of the 7th conference of the cognitive science society, 329–334. Peng, H., S. Ma, and J.M. Spector. 2019. Personalized adaptive learning: An emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments 6 (1): 9. https://doi.org/10.1186/s40561-019-0089-y.

26

The Role of Adaptive Learning Technologies and Conditional Learning

523

Penuel, W.R., J. Roschelle, and N. Shechtman. 2007. Designing formative assessment software with teachers: An analysis of the co-design process. Research and Practice in Technology Enhanced Learning 02 (01): 51–74. https://doi.org/10.1142/S1793206807000300. Reigeluth, C.M. 1999. What is instructionl design theory and how is it changing? In Instructional design theories and models, Vol 2: A new paradigm of instructional theory, ed. C.M. Reigeluth, vol. 2, 5–29. Lawrence Erlbaum Asssociates: Publishers. Rosen, Y., I. Rushkin, R. Rubin, L. Munson, A. Ang, G. Weber, G. Lopez, and D. Tingley. 2018. The effects of adaptive learning in a massive open online course on learners’ skill development. In Proceedings of the fifth annual ACM conference on learning at scale, 1–8. https://doi.org/10. 1145/3231644.3231651. Sawyer, R.K. 2005. Introduction: The new science of learning. In The Cambridge handbook of the learning sciences, ed. R.K. Sawyer, 1–16. Cambridge University Press. https://doi.org/10.1017/ CBO9780511816833.002. Sharabiani, A., F. Karim, A. Sharabiani, M. Atanasov, and H. Darabi. 2014. An enhanced Bayesian network model for prediction of students’ Academic performance in engineering programs. In 2014 IEEE global engineering education conference (EDUCON), 832–837. https://doi.org/10. 1109/EDUCON.2014.6826192. Shawky, D., and A. Badawi. 2018. A reinforcement learning-based adaptive learning system. In The international conference on advanced machine learning technologies and applications (AMLTA2018), ed. A.E. Hassanien, M.F. Tolba, M. Elhoseny, and M. Mostafa, 221–231. Springer International Publishing. https://doi.org/10.1007/978-3-319-74690-6_22. Skinner, B.F. 1958. Teaching machines. Science 128 (3330): 967–977. Tomé Klock, A.C., L.F. da Cunha, M.F. de Carvalho, B. Eduardo Rosa, A. Jaqueline Anton, and I. Gasparini. 2015. Gamification in e-learning aystems: A conceptual model to engage students and its application in an adaptive e-learning system. In Learning and collaboration technologies, ed. P. Zaphiris and A. Ioannou, vol. 9192, 595–607. Springer International Publishing. https://doi.org/10.1007/978-3-319-20609-7_56. Uusitalo, L. 2007. Advantages and challenges of Bayesian networks in environmental modelling. Ecological Modelling 203 (3–4): 312–318. https://doi.org/10.1016/j.ecolmodel.2006.11.033. Wise, A.F., and D.W. Shaffer. 2015. Why theory matters more than ever in the age of big data. Journal of Learning Analytics 2 (2): 5–13. https://doi.org/10.18608/jla.2015.22.2. Xie, H., H.-C. Chu, G.-J. Hwang, and C.-C. Wang. 2019. Trends and development in technologyenhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education 140: 103599. https://doi.org/10.1016/j.compedu.2019. 103599. Yeoman, P., and S. Wilson. 2019. Designing for situated learning: Understanding the relations between material properties, designed form and emergent learning activity. British Journal of Educational Technology 50 (5): 2090–2108. https://doi.org/10.1111/bjet.12856.

Part X The Rise and Rise of AI, VR, AR, MR, and XR

Emerging, Emergent, and Emerged Approaches to Mixed Reality in Learning and Teaching

27

Stephen Marshall

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conception of Mixed Realities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Augmented Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Virtual Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pedagogical Affordances, Applications, and Impact of Mixed Realities . . . . . . . . . . . . . . . . . . . . . . Virtual Worlds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Content in Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Visualization of the Invisible . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3D Content and Relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Collaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Presence and Immersion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Task Focus and Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Barrier Removal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Current Challenges and Limitations of Mixed Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

528 529 530 531 533 534 535 536 536 537 537 538 539 539 540 542 542

Abstract

Mixed reality technologies, including virtual and augmented reality, and also referred to as extended reality, have been in continuous development for nearly 60 years. The current generation of technologies is widely applied in diverse contexts including education. Although most educational applications are currently limited in their pedagogical framing, being dominated by content experiences, mixed realities have a number of emerging, emergent, and emerged benefits able to support a virtual university which are reviewed in this chapter. S. Marshall (*) Centre for Academic Development, Victoria University of Wellington, Wellington, New Zealand e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_27

527

528

S. Marshall

Three themes are identified: the value that these technologies play in bringing information into the environment of the learner; the ability to change the learner’s perceptions; and the implications for the virtual university as an evolving organization able to take new forms through the application of mixed reality technologies. A conceptual framework arising from the review is presented to provide a classification system for the diverse forms of new and emerging mixed reality. Keywords

Mixed reality · Augmented reality · Virtual reality · Extended reality · Metaverse

Introduction Shifting a person’s conception of reality is arguably at the core of any educational experience. Before we had writing, cave paintings provided media intended to model alternate realities and provided an interface for people to interact with those realities (Hughes 2012). Writing provided another mechanism for communication that allowed learning to be experienced through the medium of text enacted in the reader’s mind and imagination (McLuhan 1959). The development of digital computers saw the educational potential quickly recognized in the work of Heilig with his Sensorama (Heilig 1962) and by Ivan Sutherland in his early work on graphical interfaces (Sutherland 1965). Subsequently, there has been an explosion of technologies expanding the ways in which learners can engage with information, and which create different realities influencing the educational experience (Slater and Sanchez-Vives 2016). The Internet, with its vast array of useful and interrelated services and affordances, creates a powerful new reality. A virtual university is itself, in an abstract sense, an alternative reality that is constructed using modern Internet tools to have a powerful influence on the physical reality of student lives. Lawrence Lessig makes the argument in his book Code (Lessig 2006) that the affordances of software, including those used through the Internet, are as real as any other part of our lives and are enacted in parallel to those of physical reality. Technological tools aimed at expanding our senses and offering us experiences are potentially powerful educational tools in this new reality. These are variously described as mixed reality, extended reality, virtual reality, virtual worlds, the metaverse, simulations, or augmented reality. This chapter reviews the potential of these tools as enablers for learning in a virtual university, commencing with an overview of the literature, and moving onto emerging, emergent, and emerged ways these tools are applied. It should be noted that the literature on mixed realities is vast and the material cited here barely scratches the surface of the volume of research being undertaken in applying these tools in every educational context. Mixed reality is an important tool in the training space with many examples appearing in the literature describing training systems. Investment in workplace use of mixed reality dominates the commercial mixed reality environment with many emerging devices

27

Emerging, Emergent, and Emerged Approaches to Mixed Reality in. . .

529

(such as the Vive Focus) targeted at enterprise use cases rather than the consumer space. Workplace implications and work-integrated learning are addressed in ▶ Chap. 28, “Artificial Intelligence and Evolution of the Virtual University,” of this volume.

Conception of Mixed Realities The concept of the mixed reality was created to encompass any technological environment where “real world and virtual world objects are presented together within a single display” (Milgram and Kishino 1994, p. 1322). Mixed reality can be understood as covering a diverse range of experiences shaped by the information sources and the user’s perception of place (Fig. 1). This figure draws on the virtuality continuum of Milgram and Kishino (1994) and the need to consider spatiality

Fig. 1 Mixed reality classification. (Inspired by Milgram and Kishino 1994, p. 1321 and Benford et al. 1996)

530

S. Marshall

(Benford et al. 1996) but emphasizes the aspects of information source and user perception identified in this review. At the top left of this classification is the modern equivalent of the cave painting, the simulation where a computer provides an interactive environment representing an underlying model such as a business system (Cook 2013). Simulations provide users with a subset of information describing a virtual world (which may be derived from a real-world context) and provide affordances that allow the user to manipulate aspects of that world so they can observe the impact or consequences of changes over time. Typically, simulations provide information that is abstracted well away from the real world and may be deliberately simplified or biased for specific purposes. Simulations are often dependent on human imagination to translate abstract elements into real-world equivalents, but they can also be enacted though sophisticated physical representations such as those used to train health workers. Educationally, simulations can be very powerful, with Cook finding in a meta-analysis of over 600 studies that simulation was “consistently associated with large, statistically significant benefits in the areas of knowledge, skills (instructor ratings, computer scores, or minor complications in a test setting), and behaviors (similar to skills, but in the context of actual patient care)” (Cook 2013, n.p.). Moving along the dimension of place, we find the user’s perception of where they are can be influenced through the use of telepresence systems (Minsky 1980) and where the simulation or virtual elements are directly related to a physical environment other than that where the user is situated. At its simplest incarnation, telepresence is used for videoconferences or webinars in which the sensory elements are limited to video and audio, with more elaborate systems including shared access to computer screens or electronic whiteboards. These latter collaboration systems move further into the middle of the classification space as they introduce a hybrid experience of digital and physical information. Telepresence has a wide range of applications including providing a mechanism for students to “attend” classes while otherwise prevented by health issues or disability, providing access to environments where cost, safety issues, or physical constraints limit access (Torgovnick 2013), and in the form of tele-operated systems providing access to specialist equipment that can be used from remote locations more cost-effectively by a range of users through a virtual interface (Potkonjak et al. 2016).

Augmented Reality In contrast to telepresence, augmented reality technologies bring information to the physical place where the student is located. Augmented or mediated reality tools superimpose or compose virtual objects into the real world, supplementing reality rather than replacing it. Augmented reality has been defined as “augmenting natural feedback to the operator with simulated cues” (Milgram et al. 1995, p. 283). More simply perhaps, Klopfer and Squire (2008) define augmented reality as “a situation

27

Emerging, Emergent, and Emerged Approaches to Mixed Reality in. . .

531

in which a real-world context is dynamically overlaid with coherent location or context sensitive virtual information” (p. 205). Augmented reality systems currently either project information onto physical surfaces or supplement a view of the real world through a computer display, typically either a handheld or head-mounted display. Augmented reality is currently limited by the field of view of the display systems available. Devices such as Microsoft HoloLens provide a limited degree of immersion with significant limitations on the amount of information that can be provided due to the optical constraints of the lens systems and the resolution of the displays. Despite this, it is a significant improvement on the limited immersion provided through the use of handheld devices such as smartphones or tablets, although at a significant cost premium. The defining feature of augmented reality is that the user remains aware of and engaged with the physical space they inhabit. They are provided with virtual information that affects their actions in that space, and their actions in the real world directly affect the information reported virtually. Their sense of presence remains anchored in the physical reality but with augmentation of their cognitive and sensory capabilities in ways that enable insights or capabilities beyond normal experience. Relatively little dependence on human imagination is needed to realize the capabilities of the experience. Functionally augmented reality has three main characteristics: (a) the combination of virtual and real objects in a real setting; (b) people working interactively in real time; and (c) an alignment between real and virtual objects (Azuma et al. 2001). These features depend on accurate information on the location of the user in order to provide information. Two main approaches are used: marker-based and locationaware. Marker-based systems can use a variety of approaches including specially formatted images, markers such as QR codes, infrared, RFID or Bluetooth beacons, or more recently, image recognition of the environment in real time using depthsensing cameras to identify objects and environments. Location-aware AR uses real-time information on the user’s location to provide information (such as are used in the various mapping and navigation products widely used in modern smartphones). These systems typically use a combination of GPS and radio triangulation technologies to identify the location and orientation of the user. Historically, these have been limited by the need to have external line of sight to satellite systems, but increasingly location information is being derived from other network information (Joshi et al. 2020) to provide greater accuracy and also positioning indoors.

Virtual Reality At the top right of the mixed reality classification in Fig. 1 is the completely virtual environment, first described by Lanier as virtual reality (Lanier 1989). Using virtual reality technologies, the user is able to experience an entirely computer-generated environment. Within this space, users are provided with information through

532

S. Marshall

different senses normally visual and auditory, but also increasingly kinesthetic (Minogue et al. 2006), and also through smell and taste (Keruish 2019), in ways that allow the brain to create a realistic experience of presence within the virtual environment (Coelho et al. 2006; Rienties et al. 2016). Virtual reality in its modern sense consists of an illusion of a 3-D space, representations of people within that space (avatars), and tools for communication between users (Dickey 2005). Virtual reality environments may be created using real-world assets such as photographs, and panoramic video, or may be entirely generated using software. The affordances of the virtual reality environment are determined by software, mediated through a range of supporting hardware such as hand units, gloves, body suits, head-mounted displays, and external sensors. User experiences of different virtualities are heavily influenced by the evolution of different interfaces and devices used to provide the experiences. Head-mounted devices of various types are the most visible technology used to access augmented and virtual information. The most elaborate systems include full body suits with embedded touch sensors, full replacement of vision, surround sound, and the ability to move physically while remaining safely embedded in the simulation space. These expensive options are complemented by far cheaper versions including haptic gloves or sensors designed to map hand movements, fixed displays which can act as “magic mirrors” (Ma et al. 2016), and audio-visual displays that use consumer smartphones. The simplest forms of virtual reality are delivered through computer displays and are interacted with using keyboards and mice. These have the advantage of relatively low cost and a high level of user-acceptability. The use of computer screens in its most elaborated form is seen through the creation of entire rooms designed to enable group experiences of virtual realities. CAVEs (Cave Automatic Virtual Environments; Febretti et al. 2013) consist of a large number of three-dimensional displays configured into a curved wall with infrared monitoring of user gestures. These spaces have the advantage of allowing multiple people to experience and interact with a virtual environment together. Despite this, CAVEs are very expensive to create and maintain and are limited in size to a dozen or so users at a time, making the model difficult to sustain financially. More immersive forms of virtual reality involve the use of displays that completely cover the user’s vision. This conception of immersive virtual reality in the form that we think of it today was foreshadowed by Ivan Sutherland in 1965 (Sutherland 1965) and then realized with the “Sword of Damocles” head-mounted display described 3 years later (Sutherland 1968). Today, such displays fall into two major classes: untethered mobile devices which essentially place a smartphone in front of the user’s eyes (e.g., Samsung Gear VR, Google Daydream, and Google Cardboard); and tethered devices which use a high-performance desktop or more recently laptop computer in combination with a head-mounted display (e.g., HTC Vive, Oculus Rift). Virtual reality devices are being deployed commercially for entertainment purposes, and the market is growing rapidly. The ongoing development of extremely high bandwidth wireless networking suggests that consumer devices in the near future will be able to off-load the computationally intensive aspects of virtual reality,

27

Emerging, Emergent, and Emerged Approaches to Mixed Reality in. . .

533

reducing the weight and size of devices and increasing the quality of the experience. This increased use of servers will also enable growth in multiuser virtual reality such as was first illustrated by Second Life but with the ability to plausibly support entire class experiences.

Pedagogical Affordances, Applications, and Impact of Mixed Realities Mixed realities are generally well regarded by students and have positive effects on motivation, enjoyment, and engagement (Garźon et al. 2019; Ibáñez and DelgadoKloos 2018). The educational benefits of mixed realities are described in multiple surveys of published work conducted over the last decade (e.g., Garźon et al. 2019; Ibáñez and Delgado-Kloos 2018; Merchant et al. 2014; Mikropoulos and Natsis 2011). A major problem apparent in these reviews is the relatively immature state of the published literature, which is dominated by technological feasibility studies and pilots with little theory or empirical evidence of impact on learner outcomes (Jensen and Konradsen 2018). When theory can be identified, the majority of educational applications of mixed realities are implicitly influenced by models including constructivism (including social constructivism) (Mikropoulos and Natsis 2011), situated learning (Lave and Wenger 1991), and experiential learning (Kolb 1984; Schott and Marshall 2018). Very little research examines the challenges of moving mixed reality pedagogy beyond small-scale pilots to operate at a great level within the institution. Many mixed reality applications are introduced without a corresponding pedagogical change, and consequently the impact is typically limited to aspects that directly flow from the experience itself rather than influencing outcomes measured in other parts of courses or programs. The lack of a strong pedagogical framing means that many applications of mixed realities are focused on information presentation and discovery, and consequently the strongest outcomes reflect improvements in content knowledge (Garźon et al. 2019; Ibáñez and Delgado-Kloos 2018; Merchant et al. 2014), particularly in regard to three-dimensional representations (Alfalah et al. 2019; Jensen and Konradsen 2018; Yip et al. 2019), in learning abstract concepts (Arvanitis et al. 2009; Dunleavy et al. 2009; Sotiriou and Bogner 2008), and learning associated with emotion, including stressful or difficult situations (Jensen and Konradsen 2018). Mixed reality tools have also been found to support both basic and advanced skill development in areas including vocational training (Yip et al. 2019) as well as learning-associated procedures and processes. Despite the limited pedagogical models, learning undertaken in different realities has been shown to have a wide range of positive influences of student cognitive and skill outcomes including the development of independent thinking, creativity, and critical analysis. Educational affordances of different forms of reality include:

534

S. Marshall

• Access to virtual worlds that overcome limitations of the physical learning environment (de Freitas 2008) • Providing learning content in a context-specific manner supporting ubiquitous and situated learning (Dunleavy et al. 2009; Schott and Marshall 2018; Wu et al. 2013) • Visualization of the invisible or abstract in association with the physical world (Arvanitis et al. 2009; Dunleavy et al. 2009; Sotiriou and Bogner 2008) • Providing learning content in a three-dimensional representation so learners can engage with it from multiple perspectives and relationships (Kerawalla et al. 2006; Ruthberg et al. 2020; Wish-Baratz et al. 2020; Yip et al. 2019) • Enabling collaboration (Hew and Cheung 2010; Wu et al. 2013) • Supporting a sense of presence, immediacy, and immersion (Hew and Cheung 2010; Neuhofer et al. 2014) • Focusing learners on task activities including through provision of in-context formative feedback in real time (Lombard and Ditton 1997; Tussyadiah et al. 2018) • Avoidance of barriers to learning resulting from time, physical accessibility, safety, and ethical concerns (Freina and Ott 2015; Wish-Baratz et al. 2020)

Virtual Worlds Technological limitations meant that the first virtual worlds were implemented using text and required significant imagination on the part of the user. The gaming origin of these environments is evident in their naming as MUDs (Multi-User Dungeons), MOOs (MUD Object Oriented), MMOGs (Massive Multiplayer Online Games), and MMORGs (Massive Multiplayer Online Roleplaying Games); these evolved rapidly into sophisticated environments that included spaces simply created for unstructured creative play and socialization (MUVEs – Multi-User Virtual Environments). The Second Life MUVE provided one of the first major internationally adopted virtual worlds supporting a diverse range of educational experiences. Second Life was widely adopted initially, and many universities, educators, and other interested individuals licensed spaces within the virtual world to create educational spaces such as virtual campuses, museums, experiential spaces, and virtual replicas of laboratories (de Freitas 2008). This investment was complemented by an intensive program of research that investigated the technology, pedagogy, sociology, and economics of the Second Life world (Brookey and Cannon 2009; Innocenti 2017). Despite its early success, Second Life has since stalled for a variety of reasons (Crider and Torrez-Riley 2017) and many educational spaces have been discontinued, for example, the Virtual University of Edinburgh which closed in 2019 (Tate 2019), or have become quiet backwaters. The underlying systems design and resource demands (particularly network traffic) have also made the software difficult to use in large classes, limiting scalability. There is also currently no practical way to use Second Life with contemporary virtual reality hardware.

27

Emerging, Emergent, and Emerged Approaches to Mixed Reality in. . .

535

The second problem encountered was the conflict inherent in the use of a commodity platform for educational uses as well as entertainment and commerce. Famously, there were a number of incidents where inappropriate and offensive material intruded into spaces where it was unwelcome (Hutcheon 2006) and, despite the ability of institutions to create more controlled spaces, individual users could adopt avatars that could easily offend others through their appearance or behavior, or encounter material inconsistent with educational norms (Brookey and Cannon 2009). More generally, Second Life evolved into a complex social environment mirroring real-world communities (Kawulich and D’Alba 2019) and this means that student activities in different spaces need to be managed in the same way that realworld field trips are managed, making it a time-intensive mode of learning for teachers. One response to the issues with Second Life has been the adoption of universitycontrolled spaces enabled through the OpenSimulator open sourced virtual world platform (Tate 2019). This software provides a similar level of functionality to the Second Life environment but with spaces controlled and operated on university servers and with access able to be controlled. Despite addressing some of the issues with Second Life, the OpenSimulator environment has not seen a high level of uptake (Crider and Torrez-Riley 2017) and remains dominated by pilots and other small-scale investigations of the technology. Other controlled virtual spaces have started to emerge in educational use. The Mozilla Hubs (Mozilla 2021) and Spatial.io (Spatial 2021) environments provide very accessible virtual spaces that can be created at low cost for educational use. These have the advantage of working through a wide variety of devices, other than just dedicated virtual reality headsets, and can include media drawn from standard sources such as Powerpoint and the web as well as three-dimensional materials and simulations. Individual teachers can create dedicated spaces for their classes and students (Guardiola 2021). Despite the greatly reduced complexity and cost, this is unlikely to be accessible to many educators yet and issues with the number of concurrent users limit its value for medium to large classes. Larger-scale virtual worlds in the consumer space have yet to emerge; however, the recent announcement by Facebook of an intention to enact their version of a “Metaverse” (Newton 2021) may yet see a new generation of virtual reality environment hybridized with social media platforms, although the significant privacy and other concerns associated with vendors in that space may see it having minimal impact on education. These issues have already been apparent with the Facebookowned Oculus Quest hardware which in some forms requires links to social media (Egliston and Carter 2020).

Content in Context All forms of mixed realities provide information in ways that are intended to enhance comprehension, engagement, and action, so it is not surprising that the vast majority

536

S. Marshall

of educational applications are dominated by providing content in ways that contextualize it productively for learning. At a very basic level, this information provision is illustrated by the use of QR markers to provide media linked to specific objects or exhibits in museums designed to support engagement by children (Moorhouse et al. 2019). Similarly, this technology can replace manuals by providing information linked to specific equipment in ways that enable novice users to learn key features or skills needed for its application (Martín-Gutiérrez et al. 2015). More elaborate use of educational mixed reality with content in context are provided by virtual reality experiences where learners are able to discover content through exploration and interaction (e.g., Schott and Marshall 2018). The vast majority of educational mixed reality applications are shaped by experiential learning enabled by the context provided to the learner (Garźon et al. 2019; Ibáñez and Delgado-Kloos 2018; Merchant et al. 2014).

Visualization of the Invisible Mixed reality technologies are particularly useful as content delivery mechanisms when they enable learners to benefit from the addition of information beyond that which can normally be experienced in physical reality or easily observed under normal conditions (Sotiriou and Bogner 2008; Wu et al. 2013). This includes the ability to visualize forces such as electromagnetism (Ibáñez et al. 2014) and the details of chemical reactions (Klopfer and Squire 2008). Mixed reality can be used to overlay this information on the real world, for example, to teach human anatomy by overlaying anatomical information on a learner’s own body using a “magic mirror” interface (Ma et al. 2016) or to support group discussion of virtual cadavers (Ruthberg et al. 2020). Other examples include the use of virtual reality in enabling students to develop greater understanding of the complexity of cell biology with content able to be interacted with directly through a haptic (touch) interface (Minogue et al. 2006).

3D Content and Relationships The development of the computer display resulted in significant productivity and educational benefits through the ability to display information interactively in three dimensions. Rendering of three-dimensional figures using the two dimensions of a traditional display is now routine and has transformed both the practice and education of a wide range of disciplines. These include the obvious examples of designing three-dimensional objects and environments as well as the exploration of complex objects such as large molecules and multidimensional data relationships. Perhaps the most basic implementation of three-dimensional information in mixed realities is through the recreation of real-world environments in ways that enable natural behavior by learners in virtual equivalents. Tools such as Second Life

27

Emerging, Emergent, and Emerged Approaches to Mixed Reality in. . .

537

have been used to create virtual facilities in ways that exactly parallel real-world ones (Lucke and Zender 2011). The use of established norms of behavior helps novice users understand these spaces and also serves as a means for training users in how spaces are used in the real world. Mixed reality technologies are also showing significant value for learning by displaying information that can be examined from different viewing angles or perspectives. These tools have been shown to positively impact on learner abilities to engage with spatial relationships, perceiving and visualizing spatial information even in students with poor spatial skills (Lee and Wong 2014; Slijepcevic 2013). Similarly, the ability to visualize and interact with complex systems such as human anatomy using mixed reality tools has been shown to be effective in supporting learning (Alfalah et al. 2019).

Collaboration Dunleavy, Dede, and Mitchell (2009, p. 20) note that mixed reality has the “unique ability to create immersive hybrid learning environments that combine digital and physical objects, thereby facilitating the development of processing skills such as critical thinking, problem solving, and communicating through interdependent collaborative exercises.” The importance of mixed reality as a collaboration tool in education has been recognized for decades and shown to positively stimulate interaction and group work (Billinghurst and Kato 2002). Existing communication tools such as video-conferencing and screen-sharing software already provide the ability to connect learners as they engage with educational activities, and ongoing development of mixed reality tools will see these become more widely used and able to be enacted in a range of contexts. Much of the research on the use of virtual worlds is framed by their ability to enable collaboration between learners in environments which allow for natural models of interaction. The power of habit and comfort in such collaboration is evident in the way that the majority of virtual spaces created for collaboration are modeled on real-world equivalents even when the physical constraints involved are no longer relevant, i.e., individual audience seating when technically all members of the audience could occupy a single location directly in front of the speaker, something that can be realized easily once it is realized that individual users do not have to perceive a given environment in exactly the same way.

Presence and Immersion A particular feature of mixed reality occurs when the technology provides a sense of total spatial immersion in the environment, leading the user to perceive that they are physically present in a simulated environment. “Spatial immersion occurs when a player feels the simulated world is perceptually convincing, it looks ‘authentic’ and ‘real’ and the player feels that he or she actually is ‘there’” (Freina and Ott 2015, p. 1).

538

S. Marshall

The ability of mixed reality to provide authentic experiences makes it a powerful tool for education and arises from the potential to provide an illusion that is so compelling that the user is no longer aware of the technology itself, nor extraneous elements of the physical space they are in (Lombard and Ditton 1997). Completely immersing students within a particular context provides learners with rich and complex information supporting a range of cognitive, affect, and skill outcomes (Neuhofer et al. 2014). Embodiment in mixed reality generates a phenomenon known as the “body illusion” which can have significant impact on attitudes, perception, behavior, and cognition. This effect was first noted by Minsky (1980) who used the term “place illusion” to describe the feeling that the operator of a remote robotic device can experience as part of a telepresence system. This illusion arises from an interplay of sensory information and various cognitive processes enabled by the users’ natural physical and cognitive systems (Slater and Sanchez-Vives 2016). A successful mixed reality environment provides the cues needed to harness our own cognitive and visual systems rather than necessarily providing a high-fidelity simulation of the real environment being modeled (Rienties et al. 2016). The impact and consequence of immersion in alternative realities is strongly dependent on user sensemaking and intention. Jacobson (2017, p. 47) observes “Authenticity resides in both the system . . . and within the mind of the user.” Educationally, there are two major implications of this cocreation of the experience with the user. The first is to recognize that the learner’s experience can be enabled even with the relatively lower fidelity and sophistication of mixed reality that can be achieved in educational contexts, compared with the elaborate and rich experiences provided in commercial game and entertainment products. The second is the critical importance of how the user is inducted into the mixed reality experience and how their expectations are framed to enable their active participation in the illusion (Christopoulos et al. 2018; Smolentsev et al. 2017).

Task Focus and Feedback Mixed reality technology can provide feedback content directly in context as noted above. Carefully designed pedagogical experiences can be scaffolded with information designed to guide learner focus and to respond to actions in ways that cannot easily be replicated in the real world. Active engagement with mixed reality arises from the ability of these technologies to immerse them in an environment that enables natural behavior and task focus (Lombard and Ditton 1997; Tussyadiah et al. 2018; Wu et al. 2013). “A sign of presence is when people behave in a [virtual environment] in a way that is close to the way they would behave in a similar real-life situation” (Gutiérrez et al. 2008, p. 3). Mixed reality has been shown to improve task performance in training simulation and to contribute to the sense of “flow” that supports focus and interest in learning (Bressler and Bodzin 2013).

27

Emerging, Emergent, and Emerged Approaches to Mixed Reality in. . .

539

Barrier Removal Mixed reality offers many opportunities to overcome barriers to learning arising from time, physical location and accessibility, safety, and ethical concerns, which makes it potentially highly relevant to the virtual university. There are now many examples of virtual laboratories being used to provide safe and cost-effective skill development for students (Potkonjak et al. 2016). These can either be used as replacements for physical facilities, or as complements providing induction (including orientation and health and safety briefings) and opportunities to practice and plan real-world laboratory work. An example of the potential mixed tools can provide was given by Torgovnick (2013) who used the Google Glass augmented reality device to bring a group of students on a virtual field trip of the CERN supercollider facility where they were able to ask questions and engage with the teacher as he explored the facilities. Unlike a prerecorded video, this was an interactive experience that allowed students to guide the teacher’s actions and focus attention on elements that they noticed, rather than simply passively engaging with a tour. Student groups could never otherwise practically and safely explore this facility. The virtual tour also avoids the environmental cost of unnecessary travel and the disruption that outsiders bring to some environments. Mixed reality has significant potential as a tool for allowing educational experiences in fragile communities while also being far more sustainable than alternatives that depend on travel (Schott and Marshall 2018). Mixed reality can further overcome the barriers of time and imagination, allowing for the (re)creation of historical or fictional spaces (i.e., Perez-Valle and Sagasti 2012) or simplifying and condensing complex real-world environments to emphasize the pedagogical model needed to support learning (Wyss et al. 2014). Finally, mixed reality devices provide access to complex curriculum materials remotely to students who are prevented from attending traditionally offered classes, for example, during the COVID-19 pandemic (Wish-Baratz et al. 2020).

Current Challenges and Limitations of Mixed Reality Mixed reality technologies are not without their limitations. Despite significant improvements in the power of computers, battery life and weight, display quality, and network performance, existing devices remain limited in their physical usability and in their tolerance by users for extended periods of time (Rienties et al. 2016). Head-mounted systems are rapidly evolving to address a number of negative features including the weight of the devices on the head, and the resolution and quality of the displayed environment, including its responsiveness to use actions, and the problem of simulator sickness or nausea induced by the misalignment of the displayed information with the user’s physical senses (Stanney and Kennedy 2009). Motion sickness remains a technical challenge affecting many VR users, and there are many groups working on improvements aimed at reducing and mitigating this problem (Rebenitsch and Owen 2016). Despite this, issues remain with the current

540

S. Marshall

generation of technology (Yildirim 2019). This issue remains a major challenge for virtual reality experiences in education as it limits the amount of time many people can be in the space, which means that student use must typically be supervised, which in turn prevents mixed reality technology being used at scale. Scalability also remains an issue of cost. Mixed reality is currently accessed through either high-performance systems which have far fewer issues with motion sickness and can support more responsive and elaborate simulations but which depend on expensive equipment and require time and space to set up correctly for use, or through consumer handheld devices which are far cheaper but provide a much more limited experience and cannot be used for more than a short period of time by many people. The complexity and time needed to engage with mixed reality is also a barrier in educational settings. Students find augmented reality complicated (Chen and Tsai 2012; Dunleavy et al. 2009), and students often encounter technical problems (Wu et al. 2013). Many mixed reality interfaces are too complex for novice users, and in consequence, mixed reality can use excessive amounts of time for effective use. Finally, there are ethical and social concerns associated with the use of mixed reality in education. As noted above, there are concerns about the way information on learners could be used by some vendors (Egliston and Carter 2020), or used to compromise users’ freedoms and privacy (Hofmann et al. 2017). Others have raised concerns about the potential for mixed reality to cause various harms including through fraud (Hosfelt 2019), the promotion of extreme behaviors and psychological issues by blurring reality (Slater et al. 2020) as well as through the inequalities that can arise if the technology is not readily available to all.

Conclusion and Future Directions Three major themes relevant to the virtual university arise from the emerging, emergent, and emerged approaches to mixed reality described in this chapter. The first theme is the value that these technologies play in bringing information into the environment of the learner, the second theme is the ability to change the learner’s perception in ways that influence learning, and the third theme is the implications for the virtual university as an organization. Augmented reality by definition is the provision of useful information to the user in ways that respond to their situation and activities. The opportunity for emerging virtual universities is to develop educational scaffolding that can be deployed in parallel to these information feeds, enacting a pedagogical model that is delivered where the learner is, and in response to their evolving needs. Traces of this model are already emerging in the programs of microcredentials, MOOCS, and the like, particularly when these are not limited by historical conceptions of qualifications. The next step in the evolution of these must address relevance in a context of continuous education enacted using mixed reality tools.

27

Emerging, Emergent, and Emerged Approaches to Mixed Reality in. . .

541

As equipment continues to evolve and enter the consumer space from the laboratory or specialist workplace, there are likely to be significant increases in the use of mixed reality. Combined with the rapid development of information tools using very large information sets and machine learning, these should see the emergence of personal information feeds overlayed into real-world contexts. These new tools will build on the already successful models using location information through map products and search results, as well as the emerging use of machine learning tools as a mechanism for the creation of educational resources. Consumer devices such as the Google’s Home Hub and Amazon’s Echo, and virtual assistants such as Apple’s Siri and Microsoft’s Cortana, show the first wave of these information appliances, and the next generation are likely to have a far greater impact on information work including education (see ▶ Chap. 28, “Artificial Intelligence and Evolution of the Virtual University”). The ability for mixed reality tools to provide a personalized real-time information stream aligned to a learner’s focus, personal preferences, and activities represent a fundamental disruption to education (Marshall 2018). The second theme is the ease with which mixed reality tools provide access to experiences that are impractical otherwise. The vast majority of educational uses of virtual reality are experiential in focus, shaped by the environments that are provided for learners to explore. Many of the mainstream uses of mixed reality tools for information work have the ability to be enacted easily by a virtual organization provided that students have access to the necessary hardware and a high enough performing Internet connection. Integration of mixed reality learning materials into the curriculum that replace the need for physical equipment, facilities, travel, or even other people in the learning experience are much more likely to be of value in the near future. The value of three-dimensional mixed reality environments such as Second Life is strongly shaped by the collaboration they enable. Historically, universities have enabled collaboration through the creation of campus environments. The creation of a virtual campus for learners to experience is perhaps the most basic way mixed reality enables emerging virtual universities. However, the utility of recreating the traditional structures of the university is unlikely to reflect the most value for many activities with outcomes that can be better achieved through other approaches. This leads us to the last theme, the ability of the virtual university as an organization to enact itself in ways that are not just mindlessly replicating the models and affordances of a physical space. The research on mixed reality technologies is dominated by technical and experiential aspects with little engagement in the impact it might have on organizations beyond the trivial replication of existing models of space, structure, communication, and work. Much as technology-enhanced learning required universities to understand the role of a learning management system and its evolution beyond a content repository (Marshall 2019), an organizational conception of the role that mixed reality plays is needed that provides a cybernetic sensemaking conception for leaders, academics, and learners. Virtual universities need to move past the initial generation typified by Second Life to create virtual environments that are more than replicating the limitations of

542

S. Marshall

the real world with minor improvements. Mixed reality is framed around the experience of an individual and, as has been noted earlier, happens primarily in their mind and imagination. The capacity of the virtual university to enact itself within the minds of individual learners will drive its success. Enacting that strategy requires organizational leadership and educators that can similarly enact new models of mixed reality within their own minds, and that can recognize the emerging forms of mixed reality as merely a transition point to a reconception of the university.

Cross-References ▶ Preparing Students for the Future of Work and the Role of the Virtual

References Alfalah, S.F.M., J.F.M. Falah, T. Alfalah, M. Elfalah, and N. Muhaidat. 2019. A comparative study between a virtual reality heart anatomy system and traditional medical teaching modalities. Virtual Reality 23: 229–234. https://doi.org/10.1007/s10055-018-0359-y. Arvanitis, T.N., A. Petrou, J.F. Knight, S. Savas, S. Sotiriou, M. Gargalakos, and E. Gialouri. 2009. Human factors and qualitative pedagogical evaluation of a mobile augmented reality system for science education used by learners with physical disabilities. Personal and Ubiquitous Computing 13 (3): 243–250. https://doi.org/10.1007/s00779-007-0187-7. Azuma, R., Y. Baillot, R. Behringer, S. Feiner, S. Julier, and B. MacIntyre. 2001. Recent advances in augmented reality. Computer Graphics and Applications IEEE 21 (6): 34–47. https://apps. dtic.mil/sti/pdfs/ADA606245.pdf. Benford, S., C. Brown, G. Reynard, and C. Greenhaigh. 1996. Shared spaces: Transportation, artificiality, and spatiality. In CSCW '96 Proceedings of the 1996 ACM conference on Computer supported cooperative work, Boston, MA, 77–86, November 16–20, 1996. https://doi.org/10. 1145/240080.240196. Billinghurst, M., and H. Kato. 2002. Collaborative augmented reality. Communications of the ACM 45 (7): 64–70. https://doi.org/10.1145/514236.514265. Bressler, D.M., and A.M. Bodzin. 2013. A mixed methods assessment of students’ flow experiences during a mobile augmented reality science game. Journal of Computer Assisted Learning 29: 505–517. https://doi.org/10.1111/jcal.12008. Brookey, R.A., and K.L. Cannon. 2009. Sex lives in second life. Critical Studies in Media Communication 26 (2): 145–164. https://doi.org/10.1080/15295030902860260. Chen, C.-M., and Y.-N. Tsai. 2012. Interactive augmented reality system for enhancing library instruction in elementary schools. Computers & Education 59 (2): 638–652. https://doi.org/10. 1016/j.compedu.2012.03.001 Christopoulos, A., M. Conrad, and M. Shukla. 2018. Increasing student engagement through virtual interactions: How? Virtual Reality 22: 353–369. https://doi.org/10.1007/s10055-017-0330-3. Coelho, C., J.G. Tichon, T.J. Hine, G.M. Wallis, and G. Riva. 2006. Media presence and inner presence: The sense of presence in virtual reality technologies. In From communication to presence: Cognition, emotions and culture towards the ultimate communicative experience. Festschrift in honor of Luigi Anolli, ed. G. Riva, M.T. Anguera, B.K. Wiederhold, and F. Mantovani, 25–45. Amsterdam: IOS Press. Cook, D. 2013. The literature on healthcare simulation education: What does it show? Agency for Healthcare Research and Quality. https://psnet.ahrq.gov/perspective/literature-health-caresimulation-education-what-does-it-show

27

Emerging, Emergent, and Emerged Approaches to Mixed Reality in. . .

543

Crider, A., and J. Torrez-Riley. 2017. Using Google trends data to gauge interest in virtual worlds. Journal of Virtual Worlds Research 10 (1). https://doi.org/10.4101/jvwr.v10i1.7237. de Freitas, S. 2008. Serious virtual worlds: A scoping study. Bristol: Jisc. http://web.archive.org/ web/20090219082858/http://www.jisc.ac.uk/media/documents/publications/seriousvir tualworldsv1.pdf. Dickey, M.D. 2005. Brave new (interactive) worlds: A review of the design affordances and constraints of two 3D virtual worlds as interactive learning environments. Interactive Learning Environments 13 (1–2): 121–137. Dunleavy, M., C. Dede, and R. Mitchell. 2009. Affordances and limitations of immersive participatory augmented reality simulations for teaching and learning. Journal of Science Education and Technology 18 (1): 7–22. https://doi.org/10.1007/s10956-008-9119-1. Egliston, B., and M. Carter. 2020. Oculus imaginaries: The promises and perils of Facebook’s virtual reality. New Media & Society. https://doi.org/10.1177/1461444820960411. Febretti, A., A. Nishimoto, T. Thigpen, J. Talandis, L. Long, J.D. Pirtle, T. Peterka, A. Verlo, M. Brown, D. Plepys, D. Sandin, L. Renambot, A. Johnson, and J. Leigh. 2013. CAVE2: a hybrid reality environment for immersive simulation and information analysis. In Proceedings IS&T/SPIE electronic imaging, 2013. Burlingame: International Society for Optics and Photonics. https://doi.org/10.1117/12.2005484. Freina, L., and M. Ott. 2015. A literature review on immersive virtual reality in education: State of the art and perspectives. In Proceedings of eLearning and Software for Education (eLSE), Bucharest, Romania, April 23–24, 2015. http://www.itd.cnr.it/download/eLSE%202015% 20Freina%20Ott%20Paper.pdf Garźon, J., J. Pavón, and S. Baldiris. 2019. Systematic review and meta-analysis of augmented reality in educational settings. Virtual Reality 23: 447–459. https://doi.org/10.1007/s10055-01900379-9. Guardiola, P. 2021. VR/AR in education. https://www.pauguardiola.net/vrarineducation. Gutiérrez, M., F. Vexo, and D. Thalmann. 2008. Stepping into virtual reality. London: Springer. https://doi.org/10.1007/978-1-84800-117-6. Heilig, M. 1962. Sensorama Simulator. US Patent Number 3,050,870. http://www.mortonheilig. com/SensoramaPatent.pdf Hew, K. F., and W. S. Cheung. 2010. Use of three-dimensional (3-D) immersive virtual worlds in K12 and higher education settings: A review of the research. British Journal of Educational Technology 41 (1): 33–55. https://doi.org/10.1111/j.1467-8535.2008.00900.x Hofmann, B., D. Haustein, and L. Landeweerd. 2017. Smart-glasses: Exposing and elucidating the ethical issues. Science and Engineering Ethics 23(3): 701–721. Hosfelt, D. 2019. Making ethical decisions for the immersive web. arXiv preprint arXiv:1905.06995. Hughes, I. 2012. Virtual worlds, augmented reality, blended reality. Computer Networks 56: 3879–3885. https://doi.org/10.1016/j.comnet.2012.09.016. Hutcheon, S. 2006. Second life miscreants stage members-only attack. The Sydney Morning Herald, December 22, 2006. https://www.smh.com.au/technology/second-life-miscreantsstage-members-only-attack-20061222-gdp3p7.html Ibáñez, M.-B., and C. Delgado-Kloos. 2018. Augmented reality for STEM learning: A systematic review. Computers & Education 123: 109–123. https://doi.org/10.1016/j.compedu.2018. 05.002. Ibáñez, M. B., Á. Di Serio, D. Villarán, and C.D. Kloos. 2014. Experimenting with electromagnetism using augmented reality: Impact on flow student experience and educational effectiveness. Computers & Education 71: 1–13. https://doi.org/10.1016/j.compedu.2013.09.004 Innocenti, A. 2017. Virtual reality experiments in economics. Journal of Behavioral and Experimental Economics 69: 71–77. https://doi.org/10.1016/j.socec.2017.06.001. Jacobson, J. 2017. Authenticity in immersive design for education. In Virtual, augmented, and mixed realities in education, ed. D. Liu, C. Dede, R. Huang, and J. Richards, 35–54. Singapore: Springer. https://doi.org/10.1007/978-981-10-5490-7_3.

544

S. Marshall

Jensen, L., and F. Konradsen. 2018. A review of the use of virtual reality head-mounted displays in education and training. Education and Information Technologies 23 (4): 1515–1529. https://doi. org/10.1007/s10639-017-9676-0. Joshi, R., A. Hiwale, S. Birajdar, and R. Gound. 2020. Indoor navigation with augmented reality. In Proceedings of the 2nd International Conference on Communications and Cyber Physical Engineering ICCCE 2019, 159–165. Singapore: Springer. https://doi.org/10.1007/978-981-138715-9_20. Kawulich, B.B., and A. D’Alba. 2019. Teaching qualitative research methods with second life, a 3-dimensional online virtual environment. Virtual Reality 23: 375–384. https://doi.org/10.1007/ s10055-018-0353-4. Kerawalla, L., R. Luckin, S. Seljeflot, and A. Woolard. 2006. “Making it real”: exploring the potential of augmented reality for teaching primary school science. Virtual Reality 10 (3–4): 163–174. Keruish, E. 2019. Arranging sensations: smell and taste in augmented and virtual reality. The Senses and Society 14 (1): 31–45. https://doi.org/10.1080/17458927.2018.1556952 Klopfer, E., and K. Squire. 2008. Environmental detectives: The development of an augmented reality platform for environmental simulations. Educational Technology Research and Development 56 (2): 203–228. https://doi.org/10.1007/s11423-007-9037-6. Kolb, D.A. 1984. Experiential learning. Englewood Cliffs: Prentice-Hall. Lanier, J. 1989. Virtual reality: A status report. In Cyberarts: Exploring art and technology, ed. L. Jacobson, 272–279. San Francisco: Miller Freeman. Lave, J., and E. Wenger. 1991. Situated learning. Cambridge, MA: Cambridge University Press. Lee, E.A.-L., and K.W. Wong. 2014. Learning with desktop virtual reality: Low spatial ability learners are more positively affected. Computers & Education 79: 49–58. Lessig, L. 2006. Code. (Version 2.0). New York: Basic Books. Lombard, M., and T. Ditton. 1997. At the heart of it all: The concept of presence. Journal of Computer-Mediated Communication 3 (2). https://doi.org/10.1111/j.1083-6101.1997. tb00072.x. Lucke, U., and R. Zender. 2011. 3D interactions between virtual worlds and real life in an E-learning community. Advances in Human–Computer Interaction 2011. https://doi.org/10. 1155/2011/684202. Ma, M., P. Fallavollita, I. Seelbach, A.M. Von Der Heide, E. Euler, J. Waschke, and N. Navab. 2016. Personalized augmented reality for anatomy education. Clinical Anatomy 29: 446–453. https:// doi.org/10.1002/ca.22675. Marshall, S.J. 2018. Futures scenarios as tools for sense-giving and sense-making. In Shaping the university of the future. Singapore: Springer. https://doi.org/10.1007/978-981-10-7620-6_20. Marshall, S. 2019. Evolving learning management systems into learning platforms. CIO Advisor, June 2019, 33–34. Martín-Gutiérrez, J., P. Fabiani, W. Benesova, M.D. Meneses, and C.E. Mora. 2015. Augmented reality to promote collaborative and autonomous learning in higher education. Computers in Human Behavior 51: 752–761. https://doi.org/10.1016/j.chb.2014.11.093. McLuhan, M. 1959. Myth and mythmaking. Daedalus 88 (2): 339–348. Merchant, Z., E.T. Goetz, L. Cifuentes, W. Keeny-Kennicutt, and T.J. Davis. 2014. Effectiveness of virtual reality-based instruction on students’ learning outcomes in K-12 and higher education: A meta-analysis. Computers & Education 70: 29–40. https://doi.org/10.1016/j.compedu.2013. 07.033. Mikropoulos, T.A., and A. Natsis. 2011. Educational virtual environments: a ten-year review of empirical research (1999–2009). Computers & Education 56: 769–780. https://doi.org/10.1016/ j.compedu.2010.10.020. Milgram, P., and F. Kishino. 1994. A taxonomy of mixed reality visual displays. In IEICE Transactions on Information and Systems E77-D(12), 1321–1329.

27

Emerging, Emergent, and Emerged Approaches to Mixed Reality in. . .

545

Milgram, P., H. Takemura, A. Utsumi, and F. Kishino. 1995. Augmented reality: A class of displays on the reality-virtuality continuum, 282–292. Bellingham/Washington, DC: SPIE. 10.1.1.83.6861. Minogue, J., M.G. Jones, B. Broadwell, and T. Oppewall. 2006. The impact of haptic augmentation on middle school students’ conceptions of the animal cell. Virtual Reality 10 (3–4): 293–305. https://doi.org/10.1007/s10055-006-0052-4. Minsky, M. 1980. Telepresence. Omni 2 (9): 45–51. Moorhouse, N., M.C. tom Dieck, and T. Jung. 2019. An experiential view to children learning in museums with augmented reality. Museum Management and Curatorship 34 (4): 402–418. https://doi.org/10.1080/09647775.2019.1578991. Mozilla. 2021. Hubs. https://hubs.mozilla.com Neuhofer, B., D. Buhalis, and A. Ladkin. 2014. A typology of technology-enhanced tourism experiences. International Journal of Tourism Research 16: 340–350. https://doi.org/10.1002/ jtr.1958. Newton, C. 2021. Mark in the metaverse: Facebook’s CEO on why the social network is becoming ‘a metaverse company.’ The Verge, July 22, 2021. https://www.theverge.com/22588022/markzuckerberg-facebook-ceo-metaverse-interview Perez-Valle, A., and D. Sagasti. 2012. A novel approach for tourism and education through virtual vitoria-gasteiz in the 16th century. In Proceedings of the 18th international conference on Virtual Systems and Multimedia (VSMM), Milan, Italy, 615–618, 2–5 September 2012. https:// doi.org/10.1109/VSMM.2012.6365991. Potkonjak, V., M. Gardner, V. Callaghan, P. Mattila, C. Guetl, V.M. Petrovi, and K. Jovanovi. 2016. Virtual laboratories for education in science, technology, and engineering: A review. Computers & Education 95: 309–327. https://doi.org/10.1016/j.compedu.2016.02.002. Rebenitsch, L., and C. Owen. 2016. Review on cybersickness in applications and visual displays. Virtual Reality 20: 101–125. https://doi.org/10.1007/s10055-016-0285-9 Rienties, B., B. Giesbers, S. Lygo-Baker, and S. Ma. 2016. Why some teachers easily learn to use a new virtual learning environment. Interactive Learning Environments 24 (3): 539–552. https:// doi.org/10.1080/10494820.2014.881394. Ruthberg, J.S., G. Tingle, L. Tan, L. Ulrey, S. Simonson-Shick, R. Enterline, and S. Wish-Baratz. 2020. Mixed reality as a time-efficient alternative to cadaveric dissection. Medical Teacher 42 (8): 896–901. https://doi.org/10.1080/0142159X.2020.1762032. Schott, C., and S. Marshall. 2018. Virtual reality and situated experiential education: A conceptualization and exploratory trial. Journal of Computer Assisted Learning. https://doi.org/10.1111/ jcal.12293. Slater, M., and M.V. Sanchez-Vives. 2016. Enhancing our lives with immersive virtual reality. Frontiers in Robotics and AI 3: 1–47. https://doi.org/10.3389/frobt.2016.00074. Slater, M., C. Gonzalez-Liencres, P. Haggard, C. Vinkers, R. Gregory-Clarke, S. Jelley, and J. Silver. 2020. The ethics of realism in virtual and augmented reality. Frontiers in Virtual Reality 1: 1. Slijepcevic, N. 2013. The effect of augmented reality treatment on learning, cognitive load, and spatial visualization abilities. Ph.D. Dissertation. Lexington, KY, USA: University of Kentucky. Smolentsev, A., J.E. Cornick, and J. Blascovich. 2017. Using a preamble to increase presence in digital virtual environments. Virtual Reality 21: 153–164. https://doi.org/10.1007/s10055-0170305-4 Sotiriou, S., and F.X. Bogner. 2008. Visualizing the invisible: Augmented reality as an innovative science education scheme. Advanced Science Letters 1: 114–122. https://doi.org/10.1166/asl. 2008.012. Spatial. 2021. Spatial – virtual spaces that bring us together. https://spatial.io Stanney & Kennedy. 2009. Simulation sickness. In Human factors in simulation and training, ed. D.A. Vincenzi, J.A. Wise, M. Mouloua, and P.A. Hancock, 117–124. Boca Raton: CRC Press. Sutherland, I.E. 1965. The ultimate display. Proceedings of IFIP Congress 2: 506–508.

546

S. Marshall

———. 1968. A head-mounted three dimensional display. In Proceedings of the December 9–11, 1968, Fall joint computer conference, Part I, 757–764. New York: ACM. Tate, A. 2019. Vue -> Openvue. http://blog.inf.ed.ac.uk/atate/2017/11/30/vue-openvue/. Torgovnick, K. 2013. A virtual field trip to CERN, via Google Glass. http://blog.ted.com/2013/05/ 06/a-virtual-field-trip-to-cern-via-google-glass/. Tussyadiah, I.P., D. Wang, T.H. Jung, and tom Dieck, M.C. 2018. Virtual reality, presence, and attitude change: Empirical evidence from tourism. Tourism Management 66: 140–154. https:// doi.org/10.1016/j.tourman.2017.12.003. Wish-Baratz, S., A.R. Crofton, J. Gutierrez, E. Henninger, and M.A. Griswold. 2020. Assessment of mixed-reality technology use in remote online anatomy education. JAMA Network Open 3 (9): e2016271. Wu, H.-K., S.W.-Y. Lee, H.-Y. Chang, and J.-C. Liang. 2013. Current status, opportunities and challenges of augmented reality in education. Computers & Education 62: 41–49. https://doi. org/10.1016/j.compedu.2012.10.024. Wyss, J., S.-E. Lee, T. Domina, and M. MacGillivray. 2014. Cotton Island: Students’ learning motivation using a virtual world. Decision Sciences Journal of Innovative Education 12 (3): 219–232. https://doi.org/10.1111/dsji.12036. Yildirim, C. 2019. Don’t make me sick: investigating the incidence of cybersickness in commercial virtual reality headsets. Virtual Reality. https://doi.org/10.1007/s10055-019-00401-0. Yip, J., S.-H. Wong, K.-L. Yick, K. Chan, and K.-H. Wong. 2019. Improving quality of teaching and learning in classes by using augmented reality video. Computers & Education 128: 88–101. https://doi.org/10.1016/j.compedu.2018.09.014.

Artificial Intelligence and Evolution of the Virtual University

28

Jon Mason, Paul Lefrere, Bruce Peoples, Jaeho Lee, and Peter Shaw

Contents Introduction: The Terminology Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Questions Driving Inquiry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Historical Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The World of Fake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Auditing the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reducing Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ethics and Social Science: New Frontiers for Standardization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Socio-Technical Systems, Standardization, and New Frontiers for Trustworthiness . . . . . . . . . . Swarm-Based AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning from the Field of Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Disruption and Empowerment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reflecting Forward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

548 550 551 552 553 554 555 560 562 562 563 563 565 565

J. Mason (*) Faculty of Arts and Society, Charles Darwin University, Darwin, Australia e-mail: [email protected] P. Lefrere CCA-research, Milton Keynes, UK e-mail: [email protected] B. Peoples Innovations LLC, Kissimmee, FL, USA e-mail: [email protected] J. Lee Department of Computer Science, University of Seoul, Seoul, South Korea P. Shaw Oujiang Laboratory, Wenzhou, China e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_28

547

548

J. Mason et al.

Abstract

In this chapter we invite the reader to consider the importance of context in the history and evolution of both artificial intelligence and the virtual university. There are histories in the terminology as much as what they refer to. Thus, the virtual universities that emerged late last century are very different in scope and vision compared to contemporary practice. There are likewise different theoretical perspectives with Gartner’s “Hype Cycle,” one among many methods that provides perspective on the stages of AI-driven advances in technological innovation. An imperative to foreground questioning of the domain is what frames our discussion. One key question among many concerns the affordances and challenges that AI presents for a virtual university in the coming decades. A university must prepare for AI-based auditing of the integrity of its student records. AI can help spot plagiarism and fake IDs and support academic integrity. Policy-makers might use AI to spot potentially impossible situations that could give rise to big penalties. But as AI merges into our environment, it is no longer just a tool with new ethical and social issues arising. There already exist forms of colonial biases embedded into AI and machine learning systems. What practical choices exist now? Every virtual university might consider collaborating on how to ensure trustworthiness of their operational systems that use AI. Emerging public policy such as the EU’s Artificial Intelligence Act provides a legal mechanism for building a reliable AI ecosystem. Research and innovation into both AI and the kinds of services virtual universities will need into the future are now proliferating. Many futures are possible. From an educational perspective developing human agency through this next era should be a priority. Students could be taught how to use AI-supported tools to solve increasingly challenging questions. Even more imperative will be the questions they ask. Keywords

Artificial intelligence · Virtual University · Digital learning

Introduction: The Terminology Challenge For many people unfamiliar with the field of Technology-Enhanced Learning (TEL), the era of widespread personalization and automatic updating of course content through the use of artificial intelligence (AI) is only just the beginning. For those in the field, Gartner’s “Hype Cycle” provides some perspective on the stages of visibility of AI-driven advances in technological innovation: hearing about the idea behind the innovation is typically followed by overenthusiasm, hype, and disillusionment in sequence, followed by an eventual mass adoption of a productivity path (Goasduff 2021). Such perspective can also be applied to terminology itself, where qualifiers such as “smart” and “deep learning” have been appropriated by the technology sector in recent times (Mason et al. 2020). Perhaps a more resonant

28

Artificial Intelligence and Evolution of the Virtual University

549

example is that referring to “an AI” in public discourse is now commonplace – it is no longer just a research field. The message of AI’s sudden enhanced visibility and acceptance as a ubiquitous enabling technology for all, however, is amplified by the news media and validated by the latest range of consumer products that feature smart and autonomous systems. Yet, the field of AI has been developing since the middle of the last century, the world of “smart” tech is well into its second decade, and “new” has almost been with us forever. Likewise, the use of “virtual” to describe the experience of engaging with and within the digital environment has been in use for over a quarter of a century. So, what’s all the fuss about now? What has changed? What affordances and challenges does AI present for a virtual university that is being reimagined for the next decade of the twenty-first century? These and related questions are what frame this chapter and challenge us in our conceptions of a university. To set the scene, the 1970s saw a huge interest by education ministers worldwide in ways to begin to use that era’s distance teaching methods to raise enrolments dramatically while cutting overall budgets through economies of scale and scope. In principle, traditional campus-based courses could be replaced by higher-enrolment home-based courses, delivered by early modems as the technical infrastructure for some form of virtual university and thus widening access to higher education by non-elites. But while that era had answers to questions to do with massification, most of those questions led to mass market education (as in today’s Massive Open Online Courses or MOOCs) but did not address deeper issues such as how to provide each student on a massified course with some content or feedback that is affordable but is just for them, e.g., that personalizes their study experiences in a life-changing, easily remembered, and highly valued way, like anecdotes of deep insights or constructive face-to-face criticism by famous mentors. That kind of “ethical and affordable personalization” of virtual university courses has been aspired to for decades but with little success (economically, technically, scientifically) until the past 30 years. Here are some of the landmarks. In 1996 Alan Gilbert provided a “background briefing” for an international conference focused on virtual university. In opening the event, Gilbert found it useful to articulate what the core of a university is: A university is a learning community in which students, teachers, researchers and scholars share a common commitment to rational inquiry, and through it the creation, advancement, preservation and application of knowledge. This common commitment takes different forms within the different groups that comprise the university community. At the same time, the roles and identities of students, teachers, researchers and scholars also overlap, and the tasks of creating, analysing, preserving and applying knowledge cross-fertilize and reinforce each other. (Gilbert 1996)

Thus, there is much to consider if the “roles and identities . . . overlap” within digital environments replete with bots and related artificially intelligent (non-human) services. Twenty-five years ago, the boundaries of the virtual and real worlds were much more distinct than they are today, and during this time, several conceptions of the “intermeshing” of the two have been proposed (Rajasingham 2009; Walsh 2010).

550

J. Mason et al.

From a more recent commentary on the pivotal role that interoperability has in building effective and sustainable digital infrastructure, Mike Wulff (2021) suggests that: While an IT leader’s success depends in part upon their ability to navigate pressing challenges, they must occasionally take a step back and ask the following question: From a technological perspective, what should a higher education institution look like?

In probing such ideas and to help us make sense of the scope of change, Marshall (2018) critiques the IT establishment in suggesting that “technological transformation and solutionism” perpetuate “myths.” Thus, the normalization of “digital disruption” has somehow obfuscated the role of human agency and sense-making in dealing with change. So, how should we proceed?

Questions Driving Inquiry Unlike numerous accounts of the emergence of the “knowledge worker” and the rise of the “network society” that privilege the informational foundation of the so-called Knowledge Era (Castells 1998; Cortada 2009; Drucker 1959, 1994; Wong and Neck 2010), we assert that it is questioning (by humans and/or by bots) and the accompanied analytical and explanatory dimensions of millennia of human thought that drive inquiry. Thus, while we may revel in the wonders of search engines, our contemporary digital infrastructures are still dominated by a search paradigm that abbreviates questions and diminishes their nuance (Mason 2014). In addition to the questions articulated above, the following questions have emerged in the development of this chapter: • • • • • • • • • •

What can recent history tell us about AI and the virtual university (VU)? What constitutes a university in the third decade of the twenty-first century? What minimum bundle of services is necessary to constitute a university? Why did some high-profile early virtual universities not survive? In what ways can AI transform our engagement with the digital environment? In what ways might the new virtual universities re-package core services of universities? In what ways might services be unbundled and re-packaged? In what ways do the narratives of “digital disruption” and “digital transformation” so prominent within the IT sector need to be overhauled for the age of AI? In what ways can trustworthiness of digital environments be engineered? In what ways might AI revolutionize and personalize our experience of inquiry online?

Importantly, our questions are framed to drive ongoing inquiry rather than close it down with a seemingly comprehensive list of responses to a search query. Moreover,

28

Artificial Intelligence and Evolution of the Virtual University

551

they are framed differently to the list of “Standing Questions” developed as the core of the AI100 Project from Stanford University (Littman et al. 2021): SQ1. SQ2. SQ3. SQ4. SQ5. SQ6. SQ7. SQ8.

SQ9. SQ10. SQ11. SQ12.

What are some examples of pictures that reflect important progress in AI and its influences? What are the most important advances in AI? What are the most inspiring open grand challenge problems? How much have we progressed in understanding the key mysteries of human intelligence? What are the prospects for more general artificial intelligence? How has public sentiment toward AI evolved, and how should we inform/ educate the public? How should governments act to ensure AI is developed and used responsibly? What should the roles of academia and industry be, respectively, in the development and deployment of AI technologies and the study of the impacts of AI? What are the most promising opportunities for AI? What are the most pressing dangers of AI? How has AI impacted socioeconomic relationships? Does it appear “building in how we think” works as an engineering strategy in the long run?

The AI100 questions listed above are important to probe in advancing the field; however, analysis also reveals they are primarily based on extracting declarative and procedural knowledge. Where are the questions concerning context and conditionality? How do we determine when we have sufficient interdisciplinary perspective?

Historical Perspectives Back in 1991, Beverly Woolf summarized “the field of AI in education” as “concerned with development of Artificial Intelligence techniques for the study of human teaching and for the engineering of systems that facilitate human learning” (Woolf 1991, p. 1). Such a statement was made at a time when Tim Berners-Lee had just invented the enabling technologies for the World Wide Web. It remains valid today, although the field has rapidly developed into an applied science and “AI” has entered public discourse as an entity and a capability – and is much more than an academic discipline of investigation. Arguably more significant is that outside research and science fiction, mainstream teachers and learners in 1991 were then unquestionably understood as solely human; today, they are also artificial entities. In a possible future, as foreshadowed by Vernor Vinge in 1993, a “singularity” looms where “new superintelligence” will push us beyond “the human era,” upgrading itself and advancing technological capability that surpasses exponential

552

J. Mason et al.

innovation (Vinge 1993). Such an idea has had other luminaries as champions (Kurzweil 2005), and in 2008 Singularity University was established “on the premise that the world’s greatest problems are the world’s greatest opportunities. We deeply believe that the world has all the necessary ingredients to tackle our biggest challenges and create abundance for all” (Singularity Group 2022). Also in 1993, Howard Rheingold’s book on The Virtual Community became a best seller (Rheingold 1993). Inspired by William Gibson’s work Neuromancer in which the term “cyberspace” was first coined, Rheingold’s terminology not only captured the moment, but it also documented a phenomenon that had already been developing for a decade in Internet-based communities such as the WELL (the “Whole Earth ‘Lectronic Link”). Several years after Rheingold’s work, the terminology of “virtual universities” and “virtual learning environments” emerged. In many ways, Rheingold’s work was prescient when considering some of the chapter titles: “Real-Time Tribes” and “Disinformocracy.” Recently, the world has witnessed the power of both tribalism and disinformation in disrupting democracy. In 1996, the notion of the virtual university soon gathered mainstream traction, and the first international conference on the topic was hosted by the University of Melbourne in Australia during which the then vice-chancellor, Alan Gilbert, predicted that traditional universities would likely partner with news corporations to radically transform the “bricks and mortar” universities (Arnold 1999). Gilbert was not as prescient as Rheingold although the Western Governors Virtual University in the United States (founded in 1997) has developed a model of delivery that has proved to be both viable and resilient. But as we consider contemporary trends shaping the future in the following pages, there is a lot to consider when identifying emerging architectures and key components of digital infrastructures that will drive virtual universities into the future. The following sections in this chapter are each focused on critical perspectives that grapple with the emerging discourse on AI as it is currently utilized and plausible trajectories of the impact on university education. In taking such an approach, we have aimed to identify the signals of transformation that are based on an increasingly complex “ecosystem” of technological, socio-political, environmental, and economic inputs. In short, virtual universities of the future will be assembled, financed, governed, and operated in ways that draw on an increasingly diverse mix of capabilities. Yes, technology will play a big part, with AI prominent. But context is everything, and our purpose here is not to predict but to contribute to opening the discourse into topics that will need close attention.

The World of Fake That was the last project. The final piece of the puzzle. They needed the girls to finish the virtual university so they could graduate from fake IDs to fake documents for real student visas. (Gardner 2021)

28

Artificial Intelligence and Evolution of the Virtual University

553

Examples of datasets at risk in a virtual university include verifying that a registered student is a real person. Verification requires higher education’s counterparts of the modern AI-based anti-illegality systems that financial institutions are required by law to use and to open to inspection if required, so that auditors can verify that a bank’s customers are who they say they are, their assets are not the result of illegal acts, etc. By analogy, a university must therefore prepare for AI-based auditing of the integrity of its student records (to spot any false student identities with fake certificates, any changes to grades awarded) or any suspicious changes to financial records (fraudulently reducing course fees due to the university); redirecting payments, etc.; and rapid collection and analysis of data on the location, identity, and activities of anyone who is doing something that is unexpected. Security modules embodying some kinds of AI based on machine learning (ML) are increasingly being retrofitted to key administrative systems used across higher education, for example, self-improving systems that teach themselves ways to reach or exceed the ability of human auditors to spot instances of suspicious changes to any of a university’s datasets or processes or to the attack surfaces of its cyberphysical systems (Kuleto et al. 2021).

Auditing the Data Independent auditing of training data can reveal possibly worrying patterns of online use. It can also reveal cases of unsuspected bias in the selection of training data, risking that the results of ML are untrustworthy. One of the simplest trust/distrust scenarios is forgetting the historical basis for a convention that is imbued in a given dataset. Thus, each sequence (“scene”) in the first black-and-white films, circa 1896, was presented as part of a longer story (the film). But early films lacked sound tracks; in that pre-talkies era, captions were used extensively to spell out each step in a story. Even with those innovations, pre-talkies “picture houses” often had to provide audiences with “explicadors” – people skilled in public story-telling and giving book-readings, whose job was to explain innovations in a film’s story-telling techniques, before a film was shown. In addition, and still relevant today, visual shortcuts were developed for communicating the gist of a story. For example, a dress code was developed to simplify a story. Audiences learned that heroes wore white hats and clothes and were light-skinned; their oppositions wore black and were darkerskinned. The last-century original need for that filmic convention disappeared as audiences became better able to follow visual story-telling, but some form of whiteblack bias is now part of many cultures, e.g., black hat versus white hat persists in corporate training and in hacking communities. In practice, policy-makers at a virtual university might combine AI’s machine learning (ML) and its “common-sense reasoning” (CSR) to spot a supposedly impossible situation that could give rise to big penalties (e.g., embedded bias in a set of TV sequences which become re-purposed for ML to automate selection processes). This can minimize the risk of race-based biased recruitment of students and staff, which is illegal but nevertheless is happening in some residential

554

J. Mason et al.

universities (RUs). As another example of using CSR to reduce risks, we can quickly spot a “fake ID” pattern (e.g., a uniquely numbered and apparently legitimate access card suddenly appears to have an identical twin, in use in another location at the same time). Such a scenario can eventuate if internal security is weak, as in “an inside job.” The theme of “an inside job” is frequently used as the basis for novels, films, and computer games but is trumped by instances of real-life mis-use of AI spotted using common-sense reasoning. An example is spotting multiple instances of “too good to be true” levels of athletic and academic performance, plus sector-leading levels of success in fund-raising by Deans and Provosts of a big name RU. By following the sources of the funds raised (first-time donors, who were parents of prospective students who had no prior record of exceptionality), and then correlating the amounts donated, and personal payments made from the donations, versus the prowess/qualification shortfall of each successful applicant, it was possible to model corruption in the enrolment system. In such cases, competing universities make much of the reputational damage of corruption, which they assert undermines the credibility of all aspects of a university’s work, as well as reducing the perceived value of a degree from it. As yet, we are unaware of any instances of institutional corruption involving a virtual university (VU), but this may simply reflect the newness of VUs.

Reducing Risk One area where AI can help both RU and VU reputations is how they reduce the risk of false positives and negatives in detecting impersonation (identity fraud). This can be a problem, particularly if a university’s proctors and quality assessment team are lax in ensuring that every assignment and examination is being submitted by the registered student, rather than by a more-capable stand-in. AI can help spot plagiarism, fake ID (e.g., if two instances of a student’s ID are active at the same time), and other instances of passing off the work of other people. In practice the top team of a virtual university has to set priorities for allocating funds to enhancing its use of AI. Low-budget options that are widely used include subscriptions to organizations that pool their experiences of using AI costeffectively. An example is EDUCAUSE, which undertakes multi-university periodic surveys of the opportunity cost of not using AI, as well as the reputational damage that AI-based fraud or cheating can bring (Brooks 2021). Instead of using AI in ways that promote anti-social and counter-productive workarounds to existing accreditation systems, an ethical VU can encourage altruism by asking students to use AI to devise novel, affordable, useful, and easily shared innovations in teaching and learning. In practice, a VU might develop an explicit road map toward altruism, which uses today’s AI to find the shortest and most affordable paths to an industry-recognized, in-demand, and employer-sponsored qualification from a VU. Such a VU may be seen by students as a “cool” and cheaper alternative to a traditional course, offering

28

Artificial Intelligence and Evolution of the Virtual University

555

an enjoyable way to acquire and bring to a prospective employer up-to-the-minute domain knowledge and soft skills (often including feedback on progress in developing emotional intelligence and virtual team skills); success via a VU route is increasingly taken as proof of a student’s willingness to invest substantial time and money in each component of their higher education and professional development. One ethical role here for AI is to make it simple and low-effort for a VU to empower a student to create and iteratively improve multiple versions of draft versions of assignments, using a minimally aggressive analogue of today’s “Adversarial Refinement in AI” (Hao 2020). Being able to experiment like this can provide evidence of high time management skills; doing this instead of cheating by copying others’ assignments indicates likely higher ethical standards. In practice, the virtual university might consider asking its IT providers to specify an AI architecture that provides interoperability between its administrative and accreditation systems and an employer’s recruitment systems. This can make it easier for potential employers to validate an applicant’s qualifications and assess the authenticity of their claimed standout values, their ways of working in teams, and their references, to differentiate them from RU students elsewhere who lack those capabilities and qualities, and even to spot any RU students who may have cheated in prior studies (e.g., used illegal workarounds such as plagiarism or submitting someone else’s work to gain credit for an assignment). Expanding on this, cheating is an example of an area where AI can augment a key aspect of a VU, namely, its ability to get to know its students (and to quantify aspects of their performance, if needed), potentially using the improved self-explainability of coming generations of AI to generate personalized help, so that each student’s mentor can support students to systematically develop a set of above-today’s norm of qualities and ways of working that, in prior and highly selective forms of higher education, were called “graduateness” (Glover et al. 2002; Heintz 2021). Today, data scientists and AI researchers in corporations have developed “goodness of fit” complements of graduateness, such as the eponymous set of hiring criteria called “Googleyness” (Meisenzahl 2019).

Ethics and Social Science: New Frontiers for Standardization As the technologies that collectively are described today as AI keeps developing, new frontiers are emerging not just in terms of capability but in ethical and legal implications. From a human point of view, we may find new mixed reality (MR), virtual reality (VR), and autonomously dynamic learning environments incredibly enabling; however, activities that happen in such environments may transgress social norms or have implications for institutional accountability, especially from a virtual university perspective that enables “global learning.” For most people, AI is just another “black box inside a glass box” that operates similarly to the everyday experience of performing a Google search though often in a more sophisticated way. Where it becomes distinctive is to what extent the intelligence may be explicitly anthropomorphic. In this context, thought must be given to the ethics inherent in

556

J. Mason et al.

these anthropomorphic creations utilized by virtual universities. The main ethical concerns in such systems involve multilevel biases which can be viewed through the lens of colonialism (Shakir et al. 2020; Mohamed et al. 2020). Traditionally, colonialism has been understood in terms of control by one power over a dependent area or people. Historically, the colonial era began when one nation subjugated another, conquering its population and exploiting it, often while forcing its own language and cultural values upon its people. The legacy has been so profound that contemporary discourse within the social sciences is replete with inquiry framed by a decolonization agenda (Moosavi 2020). In the context of AI, most IT systems and their underlying models and algorithms are produced by superpowers such as the United States and China, utilizing data from the superpower society. Due to power and value differences, there are no guarantees one country’s systems, models, and algorithms will deliver the same results in other countries. Although mainly unintentional, power and value biases are “built into” these implementations. The fallout of AI colonization in the context of virtual universities can include how a system adapts to both educators and learners, the formation and use of knowledgebases, adherence to regulatory and governance policies, and the effects of contextual values that reflect moral, societal, or personal concerns (Shakir et al. 2020). In Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence, Shakir, Png, and Isaac (2020) present decolonial theory strategies to counter and prevent unwitting AI colonization. They elaborate on several useful tools with which to qualify the nature of power imbalances or inequitable impacts that arise from advanced technologies like AI. One such framework identifies metropoles – the centers of power – and their peripheries that hold relatively less power and contest the metropole’s authority, participation, and legitimacy in shaping everyday life (Champion 2005). Dependency theory expands on this framework, by tying colonial histories to present-day underdevelopment and continued economic imbalance between countries and regions, as well as tying resulting dependencies to historic metropole and periphery dynamics (Champion 2005). Using the lens of metropole and periphery, contemporary practices in AI development can be understood as features of colonial continuities from states and governments. Following on, today’s technology corporations and universities could likewise be described as metropoles of technological and knowledge power with civic society, consumers, and students sitting at the periphery. In drawing on historical hindsight, Shakir, Png, and Isaac (2020) further elaborate on patterns of power that shape our intellectual, political, economic, and social world. By embedding a decolonial critical approach within its technical practice, AI communities can develop foresight and tactics that can better align research and technology development with established ethical principles, repositioning vulnerable peoples who continue to bear the brunt of negative impacts of innovation and scientific progress. By fusing the fields of artificial intelligence and decolonial theories, we can take advantage of historical hindsight to develop new tools of foresight and practice. Thus, Shakir, Png, and Isaac (2020) propose a decolonial AI that can re-create the field of

28

Artificial Intelligence and Evolution of the Virtual University

557

artificial intelligence in ways that strengthen its empirical basis and operational usefulness while anticipating and averting algorithmic colonialism and harm. In practice, the virtual university might consider appropriating and implementing five tactics of Shakir, Png, and Isaac that together can form a decolonial field of artificial intelligence: creating a critical technical practice of AI, seeking reverse tutelage and reverse pedagogies, renewal of affective and political communities, inclusion of meta-perspective, and use of historical hindsight: 1. Creating a Critical Technical Practice of AI. This tactic is a self-reflexive approach in creating and implementing virtual university AI components that recognizes power imbalances and implicit value systems. This type of approach was developed by Agre (1997), who described a shift toward a Critical Technical Practice (CTP) of AI as a middle ground between the technical work of developing new AI algorithms and the reflexive work of criticism that identifies hidden assumptions along with alternative ways of functioning. CTP has been widely influential, having found an important place in specifying the operational aspects of a VU’s human-computer interaction (HCI) and design (Dourish et al. 2004; Sengers et al. 2006). By infusing CTP with decolonial perspectives, a productive pressure can be placed on technical development work by moving beyond good-conscience design and impact assessments that considered secondary tasks, to a way of working that continuously generates ethical questions and assessments of the politically situated nature of AI. It should be noted the context-aware technical development that CTP speaks to, which seeks to consider the interplay between social, cultural, and technological elements, is often referred to as heterogeneous engineering (Law 1987). Heterogeneous-critical practice thus encompasses multiple approaches for action: in research, testing, policy, and activism. These practices can include algorithmic fairness, AI safety, equity and diversity, policy-making, and even using AI as a decolonizing tool. 2. Reciprocal Engagements and Reverse Tutelage. Research in post-colonial studies highlights the essential role that users themselves have utilized in reciprocal engagements such as insurgence, activism, and organization, in changing the colonial attributes in the metropole (Gopal 2019; Gandhi 2006). It is increasingly viewed that colonialism is an act of imposition. In a reversal of roles, the metropole is beginning to learn lessons from the periphery, thereby establishing a reverse tutelage between the metropole and periphery. Modern critical practice is seeking to use this decolonial strategy to actively identify metropoles and peripheries that make reverse tutelage and the resulting pedagogies of reciprocal exchange part of its foundations. Reverse tutelage directly speaks to philosophical questions of what constitutes knowledge. There is a tension between a view of knowledge as an absolute and of data transformed into information that in a specific context forms knowledge

558

J. Mason et al.

allowing learners to create complete and encompassing abstractions of the world, versus a view of knowledge that is always incomplete and subject to multiple contextual selections and interpretation utilizing differing value systems. Deciding what counts as valid knowledge, what is included within a dataset, and what is ignored and unquestioned can be forms of colonial biases held by AI and machine learning engineers that cannot be left unacknowledged. It is in confronting these types of biases that the tactic of reverse tutelage can make its mark. 3. Renewed Affective and Political Communities. How we build a critical practice of AI for use in virtual universities depends on the strength of regional political communities to determine how they will use AI. This includes ownership of AI technologies and the mechanisms in place to contest, redress, and reverse AI technological interventions. These situations manifest through paternalistic-type thinking and imbalances in authority and choice, produced by the hierarchical orders of division and binaries established by coloniality (Gopal 2019; Said 1993; Fanon 1967; Nandy 1989). The decolonial imperative in this context calls for a move from attitudes of technological benevolence and paternalism in one type of a metropole and periphery implementation context toward solidarity in multiple types of metropole and periphery implantation contexts. The challenge to solidarity lies in how new political communities can be created that can reform systems of hierarchy, knowledge, technology, and culture at play in modern life. 4. Inclusion of Meta-perspective. Attitude, belief, and value (ABV) systems play an essential part in any crosscultural endeavor. Gaining an understanding of attitudes, beliefs, and values and recognition of understanding is referred to as meta-perspective (Peoples 2020). In education, ABVs are implicit and not clearly defined or measured. Another example of AI colonialism in developing AI systems for use in cross-cultural usage, ABVs are systems that are usually ignored. Ignoring ABVs in the development and use of AI systems and/or algorithms in cross-cultural scenarios. Utilizing ABVs in measuring meta-perspectives can be utilized as a decolonizing tool. A methodology has been developed to measure alignment of attitudes, beliefs, and values of a concept from a sender’s viewpoint (from one unique culture) to a receiver’s (from another unique culture) in a manner where the sender’s view of the receiver’s viewpoint of attitudes, beliefs, and values of the concept is understood (Peoples 2020). Although developed for humans in cross-disciplinary and educational contexts, the base formula for determining meta-perspective can be used to measure ABV alignment between any instances of technological and knowledge metropoles, and periphery users, especially in the context of contrapuntal analysis.

28

Artificial Intelligence and Evolution of the Virtual University

559

5. Use of Historical Hindsight. In combining the fields of artificial intelligence and decolonial theories, a new type of historical hindsight analysis can be developed. For example, we can develop analytical algorithms that utilize AI data and predictions (AI as an object), and the structures that support AI such as data, networks, and policies (AI as a subject) as new expressions of the colonial power (Quijano 2000; Mignolo 2007; Maldonado-Torres 2007; Ndlovu-Gatsheni 2015) and of technological power (Ricaurte 2019; Couldry and Mejias 2019; Ali 2016) that is involved in creating virtual universities. This type of historical analysis can help dismantle harmful power asymmetries and concepts of knowledge and allow creation of a “pluriversal epistemology of the future” (Mignolo 2012) that acknowledges and supports a wider radius of socio-political, ecological, cultural, and economic needs of dynamically created VUs. Even without AI, engagement with Internet services raises important questions concerning privacy, bias, and equity. The algorithms that underpin many services operate in opaque ways that collect personal data and behaviors. Such issues have led to several initiatives worldwide focused on identifying the ethical issues and complementary ethical guidelines that might be considered in the ongoing deployment of AI-enabled systems. In 2019 in partnership with CSIRO Data61, the Australian government developed an Artificial Intelligence Roadmap that provides such ethical guidelines (Australian Government 2019). Arguably, of more significance is the emergence of ethical concerns in international standards development. For example, in recent years the Institute of Electrical and Electronics Engineers (IEEE) has been a strong advocate for “ethics by design” or “ethically aligned design,” outlining a “vision for prioritizing human well-being with autonomous and intelligent Systems” (IEEE 2019). Thus, in IEEE 7000-2021: Integrating ethical and functional requirements to mitigate risk and increase innovation in systems engineering design and development, the notion of “Unintended Risk in Engineering Design” is articulated: Avoiding risk is a key concern for any organization but focusing solely on physical harms won’t provide a full picture of an end user’s experience of what you build. Artificial Intelligence Systems (AIS) driving many products and services today are driven by algorithms invisible to users that still deeply affect their data, identity, and values. Despite the best intentions of a manufacturer, without having a methodology to analyze and test how an end user interprets a product, service or system, a design process will prioritize the values of its creators. Responsible Innovation in the algorithmic era requires a values-oriented methodology that complements traditional systems engineering. This is what IEEE 7000-2021 provides. (Institute of Electrical and Electronics Engineers 2021)

In Europe, a vision is expressed more comprehensively for the Next Generation Internet (NGI) which is positioned “to shape the development and evolution of the Internet into an Internet of Humans. An Internet that responds to people’s

560

J. Mason et al.

fundamental needs, including trust, security, and inclusion, while reflecting the values and the norms all citizens enjoy in Europe” (European Commission 2021). Such efforts provide a useful segue into related standardization efforts that are more technically focused on architectures for learning. In practice, every virtual university might consider participation in global standardization, led by the IMS Global Learning Consortium, the IEEE, or various committees affiliated with the International Organization for Standardization (ISO), as outlined below.

Socio-Technical Systems, Standardization, and New Frontiers for Trustworthiness As the application of AI continues to broaden to new areas which augment the nature of socio-technical systems, trustworthiness of the next generations of those systems has become a pivotal concern for safety and reliability reasons (Safieddine et al., 2017). Thus, although how to create general-purpose AI is still largely beyond our understanding, so is far away from mass use, getting teaching machines to also learn something about human values has become known as the “alignment problem” (Christian 2020). In achieving this, self-explainability is a key element as it can increase the trustworthiness of AI systems, and to secure it, research into user requirements and subsequent standards development is actively being conducted within the field of international IT standardization. Moreover, this activity is taking place within a complex “ecosystem” of standards development that drives and is informed by digital innovation. In the context of this chapter, however, we know of no such activity that is already taking place regarding the organizational components and service aggregations that may constitute interoperable iterations of a virtual university. Indeed, in the last two decades, several new kinds of universities with limited interoperability have entered the Emerging Knowledge Era market, such as Singularity University, Ducere University, Developing Minds University, Apple University, Woolf University, and various universities with “Free” in their title. Without standardization, interoperability is more talked about than implemented, and innovation is not sustained; with it, sectoral efficiencies and economic benefits follow. In the broad field of IT, it is typically composed of terminology standards, dimensional standards, and test method standards in sequence that are aimed at increasing public interest in domains such as safety of life, environmental protection, and social responsibility. In this context, standardization related to trustworthiness of AI systems, and especially explainable AI and self-explanatory systems, is an important standardization target that contributes to the increase of public interest. Here, it is necessary to distinguish between trust and trustworthiness as separate concepts. Trust refers to the attitude or belief that a stakeholder has toward the system, while trustworthiness is an attribute that occurs between the stakeholder and the system. The distinguishing feature between AI systems and general software systems is the varying degrees of “autonomy.” Autonomy is generally interpreted as involving independence or agency, and so AI systems also make decisions and choose actions according to their own principles. An example of a representative system that determines behavior by autonomous decision-making is an autonomous vehicle. Strictly

28

Artificial Intelligence and Evolution of the Virtual University

561

speaking, the terms autonomous and automated are distinct concepts. “Automated” connotes control or operation by a machine, while “autonomous” connotes acting alone or independently. Via its administrative arm (the European Commission (EU)), the European Union (EU) has already published the Trustworthy Ethics Guidelines in 2019, which sets out seven core trustworthiness requirements, followed by the AI trustworthiness evaluation list in July 2020. By allowing AI developers and distributors to self-check the core trustworthiness requirements, users can enjoy the benefits of AI without exposing them to unnecessary risks. In practice, every virtual university might consider collaborating to reach consensus on how to ensure the trustworthiness of every one of their operational systems that uses AI. To illustrate, in April 2021, the EU announced that, through the Artificial Intelligence Act (AI Act), AI systems used in the European Union market (including VU systems and services marketed in the EU) should respect the existing European Union laws, guarantee fundamental rights, and be used safely. A legal mechanism for building a reliable AI ecosystem is presented in the Act. The level of risk following the introduction of the AI system is divided into unacceptable, high risk, and low or minimal, and the use of AI systems that might cause unacceptable risk is prohibited. Similar policies are being developed in the United States and the United Kingdom, which are reorganizing guidelines and regulations in the same context. Likewise, in Korea, in May 2021, a “reliable artificial intelligence realization strategy for human-centered artificial intelligence” was jointly prepared by relevant ministries. They are pursuing a strategy to secure trustworthiness of artificial intelligence to resolve social issues and concerns caused by the spread of artificial intelligence, maximize the benefits of artificial intelligence, and minimize risks and side effects. Korea is emphasizing the development of original technologies to enhance explainability, fairness, and robustness. As “AI Everywhere” mentioned in the promotion plan for the AI Act (EU 2021), the era of exploring universal AI is in full swing, and trustworthy digital transformation based on resilient use of AI is rapidly taking place in all fields such as technology, economy, and society. Just as the ways to generate and protect course contents in the analogue era needed to change in the digital era (through attention to gaps in the VU attack surfaces during the digitization process, which mainly meant the digitization of contents in the past) made a leap forward in domestic industry and society, so digitalization completed through digitalization that digitizes business models using artificial intelligence as a foothold for transformation will become the driving force behind the leap forward in national development. In practice, the managers of a virtual university might consider the specifics of how to secure and ensure the trustworthiness of its core AI systems as an essential element to pre-emptively identify risks and side effects of artificial intelligence systems, such as human values, ethics, and fairness, and to comprehensively cope with them in technical, social, and institutional aspects, is at the national level. It needs to be recognized as an essential element for human survival and prosperity. Given the recently articulated goals of international standardization, such as human values, safety of life, environmental protection, social responsibility, and

562

J. Mason et al.

public interest promotion, investment in AI technology research and accompanying AI trustworthiness research, there is a need for continued interest and support for research and standardization.

Swarm-Based AI During the opening ceremony of the 2020 Olympic Games in Tokyo, swarms of autonomous drones were deployed for visual effect. Similarly, swarm computing can be understood as an expression or instance of socio-technical computing. One exciting quality of socio-technical computing is that, unlike conventional AI (which relies on deep learning only), it manifests as a quasi-living thing with the potential to seem more self-directed, hence more intelligent, than earlier AI paradigms, e.g., centrally coordinated AI. Rosenberg et al. (2021) highlight that AI can be harnessed in ways that mimic ants and birds in their manifest group intelligence to perform a range of tasks. In their trials this approach was able to obtain around a 20% improvement in system performance in new areas such as stock trading. In practice, the virtual university might consider using swarm-influenced AI to provide explicit support for some of the tasks that students currently undertake collaboratively in their learning. It seems there might be potential in using analogous AI-based modeling and optimization tools to teach VU students to cooperate better than RU students and function as “intelligent teams.” Recent advances in socio-technical systems have allowed an advancing synergy of AI and human interaction. A well-known example is core Internet technologies like Wikipedia. Research by Rosenberg et al. (2021) has shown how swarm-based computing can be harnessed to produce better outcomes than either AI alone or human experts. Another well-known example is Google’s use of AI’s experts to direct their research into fusion (Norman 2017). Thus, by introducing students to socio-technical tools like swarm-based AI and the Optometrist Algorithm developed by Google and Tri Alpha Energy, students can learn new ways to conduct research and collaboration, including gestalt learning, distributed cognition, and AI-enhanced collaboration.

Learning from the Field of Deep Learning From the beginning, the field of computer science has been inspired by the human brain, e.g., the von Neumann architecture. In recent decades, neural networks have been prominent. Consequently, it is reasonable to think that breakthroughs in AI can also help us understand neurology and education better. One such area is machine learning (ML) where techniques are now being adopted in the broader community of statistics and science. Thus, if we assume that deep learning models in some way reflect human learning, then perhaps we can also learn from recent advances in AI training. This allies to both the selection of what material is best used to train AI and how best to test the AI. Ideally, the training data needs to

28

Artificial Intelligence and Evolution of the Virtual University

563

be balanced and free of bias. If it is not, then special care needs to be taken to adjust for these limitations. While the same notions exist in standard pedagogy, these concepts need to be mathematically defined and measurable in machine learning training. Rules and standards on measuring and fitting the model based on the results of these tests are well defined. A complex but compelling example of this is in using GNN to solve the vertex cover problem (Abu-Khzam et al. 2020). The neural network produced is far more reliable by computing a terse training set using obstruction (key examples that define the solution). Using ML tools, example questions given to students could be selected from a larger corpus of questions to provide a balanced and exciting subset. These questions would be varied enough to expand the student’s boundaries but given at an appropriate rate for the students to address them in differently valid ways at different points in their courses and career. The student’s perception of “interesting” here is also a qualitative measure of their learning. In practice, the virtual university might consider how to use AI to increase the perceived significance and value of its courses. As an example, by training a VU student in ways to use AI-supported tools to solve increasingly challenging questions, including open-book questions and previously unsolved and societally or commercially relevant problems, we can determine how best to adjust the added value of AI support for innovation. The UK Open University’s Performance Augmentation Lab is a pioneer in developing such tools, but similar capabilities could be put to use in most VUs. Other ideas for the practical application of AI would include cross-training. In cross-training, an AI is first taught about a general area such as images and colors before being taught the specialized area. This approach addresses limitations that apply to this field; typically, a much smaller dataset is available, or perhaps the data is biased or imbalanced in some way. One excellent example of this was training a DL model to detect otitis media from a relatively small set of photos of eardrums. Otitis media is a type of eardrum infection that occurs in children, caused by multiple pathogens. The DL model was first trained on a huge set of photos, as is usual in ML. After this, it was trained using the critically small collection of pictures of children’s eardrums. The model described the otitis media-infected eardrum as having the same color as the froth on a cappuccino (otitis media) or looking like red wine, etc. This “less is more” approach to minimizing training sets resulted in a far more accurate and helpful model (Shie et al. 2015).

Conclusions and Future Directions Disruption and Empowerment No technology is intrinsically enabling or disruptive yet referring to new innovations as always disruptive has somehow become normalized. In the case of AI, another syndrome manifests – many advances that become integrated into consumer

564

J. Mason et al.

products become normalized as simply “smart,” with standout examples being smart-text and speech-to-text systems. There is no doubt that numerous implementations of AI have been both enabling and disruptive, but describing them as such will ultimately depend on stakeholder experience. While AI might be successfully used to reduce the workload of teachers and perhaps contribute to the profit for learning institutions, the correctness of descriptions of this as enabling or disruptive depends very much on context. In the workplace, it is becoming more apparent that AI will make radical changes to our jobs and how we learn. Just as revolutions of the past have led to certain jobs being lost, new ones have been created. This is just a story of how disruption and empowerment can coexist. As the Fourth Industrial Revolution rolls on, many jobs that until now allowed workers a high degree of discretion in how to complete the task have been the staple of the middle class, such as truck driving and report writing, will soon be delegated to autonomous AI systems. Components of this change surfaced decades ago in research labs as “proofs of concept” but are now usable by hobbyists and students. For example, in 2005, an AI-authored paper on Software Engineering using Agents generated by SCIgen was accepted in an IEEE conference (Stribling et al. 2005). To demonstrate the AI’s ability to interpret technical content, a pre-digitalized version of a manual for an old reel-to-reel tape recorder was used. The resulting digitized version of the paper was updated and extended by another group of agents, to create novel content, and included agentcreated original, accurate graphs and diagrams that discussed the most topical keywords in a meaningless but grammatically accurate way. In the near 20 years since 2005, advancements in AI DL models such as GPT3 and MT-NLG (Microsoft and Nvidia’s 2021 NL-AI) to produce plausible versions of written material have increased beyond the imagination of most people (Kharya and Alvi 2021). Many students have already managed to use AI tools like GPT2 to submit passing papers at leading universities. It is even possible to get AI tools to write essays or poems (OpenAI 2021). So clearly, AI can easily do the traditional tasks of getting students to produce homework to write passable essays and other written materials. In practice, the virtual university might consider how to extend their courses to prepare students for success in an AI-supported world. AI-supported advances have implications for curricula and assessment exposing the need for teaching students both core understanding and problem-solving. For over two decades, the “twenty-first-century skills” agenda has been positioned to do this and has had a significant impact on shaping school curricula worldwide. But, as currently framed, this agenda requires recalibration to emerging contexts. Symbiotic learning of humans and machines in partnership therefore represents a frontier for curriculum designers (Mason et al. 2020). Two areas that VU curricula might accommodate this include (1) training students to identify bias in the way data is presented, especially in AI models, and (2) teaching students by addressing currently unsolved problems, both mathematical and social. In some situations, the problems might be solved, with students learning to address real needs. In other situations, AI technology could be used synergistically to help this process.

28

Artificial Intelligence and Evolution of the Virtual University

565

Reflecting Forward The use of AI in virtual universities continues to evolve, empowering human learners with technology in previously-only-fictional implementations. Advancements in AI will also likely disrupt existing practice; however, future Internetbased infrastructures will also be shaped by technologies other than AI. A prominent example of an emerging future is in the documentation associated with the Next Generation Internet led by the European Commission “to reimagine and re-engineer the Internet of tomorrow, reflecting fundamental human values” is what underpins the Next Generation Internet initiative led by the European Commission (2020). A recent manifesto on the design features of VUs argues that “permeability” will be a distinguishing feature. Just as cloud services have transformed the notion of enterprise IT architecture, the unbundling and re-packaging of services will also likely be a distinguishing feature (Stuart and Shutt 2019). Extrapolating further, a virtual university singularity will emerge, where an AI-based metropole is dynamically created for an individual human learner, where AI components making up the metropole, as well as other humans interacting in the individual’s metropole, can be considered as the periphery. This dynamically created virtual university could become known as a contextual virtual university (CVU). But as a ubiquitous learning community, it will still be based on the most important context of any virtual university. . .the individual. Over time, such a CVU will evolve to the point of generating new pedagogical constructs at both micro- and macro-levels intended for use by individual humans and artificial entities.

References Abu-Khzam, F.N., M.M.A. El-Wahab, and N. Yosri. 2020. Graph minors meet machine learning: The power of obstructions. arXiv preprint arXiv: 2006.04689. Agre, P. 1997. Toward a critical technical practice: Lessons learned in trying to reform AI. In Social science, technical systems and cooperative work: Beyond the great divide, ed. G. Bowker, S. Star, L. Gasser, and W. Turner, 131–157. Psychology Press. New York. https://doi.org/ 10.4324/9781315805849 AI HLEG. 2019. Ethics guidelines for trustworthy artificial intelligence (AI). European Commission, High-Level Expert Group on AI (AI HLEG). Brussels. https://ec.europa.eu/futurium/en/aialliance-consultation/guidelines.1.html ———. 2020. The assessment list for trustworthy artificial intelligence (ALTAI) for self-assessment. European Commission, High-Level Expert Group on AI (AI HLEG). Brussels. https://op. europa.eu/en/publication-detail/-/publication/73552fcd-f7c2-11ea-991b-01aa75ed71a1 Ali, S.M. 2016. A brief introduction to decolonial computing. XRDS: Crossroads: The ACM Magazine for Students 22 (4): 16–21. Arnold, M. 1999. Mainstreaming the digital revolution. Higher Education Quarterly 53 (1): 49–64. Australian Government. 2019. Artificial intelligence: Solving problems, growing the economy and improving our quality of life. A roadmap for Australia. CSIRO Data 61 Australia. https://data61. csiro.au/en/Our-Research/Our-Work/AI-Roadmap Brooks, D.C. 2021. EDUCAUSE QuickPoll results: Artificial intelligence use in higher education. EDUCAUSE research notes. https://er.educause.edu/articles/2021/6/educause-quickpollresults-artificial-intelligence-use-in-higher-education.

566

J. Mason et al.

Castells, M. 1998. The rise of the network society. Vol. 1. John Wiley & sons, Oxford, UK. Champion, T. 2005. Metropole and margin: The dependency theory and the political economy of the Solomon Islands, 1880–1980, 43–60. Centre and periphery, Routledge, London. https://doi. org/10.4324/9780203985151. Christian, B. (2020). The alignment problem: Machine learning and human values. WW Norton & Company. Cortada, J. 2009. Rise of the knowledge worker. Routledge, Woburn, MA. Couldry, N., and U.A. Mejias. 2019. The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford: Stanford University Press. Dourish, P., J. Finlay, P. Sengers, and P. Wright. 2004. Reflective HCI: Towards a critical technical practice. In CHI’04 extended abstracts on Human factors in computing systems, 1727–1728. Drucker, P.F. 1959. The landmarks of tomorrow. London: Heinemann. ———. 1994. The age of social transformation. The Atlantic Monthly 174 (5): 53–80. European Commission. 2020. The next generation internet: A guide to the new concept and its opportunities. https://www.ideal-ist.eu/event/next-generation-internet-guide-new-concept-andits-opportunities. European Union. 2021. AI legislative package (AI act). https://oecd.ai/en/dashboards/policyinitiatives/http://aipo.oecd.org/2021-data-policyInitiatives-27156. Fanon, F. 1967. Black skin, white masks. New York: Grove Press. Gandhi, L. 2006. Affective communities: Anticolonial thought, fin-de-si’ecle radicalism, and the politics of friendship. Durham, NC/London: Duke University Press. Gardner, L. 2021. Before she disappeared. United Kingdom: Random House. Gilbert, A. 1996. The virtual university? Gazette 1996 (Autumn): 5. Glover, D., S. Law, and A. Youngman. 2002. Graduateness and employability: Student perceptions of the personal outcomes of university education. Research in post-compulsory education 7 (3): 293–306. Goasduff, L. 2021. The 4 trends that prevail on the Gartner hype cycle for AI, 2021. Gartner.. https://www.gartner.com/en/articles/the-4-trends-that-prevail-on-the-gartner-hype-cycle-forai-2021. Gopal, P. 2019. Insurgent empire: Anticolonial resistance and British dissent. London: Verso Books. Hao, K. 2020. A new way to train AI systems could keep them safer from hackers. MIT Technology Review, July 10: 2020. https://www.technologyreview.com/2020/07/10/1005048/ai-deeplearning-safe-from-hackers-adversarial-attacks/. Heintz, F. 2021. Trustworthy AI–integrating learning, optimization and reasoning: First international workshop, TAILOR 2020, virtual event, September 4–5, 2020, Revised Selected Papers. Springer Nature. Institute of Electrical and Electronics Engineers. 2019. Ethically Aligned Design, 1st Edition: IEEE Ethics in Action in Autonomous and Intelligent Systems. https://ethicsinaction.ieee.org/ ———. 2021. IEEE 7000–2021: Integrating ethical and functional requirements to mitigate risk and increase innovation in systems engineering design and development.. https:// engagestandards.ieee.org/ieee-7000-2021-for-systems-design-ethical-concerns.html Kharya, P., and A. Alvi. 2021. Using DeepSpeed and Megatron to train Megatron-Turing NLG 530B, the world’s largest and most powerful generative language model. NVIDIA Developed.. https://developer.nvidia.com/blog/using-deepspeed-and-megatron-to-train-megatron-turingnlg-530b-the-worlds-largest-and-most-powerful-generative-language-model/. Kuleto, V., M. Ilić, M. Dumangiu, M. Ranković, O. Martins, D. Păun, and L. Mihoreanu. 2021. Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability 13 (18): 10424. Kurzweil, R. 2005. The singularity is near: When humans transcend biology. New York: Penguin. Law, J. 1987. Technology and heterogeneous engineering: The case of Portuguese expansion. The social construction of technological systems: New directions in the sociology and history of technology 1: 1–134.

28

Artificial Intelligence and Evolution of the Virtual University

567

Littman, M.L., I. Ajunwa, G. Berger, C. Boutilier, M. Currie, F. Doshi-Velez, G. Hadfield, M.C. Horowitz, C. Isbell, H. Kitano, K. Levy, K. Lyons, M. Mitchell, J. Shah, S. Sloman, S. Vallor, and T. Walsh. 2021. Gathering strength, gathering storms: The one hundred year study on artificial intelligence (AI100) 2021 study panel report. Stanford, CA: Stanford University. September 2021. http://ai100.stanford.edu/2021-report. Maldonado-Torres, N. 2007. On the coloniality of being: Contributions to the development of a concept. Cultural Studies 21 (2–3): 240–270. Marshall, S.J. 2018. Making sense of change. In Shaping the University of the Future, 387–408). Journal of ICT Standardization. Singapore: Springer. Mason, J.C. 2014. Theorizing why in digital learning: Opening frontiers for inquiry and innovation with technology. In Digital systems for open access to formal and informal learning: Research from CELDA 2012, ed. D.G. Sampson, 101–118. Cham: Springer. Mason, J., B.E. Peoples, and J. Lee. 2020. Questioning the scope of AI standardization in learning. Education, and Training 8: 107–122. Meisenzahl, M. 2019. Google made a small but important change in 2017 to how it thinks about ‘Googleyness,’ a key value it looks for in new hires. Business Insider Australia. https://www. businessinsider.com.au/google-googleyness-hiring-training-guide-change-2019-10. Mignolo, W.D. 2007. Introduction: Coloniality of power and de-colonial thinking. Cultural Studies 21 (2–3): 155–167. ———. 2012. Local histories/global designs: Coloniality, subaltern knowledges, and border thinking. Princeton: Princeton University Press. Mohamed, S., M.T. Png, and W. Isaac. 2020. Decolonial AI: Decolonial theory as sociotechnical foresight in artificial intelligence. Philosophy & Technology 33 (4): 659–684. Moosavi, L. 2020. The decolonial bandwagon and the dangers of intellectual decolonisation. International Review of Sociology 30 (2): 332–354. Nandy, A. 1989. Intimate enemy: Loss and recovery of self under colonialism. Oxford: Oxford University Press. Ndlovu-Gatsheni, S.J. 2015. Decoloniality as the future of Africa. History Compass 13 (10): 485–496. Norman, A. 2017. Google’s new algorithm wants to help researchers stabilize nuclear fusion reactions. Futurism. https://futurism.com/googles-new-algorithm-wants-to-help-researchersstabilize-nuclear-fusion-reactions. OpenAI. 2021. Summarizing books with human feedback, September 23, 2021. https://openai.com/ blog/summarizing-books/ Peoples, B.E. 2020. Reflections on interdisciplinary communications – Metaperspectives; exploring the affective domain. Journal on Systemics, Cybernetics and Informatics 18 (1): 31–36. Quijano, A. 2000. Coloniality of power and eurocentrism in Latin Americas. International Sociology 15 (2): 215–232. Rajasingham, L. 2009. The impact of artificial intelligence (AI) systems on future university paradigms. New Zealand: Victoria University of Wellington. Rheingold, H. 1993. The virtual community: Homesteading on the electronic frontier. Reading, MA: Addison-Wesley Publishing Co., A William Patrick Book. Ricaurte, P. 2019. Data epistemologies, the coloniality of power, and resistance. Television & New Media 20 (4): 350–365. Rosenberg, L., G. Willcox, M. Palosuo, and G. Mani. 2021. Forecasting of volatile assets using artificial swarm intelligence. In Proceedings, 4th international conference on artificial intelligence for industries (AI4I), 30–33. IEEE. https://doi.org/10.1109/AI4I51902.2021.00015. Safieddine, F., R. Ismail, and A. Kulakli. 2017. E-universities: Reflective analysis into technological trends. In Proceedings of the 15th international conference on e-society. Said, E.W. 1993. Culture and imperialism. New York: Vintage. Sengers, P., J. McCarthy, and P. Dourish. 2006. Reflective HCI: Articulating an agenda for critical practice. In CHI’06 extended abstracts on human factors in computing systems, 1683–1686.

568

J. Mason et al.

Shakir, M., M.-T. Png, and W. Isaac. 2020. Decolonial AI: Decolonial theory as sociotechnical foresight in artificial intelligence. Vol. 33, 659. Cornell University. https://doi.org/10.1007/ s13347-020-00405-8. Shie, C.K., C.H. Chuang, C.N. Chou, M.H. Wu, and E.Y. Chang. 2015, August. Transfer representation learning for medical image analysis. In The 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC), 711–714. IEEE, Piscataway, NJ. https://ieeexplore.ieee.org/abstract/document/7318461. Singularity Group. 2022. Our Mission. https://www.su.org/about. Stribling, J., D. Aguayo, and M. Krohn. 2005. Rooter: A methodology for the typical unification of access points and redundancy. Journal of Irreproducible Results 49 (3): 5. Stuart, M., and L. Shutt. 2019) A manifesto for the 21st century university. https://wonkhe.com/ blogs/a-manifesto-for-the-21st-century-university/. Vinge, V. 1993, March. Technological singularity. In VISION-21 symposium, 30–31, sponsored by NASA Lewis research center and the Ohio Aerospace Institute. Walsh, T. 2010. Unlocking the gates: How and why leading universities are opening up access to their courses. Princeton: Princeton University Press. https://doi.org/10.1515/9781400838578. Wong, P.S., and P.A. Neck. 2010. A practitioner’s approach to Drucker’s knowledge-worker productivity in the 21st century: A new model. Revista de Management Comparat Internat‚ional 11: 685–695. Woolf, B. 1991. AI in education. University of Massachusetts at Amherst, Department of Computer and Information Science. Wulff, M. (2021, October 18). Interoperability: How to turn a blind spot into a strength. EDUCAUSE Review – Why IT Matters to Higher Education. https://er.educause.edu/articles/ sponsored/2021/10/interoperability-how-to-turn-a-blind-spot-into-a-strength

Part XI Quality, Benchmarking, Learning, and Educational Analytics

Using Institutional Data to Drive Quality, Improvement, and Innovation

29

Sarah Dart and Samuel Cunningham

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Student Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Institution Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . External Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Strategies for Leveraging Institutional Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Strategic Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Integration into Practices, Processes, and Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Institutional Culture and Capability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contemporary Data-Driven Approaches for Advancing Educational Practices . . . . . . . . . . . . . . . Predictive Modeling of Student Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Personalized Guidance and Feedback Through Email Nudging . . . . . . . . . . . . . . . . . . . . . . . . . . . Adaptive Learning Pathways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Textual Analysis of Student-Written Passages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Benchmarking Against Peers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

572 573 574 576 578 579 579 581 582 583 585 585 586 587 588 589 590 591 591

Abstract

Although higher education has historically been a data-rich sector, the move to fully online learning and teaching dramatically increases the quantity and scope of data that institutions can collect. This is because students produce a significant amount of trace data in virtual learning environments, which can be readily captured and dynamically analyzed. When coupled with the more traditional S. Dart (*) · S. Cunningham Learning and Teaching Unit and Faculty of Engineering, Queensland University of Technology, Brisbane, QLD, Australia e-mail: [email protected]; [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_29

571

572

S. Dart and S. Cunningham

university data, analytic insights from this comprehensive dataset can be harnessed, presenting an enormous opportunity for driving evidence-based improvement. This chapter presents the various data sources that can underpin a data-driven strategy within a virtual higher education institution, and key considerations for leveraging this data to extract value. These strategies include strategic alignment, data management, integration into decision-making, and institutional culture. A series of contemporary examples are then used to illustrate how institutional data can be translated into action to successfully drive quality and innovation in a range of virtual learning and teaching situations. Keywords

Data · Analytics · Quantitative · Qualitative · Evidence-based decision-making · Quality enhancement · Continuous improvement

Introduction Although higher education has historically been a data-rich sector, the move to fully online learning and teaching dramatically increases the quantity and scope of data that institutions can collect. This is because students produce a significant amount of trace data in virtual learning environments (Leitner et al. 2017), which can be readily captured and dynamically analyzed (Pardo et al. 2019). When coupled with the more traditional university data, analytic insights from this comprehensive dataset can be harnessed, presenting an enormous opportunity for driving evidence-based improvement at all levels of virtual higher education institutions. In addition to the shift toward online learning, the emergence of “big data” and the political environment are contributing to institutional adoption of data-driven strategies (Daniel 2015; Ferguson 2012; Knight et al. 2016). With regard to big data, tools for extracting, aggregating, storing, and managing large datasets have become more accessible, while visualization tools have improved greatly (Ferguson 2012). This has led to institutions implementing increasingly sophisticated algorithms and approaches for a range of applications including to identify at-risk students for intervention, customize communications to nudge student behavior, personalize learning pathways, and provide dashboards that enable students to reflect on how they benchmark against their cohort (Liu et al. 2017). In terms of the political environment, institutions are under pressure to increase student participation while improving educational quality within a reduced funding envelope (Knight et al. 2016; Small et al. 2021). These developments are also coinciding with the diversification of students’ backgrounds through widening participation trends (Dart and Spratt 2021; Small et al. 2021). This context has meant finding highly scalable and efficient approaches to teaching and learning support that cater to student variability has become a key priority (Pardo et al. 2019). Given data can be used to implement this personalization efficiently at scale, data-driven approaches have become increasingly prevalent (Dart and Spratt 2021).

29

Using Institutional Data to Drive Quality, Improvement, and Innovation

573

While leveraging data offers great promise, there are numerous challenges that must be navigated for benefits of a data-driven strategy to be realized in practice. For example, it is important that data collection is aligned to strategic priorities, and that analytical insights are closely integrated into decision-making practices to maximize impact (Fritz 2011). Additionally, weak data governance, ethical concerns, poor institutional culture, and limited data capabilities can serve as barriers to implementation (Colvin et al. 2015). Where these issues are not overcome there is a significant risk that data-driven insights remain dormant, without ever being acted upon (Liu et al. 2017; Pistilli et al. 2014). This chapter initially outlines the data sources that can underpin a virtual university’s data-driven strategy, and then goes on to discuss key considerations for implementing such a strategy in practice. Finally, the chapter draws on contemporary examples from the literature to illustrate how institutional data can be used to successfully drive quality, improvement, and innovation within a virtual higher education landscape.

Data Sources An exceptionally wide variety of data sources can guide decision-making and developments within higher education institutions. Figure 1 summarizes specific types according to whether the data originates from students, the institution, or external entities. Although classified separately in Fig. 1, it should be noted that these varied sources can (and should) be considered in combination to facilitate deeper insight. For example, graduate outcomes can be assessed from a

Student data

Institution data

External data

•Socio-demographic background •Study attributes •Subject and educator perceptions •Program perceptions •Online engagement •Engagement with extra-curricular activities and support services •Learning outcomes •Progression, retention and completion outcomes •Staff demographic background •Staff perceptions •Professional development engagement •Curriculum information •Financial information •Graduate employment outcomes •Post-program perceptions of alumni

Fig. 1 Data sources and types

574

S. Dart and S. Cunningham

sociodemographic background perspective, while student and staff perceptions of a learning and teaching experience can be compared. The specific data types falling under each classification are discussed in more detail below.

Student Data Student data is fundamental to any data-driven strategy in the higher education context. There are several forms of data that students contribute through their relationship with an institution. This includes static data initially provided at enrolment, as well as the dynamic data progressively generated through students’ interactions with the institution.

Sociodemographic Background Universities capture sociodemographic data from students at enrolment. This includes basic information like gender, age, cultural background, citizenship, and language background, as well as measures more specific to the sector like firstgeneration student status, tertiary admission score, fee-paying type, prior study outcomes, and admission pathway (Campbell 2007; Patfield et al. 2021). Attributes like socioeconomic status and regional background are often derived by connecting measures like high school attended or postcode at enrolment to other datasets, such as those generated from census records (Patfield et al. 2021). Collection of sociodemographic data enables analysis of subcohort participation rates, with the representation of students from traditionally disadvantaged backgrounds often of interest (Patfield et al. 2021). Sociodemographic data is also vital for analyzing further datasets through an equity lens. Study Attributes Fundamentally, universities hold data about students’ study choices. This includes department, program, and course enrolment, part-time or full-time study status, level of study, majors or minors selected, and university standing (such as probation or semester honors) (Campbell 2007). The timing and frequency of changes to these attributes (such as withdrawing from a subject or taking a leave of absence) can also be valuable, as these events can be lead indicators for outcomes such as attrition (Pistilli et al. 2014). Students gradually develop an educational history within the institution, which feeds into the number of credit points achieved and failed. Credit points can also be used to determine students’ year of study within their program. Subject and Educator Perceptions Student Evaluation of Teaching (SET) surveys, also known as course evaluations, are widely used to assess student satisfaction with subjects and teaching (Goos and Salomons 2017). SET surveys are delivered toward the end of each teaching period, and typically consist of both quantitative and qualitative questions about a specific subject and the associated teaching team (Cunningham-Nelson et al. 2020). Quantitative responses (often measured on Likert scales) enable comparisons to be readily

29

Using Institutional Data to Drive Quality, Improvement, and Innovation

575

drawn across educators, courses, departments, and institutions (Goos and Salomons 2017), and trends can be tracked over time (Cunningham-Nelson et al. 2020). Qualitative free-text responses provide rich and specific feedback that can guide enhancements in teaching, curriculum design, and assessment (Cunningham-Nelson et al. 2020). Although employed relatively infrequently, student perceptions can also be gathered through focus groups and interviews. These mechanisms allow greater depth and elaboration of student responses but are much more time-consuming and resource-intensive to implement. Therefore, these data collection methods are usually only considered when there is a clear purpose that goes beyond general evaluation, such as a major subject redesign or collecting evidence for an accreditation process.

Program Perceptions Surveys can be conducted at a program level to elicit broader student perceptions data. This is appropriate when evaluating central student support services or assessing the development of skills scaffolded across a program. Program-level surveys can be run internally by an institution or via an external body. Examples of the latter include the Student Experience Survey in Australia, the National Student Survey in the UK, and the National Survey of Student Engagement in the USA (Whiteley 2016). These national surveys involve students from many institutions, with this reach and focus on quantitative questioning enabling benchmarking of key performance indicators across the respective sectors (Whiteley 2016). Online Engagement The virtual learning environment makes capturing student behavior within the learning management system (LMS) highly accessible, given student-tracking capabilities are typically included as a software feature (Ferguson 2012). Log data can provide insight into what students have engaged with on the LMS (such as assessment, discussion boards, and videos), as well as frequency and duration of access (Campbell 2007). Other data that can be drawn out include attendance at live online teaching sessions, the timing of an assessment submission (including whether a student has submitted at all), whether students have viewed their assessment feedback (Dart and Spratt 2021), the content of and interaction patterns for discussion boards (Leitner et al. 2017), and video viewing patterns such as tendency to pause and rewind (Mirriahi et al. 2018). An emergent area for online engagement data is collaboration tools (such as Microsoft Teams and Google Docs) which may be used to record individual contributions within a virtual team environment. Log data as well as the content students progressively develop within these systems may be drawn into analysis; however, it is key for these systems to be a part of the university’s learning and teaching ecosystem for the data to be readily accessible to a data-driven strategy. Engagement with Extracurricular Activities and Support Services Data can be collected on student engagement with extracurricular activities, such as participation or leadership within student clubs or societies. Data may also be

576

S. Dart and S. Cunningham

captured around student interactions with support services, such as accessing a dropin help service or using resources from the library. A virtual university context makes gathering this type of data much more straightforward than an equivalent face-toface environment, as online systems tend to automatically have these data-tracking capabilities.

Learning Outcomes Institutions collect a range of data relating to student learning outcomes. In addition to the actual artifacts that students submit, grades are recorded on individual assessment items. When criteria-based grading is utilized, grades may even be broken down into achievement against named benchmarks (Ragupathi and Lee 2020). Qualitative feedback may also be provided to students – usually this is in a written form, but it can also be communicated via audio or video recordings (McCarthy 2015). The outcomes of individual assessment items feed into students’ final subject grades, and, in turn, their grade point average (GPA) (Campbell 2007). Progression, Retention, and Completion Outcomes Higher education outcomes are measured in terms of progression, retention, and completion (Australian Government Department of Education and Training 2018). Progression measures the pass rate for individual subjects. Retention measures the percentage of a student cohort who study in one year, and then continue study or graduate in the following year (Dart 2019). This is the opposite of attrition, which captures those within the cohort who choose to withdraw from their studies without graduating. Completion measures the percentage of students within a cohort who graduate with degrees (Edwards and McMillan 2015). As students must pass individual subjects to advance through their degrees, progression underpins completion and is a vital factor influencing retention (Crosling et al. 2009). It is crucial to note that while progression data becomes available at the end of each teaching period, retention and completion outcomes lag significantly. For retention, outcomes of a given starting cohort cannot be fully finalized until the following year has concluded (although only small changes are likely as the year draws to a close) (Dart 2019). Similarly, students can take an extended period of time to complete a degree, particularly when they are studying in a part-time capacity or take a leave of absence (Edwards and McMillan 2015).

Institution Data Institution data largely relates to staff in terms of their demographic backgrounds, perceptions of students’ learning and teaching experiences, and engagement with professional development. However, two other aspects of institution data to consider are curriculum and finance.

Staff Demographic Background Staff demographic attributes include department, school, and subject alignment, appointment type (such as full-time or casual), role focus (such as teaching-intensive

29

Using Institutional Data to Drive Quality, Improvement, and Innovation

577

or research-intensive), and level (such as lecturer or professor). Like the student sociodemographic data, recording staff backgrounds can provide valuable insight when connected to other datasets. For example, staff perceptions of learner engagement may differ across departments or experience level. Understanding which staff are performing well and which are struggling enables more nuanced actions to be taken.

Staff Perceptions Staff can provide valuable perspectives on learning and teaching designs, including insight into resourcing, technology, and student engagement. Gathering these perceptions in a systematic manner is particularly important where casual staff are employed in frontline teaching roles, given they usually have few avenues for sharing their feedback but commonly facilitate smaller classes where richer student-educator interactions transpire (Hemming and Power 2021). Methods for gathering staff perceptions mirror the student perceptions data and thus center mostly on surveying toward the end of teaching periods, with focus groups and interviews used sparingly. Surveying of staff may include both quantitative and qualitative questioning, and when interpreted alongside staff demographic and curriculum information (discussed below), results can be contextualized. Surveys may be conducted internally or externally such as the Faculty Survey of Student Engagement that is run nationally in the USA (Center for Postsecondary Research 2020). Professional Development Engagement Given professional development is strongly tied to learning and teaching capability (Inamorato et al. 2019), collecting data on staff development can be valuable. Many institutions provide internal professional development through central learning and teaching units (Inamorato et al. 2019) that can form a foundation for systematic data capture. These central units may deliver ad hoc development as well as operate more formal programs such as accredited qualifications in tertiary education or educational fellowship schemes (Greer et al. 2021). Data types that can be considered include workshop attendance, engagement with professional development modules, and recognized learning and teaching expertise against specific benchmarks. As an extension of this, scholarship of learning and teaching outputs and educational research publications can be tracked (Inamorato et al. 2019). Curriculum Information Curriculum provides another source of institutional data, which can be key for contextualizing information from other sources. One aspect of curriculum data is mapping where specific skills are developed across a program, including the level to which these skills are demonstrated. Curriculum data can also include learning outcomes, subject prerequisites, assessment methods, class types (such as lecture or tutorial), credit points, and synchronous contact time. Financial Information The financial impact of decision-making is critical in higher education institutions, especially given the political pressures forcing increased operational efficiency

578

S. Dart and S. Cunningham

(Goldstein and Katz 2005; Leitner et al. 2017). Therefore, financial data needs to be represented in decision-making to ensure viability of actions from an economic perspective. Aspects to consider include student fees, and the costs of staffing, learning materials, and technology (Twigg 2003). Investment in staffing and infrastructure is also required to operationalize a data-driven strategy, which should be considered when making decisions about which approaches are pursued (Corrin et al. 2019).

External Data External stakeholders, including university graduates and industry, provide another source of data that can strongly influence learning and teaching practices. These stakeholders provide data relating to employment outcomes, graduate competencies, and accreditation.

Graduate Employment Outcomes Employment is one aspect of employability, and represents a key dataset for understanding the success of graduating students (Small et al. 2021). Employment outcomes are gathered from alumni, usually through large-scale surveys such as the Gradate Outcomes Survey in Australia (Whiteley 2016) and the National Survey of College Graduates in the USA (United States Census Bureau 2019). Data of interest includes employment or further study status, usual hours worked, employment type (such as voluntary, casual, or continuing), typical work activities, salary, industry, and overall well-being. This data may be collected shortly after students have graduated, and after alumni have developed experience in the workforce in order to support a longitudinal understanding (Whiteley 2016). Post-Program Perceptions of Alumni Past students can provide insight into the skills and capabilities developed through a program, and how they perceive this to relate to their subsequent employment. This insight can be used to pinpoint gaps or weaknesses, as well as strengths in curriculum and teaching approaches. This data may be collected via surveying where a wide sample is desirable, or through interviews and focus groups where the richness and depth of response is prioritized. Industry and Employer Perceptions Industry can provide valuable perspectives on the skills and capabilities expected of graduates when entering their respective fields. The data can help to identify curriculum gaps, while ensuring programs maintain currency with industry developments. Employers of an institution’s graduates can also provide feedback on whether graduates are meeting expectations. Industry and employer perceptions data may be collected through surveying (such as the Employer Satisfaction Survey in Australia [Whiteley 2016]), or through focus groups or interviews with industry representatives.

29

Using Institutional Data to Drive Quality, Improvement, and Innovation

579

Accreditation Outcomes Many programs undergo regular accreditation cycles, an evidence-based process for evaluating whether programs meet stated benchmarks (Borrego and Henderson 2014). Here institutions typically put forward a range of evidence for consideration by an external panel of peers and industry experts (Borrego and Henderson 2014). Alongside the judgement of whether a program should be accredited, institutions receive feedback, which serves as a further data point for informing curriculum and teaching approaches.

Strategies for Leveraging Institutional Data The previous section has highlighted the significant amount of data that virtual higher education institutions can consider feeding into an analytics strategy. However, channeling this in a way that maximizes value presents as an immense challenge. Key factors in this process are presented below, comprising alignment, management, integration, and culture.

Strategic Alignment Given the vast data institutions have at their fingertips, developing a data-driven program that aligns data collection and analytical activities to core priorities is critical for impact (Pistilli et al. 2014). A necessary starting point for this involves clarifying the overarching objectives for a data-driven program, and where it fits within institutional goals (Fritz 2011). Where objectives are unclear, a needs assessment should be completed (Spector and Yuen 2016) to establish the vital context that underpins decisions about what data is necessary for the program’s activities, as well as what should be measured to monitor the program’s performance and demonstrate its value. Logic models can be considered as a framework for connecting how a program operates to a range of measures that collectively demonstrate program success (Fig. 2) (McLaughlin and Jordan 2015; Spector and Yuen 2016). This technique first considers the resources and activities that underpin a program. Explicitly articulating these elements is useful for identifying fundamental data requirements and infrastructure necessary for a program to function. This includes data sources and types (such as those summarized in Fig. 1), specific hardware and software, and

Resources (Inputs)

Activities

Outputs (ShortTerm Results)

Outcomes (MediumTerm Results)

Impacts (LongTerm Results)

Fig. 2 Logic model structure for connecting how a program operates to its results. (Adapted from McLaughlin and Jordan 2015)

580

S. Dart and S. Cunningham

staffing. Unsurprisingly, these requirements vary considerably according to the specific objectives of the program. The second stage of logic models considers the different types of results produced, which are characterized as either outputs, outcomes, or impacts. Outputs are immediate short-term results, outcomes capture intermediate results, and impacts represent the high-level attributes changed over the long term (McLaughlin and Jordan 2015). For example, if a program’s objective is enhancing students’ employability (the impact), an output could be the number of students the program reaches, while an outcome could be improvements in students’ professional skills theorized to have a relationship with employability. Where possible, programs should seek to utilize existing datasets rather than generating new data (Pistilli et al. 2014). Often the objectives of a data-driven program are very high level, such as to improve student retention or enhance the employability of graduates. It is important to evaluate programs at this level to maintain an ongoing understanding of performance and to evidence impact. However, measuring these high-level objectives directly can take an enormous amount of time and be extremely costly (Spector and Yuen 2016). For example, retention outcomes for a newly enrolled student cohort cannot be determined until the following year, while comprehensively measuring employability requires resource-intensive longitudinal tracking of graduates in the workforce. Additionally, there may be confounding factors that influence these high-level attributes (such as the state of the employment market), thus making it difficult to isolate the program’s contribution. To address these challenges, data collected on short- to medium-term measures of program success (that is, outputs and outcomes) can act as indicative proxies (Pistilli et al. 2014). Where success can be shown across the results chain, a stronger claim can be made about the program’s influence. Identifying key measures across the results chain should be prioritized upfront in program design to ensure processes are put in place to capture the required data in a systematic manner. This is particularly important for the impact measures that must be gauged in a consistent manner over a long period of time to be highly informative and persuasive (Spector and Yuen 2016). Engaging in a continuous improvement cycle is recommended for enhancing alignment between activities and strategic objectives over time (Borrego and Henderson 2014; Spector and Yuen 2016). One popular framework for facilitating continuous improvement is “Plan, Do, Check, Action” (Bond 1999). Planning involves designing enhancement initiatives, which are then piloted in the doing phase. The influence on performance indicators is evaluated in the checking phase. The final stage of the framework involves broadly applying actions deemed successful, before repeating the cycle (Bond 1999). Over progressive iterations, improved alignment between activities undertaken and strategic aims is expected. Selecting key performance indicators is advised for guiding continuous improvement processes to overcome the potentially overwhelming amount of data available (Bond 1999). It is important to select indicators from across a logic model’s results chain, given shorter-term measures can contribute to more responsive monitoring and improvement actions but longer-term measures capture overall impact. Moreover, indicators should be drawn from a variety of sources and types (including both

29

Using Institutional Data to Drive Quality, Improvement, and Innovation

Fig. 3 “Four Quadrant” approach to triangulating data when evaluating learning and teaching interventions. (Adapted from Smith 2008)

Self-Reflection

581

Student Learning

Triangulation of Data for Evaluation Peer Review

Student Experience

quantitative and qualitative data). This is because consideration of multiple sources works to increase the external validity of conclusions made about a program, while most data collection mechanisms are not specifically tailored to evaluating a program so only become valuable when interpreted alongside other data (Smith 2008). The “Four Quadrant” approach proposed by Smith (2008) argues self-reflection (which may refer to an individual or institution), student learning, peer review, and student experience should be triangulated when holistically evaluating the impact of a learning and teaching intervention (Fig. 3). Examples of the how these recommendations can be fulfilled include internal quality assurance mechanisms that facilitate self-reflection, using learning outcomes from assessment to measure student learning, considering SET survey results for student experience, and leveraging the peer review that occurs through accreditation.

Data Management Governance is concerned with the structure and processes for distributing accountability and control in higher education institutions (Elouazizi 2014). This control is typically applied at an institutional level for key data assets (such as student and curriculum data). Often extensive information technology systems are used to bring this data together to centrally report an agreed source of truth (Goldstein and Katz 2005). Enabling access to this repository is crucial for empowering end users to ask questions of the data that are relevant to their contexts, which can rapidly grow the reach and impact of analytics efforts. However, access needs to be balanced against security and privacy concerns by giving due consideration to data management controls (Colvin et al. 2015). This is particularly critical for sensitive datasets, such as those containing extensive student information which is individually identifiable. Beyond provisioning access, ease of manipulation and interrogation of data for end users is critical for extracting value (Colvin et al. 2015). This includes how easily datasets can be connected (even to datasets that fall outside central systems), as this is a precursor to many of the more sophisticated analytical approaches (Barber and Sharkey 2012; Fritz 2011; Pistilli et al. 2014). Consequently, developing an agreed set of operational definitions for how variables are measured (including underlying assumptions) (Patfield et al. 2021; Spector and Yuen 2016) is vital for ensuring data accuracy and transparency in interpretation, and enables datasets (that each adopt the definitions) to be jointly analyzed (Colvin et al. 2015). For example,

582

S. Dart and S. Cunningham

defining how a student’s socioeconomic status is determined is critical for interpreting equity data (Patfield et al. 2021), while understanding how retention outcomes are calculated is necessary for fair comparisons, given retention may be considered at a program, institution, or sector level. Pistilli et al. (2014, p. 85) note that “absent a strong foundation of good data, any analytics effort will likely fail.” Consequently, an ongoing challenge for data-driven strategies is maintaining clean data (Barber and Sharkey 2012). For manually derived data (such as from surveys), simple steps at data capture can reduce this burden substantially. For example, where there is a finite group of categorical options, drop-down or checkbox menus can limit selection to the permissible list. Similarly, if individuals must enter numbers that sum to a certain value (such as 100%), validating the inputs is strongly recommended. To reduce incidence of missing data, forced responses can be applied. Where missing data is highly problematic, ambient data collected in a nonintrusive manner should be considered as an alternative (Pistilli et al. 2014). For example, log data from a virtual learning environment is likely a preferable substitute to surveying students directly about their engagement. Ethical protocols must underpin how data are collected, shared, and utilized (Colvin et al. 2015; Corrin et al. 2019; Pargman and McGrath 2021). Fundamental to this is the implicit expectation that analytical developments generate benefit without causing harm (Corrin et al. 2019). However, this creates questions about the level of intrusion permissible for data collection efforts (Pistilli et al. 2014), informed consent, data transparency and validity (Pargman and McGrath 2021), and institutional responsibility in intervening on the basis of analysis (Corrin et al. 2019). Managing these ethical concerns is crucial to obtaining buy-in for data-driven strategies from all stakeholders, including students, and this becomes increasingly salient as approaches to using data mature (Colvin et al. 2015; West et al. 2020).

Integration into Practices, Processes, and Policies A goal of data-driven strategies involves shifting decision-making practices to emphasize use of the evidence base, rather than pure instinct. This is because data supports identification of new opportunities, enables exploration of alternative options, and counters group thinking, ultimately leading to better outcomes (Bonabeau 2003). Core to achieving this is the integration of data into decision-making processes, such that insights are acted upon in an appropriate and timely manner (Liu et al. 2017; Pistilli et al. 2014). This is not just applicable for strategic decision-makers at upper management levels of institutions, but also educators who frequently make decisions about how to best support students (Borrego and Henderson 2014; Colvin et al. 2015). Policy is a key lever for influencing adoption of evidence-based practices (Borrego and Henderson 2014). This operates on the premise individuals and teams will “collect and analyze their own evidence to evaluate and improve their ability to meet stated goals” (Borrego and Henderson 2014, p. 235). Policies may

29

Using Institutional Data to Drive Quality, Improvement, and Innovation

583

signal specific sources of evidence that need to be considered in a decision-making process. Examples of where this is commonly employed at an institutional level include program accreditation, curriculum design, and academic integrity management. At an individual level, academic promotion policies can substantially influence data usage in learning and teaching contexts, such as when SET scores are nominated as a key indicator of educator quality (Heffernan 2021). However, setting narrow definitions for measuring quality risks perverse consequences. For example, SET scores have been shown to disadvantage educators from specific minority backgrounds (Heffernan 2021) and can mask other factors at play. Consequently, triangulation of multiple indicators should be encouraged (Fig. 3). The extent to which data drives decision-making within an institution depends on the approaches employed. Goldstein and Katz (2005, p. 60) proposed a five-stage hierarchy for classifying institutions’ data usage maturity. Immature institutions relied on extracting and reporting transaction-level data (Stage 1), which then evolved to basic analysis to monitor operational performance (Stage 2) and support “what-if” planning decisions (Stage 3). As institutions further developed, they pursued predictive modeling (Stage 4), and eventually embedded automatic triggers into business processes, such as when a key metric fell outside a desired range (Stage 5). From this hierarchy it can be observed that increasing the sophistication of data-driven approaches creates opportunities for more informed, relevant, and timely decisionmaking. In terms of educators, an area of interest is how technological tools can improve decision-making related to teaching and student support (Ferguson 2012). This is particularly relevant to virtual environments for two key reasons. First, educators lack traditional student engagement cues when teaching online, such as visually observing whether a student is actively participating and on track, or overwhelmed and absent (Ferguson 2012). Second, virtual environments generate a substantial amount of trace data related to engagement and performance (Leitner et al. 2017). Learning analytics has sought to find ways of unifying this data with reporting and analysis that supports educators in observing what would otherwise go unseen (Liu et al. 2017). However, benefits will only be realized when educators integrate these tools into routine practices by deploying actions based on the evidence (Liu et al. 2017). This may be at the macrolevel, where teaching modifications are made for an overall cohort, or at the microlevel, where adjustments are made for individual students. Ensuring analytical developments are responding to the real needs of educators in their teaching is core to achieving this goal (Liu et al. 2017).

Institutional Culture and Capability Institutional culture plays a key role in the adoption of evidence-based practices, and the extent to which individuals embrace measurement and improvement as an ongoing process (Borrego and Henderson 2014; Pistilli et al. 2014). The commitment of those in leadership positions to data-driven practices contributes to motivating this culture (Goldstein and Katz 2005) due to the strong influence over data

584

S. Dart and S. Cunningham

availability, including brokering systematic collection mechanisms and mediating the relationship between data owners and end users (Liu et al. 2017). This is particularly critical in the virtual university context where selection of software tools greatly influences the type of data available. Leadership is also required in ensuring quality in datasets and analysis tools (Colvin et al. 2015). The interplay between data capture, sharing, and utilization shapes desire within the institution to engage, and sets the tone for analytic efforts growing or fading. In fact, Colvin et al. (2015) found formation of a shared vision that effectively responded to institutional needs to be a core factor in ensuring sustainable analytics implementations. Additionally, leadership establishes the risk appetite. Where individuals feel supported to experiment with new approaches to using data, one can expect innovations to emerge. This contrasts against a culture where data is tightly regulated and where evaluation is used as a “hammer” to exert control (Pistilli et al. 2014). Strategic capability is crucial within a data-driven environment (Colvin et al. 2015). This is because specific expertise is required to guide decisions around what data is collected to align with needs, the development of strategies for managing this data appropriately, and how developments can be diffused through the institution for maximum impact. Consequently, a commitment to resourcing a highly skilled workforce with strong capabilities in technology, data, analysis, and evaluation is required (Goldstein and Katz 2005). In fact, Goldstein and Katz (2005) emphasize that it is the skills of staff (especially in understanding and manipulating data) that limit what can be achieved when embarking on analytical approaches, rather than the technology. However, it is worth noting that a strong understanding of the educational context is also important in guiding developments. Professional development, secondments, and supporting staff to complete postgraduate study have been recommended for capacity building (Colvin et al. 2015). Individuals can be encouraged to evolve toward data-driven practices through a range of mechanisms. One such path involves building awareness of the opportunities and advantages of data-driven approaches, such as through planned communication strategies or word of mouth. As individuals progressively choose to adopt these approaches, a “tipping point” is reached where it becomes a normal part of practice anchored in the culture (Borrego and Henderson 2014). Professional development can also be used to influence adoption decisions through raising capacity and confidence to engage with analytics (Colvin et al. 2015). Providing these developmental opportunities coupled with ongoing support is particularly important for ensuring data is embraced on mass by those in a wide range of roles, not just those who are enthusiastic about data and technology (Liu et al. 2017). Training can also minimize data misuse, such as where messiness in underlying datasets leads to misinterpretation and inappropriate action (Liu et al. 2017). Colvin et al. (2015) further highlight the importance of empowering students in analytical developments, especially for those interventions that rely on students exhibiting their agency as part of self-regulated learning processes. Here students require sufficient skills in understanding and interpreting data-driven insights to take responsibility in driving their learning effectively.

29

Using Institutional Data to Drive Quality, Improvement, and Innovation

585

Motivating educators to approach their teaching as a scholarly activity is another avenue for increasing adoption of evidence-based approaches (Borrego and Henderson 2014). This involves shifting educators to be consistently thinking about the effectiveness of their teaching methods and how they could improve. Means for this exist on a spectrum ranging from personal reflection to formal educational research, with data integral to the process regardless of the pathway (Borrego and Henderson 2014). Higher Education Academy fellowships, which facilitate reflection against a Professional Standards Framework, have been used to strategically support this shift (Greer et al. 2021). Rewarding learning and teaching quality (including educational research) through academic promotion processes has also been recommended as a lever for stimulating cultural change (Borrego and Streveler 2015). Fostering communities of practice can complement scholarly teaching endeavors. This involves creating spaces to connect those across an institution with interest in specific topics (Liu et al. 2017), such as using a particular software tool or deriving meaning from a specific dataset. These forums facilitate practice sharing, contributing to the development of increasingly mature evidence-based practices and a culture of continuous improvement. This approach can also create change by linking data experts with end users to collaboratively work on solving context-specific problems.

Contemporary Data-Driven Approaches for Advancing Educational Practices Pistilli et al. (2014, p. 80) emphasize “institutions cannot simply collect and report on data. . . [They] must take specific actions to enhance student success.” As institutions have incorporated increasingly sophisticated data-driven strategies, a range of these actions have emerged including “prediction, intervention, recommendation, personalization and reflection” (Leitner et al. 2017, p. 6). Contemporary examples of these actions applicable to the virtual university context are discussed below.

Predictive Modeling of Student Outcomes Predictive modeling represents one of the most popular data-driven approaches employed by higher education institutions (Corrin et al. 2019; Goldstein and Katz 2005; Leitner et al. 2017). This relies on data from past cohorts to make predictions about those that follow, with subject-based performance and retention the most common outcomes to predict (Corrin et al. 2019). Predictive models can methodically flag at-risk students, with educators or support staff often used to contact these students directly with tailored support interventions (Barber and Sharkey 2012; Colvin et al. 2015). Predictive models can also enhance understanding of key risk factors (Dart 2019), enabling more strategic actions to be taken at the cohort level (such as changing program admission requirements, subject prerequisites, or assessment approaches).

586

S. Dart and S. Cunningham

A wide range of statistical techniques have been employed for predictive models including regression, decision trees, support vector machines, and neural networks (Corrin et al. 2019; Kotsiantis et al. 2010; Marbouti et al. 2016). These techniques differ greatly in terms of complexity and computational cost, with suitability highly dependent on underlying data and model purpose. Predictive modeling of at-risk students may be conducted at a subject level (e.g., Marbouti et al. 2016) as well as within or across programs (e.g., Campbell 2007). While high-level models can draw in larger numbers of observations to improve predictive power, this comes at a cost of being limited to considering students’ demographics, educational backgrounds, and high-level LMS engagement as these are the only variables common across all students (Liu et al. 2017). Conversely, subject-level models can consider very specific variables, such as performance in assessment items and engagement with individual activities, which have been shown to substantially improve model accuracy (Dart 2019; Kotsiantis et al. 2010; Marbouti et al. 2016). It is worth noting that a virtual learning and teaching landscape dramatically increases the number of indicators available for predictive models, given engagement and performance data are often captured by online tools in a highly structured manner that minimizes missing data (Ferguson 2012). There are several ethical issues that need to be considered for predictive modeling. In particular, Liu et al. (2017, p. 146) highlight that making predictions based only on students’ background characteristics “risks limiting our view of students’ ability to their past performance and, at worst, perpetuates stereotypes.” Corrin et al. (2019) emphasize ethics when selecting variables for inclusion in modeling, while expert oversight is critical when developing and interpreting results to mitigate against validity concerns. Missing data and data quality issues can compromise predictive modeling efforts (Barber and Sharkey 2012). Similarly, where data lies in disparate systems, a significant amount of work is required to unify sources (Barber and Sharkey 2012). This is becoming a greater challenge as educators rely on a wider variety of online learning technology tools and students look to sources outside the LMS (such as YouTube), which means trace data is not always recorded in institutional systems (Dart et al. 2020; Pistilli et al. 2014).

Personalized Guidance and Feedback Through Email Nudging Personalized email communications have emerged as a scalable method for nudging students toward certain actions, with data-driven insights used to tailor messaging (Dart and Spratt 2021). Data underpinning this approach typically includes students’ demographic and educational backgrounds, engagement (especially with online systems), and assessment performance (Liu et al. 2017). The approach is well suited to a virtual university context given the extent of the systematically collected data available (especially relating to learner engagement with online tools). Mail merging (Dart and Spratt 2021) or specialist software (Lim et al. 2019; Liu et al. 2017; Pardo et al. 2018) can be used for disseminating emails. However, the latter is preferable as

29

Using Institutional Data to Drive Quality, Improvement, and Innovation

587

it brings together data, analysis, and the email interventions in a unified workflow (Liu et al. 2017). Early developments in the personalized email space tended to focus on students deemed likely to fail a subject or withdraw from their program (Liu et al. 2017). This meant predictive modeling was often a precursor step necessary to identify target students. One of the most well-known examples is Course Signals (Arnold and Pistilli 2012) where LMS engagement, performance, and background data were used to generate a risk rating for each student. Educators developed personalized emails for those most at risk to guide these students toward supports and remedial actions. More recently, there has been a move away from the deficit approach that focuses on at-risk students to instead consider how personalized messaging can be embedded as a support mechanism in routine learning and teaching practices (Dart and Spratt 2021). In these situations, statistical methods are not employed to calculate risk. Instead, educators’ intrinsic understanding of behaviors that demonstrate effective learning practices guide what data is consulted and the associated messaging. This centers the relationship between the educator and student, and firmly contextualizes recommended actions within the learning environment (Liu et al. 2017). Specific examples of where this has been employed include to aid student transitions to university by “fostering feelings of belonging, supporting effective engagement, and easing navigation of university systems and processes” (Dart and Spratt 2021, p. 10), providing feedback to students showing different levels of interaction with learning resources (Pardo et al. 2019), and prompting engagement for online students showing signs of disengagement (Lawrence et al. 2019). Personalized email interventions have been shown to translate to increased perceptions of support (Lawrence et al. 2019), enhanced course satisfaction (Dart and Spratt 2021; Pardo et al. 2019), and improved grade performance (Dart and Spratt 2021; Lim et al. 2019).

Adaptive Learning Pathways Online learning provides substantially more opportunities for personalizing experiences than traditional face-to-face environments, enabling more efficient and effective learning (Vie et al. 2017). This is because the systematically captured engagement and performance data can be readily used to customize learning to individuals’ needs, which is becoming increasingly important as cohort diversity increases with widening participation (Small et al. 2021). In simplistic implementations, students are asked multiple-choice or numerical response questions, with feedback given according to chosen answers. This feedback can include whether students have answered the question correctly, clarifying reasoning, and guidance on other resources to engage with for further support. More sophisticated implementations respond to each students’ needs by dynamically changing the content presented according to digital trace data (Corrin et al. 2019; Khosravi et al. 2017). Systems incorporating this more advanced approach are often referred to as adaptive learning platforms (Corrin et al. 2019).

588

S. Dart and S. Cunningham

Decisions about how the learning pathway is adapted can be made using “if-then” rules. This draws upon the expertise of educators in understanding how learning progresses within their context. For example, students may be given a diagnostic activity at the beginning of a course. According to their performance, they can be directed to content that addresses the specific gaps identified in their assumed knowledge (Vie et al. 2017). Alternatively, variations on a problem may be given until the student is verified to possess robust understanding (Corrin et al. 2019). Here the real-time feedback educators receive from the system on students’ progress is important for driving critical reflection that leads to content enhancements and teaching adjustments in corresponding synchronous class components (Prusty et al. 2011). Learning pathway decisions may also be based on models generated from other users’ data (Vie et al. 2017). An example of an adaptive learning system using this approach is the Recommendation in Peer-Learning Environments (RiPLE) system of Khosravi et al. (2017). This was designed to improve the relevance of crowd-sourced questions by suggesting content corresponding to individuals’ interests and knowledge gaps. It provides students with rich, relevant, and timely peergenerated feedback to further drive learning.

Textual Analysis of Student-Written Passages Data in the form of numbers and categories represents structured data, while unstructured data comes in formats such as text-based documents, video, audio, and images (Daniel 2015). Most institutions rely heavily on structured data for their analytics as this is far easier to analyze, but with developments in analysis and visualization tools, mining the more complex unstructured data for meaningful patterns is on the rise (Daniel 2015). Examples of natural language processing in a learning analytics context include assessing students’ written language (Peña-Ayala et al. 2017) and conceptual understanding (Cunningham-Nelson et al. 2018) to provide immediate feedback, exploring connections between ideas in discussion forum or social media settings (Peña-Ayala et al. 2017), and extracting trends from institutional evaluation data containing a large number of qualitative comments (Cunningham-Nelson et al. 2019). Again, application of this analysis technique is well suited to the virtual university environment where this type of data is automatically collected as part of routine practices. One common technique for analyzing large samples of qualitative comments is sentiment analysis. This involves labeling data as either positive or negative (Medhat et al. 2014), which can be on a binary scale (positive/negative) or on continuous scale (such as 5 to +5). This enables comments to be grouped based on positive and negative sentiment, as well as sorted from most to least positive for further consideration. Another method for analyzing textual data is thematic analysis. Traditionally, this has been performed manually by individuals or teams defining common codes, tagging where these are evident in the data, and then following a verification process (Braun and Clarke 2012). While effective, this thematic analysis is not time efficient and not easily replicable. A more recent alternative approach is topic

29

Using Institutional Data to Drive Quality, Improvement, and Innovation

589

Fig. 4 Example of sentiment and topic visualization for a subject

modeling, which can be used to automatically extract key terms and themes from textual data (Murakami et al. 2017). This approach can identify recurring patterns in large quantities of text, and in turn provide a summary of areas that may need to be addressed with action. Cunningham-Nelson et al. (2019, 2020) provide examples of how sentiment analysis and topic modeling can be combined to generate visualizations of SET data, such as in Fig. 4. The visualizations include automatically generated key topics or themes on the horizontal axis and a sentiment count on the vertical axis. This allows educators to easily identify topics commonly mentioned, and whether students are discussing those in a positive or negative way, thus supporting educators to prioritize responsive actions. The visualizations also allow for comparisons to be made between the same subject across teaching periods, highlighting trends in student perceptions over time.

Benchmarking Against Peers Benchmarking involves drawing comparisons between an entity and a broader sample from the population. Benchmarking has historically been conducted across institutions (Tasopoulou and Tsiotras 2017), especially through large-scale national surveys measuring student experience and graduate employment outcomes (Whiteley 2016). These surveys tend to have a strong emphasis on quantitative questioning to facilitate simple numerical comparisons across the sector. Engaging in this type of metric benchmarking process enables institutions to systematically compare themselves to similar entities for the purpose of identifying relative strengths and weaknesses (Tasopoulou and Tsiotras 2017). This insight can be

590

S. Dart and S. Cunningham

used within continuous improvement processes to drive quality enhancement (Bond 1999). However, it is important to set reasonable expectations for benchmarking indictors, as unrealistic goals can have a harmful impact on motivation (Tasopoulou and Tsiotras 2017). A more recent innovation with regard to benchmarking involves student-facing dashboards that compare a student to their cohort. These provide students real-time feedback on their engagement and performance throughout the semester, and can in turn support self-regulated learning (Fritz 2011). This can also reduce the burden on educators to monitor and act, which is particularly advantageous in large classes where instructor time is stretched (Pardo et al. 2019). An example of where this has been implemented is Course Signals, where the risk rating developed from predictive modeling was displayed as a traffic light signal on each students’ LMS home page (Arnold and Pistilli 2012). Similarly, Fritz (2011) implemented a “Check My Activity” tool that compared students’ grade and LMS activity to their peers anonymously. However, in these student-facing systems due consideration needs to be given to ethics, especially around the types of data used (West et al. 2020) and the influence of negative judgments in demotivating students (Corrin et al. 2019). For example, Arnold and Pistilli (2012) noted a handful of students felt “demoralized” by negative feedback, while signals that were not regularly updated risked giving false indications. Poorly designed dashboard systems can also be confusing for students, causing unintended stress and damage (Corrin et al. 2019). The significance of onboarding students has also been highlighted, with Fritz (2011) stating students needed to be proactively introduced to their tool to understand how and why it would be useful.

Conclusion and Future Directions This chapter has presented the various data sources and types that can underpin a data-driven strategy within a virtual higher education institution, and key considerations for leveraging this to extract value. These strategies included strategic alignment, data management, integration into decision-making, and institutional culture. A series of contemporary examples were then used to illustrate how institutional data can be translated into action to successfully drive quality, improvement, and innovation in a range of learning and teaching situations. Contextual factors continue to disrupt the higher education landscape including through growing student diversity, changes in funding structures, greater external accountability expectations, and evolutions in technology (Ferguson 2012; Knight et al. 2016). The latter has been particularly spurred by the COVID-19 public health crisis, which has forced universities to redefine how teaching is delivered and learning is undertaken in fully online modes. This recent shift has meant the extent of the data opportunities in virtual learning environments continues to grow rapidly. Thus, as we move beyond this period of peak disruption, we would expect institutions to be increasingly looking to data-driven approaches to guide their decision-

29

Using Institutional Data to Drive Quality, Improvement, and Innovation

591

making when responding to external pressures, while implementing the more sophisticated and innovative advances made possible within a virtual environment.

Cross-References ▶ The Role of Analytics When Supporting Staff and Students in the Virtual Learning Environment ▶ The Role of Standards and Benchmarking in Technology-Enhanced Learning

References Arnold, K.E., and M.D. Pistilli. 2012. Course signals at Purdue: Using learning analytics to increase student success. In 2nd international conference on learning analytics and knowledge, Vancouver, Canada. https://doi.org/10.1145/2330601.2330666. Australian Government Department of Education and Training. 2018. Higher education standards panel final report – Improving retention, completion and success in higher education. https:// docs.education.gov.au/system/files/doc/other/final_report_for_publishing.pdf. Barber, R., and M. Sharkey. 2012. Course correction: Using analytics to predict course success. In 2nd international conference on learning analytics and knowledge, Vancouver, Canada. https:// dl.acm.org/doi/abs/10.1145/2330601.2330664 Bonabeau, E. 2003. Don't trust your gut. Harvard Business Review 81 (5): 116–123. https://hbr.org/ 2003/05/dont-trust-your-gut. Bond, T. 1999. The role of performance measurement in continuous improvement. International Journal of Operations & Production Management 19 (12): 1318–1334. https://doi.org/10.1108/ 01443579910294291. Borrego, M., and C. Henderson. 2014. Increasing the use of evidence-based teaching in STEM higher education: A comparison of eight change strategies. Journal of Engineering Education 103 (2): 220–252. https://doi.org/10.1002/jee.20040. Borrego, M., and R.A. Streveler. 2015. Preparing engineering educators for engineering education research. In Cambridge handbook of engineering education research, ed. A. Johri and B.M. Olds, 457–474. New York: Cambridge University Press. Braun, V., and V. Clarke. 2012. Thematic analysis. In APA handbook of research methods in psychology, Vol 2: Research designs: Quantitative, qualitative, neuropsychological, and biological, 57–71. American Psychological Association. https://doi.org/10.1037/13620-004. Campbell, J.P. 2007. Utilizing student data within the course management system to determine undergraduate student academic success: An exploratory study. West Lafayette: Purdue University. https://docs.lib.purdue.edu/dissertations/AAI3287222/ Center for Postsecondary Research. 2020. Faculty survey of student engagement. Retrieved 15 February from https://nsse.indiana.edu/fsse/about-fsse/index.html Colvin, C., T. Rodgers, A. Wade, S. Dawson, D. Gasevic, S.B. Shum, K.J. Nelson, S. Alexander, L. Lockyer, and G. Kennedy. 2015. Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Final report. https://research.usc.edu. au/discovery/fulldisplay/alma99449564202621/61USC_INST:ResearchRepository Corrin, L., G. Kennedy, S. French, S.B. Shum, K. Kitto, A. Pardo, D. West, N. Mirriahi, and C. Colvin. 2019. The ethics of learning analytics in Australian higher education: A discussion paper. https://melbourne-cshe.unimelb.edu.au/research/research-projects/edutech/the-ethicaluse-of-learning-analytics

592

S. Dart and S. Cunningham

Crosling, G., M. Heagney, and L. Thomas. 2009. Improving student retention in higher education: Improving teaching and learning. Australian Universities' Review 51 (2): 9–18. https://search. informit.org/doi/abs/10.3316/ielapa.159225407205474. Cunningham-Nelson, S., M. Mukherjee, A. Goncher, and W. Boles. 2018. Text analysis in education: A review of selected software packages with an application for analysing students’ conceptual understanding. Australasian Journal of Engineering Education 23 (1): 25–39. https://doi.org/10.1080/22054952.2018.1502914. Cunningham-Nelson, S., M. Baktashmotlagh, and W. Boles. 2019. Visualizing student opinion through text analysis. IEEE Transactions on Education 62 (4): 305–311. https://doi.org/10. 1109/TE.2019.2924385. Cunningham-Nelson, S., M. Laundon, and A. Cathcart. 2020. Beyond satisfaction scores: Visualising student comments for whole-of-course evaluation. Assessment & Evaluation in Higher Education: 1–16. https://doi.org/10.1080/02602938.2020.1805409. Daniel, B. 2015. Big data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology 46 (5): 904–920. https://doi.org/10.1111/bjet.12230. Dart, S. 2019. Developing predictive models of student success in undergraduate engineering mathematics courses. In 30th Australasian Association for Engineering Education annual conference, Brisbane. https://aaee.net.au/wp-content/uploads/2020/07/AAEE2019_Annual_ Conference_paper_38.pdf Dart, S., and B. Spratt. 2021. Personalised emails in first-year mathematics: Exploring a scalable strategy for improving student experiences and outcomes. Student Success 12 (1): 1–12. https:// doi.org/10.5204/ssj.1543. Dart, S., E. Pickering, and L. Dawes. 2020. Worked example videos for blended learning in undergraduate engineering. Advances in Engineering Education 8 (2): 1–22. https://advances. asee.org/worked-example-videos-for-blended-learning-in-undergraduate-engineering/. Edwards, D., and J. McMillan. 2015. Completing university in a growing sector: Is equity an issue? https://research.acer.edu.au/higher_education/43/. Elouazizi, N. 2014. Critical factors in data governance for learning analytics. Journal of Learning Analytics 1 (3): 211–222. https://doi.org/10.18608/jla.2014.13.25. Ferguson, R. 2012. Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning 4 (5/6): 304–317. https://doi.org/10.1504/IJTEL.2012. 051816. Fritz, J. 2011. Classroom walls that talk: Using online course activity data of successful students to raise self-awareness of underperforming peers. The Internet and Higher Education 14 (2): 89–97. https://doi.org/10.1016/j.iheduc.2010.07.007. Goldstein, P.J., and R.N. Katz. 2005. Academic analytics: The uses of management information and technology in higher education. https://www.educause.edu/ir/library/pdf/ers0508/rs/ ers0508w.pdf. Goos, M., and A. Salomons. 2017. Measuring teaching quality in higher education: Assessing selection bias in course evaluations. Research in Higher Education 58 (4): 341–364. https://doi. org/10.1007/s11162-016-9429-8. Greer, D.A., A. Cathcart, and G.C. Swalwell. 2021. Compounding the impact of teaching development programs in China and Hong Kong SAR: Using the Professional Standards Framework to deepen learning and improve teaching self-efficacy. International Journal for Academic Development: 1–15. https://doi.org/10.1080/1360144X.2021.1878032. Heffernan, T. 2021. Sexism, racism, prejudice, and bias: A literature review and synthesis of research surrounding student evaluations of courses and teaching. Assessment & Evaluation in Higher Education: 1–11. https://doi.org/10.1080/02602938.2021.1888075. Hemming, A., and M. Power. 2021. Student ‘voice’ and higher education assessment: Is it all about the money? Journal of University Teaching & Learning Practice 18 (1): 1–16. https://ro.uow. edu.au/jutlp/vol18/iss1/6/. Inamorato, A., S. Gaušas, R. Mackevičiūtė, A. Jotautytė, and Ž. Martinaitis. 2019. Innovating professional development in higher education: Case studies. https://doi.org/10.2760/712385

29

Using Institutional Data to Drive Quality, Improvement, and Innovation

593

Khosravi, H., K. Cooper, and K. Kitto. 2017. RiPLE: Recommendation in peer-learning environments based on knowledge gaps and interests. Journal of Educational Data Mining 9 (1): 42–67. https://doi.org/10.5281/zenodo.3554627. Knight, D.B., C. Brozina, and B. Novoselich. 2016. An investigation of first-year engineering student and instructor perspectives of learning analytics approaches. Journal of Learning Analytics 3 (3): 215–238. https://doi.org/10.18608/jla.2016.33.11. Kotsiantis, S., C. Pierrakeas, and P. Pintelas. 2010. Predicting students’ performance in distance learning using machine learning techniques. Applied Artificial Intelligence 18 (5): 411–426. https://doi.org/10.1080/08839510490442058. Lawrence, J., A. Brown, P. Redmond, and M. Basson. 2019. Engaging the disengaged: Exploring the use of course-specific learning analytics and nudging to enhance online student engagement. Student Success 10 (2): 47–59. https://doi.org/10.5204/ssj.v10i2.1295. Leitner, P., M. Khalil, and M. Ebner. 2017. Learning analytics in higher education – A literature review. In Learning analytics: Fundaments, applications, and trends: a view of the current state of the art to enhance e-learning, ed. A. Peña-Ayala, 1–23. Springer. https://doi.org/10.1007/ 978-3-319-52977-6_1. Lim, L.-A., S. Gentili, A. Pardo, V. Kovanović, A. Whitelock-Wainwright, D. Gašević, and S. Dawson. 2019. What changes, and for whom? A study of the impact of learning analyticsbased process feedback in a large course. Learning and Instruction 2019: 101202. https://doi. org/10.1016/j.learninstruc.2019.04.003. Liu, D.Y.-T., K. Bartimote-Aufflick, A. Pardo, and A.J. Bridgeman. 2017. Data-driven personalization of student learning support in higher education. In Learning analytics: Fundaments, applications, and trends: A view of the current state of the art to enhance e-learning, ed. A. Peña-Ayala, 143–170. Cham: Springer. Marbouti, F., H.A. Diefes-Dux, and K. Madhavan. 2016. Models for early prediction of at-risk students in a course using standards-based grading. Computers & Education 103: 1–15. https:// doi.org/10.1016/j.compedu.2016.09.005. McCarthy, J. 2015. Evaluating written, audio and video feedback in higher education summative assessment tasks. Issues in Educational Research 25 (2): 153–169. http://www.iier.org.au/ iier25/mccarthy.html. McLaughlin, J.A., and G.B. Jordan. 2015. Using logic models. In Handbook of practical program evaluation, 4th ed., 62–87. Hoboken, New Jersey: Wiley. Medhat, W., A. Hassan, and H. Korashy. 2014. Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal 5 (4): 1093–1113. https://doi.org/10.1016/j.asej.2014. 04.011. Mirriahi, N., J. Jovanovic, S. Dawson, D. Gasevic, and A. Pardo. 2018. Identifying engagement patterns with video annotation activities: A case study in professional development. Australasian Journal of Educational Technology 34 (1): 57–72. https://doi.org/10.14742/ajet.3207. Murakami, A., P. Thompson, S. Hunston, and D. Vajn. 2017. ‘What is this corpus about?’: Using topic modelling to explore a specialised corpus. Corpora 12 (2): 243–277. https://doi.org/10. 3366/cor.2017.0118. Pardo, A., K. Bartimote, S.B. Shum, S. Dawson, J. Gao, D. Gašević, S. Leichtweis, D. Liu, R. Martínez-Maldonado, and N. Mirriahi. 2018. Ontask: Delivering data-informed, personalized learning support actions. Journal of Learning Analytics 5 (3): 235–249. https://doi.org/10. 18608/jla.2018.53.15. Pardo, A., J. Jovanovic, S. Dawson, D. Gašević, and N. Mirriahi. 2019. Using learning analytics to scale the provision of personalised feedback. British Journal of Educational Technology 50 (1): 128–138. https://doi.org/10.1111/bjet.12592. Pargman, T.C., and C. McGrath. 2021. Mapping the terrain of ethics in learning analytics: A systematic literature review of empirical research. Journal of Learning Analytics: 1–17. https://doi.org/10.18608/jla.2021.1. Patfield, S., J. Gore, and N. Weaver. 2021. On ‘being first’: The case for first-generation status in Australian higher education equity policy. The Australian Educational Researcher. https://doi. org/10.1007/s13384-020-00428-2.

594

S. Dart and S. Cunningham

Peña-Ayala, A., L.A. Cárdenas-Robledo, and H. Sossa. 2017. A landscape of learning analytics: An exercise to highlight the nature of an emergent field. In Learning analytics: Fundaments, applications, and trends: A view of the current state of the art to enhance e-learning, ed. A. Peña-Ayala, 65–112. Springer. https://doi.org/10.1007/978-3-319-52977-6_1. Pistilli, M.D., J.E. Willis, and J.P. Campbell. 2014. Analytics through an institutional lens: Definition, theory, design, and impact. In Learning analytics: From research to practice, ed. J.A. Larusson and B. White, 39–60. New York: Springer. Prusty, G.B., C. Russell, R. Ford, D. Ben-Naim, S. Ho, Z. Vrcelj, N. Marcus, T. McCarthy, T. Goldfinch, R. Ojeda, A. Gardner, T. Molyneaux, and R. Hadgraft. 2011. Adaptive tutorials to target threshold concepts in mechanics-a community of practice approach. In 22nd Australasian Association for Engineering Education annual conference, Fremantle. https://aaee.net.au/ wp-content/uploads/2018/10/AAEE2011-Prusty_Russell_Ford_et_al-Adaptive_tutorials_to_ target_Threshold_Concepts_in_Mechanics.pdf Ragupathi, K., and A. Lee. 2020. Beyond fairness and consistency in grading: The role of rubrics in higher education. In Diversity and inclusion in global higher education, ed. C.S. Sanger and N.W. Gleason, 73–95. Singapore: Palgrave Macmillan. Small, L., R. McPhail, and A. Shaw. 2021. Graduate employability: The higher education landscape in Australia. Higher Education Research & Development: 1–15. https://doi.org/10.1080/ 07294360.2021.1877623. Smith, C. 2008. Building effectiveness in teaching through targeted evaluation and response: Connecting evaluation to teaching improvement in higher education. Assessment & Evaluation in Higher Education 33 (5): 517–533. https://doi.org/10.1080/02602930701698942. Spector, J.M., and A. Yuen. 2016. Educational technology program and project evaluation. New York: Routledge. Tasopoulou, K., and G. Tsiotras. 2017. Benchmarking towards excellence in higher education. Benchmarking: An International Journal 24 (3): 617–634. https://doi.org/10.1108/BIJ-032016-0036. Twigg, C. 2003. Improving learning and reducing costs: Lessons Learned from Round I of the Pew Grant Program in Course Redesign. https://www.thencat.org/PCR/Rd1Lessons.pdf. United States Census Bureau. 2019. What is the national survey of college graduates? Retrieved 15 February from https://www.census.gov/programs-surveys/nscg/about.html Vie, J.-J., F. Popineau, É. Bruillard, and Y. Bourda. 2017. A review of recent advances in adaptive assessment. In Learning analytics: Fundaments, applications, and trends: A view of the current state of the art to enhance e-learning, ed. A. Peña-Ayala, 113–142. Springer. https://doi.org/10. 1007/978-3-319-52977-6_1. West, D., A. Luzeckyj, B. Searle, D. Toohey, J. Vanderlelie, and K.R. Bell. 2020. Perspectives from the stakeholder: Students’ views regarding learning analytics and data collection. Australasian Journal of Educational Technology 36 (6). https://doi.org/10.14742/ajet.5957. Whiteley, S. 2016. Creating a coherent performance indicator framework for the higher education student lifecycle in Australia. In Positioning higher education institutions, ed. R.M.O. Pritchard, A. Pausits, and J. Williams, 143–160. Rotterdam: Brill Sense.

The Role of Standards and Benchmarking in Technology-Enhanced Learning

30

Stephen Marshall and Michael David Sankey

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Implementing a TEL Quality System for the Virtual University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Benchmarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fully Online Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Baseline Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TEL Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Governance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Assessing the TEL Frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Leading Change with Quality Frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

596 597 601 602 604 605 607 608 610 612 613 613

Abstract

Standards and benchmarking frameworks are key enablers of the future virtual university. This chapter reviews key concepts of organizational quality improvement using technology to stimulate and enable change. It presents a model of four key elements, TEL policy and procedures, the institutional TEL framework, baseline standards, and standards for fully online education. This model is used to analyze the major current TEL quality frameworks providing a guide to

S. Marshall (*) Centre for Academic Development, Victoria University of Wellington, Wellington, New Zealand e-mail: [email protected] M. D. Sankey Director Learning Futures and Lead Education Architect, Charles Darwin University, Darwin, NT, Australia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_30

595

596

S. Marshall and M. D. Sankey

institutional leaders working to enact quality standards and benchmarking for the improvement of TEL as they lead their institutions toward the future virtual university. Keywords

Standards · Quality · Benchmarking · Change · Technology-enhanced learning

Introduction The nice thing about standards is that there are so many to choose from. (Tannenbaum 1981, p. 221) . . .a truly practical standard is one that will be used because it is simple enough to follow and flexible enough to allow for creativity . . . a tool that allows you to do more, rather than a grim necessity to which you must adhere. (Welsch 2002)

Take for example an architect, who has a whole range of building codes and standards that they need to adhere to when creating their designs. A good architect will design for an individual but, in doing so, works within a recognized framework, not to constrain them, but to give them the means to provide an individualized quality outcome. These standards have of course evolved over time due to being critically examined and in some cases found wanting while at the same time adjusting to the needs and wants of society as it evolves and as tastes change. The virtual university is no different; it is by implication a significant evolution of the modern university with consequent differences in the way that education is experienced and the systems that enact it. These differences suggest that current conceptions of success, and the tools that enable systematic change toward greater success, need to be re-examined to ensure that they are enabling the virtual university to do more, and not simply reinforcing a model out of inertia and unwillingness to move practices away from those enabled by sunk investment costs. Standards and benchmarking frameworks are tools that enact a quality culture within an organization. The choice of which instruments to use is an important factor in the leadership of change toward the implementation of TEL in the future virtual university. Misalignment of these tools runs the risk of degradation rather than improvement through a mechanism known as Gresham’s Law (Ricketts 2015). This describes the phenomenon commonly expressed as “bad money drives out good” or more generally the trend for poor-quality activities to replace higher-quality ones if something acts to obscure the differentiation, or imposes a regulatory environment that mandates poor quality activities or measures. In any complex space where choices are made on the basis of incomplete information, such as the choice of a course by a student (Ricketts 2015), the purchase of a second-hand car (Akerloff 1970), or the planning for future change in a university’s operations, there is a risk of suffering from the effects of the law. While in some cases those with specialist “insider” knowledge can operate what is known as inscrutable markets

30

The Role of Standards and Benchmarking in Technology-Enhanced Learning

597

(Gambetta 1994), the effect is just as likely to occur when all participants are equally unlikely to be able to discern the fundamental qualities of different activities, or share common misperceptions of what constitutes quality without validating these with empirical evidence. Such shared misperceptions in a quality culture arise particularly when the complex interplay of drivers defining and sustaining that culture are not acknowledged. Blanco-Ramírez and Berger (2014) identified that quality cultures in universities operate within a multidimensional space reflecting bureaucratic, political, symbolic, systemic and collegial drivers. The bureaucratic dimension recognizes the formal structures and regulations enacted by the university, such as policies and procedures. The political dimension describes the power relationships that drive quality activities, reflecting the choices of priority and the strategies and agendas that are being advanced. The symbolic dimension captures the importance that values play in the work of the university and the meanings associated with its quality activities by those involved and those observing. The systemic dimension acknowledges the wider context the university operates within and the inevitable way that external forces drive aspects of the quality culture and activities. Finally, the collegial dimension reflects the unique culture of the university and the way that academic work is framed through a mixture of autonomous activity and peer review, enabling a collectively supportive quality environment (see ▶ Chap. 4, “Laying and Maintaining the Foundations for Quality,” for further discussion). Recognizing the existence of these dimensions, and the need to address the organizational tensions or challenges inherent in them, is essential if a university is intending to engage in a significant shift toward a deeper implementation of technology-enhanced learning environments. Despite the attempts of some to create completely new organizations to enact the virtual university of the future (Staley 2019), it is much more likely that the existing multitude of universities will shift over time to respond to the wider forces driving change in university systems. This change will occur through an inevitable technocratic disruption (Carey 2015; Christensen and Eyring 2011; KPMG 2020) or creative destruction (Schumpeter 1976), flawed and broken models as they are (Morozov 2013; Njenga and Fourie 2010); rather, it will be shaped for the vast majority of universities through an incremental process responding to short- and longer-term strategies, enacted through organizational systems affected by the quality culture dimensions, and operating on multiple organizational levels.

Implementing a TEL Quality System for the Virtual University Quality in TEL happens at many levels across an institution, with many players within that institution being involved at the institutional level (for the business), program level (for the academic), and individual level (for the student), all of which need to be working together. For example, in Fig. 1 we see that there are typically four key elements that need to be in place: TEL policy and procedures, an

598

S. Marshall and M. D. Sankey

Fig. 1 Elements of TEL quality within an institution

institutional TEL framework, baseline standards for programs and courses, and specific standards for those that are fully online. The first key element relates to an institution having policies and procedures in place that govern the use of TEL. This may take the form of a specific TEL policy, or it may be embedded within other policies, like the learning and teaching policy. Ideally there is some form of governance committee that is responsible for this policy and indeed for all TEL activities more broadly. The political dimension of this element is fundamental to its impact on the ability of an institution to make significant improvements in its TEL activities, as it moves to a virtual model. The importance of this has been particularly highlighted over recent years as institutions across the globe have had to move rapidly to a heightened use of online learning, which was recognized reasonably early by various governing agencies (Martin 2020). Where the agenda is dominated by those seeking preservation of the traditionally framed university dominated by campus experiences (Bailey and Freedman 2011; Coaldrake and Stedman 2016; Collini 2017), the TEL policy will be conservative, aimed at sustaining existing practices and resistant to shifts in modes and experiences that expand the scope of the university into the virtual. If the power relationships are influenced by the systemic dimension, and if external stakeholder interests dominate, then the policy may see a push toward a TEL future that is aligned to those interests, through activities such as work-integrated learning and closely aligned technology systems defined by industry and dominant commercial actors (McMillam Cottom 2017; Schierenbeck 2013). An alternative is illustrated by the approach taken by MIT (Willcox et al. 2016), where the TEL policy is informed and shaped by scholarly and research-driven framing of their activities and the intention

30

The Role of Standards and Benchmarking in Technology-Enhanced Learning

599

that TEL be a tool closely aligned to the science and technology they develop themselves for the future. The main point to be made here is that TEL policy, if it is to be anything other than a passive deflection of change, must be recognized as enacting an agenda, and not simply shaped by the features of the bureaucratic dimension that dominate the next element. These are necessary, but not sufficient for this element of the quality system to operate effectively in enabling the other elements. The second element in the TEL quality system is where policy and procedure are translated into practice. This element enacts the bureaucratic dimension of quality most visibly but is also implicitly shaped by the symbolic dimension as the institutional choices regarding responsibility for key activities, scope and scale of investment, visibility of the outcomes, and association with key internal and external stakeholders affect the perception of the value and significance of the practices. The range of practices needed to enact the TEL quality system are captured by organizational frameworks such as the ACODE TEL Framework (McCarthy and Halley 2018) that was formed out of a collaboration of 14 universities across Australia and New Zealand. This framework provides “an adaptable mechanism to assist the collaborative planning, implementation, support and review for TEL across Higher Education Institutions” (p. 4). The Framework is a companion piece to the popular ACODE Benchmarks for TEL (Sankey et al. 2014) that have been used in a formal way by 59 universities across five countries since 2014, to ensure an institution has all the required mechanisms in place to assure quality practice in relation to TEL. The ACODE TEL Framework guides organizations to reflect on eight functional areas of the quality system addressing strategy, quality assurance, technology infrastructure, operational services, and staff and student development and support. This quality assurance of practice element is perhaps most at risk of failing to enable a change agenda aligned to a future aspiration for the university. Activities must not be mistaken as outcomes or a quality theater will occur with activities undertaken virtually or ritually, deflecting attempts to analyze or enact effective improvement (van Kemenade and Hardjono 2010). All too often, the practices chosen lack the capacity to identify cues for change (Marshall 2016) and rather simply validate the operational investments by accounting for input measures, such as the number of reviews undertaken or the resources expended. The third element in the quality system relates to the expectations and requirements that are imposed across the program of learning, irrespective of the mode of learning. Normally, as these apply as minimum expectations, they are generalized requirements like the statement of key policies and information, such as learning objectives and assessment details, the alignment of assessment to the learning objectives, the ongoing communication of key course information including feedback, and the use of important infrastructure such as the learning management system and associated content management tools for large media and copyright works. Given the focus, these are clearly representative of the bureaucratic dimension, but they are also often dominated by the collegial dimension, as they relate to academic choices and control of learning and teaching, which is typically delegated

600

S. Marshall and M. D. Sankey

to faculties, departments, or schools, and where it is not (such as core infrastructure or legal compliance mechanisms), these are seen as practical mechanisms that sit outside the collegial norm. A feature of the quality aspects of this element is that it is less about the learning outcomes and experience within specific course learning activities, and more about the management, administration, and reporting of learning and teaching operations. Even when they relate to key pedagogical tools such as learning objectives, the focus is on the system-driving activities that are being managed to ensure learning occurs, but without engagement with the nature and experience of that learning for the individual learner. As such, the components of this element necessarily balance specificity and fidelity of measurement against the need to be relevant to a wide diversity in pedagogical practice across the university, within the constraints of resourcing needed to sustain the quality measurements. In some institutions, these norms are shaped primarily by the collegial dimension, perhaps through the impact of political and systemic influences in the higher elements of the quality system, resulting in a significant weakening of the bureaucratic dimension. This is apparent when distinct choices are made in the selection and application of key learning infrastructure such as the LMS. Historically this has seen a proliferation of learning environments within the university. As discussed elsewhere in this volume (▶ Chap. 16, “The Future of The Learning Management System in The Virtual University”), the evolution of the LMS and university information technology infrastructure has seen this proliferation countered by systemic aspects, such as the application of regulations and controls (i.e., the Family Educational Rights and Privacy Act (FERPA) in the United States) and by the growth in dominant providers of key systems with greater functionality and larger market shares. The last element in the quality system refers to those aspects that have been created to define detailed standards specifically for fully online courses, and by extension those required for the virtual university. It needs to be recognized that the dichotomy between such courses and the increasingly indistinct “traditional” faceto-face course is an artificial one, a consequence of the symbolic quality dimension operating in many universities in a manner that treats the use of TEL as something unusual and distinct from “normal” practice. There is often a strongly political dimension as well to these aspects of the quality system, reflecting the history of the application of technology to learning. The tensions of this (Noble 2002) are situated between the perceived inherent autonomy of face-to-face teaching and the pseudo-industrial models often applied in TEL when it draws on the distance learning paradigm (Peters 1994). In practice some national regulators have expressed concerns that institutions need to ensure that what is being offered online is of an equivalent standard to what is offered face-to-face (Martin 2020). The operation of this last element of the quality system reflects the interplay of the political and symbolic quality dimensions, and their impact on the bureaucraticcollegial tension that plays out in the definition of the activities undertaken in service of this element. They can simply reflect a commercial quality mindset with features prioritized on the basis of their visibility in a market. Dominance of the bureaucratic

30

The Role of Standards and Benchmarking in Technology-Enhanced Learning

601

perspective is evident when the tools have an outcome that is used externally primarily in the form of a quality mark or rating that acts as a proxy for quality in marketing the university’s offerings. A more collegially prioritized alternative is evident when the focus shifts to creating an environment that responds to the needs of the individual student. This can simply reflect the need for an extra level of consistency applied to these courses to reduce the cognitive load on students (Dao 2020). This is typically achieved by some form of template (or agreed configuration) to online course sites that help teaching staff scaffold where important information for students may be found (Scutelnicu et al. 2019). For example, if a student is studying four online courses per semester, it is really helpful if the information about the assessment in those courses is found in the same place. The same applies to readings, module information, and so on. The internal quality system elements described in this section provide guidance to institutional TEL leaders and managers, resulting in a level of surety for an institution wanting a level of consistency, coherence, and structure across its TEL practice. However, no university is an island, and the question inevitably arises how that practice compares to that of other institutions. This is where benchmarking enters the scene.

Benchmarking . . . as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns – the ones we don’t know we don’t know. . . . it is the latter category that tend to be the difficult ones. (Rumsfeld 2002, n.p.)

Benchmarking is the operationalization of quality standards into a tool that allows comparisons to be made between organizations with the objective of identifying areas for improvement and mechanisms for engaging in that improvement. Camp (1989), one of the originators of benchmarking in its early incarnation at Xerox Corporation, defined it as follows: Benchmarking is the search for and implementation of best practices. The adoption or adaptation of the best practices allows an organization to raise the performance of its products, services and business processes to leadership levels. Benchmarking performance measurements are useful means to identify organizations whose performance is significantly better and who, therefore, may have best practices. The real benefit of benchmarking, however, comes from understanding the practices that permit the performance and the reasoned transfer to the organization. (Camp 1989, pp. 15–16)

Benchmarking frameworks combine three key features that shape their application and, by implication, the outcomes they generate. In their most general form, they embody a set of performance indicators or management tools, implicit or explicit scales or standards associated with those indicators (which may be qualitative or

602

S. Marshall and M. D. Sankey

quantitative, ordinal or cardinal, absolute or comparative, as noted above by Cave), and mechanisms that stimulate an improvement response. Traditional benchmarking approaches determine these through a “best in breed” analysis shaped by the activities being improved (Camp 1989). In this conception of benchmarking, the analysis identifies the features of a more successful organization with regard to the area of interest and then attempts to make a comparison between that organization and the one conducting the benchmarking. Most higher education and TEL benchmarks abstract this process, identifying an idealized higher-education organization to benchmark against with measures or indicators chosen in a variety of ways. Over the years, the need for standards and benchmarking frameworks has been recognized over most educational jurisdictions and associations, who have, as a result, developed a range of tools to assist institutions achieve their online quality goals. Table 1 describes some of the tools that have been developed in this space and deployed/used in a consistent way. This is not a comprehensive list, but it does reflect those tools and frameworks that have demonstrated persistence in the sector and which have an international profile (Bates 2010; Ossiannilsson et al. 2015; Sankey 2019; SNAHE 2008). The focus of the frameworks in Table 1 on each on the four elements described in Fig. 1 was identified by the authors’ review of each framework and the published case studies of their use, where available. The rationale for the strength mapping displayed is outlined in more detail for each focus element in the sections that follow.

Fully Online Standards Using the TEL quality elements (Fig. 1) is a useful way to identify which of these quality frameworks could usefully be deployed within an institution wanting to act to improve their TEL activities. Often the easiest place to start is with the fourth element, namely, those activities that directly relate to the delivery of online courses. Nine of the quality frameworks listed in Table 1 address this element to a greater or lesser extent ranging from detailed checklists and guidance through to more abstract general concerns. The ASCILITE TELAS, Quality Matters, and OLC Quality Scorecard suite all provide detailed checklists of specific features, functions, tools, and processes that need to be enacted in successful online courses. These include learner experience and support, administrative functions and services, interaction facilities with learners and teachers, assessment and other activities, and content delivery and management. Each of these draw their items from a long period of engagement with the sector and operate primarily as a form of structured self-review supported by peer feedback. The Jisc eLearning Quality Standards are similar but are more limited in scope, focusing primarily on those aspects relating to assessment using digital tools. These are all useful for institutions operating in a highly devolved manner or wishing to provide tools directly to teachers and support staff rather than their managers.

30

The Role of Standards and Benchmarking in Technology-Enhanced Learning

603

Table 1 Summary of a selection of TEL quality frameworks aligned to the TEL quality elements

4: Fully online standards

3: Baseline standards

2: TEL Framework

TEL Quality Element 1: Governance and Policy

TEL Quality Framework

References Theory of Change or Quality

ASCILITE Technology Enhanced Learning Accreditation Standards (TELAS)

Peer Review

ASCILITE (2020)

Quality Matters (QM)

Peer Review

https://www.qualitymatters.org

Online Learning Consortium Quality Scorecard Suite (OLCQSS)

None

OLC (2021)

The Joint Information Systems Council (JISC): eLearning Quality Standards

None

JISC (2004; 2021)

eLearning Guidelines (New Zealand) (eLGNZ)

TQM

EFMD Online Course Certification System (EOCCS)

None

EFMD (2021)

International Council of Distance Education (ICDE): Open, Online, Flexible and Technology Enhanced Learning (OOFAT)

None

Orr, Weller & Farrow (2018)

E-Learning Maturity Model (eMM)

Maturity Model

http://e-learning.geek.nz/emm/

EADTU E-xcellence Label

Peer Review

Varonis (2014)

http://elg.ac.nz Suddaby and Milne (2008)

Marshall (2006) https://e-xcellencelabel.eadtu.eu Ehlers (2012) EADTU (2016) E-learning Quality Model (ELQS) from Sweden

TQM

SNAHE (2008)

ACODE Benchmarks

Collaborative Benchmarking

http://www.acode.edu.au/ course/view.php?id=23

Commonwealth of Learning Technology-enabled Learning Implementaon Handbook (CoL)

TQM

Kirkwood & Price (2016)

Sankey et al. (2014)

Legend Dark gray indicates strong focus on this element light gray weaker focus or only in regard to some aspects white indicates elements not addressed

604

S. Marshall and M. D. Sankey

Similar aspects are also addressed by the two New Zealand developed frameworks, the e-learning Maturity Model and the eLearning Guidelines. As well as much more detailed and specific measures, these address in detail the systems that indirectly enable learner experiences as well as the tools used for learning. The eLGNZ is the only framework that attempts to adopt a learner perspective when undertaking quality reviews, although none of these frameworks are explicitly implemented with the intention that students would undertake aspects of any assessment. These two frameworks are extensive in their coverage and would suit institutions that wish to build on a foundation of existing online activity in a systematic way. The last three frameworks addressing the fourth TEL quality element operate at a more abstract level in their engagement with online courses, considering them in aggregate across the institution without detailed examination of the specifics for individual courses and programs. The Swedish E-Learning Quality Model and the EADTU E-xcellence Label have the higher elements of TEL Quality as their primary focus but also address the implications of these in relation to the elements of online courses. The EFMD Online Course Certification System also operates in a more abstract way, reflecting its focus on institutional systems and accreditation perspectives, as opposed to a more generalized quality improvement focus. These three frameworks are better suited to institutions developing and building their portfolios of online courses and implementing a management system to support ongoing use.

Baseline Standards TEL quality frameworks are generally weak when speaking to the mainstream course and program standards within universities as there has historically been an expectation that these business-as-usual aspects are addressed largely through the well-established systems used for traditional course delivery. Where they focus on this quality element, it generally reflects the need to engage with modern responses to issues such as the constructive alignment of course activities and assessments with formally stated learning objectives, the compliance of course content with copyright licenses, and the inclusivity of course content and activities. Standards such as the OLC Quality Scorecard speak directly to courses designed with TEL to operate other than exclusively online, in what is often described as blended learning, in response to the ongoing process of mainstreaming TEL into the primary modes of delivery of historically face-to-face institutions. Others, such as the e-learning Maturity Model, ASCILITE TELAS, and Quality Matters, are designed to avoid any presumption of the mode of delivery and consequently address this element as thoroughly as they speak to the online course quality element (Parrish et al. 2020). Coverage in the Swedish E-Learning Quality Model and the EADTU E-xcellence Label is essentially incidental to their focus on the policies and operational systems used to offer all forms of learning. Institutions focusing on this element are likely evolving their traditionally delivered face-to-face and distance offerings into blended courses, with TEL used to

30

The Role of Standards and Benchmarking in Technology-Enhanced Learning

605

achieve a range of operational and strategic goals. A prime example of this is the work RMIT University has done in shifting their Bachelor of Business to fully online, stating that “the decision to expand RMIT offerings in the online undergraduate space represents a strategic move from the University to prioritise flexible learning models amid significant disruption” (RMIT 2021). When doing this, it should be done as part of a wider project that also addresses the first and/or second quality elements, suggesting that one of the models that is stronger in those elements should be selected. In the absence of that wider mandate, the focused assessments offered by the ASCILITE TELAS and Quality Matters frameworks are likely to be more effective as they can be used in an incremental or targeted way, by focusing only on operationally important courses and programs.

TEL Framework All but two of the frameworks to some extent assess the need for coordinated systems, processes, and responsibilities, enacting TEL at scale within the institution. This element is the main focus of the International Council of Distance Education Open, Online, Flexible and Technology-Enhanced Learning (OOFAT) framework, which is intended to assist organizations in articulating how open educational models in particular are enacted through the organization and creating an effective operating model. This strong focus on developing an institution’s thinking about how to structure its engagement with TEL is also apparent in the Commonwealth of Learning Technology-Enabled Learning Implementation Handbook, which consists primarily of high-level propositions for consideration by the institution’s leadership. These frameworks are excellent choices for institutions that have yet to make any significant investment in TEL and need to consider options, before working with collaborators, vendors, and other consultants to enact detailed plans in this space. The majority of the remaining frameworks addressing this quality element are aimed at assisting the institution in examining, in detail, established systems, processes, and operational groups’ engagement with TEL. The e-learning Maturity Model, ACODE Benchmarks, New Zealand and Swedish e-learning guidelines, EFMD Online Course Certification, and EADTU E-xcellence Label all support detailed and comprehensive assessments of the range of activities needed to enact a framework supporting TEL, which cover planning, technology systems and services, and staff and student support. The main consideration in which of these frameworks to use is the type of change process that is needed to lead the institution in the desired improvements. Institutions who have access to either the European (EADTU) or Australasian (ACODE) university networks should seriously consider the value of drawing on their collaborative implementations of the benchmarking and assessment processes. The original benchmarking process depended very much on the learning that can be drawn from comparing an institution’s processes and systems with others, and peerreview processes often provide important reference points for the analysis of TEL capability. At minimum, some of these values can be obtained by using these tools

606

S. Marshall and M. D. Sankey

with a group of internal stakeholders, as this often also helps generate the internal capability needed to lead and undertake change programs. The collaborative approach used inside institutions, as part of the ACODE benchmarking system, has a number of benefits (Marshall and Sankey 2017): • The data is more likely to reflect reality, having been informed by multiple perspectives and experiences. • Activities which are not shared outside specialist groups are more likely to be identified. • Activities which fall across organizational structures or boundaries are more likely to be understood completely. • The collaboration process creates a potential team of informed staff engaged with the problem and able to contribute to improvement activities; collaboration stimulates critical thinking and creativity, leading to a greater diversity of potential strategies for improvement. • Collaboration stimulates commitment and encourages the development of distributed leadership capability able to strengthen organizational agility and flexibility. Both the Quality Matters and e-learning Maturity Model enact specific change mechanisms as a particular focus. Quality Matters includes a well-designed program of professional development that is intended to create a supportive TEL framework within the institution. This systematization of capability is key to building the people component needed to sustain change in a large institution. The e-learning Maturity Model is based on the capability maturity model (Curtis 1994; Humphrey 1987), which has as its main focus the development of institutional capability, processes, and systems from ad hoc, through to fully optimized and continually improving systems. Both of these frameworks require a greater investment than others; the Quality Matters framework requires investment in the professional development program at scale and over a number of years to deliver its benefits, while the e-learning Maturity Model is very comprehensive, requiring significant time to use as designed. Business-oriented institutions with accreditation as a priority may find the EFMD Online Course Certification useful, with its direct links to other external accreditation bodies and specific focus on their disciplinary focus. The remaining frameworks serve as useful tools for an institution’s leadership interested in conducting an internal assessment. The New Zealand e-Learning Guidelines examine TEL systems from the perspectives of learners, teachers, managers, and organizational leaders, both in terms of implementing and enhancing these. The checklists provided prompt examination of a comprehensive range of activities. Similarly, the Swedish E-learning Quality Model prompts analysis of the implementation of strategies and policies across 10 aspects of TEL including course content, the learning environment and interactive features, assessment and feedback, support for students and staff, and management of resources and processes. Finally, the Online Learning Consortium Quality Scorecard Suite includes scorecards addressing administrative and support aspects of TEL.

30

The Role of Standards and Benchmarking in Technology-Enhanced Learning

607

It should be noted that these last frameworks do not provide the detailed information needed to enact the various TEL systems, or provide specific measures with aligned standards. Any institution using these frameworks would need to draw on experienced assessors able to reference any assessments against good practice and evidence drawn from comparable institutions. The quality model they enact is essentially a Total Quality Model (TQM, Asif et al. 2013; Houston 2007) approach, whereby every key activity, process, or system is examined for possible improvements.

Governance The last element in the model shown in Fig. 1 is the institutional policies and procedures which shape the organizational environment the other elements operate within. Articulating and communicating the institution’s vision and plans for TEL through strategies, policies, and effective governance is a major focus of five of the TEL quality frameworks. As noted in the third element, the Commonwealth of Learning Technology-Enabled Learning Implementation Handbook has the objective to support institutions in articulating a clear governance framework for TEL and to guide their leadership groups in undertaking a comprehensive change program aimed at implementing or improving TEL activities in their institutions. This makes it a good match for institutions that have yet to make any major investment into the TEL space and need direction as to the priorities for their initial engagement. A major focus of this TEL quality element is an effective policy framework that enacts a clear strategy for TEL, and which speaks to the full range of operational activities needed in the TEL framework element. The EADTU E-xcellence Label, ACODE Benchmarks, and the Swedish and New Zealand e-learning guidelines all provide detailed lists of policy and planning activities covering course content, the learning environment and interactive features, assessment and feedback, support for students and staff, and the management of resources and processes. These provide useful reference points for institutional leaders needing regular checks as to the state of TEL, without substantial investment in a detailed assessment. It is not sufficient for governance to simply define strategy and policy for TEL; the complexity of the space and the rapidly changing institutional, sector, and international context for higher education, mean that these tools must be constantly re-assessed and success sustained in new and evolving ways. The enhancing perspective of the New Zealand e-learning guidelines has the goal of testing whether established activities are continuing to deliver the best possible outcomes from the perspectives of learners, teachers, managers, and institutional leadership. Similarly, the e-learning Maturity Model has a strong focus on continuous improvement through the assessment provided in its optimization dimension, particularly with regard to the organizational activities that frame the engagement with TEL. These frameworks are useful as tools for institutions wanting to further lift their TEL outcomes beyond the success they have already achieved.

608

S. Marshall and M. D. Sankey

Assessing the TEL Frameworks Looking back over Table 1 at the four TEL quality elements and their coverage by these quality frameworks, a number of further observations can be made. All but two of the quality frameworks position their analysis to some extent in line with the TEL framework element. This is consistent, with the drivers for such tools coming in many cases from sector organizations with a strong operational focus in their work, such as the ACODE, EADTU, and ICDE. Other than that common ground, the quality frameworks generally split into those that approach their analysis and improvement activities either from the bottom up, or from the top down. Bottomup quality frameworks are typically targeted at addressing the needs of those enacting TEL within courses and those supporting them operationally. Top-down tools are aimed more at helping institutional leaders understand the strategic implications of TEL and then engaging in institution-wide activities aimed at systematically improving this. Bottom-up tools generally build on expert opinion and practical concerns, rather than having a formally enacted model of change, while those attempting to operate from the top across the whole institution embody quality models that align with those formalized in other industries and sectors. A consequence of these approaches is the need to carefully examine the specifics of the measures used with any framework, particularly a bottom-up quality framework, which will be addressed in some detail below. The most visible feature of any benchmarking framework is the list of measures or performance indicators. These form the reference point against which benchmarking occurs. A major issue for our purposes here is whether the indicator speaks to a feature that can be improved and will result in improved outcomes relevant to the Virtual University, as opposed to items that reflect a historical perspective on higher education. As noted in the introduction, it is easy to select measures that do not meaningfully drive improvement or, worse, promote ineffective strategies over more effective ones. It is easy to fall into the “big data” fallacy with many measures, the assumption that if enough data is collected from enough organizations, a meaningful and useful insight can be obtained from emergent patterns. In reality, the patterns observed are far more likely to arise from pure chance, a statistical likelihood that becomes certainty if enough patterns are aimlessly generated. This problem is noted by Hagner (2001, p. 31), who identifies the risk of poor choices of measures: . . .the author had envisioned the presentation of a wide range of ‘best practices’ that would resemble a menu-like opportunity for interested institutions to choose from. This original intent was misguided. ... ‘cherry-picking’ a variety of practices is not recommended. Instead of focusing on ‘best practices’, a more profitable emphasis should be placed on ‘best systems.’

The phrase “best systems” speaks to the need to align the benchmarking approach with the TEL quality system and to choose sets of measures that are coherent to the systems change being modelled and undertaken. This is more complex than it might

30

The Role of Standards and Benchmarking in Technology-Enhanced Learning

609

seem. Reviews of quality measures undertaken by Chalmers (2007) and Gibbs (2010) have found them dominated by convenience measures, the need to achieve simplified comparability for political accountability, and very little empirical evidence of validity as a tool for generalized comparisons. The evidence base for improvements in TEL generating strategic improvements in the virtual university is nascent, reflecting the dynamic and emergent states of the field. The measures currently used reflect this limited validation state (Inglis 2008), dominated as they are by relatively limited research literature, and supported with pilot studies and surveys, as well as the expert opinions of the same researchers responsible for that knowledge base. Until better evidence becomes available, heuristics such as that of Marshall (2018) are the best tests for the measures being used within a benchmarking framework: 1. Is each measure within scope for the domain being assessed? Does it have “content validity”? 2. Does the measure satisfy completeness? 3. Does each measure have “face validity”? 4. Does the indicator measure what it claims to and is it logically appropriate? 5. Is each measure singular in focus, defined independently of other measures, and describing only one aspect of an activity? 6. Does each measure describe an important and necessary outcome or characteristic of an activity? 7. Does each measure avoid specifying a particular technology, process, or mechanism for undertaking the activity? 8. Measures must discriminate; do they support the application of judgement and decision-making by those using the model? 9. Is the measure able to be reliably used? 10. Is the measure consistent over time and location? 11. Is the measure timely? 12. Does the measure have clarity and transparency with respect to known limitations? 13. Is the measure accessible and affordable? 14. Is aggregated data respecting the underlying abstractions and meanings? 15. Is there evidence supporting the importance of the measure and validating its inclusion? 16. Does the measure enable improvement to occur? The last one of these questions is perhaps the most important in the context of benchmarking, as opposed to forms of quality aimed at external accountability. As an example, consider the measure of student completion rates for qualifications. This is an important measure used for funding and oversight of universities in many countries, describing an important outcome measure that should be monitored over time. That said, it reflects such a complex intersection of factors that it is of no particular value in a benchmarking system itself. It could be used to select successful universities to benchmark against if the Xerox model was being followed, but even

610

S. Marshall and M. D. Sankey

here, there is a risk that differences would be a result of factors that are outside of the direct scope of control of the university, such as the wider economic and demographic features of the communities it serves, and is more likely to result in a compliance model sustaining a status quo (Chalmers 2007; Gibbs 2010; Law 2010). The identification of what is to be measured leads to the question of how it should be quantified or examined. As Cave et al. (1991) noted, there are a range of possibilities: Performance Indicator: an authoritative measure – usually in quantitative form – of an attribute of the activity of a higher education institution. The measure may be either ordinal or cardinal, absolute or comparative. (p. 24)

Generally, each measure identified leads very quickly to the form of its measurement, but particularly in TEL, and given the weakness of the evidence in support of the measures noted above, there needs to be careful consideration about the question of who collects the measurements, how they document their analysis, and how they provide a reference point that can be reliably used in the future to see whether a change has occurred or not. Many of the quality frameworks are defined as questions, often without any explicit standards or defined reference points, and consequently, as noted earlier, their effective use depends on the support provided by professional bodies or networks of collaborating peers to provide some confidence in the certainty and reliability of the measurements. This becomes even more important when measures are aggregated or summarized to generate overall assessments for use throughout the institution. The TEL benchmarking space is just as susceptible to the systematic criticisms that are made of university ranking systems (Bekhradnia 2016; Blanco-Ramírez and Berger 2014) with the difference that particularly when enacting significant change, most of the criticisms will come from the vested positions of internal stakeholders. Addressing the established interests and appetite for change is one of the factors that influences whether the expectations set by quality frameworks align to the future possibilities of the virtual university or constrain future change to more familiar models. This relates to the last feature of a quality benchmarking framework, which is the sociopolitical space that it sits within. Even before deciding which quality framework to use, institutional leaders need to consider the type of change they need for their institution’s context and current challenges. This is the focus of the last section of this chapter.

Leading Change with Quality Frameworks “‘Professionalism’ is commonly understood as an individual’s adherence to a set of standards, code of conduct or collection of qualities that characterise accepted practice within a particular area of activity” (Universities UK et al. 2004, p. 2). The use of standards and codified knowledge are the mark of professionalism. The evolution of these tools reflects the growing confidence and capability that

30

The Role of Standards and Benchmarking in Technology-Enhanced Learning

611

universities are developing in their adoption of technology in learning and teaching as well as the growing focus on such tools by institutional leaders looking for tools that can guide their organization toward a virtual university. This chapter has presented a high-level sense-making framework intended to help leaders of virtual universities, and those intended to lead them, frame their plans for improvements in the TEL capability of their institution. We have shown the need to consider the four key elements of TEL Quality and how to use these to select quality frameworks that align both with the immediate situation of the institution and its needs and capacity for change. Institutional leaders need to critique quality frameworks in light of that capacity and the realities of the nature of the change that they choose to undertake. Marshall (2010) has suggested that when undertaking this critique, leaders should reflect on the way that standards and quality frameworks: • reflect the diversity of student learning capabilities and desired outcomes; • are designed to evolve to meet the challenges of new forms of technology, and new types of pedagogy, and ideally they should stimulate the discussion, application, and research that result in that evolution; • be enablers of effective practice rather than constraints on the creativity and burdens to the passion of teachers; • be informed by an evidence base of effective teaching practice and research into ways of improving student learning, but not limited by conceptions that are misaligned to the virtual university; • be expressed in a way that enables efficient determination of performance and an ability to “benchmark” or document that performance over time in a coherent and reliable way; • support the management of institutions in identifying areas in need of development and strategic decisions regarding the future direction of the virtual university; • support the development of TEL capability across networks of practice, rather than encouraging piecemeal and isolated initiatives. As well as the framework, leaders must understand the mix of change mechanisms that are relevant to their circumstance. DiMaggio and Powell (1983) described the change mechanisms that institutions use, based on the source of the motivation for change, as falling into three different archetypes. Coercive isomorphism is change imposed from without, and the responses are rational but driven by compliance, with the goal being the symbolism of the outcomes more so than the substance. Quality frameworks that provide abstracted lists of high-level activities, which can be evidenced through policies, process documentation, and high-level self-assessments, are a natural fit to this type of change. Mimetic isomorphism, by contrast, is change driven from within the institution through reference to the best practices of others, which are imitated and replicated as closely as possible. Marginson and Considine (2000) have observed that this strategy is ultimately a recipe for mediocrity:

612

S. Marshall and M. D. Sankey

. . . institutional strategy is caught in the logic of the ‘simulacrum’ . . . a state of replication in which the difference between the copy and the original disappears. . . . The problem is that universities are position-bond, and success is a product not just of clever strategies, but of history and geography. The positional power of the leader stays intact. The ‘simulacra’ are exposed as inferior copies. (p. 221)

This type of change is enabled by quality frameworks that enumerate lists of measures drawn from the experience of leading institutions and the judgment of experts, but without a mechanism that provides them with a future-oriented theory or model of change, they cannot move beyond mainstream practice to stimulate leadership to adopt novel, future-focused approaches. The last form of change, normative isomorphism, reflects the ongoing sense-making of professionals within the institution, using their experiences and expertise framed with “legitimated professional practices” that drive an ongoing re-normalization of practices. Frameworks that build the collective collegial capacity of the institution’s staff are well suited to this form of change. A challenge in using this form is that it requires a leadership prepared to devolve and distribute power and to be less in control of a pre-defined mechanism for achieving the institution’s strategic goals. A final point is important to be emphasized. All three of these forms of change will likely co-exist to a different extent in different institutions, further complicating the challenge facing institutional leaders. Any institution will need a clear direction as to the range of tools and strategies being implemented, and leaders must be able to show how the change mechanisms are aligned but also differentiated according to the needs and priorities of the university if they are to help stakeholders accept and engage with the necessary complexity of ambiguity that defines the context for all of us.

Conclusion and Future Directions It is all too easy to see established standards and benchmarking tools as guides to successful leadership, particularly when they are presented and promoted by Government agencies, accreditors, and respected professional bodies. This chapter has shown that the reality is unfortunately not that simple. Standards and benchmarking tools must themselves change and evolve to respond to organizational capabilities and the rapidly shifting contexts defining the pathway toward the virtual university. The bullet points outlined above suggest the minimum that needs to be considered both by those maintaining and developing frameworks and by those using the frameworks, to enact any of the change mechanisms described above. This includes the agencies, regulators, and accreditors working to enhance the qualities and outcomes needed from higher education in all societies. COVID-19 has emphasized the need to focus strongly on the diversity of needs throughout the student population and the range of contexts that they must learn from, as much as it has shown the importance of modern communication and collaboration tools for all forms of information work. These technological aspects

30

The Role of Standards and Benchmarking in Technology-Enhanced Learning

613

demand a pedagogical response and a re-engagement with the impact that such changes are having on the learners, educators, the workplace, and the society at large. Frameworks need to be able to respond to rapidly changing technologies and pedagogies while also enabling and driving the collection of evidence to inform and shape that change. They need to be explicitly designed to change themselves, even though enabling change is a major challenge to those responsible for quality frameworks. The epistemology of the virtual university is built on creativity and imagination, both pedagogically and organizationally. The frameworks must contain enough flexibility in themselves to provide the opportunity for leaders to demonstrate new ideas that harness that creativity and imagination in powerful and often unexpected ways. Leaders need to also be willing to anchor their own ambitions with evidence and to make decisions that reflect comprehensive and accurate assessments of the strengths and weaknesses of the institution (see ▶ Chap. 29, “Using Institutional Data to Drive Quality, Improvement, and Innovation,” in this volume for an extended discussion of this important aspect). They further need to consider the ways that change can be connected and capability developed in networks of practice that operate across the entire sectors of education and societies. Quality frameworks should provide common points of reference and a language for engaging in genuinely collaborative initiatives, responding to the shared challenges facing all universities. This latter point is fundamental to normative change and essential if universities are to lead their own destiny as a virtual university.

Cross-Reference ▶ Using Institutional Data to Drive Quality, Improvement, and Innovation

References Akerloff, G. 1970. The market for ‘lemons’: Qualitative uncertainty and the market mechanism. Quarterly Journal of Economics 84 (3): 488–500. ASCILITE (2020). TELAS Technology Enhanced Learning Accreditation Standards. https://www. telas.edu.au/wp-content/uploads/2020/07/TELAS-Framework-1-July-2020.pdf Asif, M., M.U. Awan, M.K. Khan, and N. Ahmad. 2013. A model for total quality management in higher education. Quality and Quantity 47 (4): 1883–1904. https://doi.org/10.1007/s11135-0119632-9. Bailey, M., and D. Freedman. 2011. The assault on universities: A manifesto for resistance. London, UK: Pluto Press. Bates, T. (2010). E-learning quality assurance standards, organizations and research. https://www. tonybates.ca/2010/08/15/e-learning-quality-assurance-standards-organizations-and-research/ Bekhradnia, B. 2016. International university rankings: For good or ill? Oxford, UK: Higher Education Policy Institute. Blanco-Ramírez, G., and J.B. Berger. 2014. Rankings, accreditation, and the international quest for quality. Quality Assurance in Education 22 (1): 88–104. https://doi.org/10.1108/QAE-072013-0031.

614

S. Marshall and M. D. Sankey

Camp, R.C. 1989. Benchmarking: The search for industry best practices that lead to superior performance. Milwaukee, WI: ASQC Quality Press. Carey, K. 2015. The end of college: Creating the future of learning and the university of everywhere. New York, NY: Riverhead Books. Cave, M., S. Hanney, and M. Kogan. 1991. The use of performance indicators in higher education (2nd edition). London, UK: Jessica Kingsley Publishers. Chalmers, D. 2007. A review of Australian and international quality systems and indicators of learning and teaching. Chippendale, NSW, Australia: Carrick Institute for Learning and Teaching in Higher Education Ltd. Christensen, C.M., and H.J. Eyring. 2011. The innovation university: Changing the DNA of higher education from the inside out. San Francisco, CA: Jossey-Bass. Coaldrake, P., and L. Stedman. 2016. Raising the stakes: Gambling with the future of universities. 2nd ed. Brisbane, QLD, Australia: University of Queensland Press. Collini, S. 2017. Speaking of universities. London, UK: Verso. Curtis, B.A. 1994. A mature view of the CMM. American Programmer 7 (9): 19–28. Dao, D.D. 2020. Best practices for monitoring students’ cognitive load in online courses: A case study at a university in Iowa. International Journal of Education and Social Science 7 (2): 25–39. www.ijessnet.com/uploades/volumes/1598598325.pdf. DiMaggio, P., and W. Powell. 1983. The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review 48: 147–160. EADTU. 2016. Quality assessment for e-learning: A benchmarking approach. 3rd ed. European Association of Distance Teaching Universities. https://e-xcellencelabel.eadtu.eu/images/ E-xcellence_manual_2016_third_edition.pdf. EFMD. 2021. EFMD online course certification system. https://www.efmdglobal.org/assessments/ online-courses/eoccs/ Ehlers, U.D. 2012. Quality assurance policies and guidelines in European distance, and e-learning. In Quality assurance and accreditation in distance education and e-learning, ed. I. Jung and C. Latchem, 79–90. New York, NY: Routledge. Gambetta, D. 1994. Inscrutable markets. Rationality and Society 6 (3): 353–368. https://doi.org/10. 1177/1043463194006003005. Gibbs, G. 2010. Dimensions of quality. York, UK: Higher Education Academy. Hagner, P.R. 2001. Interesting practices and best systems in faculty engagement and support. Paper presented at the National Learning Infrastructure Initiative Focus Session, Seattle, USA. Houston, D. 2007. TQM and higher education: A critical systems perspective on fitness for purpose. Quality in Higher Education 13 (1): 3–17. Humphrey, W. 1987. Characterizing the software process: A maturity framework. IEEE Software 5 (2): 73–79. https://doi.org/10.1109/52.2014. Inglis, A. 2008. Approaches to the validation of quality frameworks for e-learning. Quality Assurance in Education 16 (4): 347–362. JISC. 2004. Effective practice with e-learning. JISC. https://www.webarchive.org.uk/wayback/ archive/20140613220103/http://www.jisc.ac.uk/media/documents/publications/effectiveprac ticeelearning.pdf. ———. 2021. Quality assurance and standards.. https://www.jisc.ac.uk/guides/transformingassessment-and-feedback/quality-assurance Kirkwood, A., and L. Price. 2016. Technology-enabled learning implementation handbook. Commonwealth of Learning. http://oasis.col.org/bitstream/handle/11599/2363/2016_TELI-Handbook.pdf KPMG. 2020. The future of higher education in a disruptive world. https://home.kpmg/xx/en/home/ industries/government-public-sector/education/the-future-of-higher-education-in-a-disruptiveworld.html Law, D.C.S. 2010. Quality assurance in post-secondary education: Some common approaches. Quality Assurance in Education 18 (1): 64–77. Marginson, S., and M. Considine. 2000. The enterprise university: Power, governance and reinvention in Australia. Cambridge, UK: Cambridge University Press.

30

The Role of Standards and Benchmarking in Technology-Enhanced Learning

615

Marshall, S. 2006. eMM version two process guide. Wellington, NZ: Victoria University of Wellington. ———. 2010. E-learning standards: Beyond technical standards to guides for professional practice. In Handbook of research on E-learning standards and interoperability: Frameworks and issues, ed. F. Lazainis, S. Green, and E. Pearson, 170–192. Hershey, PA: IGI Global. ———. 2016. Quality as sense-making. Quality in Higher Education 22 (3): 213–227. https://doi. org/10.1080/13538322.2016.1263924. Marshall, S.J. 2018. Using evidence to guide sense-making. In Shaping the university of the future: Using technology to catalyse change in university learning and teaching, ed. S. Marshall, 349–386. Singapore: Springer. https://doi.org/10.1007/978-981-10-7620-6_16. Marshall, S., and M. Sankey. 2017. The ACODE benchmarks for technology enhanced learning. In Proceedings of the THETA 2017 the higher education technology agenda conference, 1–13. New Zealand: Auckland. Martin, L. 2020. Foundations for good practice: The student experience of online learning in Australian higher education during the COVID-19 pandemic. Canberra, ACT, Australia: Australian Government, Tertiary Education Quality and Standards Agency. https://www.teqsa. gov.au/latest-news/articles/new-teqsa-report-details-student-experiences-switch-onlinelearning. McCarthy, S., & Halley, K. (2018). TEL framework: A template for higher education institutions. Australasian council on open distance and eLearning (ACODE). https://www.acode.edu.au/ pluginfile.php/3295/mod_page/content/9/ACODE%20TEL%20Framework%20Pilot% 20Version.pdf McMillam Cottom, T. 2017. Lower ED: The troubling rise of for-profit colleges in the new economy. New York, NY: The New Press. Morozov, E. 2013. To save everything, click here: The folly of technological solutionism. New York, NY: Public Affairs Books. Njenga, J.K., and L.C.H. Fourie. 2010. The myths about e-learning in higher education. British Journal of Educational Technology 41 (2): 199–212. Noble, D. 2002. Digital diploma mills: The automation of higher education. Toronto, Canada: Between the Lines. OLC. 2021. OLC quality scorecard suite. https://onlinelearningconsortium.org/consult/olc-qualityscorecard-suite/ Orr, D., M. Weller, and R. Farrow. 2018. Models for online, open, flexible and technology enhanced higher education across the globe – A comparative analysis. Oslo: International Council for Open and Distance Education. Ossiannilsson, E., K. Williams, A.F. Camilleri, and M. Brown. 2015. Quality models in online and open education around the globe: State of the art and recommendations. Oslo: International Council for Open and Distance Education. Parrish, D., Campbell, C., and A. Christie. 2020. ASCILITE technology enhanced learning accreditation standards update. ASCILITE TELall Blog (15 May). https://blog.ascilite.org/ ascilite-technology-enhanced-learning-accreditation-standards-update/ Peters, O. 1994. Distance education and industrial production: A comparative interpretation in outline. In Otto Peters on distance education: The industrialization of teaching and learning, ed. D. Keegan, 107–127. London, UK: Routledge. Ricketts, M. 2015. Adverse selection, Gresham’s law and state regulation. Economic Affairs 35 (1): 109–122. https://doi.org/10.1111/ecaf.12108. RMIT. 2021, 11 August. Next-gen Bachelor of Business courses designed to meet the future of education. RMIT News. https://www.rmit.edu.au/news/all-news/2021/aug/next-gen-bachelorbusiness Rumsfeld, D.H. 2002. DoD news briefing. http://archive.defense.gov/Transcripts/Transcript.aspx? TranscriptID¼2636 Sankey, M. 2019. Quality assuring online learning: ACODE’s work on benchmarking. https:// www.linkedin.com/pulse/quality-assuring-online-learning-acodes-work-michael-sankey

616

S. Marshall and M. D. Sankey

Sankey, M., H. Carter, S. Marshall, R. Obexer, C. Russell, and R. Lawson, eds. 2014. Benchmarks for technology enhanced learning. Australasian council on open, Distance and E-Learning. http://www.acode.edu.au/pluginfile.php/550/mod_resource/content/3/TEL_Benchmarks.pdf. Schierenbeck, C. 2013. Fixing higher education: A business manager’s take on how to boost productivity in higher education. Berlin: Springer Gabler. Schumpeter, J.A. 1976. In Capitalism, socialism and democracy, ed. R. Swedberg. London, UK: George Allen and Unwyn. (Original work published 1942). Scutelnicu, G., R. Tekula, B. Gordon, and H.J. Knepper. 2019. Consistency is key in online learning: Evaluating student and instructor perceptions of a collaborative online-course template. Teaching Public Administration 37 (3): 274–292. https://doi.org/10.1177/ 0144739419852759. SNAHE. 2008. E-learning quality: Aspects and criteria for evaluation of e-learning in higher education. Swedish National Agency for Higher Education. https://web.archive.org/web/ 20101008210201/http://www.hsv.se/download/18.8f0e4c9119e2b4a60c800028057/0811R.pdf Staley, D.J. 2019. Alternative universities: Speculative design for innovation in higher education. Baltimore, MA: John Hopkins University Press. Suddaby, G., and J. Milne. 2008. Coordinated, collaborative and coherent: Developing and implementing e-learning guidelines within a national tertiary education system. Campus-Wide Information Systems 25 (2): 114–122. Tannenbaum, A. 1981. Computer networks. 2nd ed. New York, NY: Prentice-Hall. Universities, U. K. The Standing Conference of Principals, The Higher Education Academy, The Higher Education Funding Council for England, The Scottish Higher Education Funding Council, The Higher Education Funding Council for Wales & the Department for Employment and Learning, Northern Ireland (2004). Towards a framework of professional teaching standards. http://www.scop.ac.uk/downloads/ProfStand.doc. University of London. van Kemenade, E., and T.W. Hardjono. 2010. A critique of the use of self-evaluation in a compulsory accreditation system. Quality in Higher Education 16 (3): 257–268. https://doi. org/10.1080/13538322.2010.506715. Varonis, E.M. 2014. Most courses are not born digital: An overview of the quality matters peer review process for online course design. Campus-Wide Information Systems 31 (4): 217–229. https://doi.org/10.1108/CWIS-09-2013-0053. Welsch, E. 2002. SCORM: Clarity or calamity? Online Learning Magazine. http://www. onlinelearningmag.com/onlinelearning/magazine/article_display.jsp?vnu_content_ id¼1526769 Willcox, K.E., Sarma, S., and P.H. Lippel. 2016. Online education: A catalyst for higher education reforms (online education policy initiative final report). Massachusetts Institute of Technology. https://oepi.mit.edu/sites/default/files/MIT%20Online%20Education%20Policy%20Initiative% 20April%202016_0.pdf

Part XII Concluding Thoughts

The Virtual University in Practice Michael David Sankey

, Henk Huijser

31

, and Rachel Fitzgerald

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Laying a Solid Foundation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Virtual Learning Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Supporting Staff and Students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning Theories and the Application of TEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . New and Emerging Forms of Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Role Openness Plays in the Virtual University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gamification, Adaptive and Conditional Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gamification, Adaptive and Conditional Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Rise and Rise of AI, VR, AR, MR, and XR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quality, Benchmarking, Learning, and Educational Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concluding Thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

620 621 623 624 626 629 631 633 634 635 636 637 638 638

Abstract

The virtual university is not a figment of one’s imagination; it actually exists in many forms already. What this chapter does, however, is pull together all the thoughts and ideas of multiple scholars from around the world, to provide a M. D. Sankey (*) Director Learning Futures and Lead Education Architect, Charles Darwin University, Darwin, NT, Australia e-mail: [email protected] H. Huijser Learning and Teaching Unit, Queensland University of Technology, Brisbane, QLD, Australia e-mail: [email protected] R. Fitzgerald The Faculty of Business, Economics and Law, University of Queensland, Brisbane, QLD, Australia e-mail: rachel.fi[email protected] © Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4_31

619

620

M. D. Sankey et al.

cohesive suite of options one should consider if looking to establish a virtual university from scratch. This is doable because of the breath of experience that resides within the body of authors contributing to this volume. This chapter clearly demonstrates the imperative nature of the virtual university in a world that has been through substantial change over recent years, and where many ideas that where seen as fundamental to the successful application of higher education have been challenged. That is not to say that higher education itself have been left wanting, rather that the option to conduct higher education has been shown to not just survive, but to flourish in the virtual space. Throughout this chapter, the lessons that have been learned by some 54 scholars will be summarized and placed into a solid agenda for consideration. It will not hold all the answers, but it will provide the reader with a fantastic place from which to start their journey when looking to establish a virtual university that takes the affordance of technology-enhanced learning and makes this vision not just doable, but desirable. Keywords

Virtual university · Technology-Enhanced Learning · Higher education · TEL · Online · Quality

Introduction We have come on quite the journey over 30 chapters. It is anticipated that although you may not have read all these chapters in detail, you will get the sense that for the virtual university to become a reality there are some significant affordances that can be gained by applying the principles outlined in this book. This chapter provides a summary of the key elements that would need to be addressed, if you are looking to establish a virtual university, and it will provide this in the form of a series of “main points to consider,” covering each of the major elements found in the different sections of this book. In isolation, these chapters are complete in themselves, but as a collective of ideas, they have a dynamic that can be seen as foundational. If you find something in these summaries that particularly interests you, we encourage you to explore the chapters themselves in more depth. To move the virtual university from fiction to fact requires a retrospective look at the lessons learned through the harder times of COVID-19, and a future focus that embraces the opportunities presented by newer forms of technology available to us linked with the affordances presented by artificial intelligence. These two factors, above anything else, have led educators to fundamentally rethink the processes required to empower a new generation of learners. Learners are not empty vessels that come to us waiting to be filled, but rather people who, when empowered by the opportunities that now exist, can be cocreators of new knowledge facilitated by their academic mentors. There is a very real sense that students can themselves be critical producers and not just consumers of knowledge. It is therefore the role of the virtual

31

The Virtual University in Practice

621

university to seize upon these opportunities within the context of students’ lived experience and linked to the fundamentals of the professions they aspire to, even if they create those themselves. Student productivity and cocreation are not new thoughts of course, but they have risen to prominence since the wide-spread availability of new-generation artificial intelligence tools and emergence of user-friendly large language models, such as ChatGPT in late 2022. Indeed, educational institutions worldwide have been adapting their learning and teaching practices to leverage opportunities presented by the rapidly evolving landscape of technology and digital tools. While there has been a sense of excitement and potential, there has also been a significant amount of apprehension and caution too. This of course plays right into the hands of, and the potential for, the virtual university, as these new functionalities work almost seamlessly with other online environments to provide more holistic platforms to promote student productivity. We return to this point later in this chapter as we look to provide a series of checklist elements to consider in embracing AI (artificial intelligence) in the curriculum offered by the virtual university. Importantly, when considering the lessons found in this volume, please do not view them through the lens of a traditional institution. We are talking here about a new type of institution that learns from the past but is not encumbered by it. A great example of this is the challenges that institutions are currently facing due to the rise of AI. Back in early 2018 the Smithsonian Institute were warning society about the impending challenges AI would bring to education, suggesting that, along with automation, it would challenge, and even threaten, traditional forms of learning (Kak 2018). And yet, only now in 2023, 5 years later, are our universities seriously discussing the implications of this (Chen 2023). These new approaches, however, only go to strengthen the case for the virtual university and the affordances offered to it by technology. It is therefore hoped that considering all the elements discussed in this book will go some way in forming the foundations of contemporary quality online practice. To help us embrace this, we now take you on a journey of discovery, to help you embrace many of the suggested practices that are on offer here.

Laying a Solid Foundation It is fair to say that all universities start with, and continue to evolve, their strategic plans, and this is no less true for the virtual university. Aligning a vision for technology-enhanced learning with a master plan, which is then aligned with an institution’s policies and procedures, provides the foundation for any university, including the virtual university. In ▶ Chap. 2, “Aligning the Vision for TechnologyEnhanced Learning with a Master Plan, Policies, and Procedures,” Ashford-Rowe et al. clearly define how to create a strategy for the virtual university through the following steps:

622

M. D. Sankey et al.

• Establish a vision for digital transformation in learning and teaching that clarifies understanding of the concept of virtual university at the institutional level. • Embrace digital transformation and focus on how to engage stakeholders. • Create a master plan, a vision and planning instrument, that embodies policies and procedures that detail the critical steps for implementing digital transformation and keeping the processes transparent. • More explicitly this would mean making particular attention to the fiscal resources associated with innovation adoption. • Being clear about the institution’s infrastructure such as the hardware, software, facilities and network capabilities in support of teaching resources, production resources, communication resources, student resources, and administrative resources. • Being articulate about the needs, hopes, values, skills, and experiences of the people involved. • Seeing a clear and definite alignment of institutional policies and procedures. • Charting out the relationship between the technology and learning outcomes. • Scheduling a regular evaluation and review, including the impact of the technology on learning goals. • Seeing this through the lens of the support systems and then documenting the scaffolding required to ensure successful implementation. Transparency plays a crucial role in change management as it fosters trust, minimizes resistance, and enables effective communication throughout the change process. Thus, for the virtual university, transparency is vital. However, too much transparency can be a double-edged sword. Smallman & Ryan muse on this in ▶ Chap. 3, “Transparency in Governing Technology Enhanced Learning” as they consider how transparency should underpin the governance processes of the virtual university. In this chapter, they highlight that • Transparency is the core principle of good governance and is demonstrated by the willingness of an organization to provide clear information to all stakeholders. • Too much transparency can paradoxically cause distrust; therefore, good governance frameworks, staff, and policy are essential to the future of the virtual university. • Technology sits at the heart of developing transformation, so we need to position ourselves to accelerate the adoption and diffusion of Technology Enhanced Learning (TEL). • Do not try to replicate the in-class experience online. Instead, consider design that exploits TEL delivered, supported by transparent academic governance. • LMSs are brought to life by the careful curation of educators, but we should also prepare for future change in this space. • Ed Tech (TEL) creates scale, and scale increases both access (social good) and revenue. • Be clear in what makes up your TEL toolbox, and understand how we use this to expand our higher education experience.

31

The Virtual University in Practice

623

• Academic governance and leaders must consciously lead in changing the mindsets and behaviors to fully integrate the practices of faculty and students. • Properly train teachers in the context of TEL. Of course, there is more to maintaining the quality of the virtual university than governance, and a range of other factors should be considered when looking to lay the right quality foundations and then maintaining those fundamental elements. This was partly discussed in ▶ Chap. 3, “Transparency in Governing Technology Enhanced Learning,” and is clearly linked with institutional vision from ▶ Chap. 2, “Aligning the Vision for Technology-Enhanced Learning with a Master Plan, Policies, and Procedures”; however, ▶ Chap. 4, “Laying and Maintaining the Foundations for Quality,” takes us even deeper into this realm, providing us with some very practical considerations, including: • Technology shapes the activities of the university and so executes and enables its strategies. • TEL is not limited or defined by the strategic interests of its stakeholders, but its success is aligned to an investment plan to sustain a differentiated and capable suite of platforms. • Technology platforms that use chaos to enable robustness and resilience are more likely to be enabled a rich mix of focused tools integrated by open standards. • Management of information flows are key to successful operations and contribute to sense-making and quality improvement activities. • Many quality systems fail due to a misalignment between strategic goals and operational measures. Quality measures and improvement activities need to avoid the trap of facile representations of brands and marketing. • The quality foundations of the virtual university need to provoke improvements that positively engage with the mission and values of the institution.

The Virtual Learning Environment Due to the ubiquitous nature of the virtual university and the propensity for this form of institution, by its nature, to be more open than more traditional institutions, the principles of social equity are in its very DNA. Of course, all institutions address this in various shapes and forms, but for the virtual university, ensuring there is an appropriate social equity framework in place is vital. We were cautioned on allowing technology to lead strategy in ▶ Chap. 4, “Laying and Maintaining the Foundations for Quality,” and in ▶ Chap. 5, “A Social Equity–Based Framework Toward the Development of the Virtual University,” Tay reminds us that digital inequity is growing despite the widespread use of technologies in education. • In this chapter, we are asked to consider a framework that reduces social inequity through purposeful consideration of equitable access and opportunity.

624

M. D. Sankey et al.

• Enables leaders of the virtual university to recognize barriers and develop innovative strategies from the outset. Unlike more traditional university practices, the virtual university can learn many lessons from institutions that have forged the way in newer forms of pedagogies that can be applied to the virtual environment. Much of this new practice revolves around academic engagement that involves both teachers and students. This pedagogic transformation has been discussed in ▶ Chap. 6, “Academic Engagement in Pedagogic Transformation,” where Maxwell and Armellini provide us with these key considerations that • Strong strategic leadership and the vision set the direction of travel. • Used appropriately, learning technology can enable pedagogic innovation, but used inappropriately technology can be a barrier to both learning and innovation. • The quality of teaching practice is of utmost importance in the virtual university. Notwithstanding the need for a firm pedagogical underpinning for the practices of the virtual university, it is also incumbent on such an organization to be at the forefront of innovation in its use of learning technologies, particularly emerging technologies. Through specific cases, Lai and Markauskaite in ▶ Chap. 7, “Innovation and the Role of Emerging Technologies,” provide us with a view of the role that emerging technologies play and help us see some of the required considerations. These include • The importance of being informed by learning theory and research on how learners learn across time and space. • Digital and learning technologies should be used to enable higher level cognitive engagement, as we prepare students for uncertain futures.

Supporting Staff and Students As there is a growth in the types of technologies that are being deployed across universities, primarily brought about by, and in response to, the COVID-19 pandemic, so too is the virtual university required to expand the innovative use of technology, as noted in ▶ Chap. 7, “Innovation and the Role of Emerging Technologies.” However, that creates a simultaneous need to adequately and completely equip the staff of the virtual university with the ability to make maximum use of these technologies. To do so, a range of staff-focused professional development in the areas of technology-enhanced learning are required. These models were investigated and outlined in ▶ Chap. 8, “Models of Professional Development for Technology-Enhanced Learning in the Virtual University,” where Sim and Huijser provided a comprehensive view of key techniques that could be employed and that focus on enhancing digital literacy and improving practice in a virtual university. Some hits include but are not limited to:

31

The Virtual University in Practice

625

• Be clear about the relationships between humans and digital teaching and learning environments. • Engaging stakeholders, training them, and supporting them in the use of digital tools by targeting what their digital literacy needs are. • Frame for staff and students the interconnections between themselves and the systems (and subsystems) that they interact with. • Help them be clear on the actions, decisions, and judgments about the use of TEL. • Develop a supported community of practice to help build on prior knowledge in organic ways. • Avoid isolated professional development instances, rather plan against the bigger picture. As part of the ongoing pressure for academic staff to reach high standards in their online practice, an additional area that is particularly vital for the virtual university is the need for teachers in the virtual space to understand how others may work in similar situations: how teachers are learning from other teachers through the peer observation of others. This benefits both the teachers who are observed and receive feedback on their teaching activities, and the observer who learns through the act of observing. Crehan, Munro, and O’Keeffe have unpacked some of the factors for success in implementing such a program in ▶ Chap. 9, “Peer Observation of Teaching in The Virtual University: Factors for Success.” They suggest that • Trust and collegiality are as important to foster in the virtual university as in a brick and mortar model, to develop authentic partnerships and conversations (and therefore learning). • Online observation of teaching may seem challenging but can contribute to improved quality, ongoing learning conversations, and reflection on practice improving both teacher and student experience. As noted in ▶ Chap. 6, “Academic Engagement in Pedagogic Transformation,” although a start-up virtual university does not strictly speaking go through a change management process, many of the teaching staff will come from more traditional universities and will go through a change process as individuals. In ▶ Chap. 9, “Peer Observation of Teaching in The Virtual University: Factors for Success,” Wheaton and Young reflect on such transition and provide some practical and conceptual guidance to support transition to a virtual university; they observe that • Two changes need to occur – pedagogy and technology. • As noted in ▶ Chaps. 8, “Models of Professional Development for TechnologyEnhanced Learning in the Virtual University,” and ▶ 9, “Peer Observation of Teaching in The Virtual University: Factors for Success,” also notes that professional staff development may be required to align pedagogy and technology. When considering how we support our staff and students, data are increasingly seen as gold. So much of what we see, particularly when dealing with virtual

626

M. D. Sankey et al.

environments, is informed by large sets of data. How else can we know how people are interacting online when we cannot see them physically. For the virtual university, this has led to a greater uptake in the use of learning analytics to support, encourage, and inform both our staff and students, and to provide them with insights into their engagement and to design learning. Some key considerations in this respect were discussed by Jones and Fitzgerald in ▶ Chap. 11, “The Role of Analytics When Supporting Staff and Students in the Virtual Learning Environment.” They suggest, among other things, that one needs to: • Provide professional development and support to empower a more data-led approach to understanding student behavior to improve learning experiences. • The implementation of data-led approach to the design of learning may require change management techniques to empower users to confidently adopt\change.

Learning Theories and the Application of TEL There is no shortage of theories and approaches to teaching with technology; however, at some point one has to settle on a range of approaches that suit the theories that underpin one’s practice. Sounds simple right? Well, not so much. To understand your practice, it helps to see where this practice fits into greater theoretical frameworks that have been evolving for decades (for online education that is). Campbell and Tran have provided us with some grounded advice in ▶ Chap. 12, “The 3C Merry-Go-Round: Constructivism, Cognitivism, Connectivism, Etc.,” based on some of the more prominent learning theories of the last 20 years, yet they have done so with an eye on current practice. They suggested that • As the knowledge of TEL matures, the appreciation of the importance of theory deepens, and it becomes clear that theory and practice must be aligned within a coherent and workable model for the virtual university. • Learning theories are a dynamic and fluid part of knowledge that evolve with the new technologies that emerge and transform intellectual, social, and economic horizons. • Learning theories only explain how different learners learn without telling them how to learn, resulting in learners reluctance and struggle due to the lack of learner support. • Education needs to build upon and integrate influential learning theories to reform TEL in the digital age, which is characterized by connectivity, collaboration, accessibility, and rapidly emerging technologies. • There is no need to have a unified theory of TEL, as existing learning theories can be combined, modified, and/or directly applied by the virtual university. • Technology has been found to be an influencing factor to the development of learning theories that it may not always be the case moving forward, thanks to the evolution of both established and emerging learning theories.

31

The Virtual University in Practice

627

Like all good theories, there are multiple perspectives on how and why they should be adopted in a particular context. Extending ▶ Chap. 12, “The 3C MerryGo-Round: Constructivism, Cognitivism, Connectivism, Etc.,” Czaplinski and Huijser have provided another perspective on the role and application of learning theories that are particularly relevant for the virtual university in ▶ Chap. 13, “The Role and Application of Learning Theories in the Virtual University.” This chapter left us in no doubt as to the importance of: • The relationship between an academic and the level of familiarity with TEL is important. • The institutional parameters that define academic roles might need to be set up differently in the virtual university. • Establishing pedagogical preparedness to teach with TEL is an important factor, particularly for sessional staff. • In the TEL space, good teachers are super helpful, really caring, approachable, and hands-on. • Staff professional development should also emphasize learning design and facilitation, as these have a strong and positive impact on students. • Teaching academics in the virtual university often fulfills a triple role: content expert, designer of learning activities, and facilitator of knowledge transfer. • It was again highlighted that a TEL community of practice is important to nurture teachers and learning designers, particularly when this is grounded in learning theories and supported by evidence-based examples of good practice. • Finally, when supported by a collaborative space in which the pedagogical virtue of TEL can be supported, effective design and enjoyable activities can be cocreated. One of the exciting things about working in the area of technology-enhanced learning is that the field is ever-evolving. This means that we have to stay vigilant and be willing to both adapt our practices and be open to the creation of new theories that can explain our practice. This does not happen by chance but is rather spurredon by the ongoing research of our scholarship that seeks to make sense of technology-enhanced learning. To help us frame this for the virtual university, Ostashewski has provided us in ▶ Chap. 14, “Adapting and Creating New Theories Through the Ongoing Research of Technology-Enhanced Learning,” with a neat framework that he and others have been developing around their online practice. In this, he identifies several elements that students have identified as important for their satisfaction and success. These elements include: • Lecturers’ knowledge, experience, and pedagogical capacity • Quality of the feedback they get on activities students carry out • On the speed and efficiency of having their questions being answered The implication of this for the virtual university are: • The need for robust and reliable technological tools, e.g., an LMS that allows for many different types of learner interactions, both asynchronous and synchronous.

628

M. D. Sankey et al.

• As on-campus social activities are not viable, an internal social media platform for students to participate in scheduled online activities, sitting alongside the curriculum. • The need for effective and responsive support systems for both learners and instructors, especially where there is a large dependency in technological knowledge. • The need to be able to access IT technology supports and training opportunities on how to make best use of the TEL tools. • Opportunities to engage with other educational programs and personal wellness supports. • Instructors need to be competent in not only their discipline, but also in their technological pedagogical content knowledge. Not surprisingly, the main players in online learning have been systems that have largely corralled students into a type of walled garden approach to higher education, that is, until Web 2.0 technology came along (Huijser and Sankey 2011), which ushered in a suite of new innovations, with social media being particularly prominent. Panke, in ▶ Chap. 15, “Social Media: Friend and Foe,” investigated this in light of the virtual university and had the following suggestions: • Higher education institutions are largely missing the opportunity to foster productive debate on social media. Typically their social media policies tend to favor institutional reputation over those of academic freedom. • Educators should provide nuanced advice to help students chart their own path through purposeful activities instead of either vilifying or hailing social media. • They can help students understand their social media landscape, and which of them can make meaningful contributions to their learning network. • Importantly, acknowledge the role of social media for informal learning, while being at the same time cautious about digital well-being and social division. • Instructors can also help students reflect on a balanced use of social media by encouraging them to go offline for a day or two, and to document what they miss – and gain. • Students need to be shown how to evaluate the information they get through their social media streams. Despite the advent of social media and its impact on learning and teaching, the notion of a coherent system to mediate learning, such as the learning management system, has prevailed. However, with faster Internet and cheaper data there has simultaneously been a large uptake in the use of other forms of media to supplement more traditional learning materials. This in turn had led to the development of specialized systems that link with, but stand separate to, the LMS. This in itself does not mean the LMS has become redundant, but it does call the privileged place it has enjoyed in education over the last 20 years into question. Marshall and Sankey have put this to test in ▶ Chap. 16, “The Future of the Learning Management System in the Virtual University,” unpacking some of the future trends in technology and

31

The Virtual University in Practice

629

more so what that might mean for the TEL ecosystem in the virtual university. They suggested that: • The success of the virtual university relies on establishing learner communities. • TEL environments are both stable to maintain operations, and flexible enough to accommodate change and growth. • University strategy will carefully position and enact their TEL plans and be open to future evolution. • There is a reliance on a technology partnership between the institution, academics, and students, and this requires an effective governance mechanism that includes those on the periphery of the platform. • Develop a vision for how a product, technology, or service is an essential part of a university learning ecosystem. • Build a coalition around the platform that shares the vision and rallies technology partners, academics, and students into cocreating a vibrant ecosystem. • Continually evolve the ecosystem while maintaining the collegial and intellectual values and direction of the university. • Adoption and use of online tools is strongly related to the role and professional identity of the academic; this stance treats them as active partners rather than clients or users and is a powerful mechanism to enact the collegial university model of distributed leadership. • The virtual university requires systems that reflect engagement with diverse learners and a focus on expanding the reach and impact of the university into new contexts. • Many of the new affordances of TEL reflect changed patterns of work and the dynamic networks learners participate in for their social lives and employment. • The TEL ecosystem of the virtual university is capable of sustaining an evolution of existing operations into new and uncharted spaces, one framed by clear standards and business processes that protect the user but also allow for innovation and expansion of pedagogical knowledge.

New and Emerging Forms of Assessment As we move into the online learning environment for our learning and teaching, the option to do in-class assessments is minimized. We say minimized as clearly there are options in relation to using tools such as Zoom or Teams where synchronicity is not lost. We look to maximize opportunities like this, rather than suggesting that this is not as good. Decades of research suggest that the options for online education provide just as much opportunity for students to engage in meaningful learning and assessment as their face-to-face counterparts (Nguyen 2015). Hillier, in ▶ Chap. 17, “Making Online Assessment Active and Authentic,” has begun to discuss the value proposition of making online assessment active and authentic, suggesting that there are ample opportunities to embrace the many and varied tools we have at our disposal in our online environments. Among other things, he suggests:

630

M. D. Sankey et al.

• With the increasing use of automation, robotics, and generative AI in education and life, graduates require lifelong learning skills to adapt and thrive in this rapidly changing environment. • Universities traditionally do this through defining a set of graduate attributes, at the program (degree) and unit (subject) level. These define a specific set of intended learning outcomes relevant to the discipline being learnt. • Assessment is the key component in how we assure students can do what they claim they can, requiring an alignment with the desired learning outcomes. • Traditional assessments, like essays and multiple-choice tests, are likely to fall short in this era of generative AI. Instead, assessment of complex problemsolving tasks, innovation, and creativity are now required. • Tasks designed to be active and authentic are thought to improve student learning outcomes. • Active learning occurs when students are “doing things” and actively thinking about what they are doing, engaging in the application of knowledge, analysis, synthesis, evaluation, and creation. • If the task is largely artificial or trivial, it may not challenge the student in ways reflective of the complexity required of professional practice. • Authentic assessment tasks are designed with characteristics that are, to various extents, reflective of the “world of work” and the “social world” where problems can be messy, dynamic, and complex. • “active” assessment moves students away from passive memorization toward the need to take action and engage in problem-solving. • “authentic” assessment tasks go beyond contrived activities by utilizing the characteristics of complex problems found in professional practice. • The virtual university has the opportunity to reconceptualize assessment in light of TEL and enable higher-order capabilities to be assessed and is already possible by taking advantage of existing technologies. One of the affordances of online assessment that has really risen to prominence over recent years has been the ease with which peer and collaborative assessment can be undertaken. A range of online environments have been developed to facilitate this form of assessment, which coincidentally is used equally as much as in more traditional class settings. That is, it is not ubiquitous to online education, but it is something that could now be considered native to the virtual university. Gunning, Adachi, and Tai have discussed this in ▶ Chap. 18, “Peer and Collaborative Assessment,” and argue that the effective deployment of this approach should become mainstream in the virtual university. They expand on this with the following points: • Student confidence and ability to assess the work of others critically and honestly can be fostered through development of evaluative judgment and engagement in quality feedback processes, which requires a deliberative approach to develop the skills students need to make it meaningful. • As part of this development, they note that relevant learning outcomes should be scaffolded across programs and measured through a combination of selfassessment, peer assessment, and teaching teams’ judgments.

31

The Virtual University in Practice

631

• Importantly, peer and collaborative assessment activities require institutionallevel policy and provision of technology to engage and support teaching teams. As with many contemporary universities, the virtual university is primarily focused on preparing students for the future of work and thus has a unique role to play because of the affordances of the virtual space. Dean et al. discussed many of these affordances in ▶ Chap. 19, “Preparing Students for the Future of Work and the Role of the Virtual,” where they make a clear case that • Virtual models of work-integrated learning (WIL) can provide a bridge between the virtual university and the reality of the workplace. • Emerging pedagogical approaches, such as virtual internships, digital service learning, and online placements, can enable students to engage with experiential learning through WIL in the virtual university. • The entanglement of technology, work, and learning require new models of WIL in which digitally enabled work shapes how WIL is designed, and where technology, present in specific skills or jobs, informs and shapes the pedagogical choices for virtual WIL models. Online learning, as we have seen, is not exactly a new phenomenon, yet we need to be realistic and not minimize the challenges there may be in relation to identity in the online space. Over the decades of experience we now have, numerous strategies have been adopted to ensure a level of authenticity to our online practice. It is particularly important, when it comes to considering assessment, that we are taking advantage of the many lessons that have been learned about originality and minimizing opportunities for people to cheat. In ▶ Chap. 20, “Authenticity, Originality, and Beating the Cheats,” Thomson, Amigud, and Huijser have provided us with a range of strategies that have proven to be particularly robust, which include: • Adopting a holistic approach that considers various aspects of academic integrity, which is necessary to develop a comprehensive solution • Creating a learning community, developing authentic and meaningful assessment, and adopting the best available verification and authentication technologies • Prioritizing student engagement and building relationships of trust, to foster a culture of academic integrity that benefits both students and faculty

The Role Openness Plays in the Virtual University Given you can now find almost anything you want (and not want) on the Internet, this can both be a positive and a negative for the virtual university. The trick to make it a positive experience is to pursue and cherish the techniques that have been established to check the variety of information we seek. One of the many concerns for those in the educational research space is that much of this information sits behind paywalls that makes it difficult for those in less affluent economies to access

632

M. D. Sankey et al.

quality information. This in turn leads to inequities in our globalized educational systems (Zajda 2022). The two relevant chapters in this section of the volume (▶ Chaps. 21, “Open Educational Practice as an Enabler for Virtual Universities,” and ▶ 22, “The Affordances of Openness for the Virtual University”) have made it very clear that there are numerous affordances for nearly all concerned to celebrate Open Educational Practice, which should particularly be the case for the virtual university. Bossu and Ellis in ▶ Chap. 21, “Open Educational Practice as an Enabler for Virtual Universities,” have provided some keys to how this can be understood and ultimately attained. They argue that, if carefully and thoughtfully harnessed and implemented, the promise of OEP for virtual universities is substantial. To capitalize on this promise requires both top-down and bottom-up approaches: • A holistic and multidirectional approach to be adopted across all levels – institutional, national, and international • Engagement from not only educators but also learners. A need to adopt a wider view beyond western-centric and English language dominance in OEP • Encouragement of active participation from those in the Global South as well as the global north More specifically, Mishra in ▶ Chap. 22, “The Affordances of Openness for the Virtual University,” has provided a solid framework on which to place one’s practice within. The affordances of openness for the virtual university are many and, if established from the beginning, can set the institution on a solid global footing with the potential to have a positive educational impact on a large portion of the world’s population. Mishra has provided ten clear recommendations in his chapter, based on the ten dimensions of openness to support equity and inclusion, and access to quality educational opportunities globally, in support of United Nations Sustainable Development Goal 4 – ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. He further argues that those recommendations could help build a resilient system to face future pandemics. • Adopt a flexible entry policy for anyone interested in pursuing a course and create a new revenue model for sustainable higher education • Offer programs in multimodal pathways to enable learners to study from any location without being fixed to one system • Minimize synchronous meetings to those necessary to meet the learning outcomes • Provide a variety of courses to choose from, and help the learners design their curriculum • Build the capacities of teachers to adopt collaborative learning strategies online • Use technology tools that are open source and reduce the cost of access to learners • Embrace the use of open educational resources and open textbooks by adopting a policy for OER and curating relevant open textbooks

31

The Virtual University in Practice

633

• Adopt a more flexible approach to assessment, providing how and when the learner may provide evidence of learning • Offer courses in a modular and stackable manner to accumulate credentials within a lifelong learning framework

Gamification, Adaptive and Conditional Learning As models of delivery change within higher education, so do opportunities for new approaches to the way we credential learning (Selvaratnam and Sankey 2021). As major economies now take the microcredentialing movement very seriously, they are releasing new national microcredentialing frameworks. Just two examples of this are first the European approach to microcredentials (The European Commission 2022). In June 2022, the Council of the European Union (EU) adopted a recommendation to support the development, implementation, and recognition of microcredentials across institutions, businesses, sectors, and borders. Similarly, the Australian government, in March 2022, released its National Microcredentials Framework, providing education providers with a clear way forward (Australian Government 2022). This highlights the different models and practices that have emerged due to a lack of a common definition. Selvaratnam has discussed this in ▶ Chap. 23, “Microcredentialing Models and Practice,” and suggests that • More research is needed to establish robust success measures to ensure the sustainability of microcredentialing initiatives for the longer-term assurance of quality and relevance. • The learner’s agency is key for the larger growth and success of any microcredentialing effort. • A rapidly evolving technological landscape makes any single static model obsolete very quickly and thus needs agile responses and dynamic, forward thinking. Earlier in 2023, UNESCO released a new policy paper entitled, “Short courses, micro-credentials, and flexible learning pathways: a blueprint for policy development and action” (Van der Hijden and Martin 2023), which again provides another useful definition of what may be considered a microcredential. Presumably we will reach a common definition at some point, but while this redefining process is taking place, the rest of the world is moving on. A great example of this is found in ▶ Chap. 24, “The Opportunities and Challenges in the Portability and Authentication of Micro-credentials and Short Courses in a Post-COVID Landscape,” where Fitzgerald and Huijser have unpacked many of the opportunities and challenges in the microcredential and shortcourse landscape. They encourage the virtual university to adopt a strategic and coherent approach that enables collaborative and innovative approaches to higher education delivery, as there are considerable risks in ad hoc engagement with short courses for skills development. They further suggest that

634

M. D. Sankey et al.

• The virtual university is in a position to lead on how to assure quality and cohesion to guide learners on how microcredentials add value to their learning journey. • The virtual university must engage with flexible models to be able to compete in a global context and to best serve the learners that a virtual university attracts, which is a potentially large population. • Short courses and microcredentials attract educated, professionally skilled, and employed learners and offer equitable opportunities and pathways that may otherwise not be available for all learners, which potentially allows the virtual university to play a key role in creating such opportunities.

Gamification, Adaptive and Conditional Learning An important development that has particular importance for the virtual university is how we keep students engaged in the online space. However, choosing the right gaming elements is a challenge for designers and practitioners alike, due to the dearth of proven design approaches. Fortunately, Bell, in ▶ Chap. 25, “Developing and Quantifying Intrinsically Motivating Instruction: Models and Principles of Gameful Design, Adaptive and Online Experiential Learning,” has worked us through many considerations when developing and quantifying intrinsic motivation in relation to our instruction approaches. He has provided us with a very userfriendly model that is backed by principles of gameful design. When applied, these lessons can empower staff and students to adapt quickly to these newer forms of online experiential learning. Bell demonstrates that • Many online games use AI to generate competition at an appropriate skill level to challenge participants “just enough,” which the virtual university can capitalize on. • Skilled instructional design, some automated feedback, and intrinsically motivating materials that will encourage engagement and preparation prior to dynamic, challenging, even fun in-class sessions can be an attainable goal. For the virtual university, the affordances of adaptive learning technologies are gold. This is a far cry from the inflexible pedagogical approaches that have been routinely adopted in more traditional contexts of instruction (Graesser et al. 2022). Adaptive technologies linked with conditional learning lead us to one of the more contemporary approaches to learning, which have their roots in sound research. Thompson et al. have taken us on this journey in ▶ Chap. 26, “The Role of Adaptive Learning Technologies and Conditional Learning,” and provided us with the following considerations: • The risk to not considering rigorous, open, evidence-informed ways to approach the use of adaptive learning technologies in higher education is that priorities could become driven not by learning objectives but only by objectives related to gaining competitive advantage or financial performance.

31

The Virtual University in Practice

635

• Adaptive learning technologies can provide support for data-driven decisionmaking in the design of learning situations as well as during the implementation of these designs, assessing student progress to inform feedback, learner paths, content, and the development of students’ metacognitive skills such as selfregulated learning.

The Rise and Rise of AI, VR, AR, MR, and XR The virtual university, because of its emphasis on the affordances of technology, offers the option to shift our practice away from customary cognitive tasks toward contemporary interactive tasks. This is clearly seen in technologies such as AI, which have proven, particularly since the end of 2022, to have a real impact upon higher education. AI and its associated technologies are not just another innovation but represent a fundamental change in the relationship between higher education and its broader socioeconomic interests (Bearman et al. 2022). When we add in the many and varied contemporary approaches to media-rich education, such as different forms of mixed realities, this provides a cornucopia of options for educators in the online space. Much of this practice that has emerged in the mixed reality realm to support learning and teaching has been discussed by Marshall in ▶ Chap. 27, “Emerging, Emergent, and Emerged Approaches to Mixed Reality in Learning and Teaching,” both from an ontological and epistemological perspective. Again, this has provided us with a solid framework on which to build a consistent platform of practice. Three key themes are identified that should be taken into account by those who design for learning in the virtual university: • The value that these technologies add to bringing information into the environment of the learner • The ability to change the learner’s perceptions. The recognition of the virtual university as an evolving organization that is adaptable enough to apply mixed reality technologies in meaningful ways Mason et al., in ▶ Chap. 28, “Artificial Intelligence and Evolution of the Virtual University,” then took us on a deeper dive into the role that Artificial Intelligence can play in the evolution of the virtual university. It is not too much of a stretch to suggest that this is probably the most fundamental shift to higher education in the last decade (Chen 2023), and understanding this shift is crucial for the forthcoming generation of practitioners. This chapter has clearly laid out the affordances that the virtual university can profit from, including: • AI can help spot plagiarism and fake IDs and support academic integrity. • The virtual university might consider collaborating on how to ensure trustworthiness of their operational systems that use AI. • From an educational perspective, developing human agency should be a priority. Students could be taught how to use AI-supported tools to solve increasingly challenging questions.

636

M. D. Sankey et al.

Quality, Benchmarking, Learning, and Educational Analytics It is no accident the last two chapters at the conclusion of this volume deal with quality in the technology-enhanced learning space. We started this volume with a series of chapters dealing with laying the appropriate foundations on which to build the virtual university. Then, in between these opening and closing chapters, we have dealt with putting up the walls, making sure the plumbing and fixtures are in the right place, ensuring the windows have a nice view and the painting gives it a sense of homeliness and belonging. The last thing that happens when building a house is that the quality inspector comes in to sign off on the build before it can be populated. Well, that is exactly what we are doing here, with the only difference being that this is not a one-off, but an ongoing task. Why? Because we are continually building, iterating, and improving our practice. That is precisely why we put in place the right quality measures and understanding of how we can measure ourselves against such measures. There is no point putting in place a strategy or initiative if we cannot also have the data at hand to effectively evaluate them. In other words, this helps us understand that what we are doing is hitting the intended marks. For the virtual university, this is all about the digital footprint of our students and staff and how we are using our institutional data to drive quality, improvement, and innovation. Dart and Cunningham, in ▶ Chap. 29, “Using Institutional Data to Drive Quality, Improvement, and Innovation,” have provided us with some of the keys to this quality house, and the strategies they present include strategic alignment, data management, integration into decision-making, and institutional culture. • Hosting the virtual university dramatically increases the quantity and scope of data that can be collected and analyzes driving evidence-based improvement at all levels of the institution. • This “big data” drives the adoption of data-driven strategies through tools that extract, aggregate, store, and manage these large datasets, while visualization tools have been improving greatly. • Institutions are increasingly implementing sophisticated algorithms to identify at-risk, customize communications, personalize learning pathways, and provide dashboards that enable students to reflect on how they fair. • Given data can be used to implement personalization at scale, data-driven approaches have become increasingly prevalent. • Importantly, data collection is to be aligned to strategic priorities, and analytical insights closely integrated into decision-making practices. But there is a significant risk when data-driven insights remain dormant, never being acted on. • Recent shift spurred by COVID-19 has extended the data opportunities for virtual learning. So looking to data-driven approaches to guide decision-making while implementing more sophisticated and innovative TEL is gold for the virtual university. Once we have our house in order in relation to our institutional practices, we lastly need to think about how this might compare to what other institutions are doing who

31

The Virtual University in Practice

637

are working in similar fields. In this particular case, with the virtual university being predominantly operating in the online space, the role of standards and benchmarking for technology-enhanced learning is paramount. ▶ Chapter 30, “The Role of Standards and Benchmarking in Technology-Enhanced Learning,” by Marshall and Sankey, has taken us on this final journey to discover many of the keys to putting in place a fulsome approach to benchmarking. They suggest that leaders of the virtual university should reflect on the way standards and quality frameworks can work to enhance their practice. • They can help leaders reflect on the diversity of student learning capabilities and desired outcomes. • They are designed to evolve to meet the challenging nature of technology, and new types of pedagogy. • Ideally they should stimulate discussions, and stimulate research that result in that evolution. • Enable effective practice, rather than constrain creativity and be a burden to the passion of teachers. • They are informed by an evidence base of effective teaching practice and research into ways of improving student learning, but not limited by conceptions that are misaligned to the virtual university. • Expressed in a way that enables efficient determination of performance and documents that performance over time in a reliable way. • Support, manage, and identify areas in need of development and strategic decisions regarding the future direction of the virtual university. • Support the development of TEL capability across networks of practice, rather than encouraging piecemeal and isolated initiatives.

Concluding Thoughts This book has taken us on quite the journey and has provided us with the views of some 54 authors from eight countries contributing their expertise to the vision for the virtual university. Their experience has provided us with a strong set of principles that should be considered for those wishing to thrive in this space. For any university, but particularly a new breed of university, unencumbered by tradition, the advent of a more versatile approach to learning and teaching, enabled by technology, ensuring that a comprehensive suite of underlying elements is in place is a must. Many of the elements presented in this volume, in the view of the editors, are nonnegotiables. They include, but are not limited to, ensuring the vision, purpose, and design of learning and teaching is supported by a rigorous approach to policy and sound governance. Of course, the virtual university lives and breathes in the virtual environment, so ensuring that the technology suite that is adopted meets the requirements of, and adequately supports, its staff and students which is paramount. Having the right systems is great (and necessary), but if they are not made to sing by the application of an appropriate mix of learning theories to suit the specific

638

M. D. Sankey et al.

learning context, then the distinct advantage the virtual university enjoys may well be lost. These newer pedagogies may also lead to new and more authentic forms of interaction and assessment, many of which have been discussed at length in this volume. Less “traditional” perhaps, but something this volume strongly endorses for the virtual university, is openness and particularly open education practice. This is closely aligned with how the global education market is shifting its thinking in relation to credentialing learning, and adding newer forms of microcredentials to the existing educational mix, as different nations slowly come to terms with this new microeconomy. Once the virtual house is built, it then opens the door to a whole range of newer forms of online learning: gamification, adaptive and conditional learning, virtual and mixed realities, and of course the ways we look to use AI. There is so much potential to excite and stimulate learning, and the prospects of using a wide range of strategies in the online space are immense, as touched on in great detail in this volume. However, ensuring we are not overreaching, and that we fully understand the needs of our students and take full advantage of what we are providing them, requires a robust approach to quality measures, benchmarking, and acting on our learning and educational analytics to drive a culture of continuous improvement. It has been a joy to pull this comprehensive volume of ideas and approaches together, and we trust that when viewed in its entirety the lessons shared here will enable a whole-of-institution approach to providing a virtual university that is fully and comprehensively underpinned and enabled by technology-enhanced learning approaches. Importantly, these approaches are never finalized, for if there is one thing this volume has taught us, it is that the virtual university is a dynamic and iterative concept that needs continuous rethinking in the face of new developments, much like learning itself.

Cross-References ▶ The Virtual University: Moving from Fiction to Fact

References Australian Government. 2022. National microcredentials framework. Canberra: Department of Education, Skills and Employment. Retrieved from https://www.education.gov.au/highereducation-publications/resources/national-microcredentials-framework Bearman M, J. Ryan, and R. Ajjawi. 2022. Discourses of artificial intelligence in higher education: A critical literature review. High Education. Open Access. https://doi.org/10.1007/s10734-02200937-2. Chen, C. 2023. AI will transform teaching and learning. Let’s get it right. Stanford University, Human Centred Artificial Intelligence. 9 March. Retrieved from https://hai.stanford.edu/news/ ai-will-transform-teaching-and-learning-lets-get-it-right

31

The Virtual University in Practice

639

European Commission. 2022. A European approach to micro-credentials. European Education Area. Retrieved from https://education.ec.europa.eu/education-levels/higher-education/microcredentials Graesser, A.C., J.P. Sabatini, and H. Li. 2022. Educational psychology is evolving to accommodate technology, multiple disciplines, and twenty-first-century skills. Annual Review of Psychology 73 (1): 547–574. https://doi.org/10.1146/annurev-psych-020821-113042. Huijser, H., and M.D. Sankey. 2011. You can lead the horse to water, but. . .: Aligning learning and teaching in a Web 2.0 context and beyond. In Web 2.0-based e-learning: Applying social informatics for tertiary teaching, ed. M.J.W. Lee and C. McLoughlin. Hershey: IGI Global. https://doi.org/10.4018/978-1-60566-294-7.ch014. Kak, S. 2018. Will traditional colleges and universities become obsolete? The conversation. Smithsonian Magazine. 10 January. Retrieved from https://www.smithsonianmag.com/ innovation/will-traditional-colleges-universities-become-obsolete-180967788/ Nguyen, T. 2015. The effectiveness of online learning: Beyond no significant difference and future horizons. Journal of Online Learning and Teaching 11 (2). Retrieved from http://jolt.merlot.org/ Vol11no2/Nguyen_0615.pdf Selvaratnam, R.M., and M.D. Sankey. 2021. An integrative literature review of the implementation of micro-credentials in higher education: Implications for practice in Australasia. Journal of Teaching and Learning for Gradate Employability 12 (1): 1–17. https://doi.org/10.21153/ jtlge2021vol12no1art942. van der Hijden, P., and M. Martin. 2023. Short courses, micro-credentials, and flexible learning pathways: A blueprint for policy development and action. Policy Paper. UNESCO, International Institute for Educational Planning. Retrieved from https://www.iiep.unesco.org/en/publication/ short-courses-micro-credentials-and-flexible-learning-pathways-blueprint-policy Zajda, J. 2022. Discourses of globalisation and education reforms: Overcoming discrimination. In Discourses of globalisation and education reforms. Globalisation, comparative education and policy research, vol. 31. Cham: Springer. https://doi.org/10.1007/978-3-030-96075-9_8.

Index

A Academic analytics, 293 Academic community, 94 Academic developers, 130, 142 Academic development, 141–143 Academic dishonesty, 399 Academic governance, 46, 54, 55 Academic integrity, 318, 396 administration, 403 associations and quality assurance agencies, 404 collaboration and outsourcing, 398 continuous assessment, 400 decisions, 396 discourse, 398 educational process, 399 educator approaches, 408 effectiveness, 407 higher education, 396 holistic approach, 408 inconsistency and confusion, 404 learner’s identity, 400 notion, 395 plagiarism and contract cheating, 394 research, 399 societal context, 398 steps, 403 virtual university, 397 See also Academic misconduct Academic logic, 172 Academic misconduct interpretation, 398 manifestations, 398 remote learning, 397 research, 399 wicked problem, 395–396 See also Academic integrity Academic preparedness, 364

Academic standing, 510, 513, 514 Accessibility, 81, 84–87, 122 Accreditation, 468, 470, 471 ACODE TEL framework, 599 Active assessment, 311, 313, 319 Active Blended Learning (ABL), 94 constituent, 94 emergency remote teaching, 100–102 implementation of, 98–100 University of Northampton, 96–98 virtual university, 102–104 Active engagement, 289 Active innovators, 99 Active learning, 228, 230, 232, 233, 235, 236, 239, 249, 250, 259, 310, 314, 334 approaches, 240 importance of, 241 mode, 113 Active participant, 258 Activity-based working, 100 Activity centred analysis and design (ACAD), 506, 507 Adaptable vs. adaptive element, 503 Adaptive assessment, 503 Adaptive educational system, 502, 506 Adaptive learning, 494–495, 503, 504, 587–588 Adaptive learning system, 292 algorithms, 504 assumptions about learning, 506 BNs, 502 characteristics, 503, 507 classification, 507 elements, 507 implementation, 504 operationalization, 504 postgraduate scenario, 515, 516, 518 undergraduate scenario, 513–515

© Springer Nature Singapore Pte Ltd. 2023 M. D. Sankey et al. (eds.), Technology-Enhanced Learning and the Virtual University, University Development and Administration, https://doi.org/10.1007/978-981-99-4170-4

641

642 Adaptive learning technologies assessment, 502, 504 big data learning architecture, 505 BN approach, 505, 506 data-driven decision-making, 520 features, VU’s methodology, 518, 519 higher education, 520 implementation, 503 implications, 520 instructors, 504 limitations, 519 methods, 508, 510 node descriptions and states, 510, 511 personalise, 503 virtual university, 502 See also Adaptive learning system Adaptive technology, 503 Adoption, 167, 168, 170–172, 174, 178, 179, 181, 183 Affordances, 249, 426, 427, 436–437 Affordances of technology, 315 Agent-based models (ABMs), 116 AI100 project, 551 AI-based anti-illegality systems, 553 AI chatbots, 88 AI safety, 557 Algorithmic colonialism, 557 Alignment problem, 560 Apple University, 560 Architectures for learning, 560 Arizona State University (ASU), 111 Artificial Intelligence Act (AI Act), 561 Artificial intelligence (AI), 120, 292, 293, 377, 386, 404, 471, 498, 621, 630, 634, 635, 638 disruption and empowerment, 563, 564 ethics and social science, 555–560 historical perspectives, 551, 552 inquiry, 550, 551 mass-market education, 549 questioning, 550, 551, 563 roadmap, 559 techniques, 551 technological innovation, 548 trustworthiness, 560–563 validated, 549 visibility and acceptance, 549 Artificial Intelligence Systems (AIS), 559 Artificial peer collaborator, 121 ASCILITE TELAS, 602, 604, 605 Assessment approach, 441 Assessment for learning, 313

Index Assessment security, 400 Asynchronous discussion board, 112 Asynchronous learning, 247, 257 Atomic force microscope, 115 Attention spans, 482, 483 Attitude, belief and value (ABV), 558 Augmented reality (AR), 339, 528, 530–531 Australasian Council for Open, Distance and e-Learning (ACODE), 459 Australian Computer Society (ACS), 469 Australian Federation of Travel Agents (AFTA), 469 Australian National Microcredentials Framework, 467 Australian Qualifications Framework (AQF), 453 Authentic activities, 254 Authentic assessment, 112, 311, 364, 370, 394, 409 Authentic context, 252 Authenticity, 28, 311, 400, 403, 405 Authentic knowledge artifacts, 114 Authentic learning, 158, 248, 251–254, 471 Automatic motivation, 191 Autonomous Universities (AU), 456 Autoregressive language models, 405

B Barriers, 84 cultural and linguistic barriers, 84 economic barriers, 84 technological barriers, 84 Baseline standards, 604 Bayesian Network (BN), 502, 505–510, 512, 515, 517 Bboard, 215 Behavior change techniques (BCT), 191–193 Behavior change wheel (BCW) application, 191–193 cultural change, 191 definition, 191 educational technology adoption, 190 Benchmarking, 589–590, 601–602, 605, 608, 609, 612 Bias, 553, 556, 564 Big data, 572 Blackboard, 284 Blackboard Discussion Boards, 215 Blended learning, 95, 101 Block chain models, 473 Blogs, 264, 266, 272, 274, 278

Index Bloom’s revised taxonomy, 310, 321, 346 Blueprint 6, 31 Body VR, 118 Branching simulations, 116

C Campus-based courses, 549 Canvas, 277, 284 Capability, opportunity and motivation and behaviour (COM-B) model, 191 Capacity building, 421, 423, 424, 426 Cape Town Open Education Declaration, 421 Carbon nanotubes, 118 Career development, 468 Case study, 360, 361, 366 Cave Automatic Virtual Environments (CAVEs), 532 Community of support, 88 Change management, 190 Cheating, 555 Cheating minimization strategies, 398 Cheating pandemic, 397 Citizen science, 272, 278 Cmap, 120 Co-constructing knowledge, 30 Cognitive engagement, 113 Cognitive load theory, 115, 116 Cognitive presence (CP), 256 Cognitive realism, 253 Cognitive revolution, 207 Cognitivism, 205–212, 219, 220, 230 TEL, 209 Collaboration, 402, 534 Collaborative assessment, 355, 356, 358 Collaborative-constructivist process, 256 Collaborative learning, 252 Collaborative learning strategies, 440, 443 Collegiality, 149, 153, 155, 156, 158, 160 Colonialism, 556 Common-sense reasoning (CSR), 553 Communities, 248 Communities of practice, 239, 241, 585 Community, 155, 158, 159 Community logic, 172, 173, 180 Community of Inquiry (CoI), 158, 254–257 Community of practice, 88 Computers, 404–406 Computer science, 562 Concept mapping tool, 120 Conceptual scenarios, 502, 509 Conditional probability distributions, 508, 509

643 Connectivism, 230–232, 238, 240 concepts of, 239 staff professional learning, 216 TEL, 210 Constructive alignment, 314, 315 Constructive learning mode, 113, 116 Constructivism, 205, 533 Echo360 ALP, 211 Graduate Certificate, 212 Microsoft Teams, 215 PebblePad ePortfolio, 214 TEL, 210 Constructivist, 250 Contemporary learning management systems (LMS), 277 Context-aware, 557 Contextual virtual university (CVU), 565 Contingency theory, 47 Continuing Education and Training (CET), 456 Continuing professional development (CPD), 428 Continuous improvement cycle, 580 Corporate governance, 49 Corporation logic, 173 Costs of education, 443 Course design, 95, 98 COVID-19 pandemic, 44, 46, 54, 94, 100–105, 172, 177–181, 204, 205, 228, 229, 308, 343, 434, 439, 590, 612 Credentialing management systems (CMS), 457 Critical practice of AI, 558 Critical technical practice (CTP), 557 Cultural change processes, 190 Culturally and linguistically diverse (CALD) students, 84 Culturally deficient, 82 Curricular flexibility, 440 Curriculum data, 315, 577 Customized career services, 89 Cyber-cultural logic, 173, 180, 181 Cyber Forensics Assurance, 318 Cyber-physical systems, 553 Cyber security, 469

D Data, 573 external stakeholders, 578–579 institution data, 576–578 student data, 574–576 Data analytics, 85, 86

644 Data-driven approaches, 195 Data-driven decision-making, 502 Data management, 581–582 Data strategy approach, 196 Decision-making practices, 27, 582 Decolonial theory, 556, 558, 559 Deep learning, 113, 548, 562, 563 Democracy, 81 Dependency theory, 556 Design-based research, 254 Design for learning, 502, 504, 507, 519 Design framework, 506 Developing Minds University, 560 Developmental-incremental enhancements, 103 Developmental learning approach, 407 Digital at heart, 26, 29–30, 32, 33, 35, 36, 38 Digital campus, 25, 27–31, 36 Digital campus master plan, 26, 32–36 Digital capabilities, 134 Digital citizenship, 265, 272, 408 Digital collaboration, 28 Digital disruption, 550 Digital education, 101 Digital environments, 394, 549 Digital equity, 80, 81–82 Digital infrastructure, 100, 552 Digital learning ecosystem, 31, 33, 36, 38 Digital literacy, 24, 84, 86–87, 132, 139, 141, 142 Digital marketing, 471 Digital media, 45, 514 Digital representations, 252 Digital revolution, 24 Digital service learning, 377, 385, 388 Digital skills, 99 Digital teaching and learning (DTL), 101 Digital technologies, 52 design and teaching phases, 111 knowledge products, 111 (see also Learning activities) online study models, 111–113 pedagogical strategies, 113 roles, 121 students' learning, 110 usage, 110 Digital transformation, 24–27, 29, 30, 32–35, 37, 38, 455 Digital wellbeing, 265, 276 Directed acyclic graph (DAG), 508 Direct effects, 296 Disabilities, 82, 87 Disadvantaged students, 82, 88, 89 Disruption, 563, 564

Index Distance education, 249, 250, 255, 434, 435 Distance learning model, 110, 396 Distance teaching methods, 549 Distributed learning model, 396 Diverse backgrounds, 81, 87 Diverse cultures, 85 Diversity, 557 Douyin, 268 Ducere University, 560 Dynamic work environments, 388–389

E EADTU E-xcellence Label, 604, 605, 607 E-cheating, 359 Echo360 Active Learning Platform (ALP), 210–212, 219 Echo chamber, 269, 278, 279 Ecological change models, 297 Ecological systems, 297 Economic barriers, 85, 86 Economic constraints, 84 Ecosystem approach, 436 Educational approaches, 254 Educational decision making, 505 Educational designs, 115 Educational developer, 149–151, 156, 158 Educational mixed reality, 536 Educational reforms, 110 Educational technology, 366 Education design patterns, 504 EFMD Online Course Certification System (EOCCS), 604–606 eLearning guidelines (eLGNZ), 604 E-Learning Maturity Model (eMM), 604–607 Electronic WIL (eWIL), 381, 387 Embodied learning experiences, 118 Emergency remote teaching (ERT), 100–102, 397 Emotional barriers, 84 Employability, 25, 88, 89, 376–378, 383, 385–388 Employer-university partnership model, 458 Employment, 578 Empowerment, 563, 564 Engagement, 118, 167, 168, 170–176, 178, 179, 181, 183, 252 Entry requirements, 438 Environmental protection, 560 e-payments, 270 Equitable access, 25, 80–82, 85–89 digital literacy, 86–87 financial resources, 85–86

Index Equitable distribution of financial resources, 85 Equitable opportunities, 82, 89 community of support, 88 inclusive pedagogical strategies, 87 social networks, 88 Equitable outcomes, 88–89 Equitable success, 82, 89 at-risk students, 88 employability, 88 Equity, 557 Equity-based policies, 85 Equity groups, 82 Esports, 273 Ethically aligned design, 559 Ethical principles, 556 Ethical protocols, 582 Ethical rights, 81 European Bologna process, 63 European Credit Transfer System, 442 European Higher Education Area (ESG), 96 European MOOC Credential Framework (EMC), 470 European Union (EU), 561 Evaluative judgement, 357, 359, 363 Eva’s story, 273 Evidence-based approaches, 196 Expensive scanning tunneling microscope, 115 Experiential learning, 490, 492 Explicadors, 553 Exploratory Learning Analytics Toolkit (eLAT), 189 Explore Learning and Teaching website, 206, 211, 216, 219 Extended reality, 528 External data, 578 accreditation cycles, 579 employers, 578 employment outcomes, 578 industry, 578 post-program perceptions of alumni, 578 Eye-tracking, 195

F Facebook, 267 Face-to-face learning, 246, 247 Faculty Sparks, 216, 219 Fairness, 81, 561 Fake auditing data, 553, 554 certificates, 553 datasets, 553 reducing risk, 554, 555

645 Fake news, 270 Family Educational Rights and Privacy Act (FERPA), 600 Fanfiction, 273 Feedback, 313, 358, 530, 534, 538 Feedback literacy, 357 Feed-forward, 313 Financial aid, 84, 86 Financial barrier, 83, 85, 86 Financial support, 85–87 First-in-family, 84 Flexibility, 28, 87 FlipGrid, 277 Flipped learning, 95 Flow (Csikszentmihalyi), 486 Formative assessment, 313 Formative feedback, 534 Fourth industrial revolution, 485, 564

G Gameful design, 484, 486–488 Gamification, 377, 385 Gibson, W., 552 Gig economy model, 472 Gilbert, A., 549, 552 Global learning, 555 Global profiles, 454 Gold nanoparticles, 117 Google Glass augmented reality device, 539 Googleyness, 555 Governance, 44, 55, 298 framework, 49 structure, 48 systems, 54 Grade guessing, 359 Graduate Certificate, 212 Graduates’ employability, 358 Gresham’s Law, 596 Group discussions, 104

H Head-mounted systems, 539 Hearing-impaired students, 87 Hearing impairments, 87 Heterogeneous engineering, 557 Higher education, 4, 5, 7, 9, 11, 25–27, 285, 622, 628, 632–635 Higher education institutions (HEIs), 44, 46, 48–50, 52–54, 550 Higher Education Leadership Institute (HELI), 48, 49

646 Higher Education Statistics Agency (HESA), 98 Holistic approach, 395 academic integrity, 408 coherent, 409 Holistic quality, 63 How students learn, 113, 114 Human-centered artificial intelligence, 561 Human-computer interaction (HCI), 557 Human values, 561 HyFlex model, 453 Hype Cycle, 548

I I Framework, 189 Inclusion, 82, 87, 89 Inclusive pedagogical strategies, 87 Independent learning, 253 Indigenous students, 82 Industry 4.0, 377, 378, 381, 382, 388 Informal learning, 264, 271–273, 276, 278 Information and communication technologies (ICT), 45, 386 Information Assurance, 318 Information technology, 455 Information Technology Services (ITS), 174 Innovative, 468 Inquiry, 550 Instagram, 267 Institute of Electrical and Electronics Engineers (IEEE), 559 Institutes of Higher Learning (IHLs), 456 Institutional change, 166, 180 Institutional context, 406 Institutional culture, 583–585 Institutional guidelines, 427 Institutional integrity process, 408 Institutional policy, 403 Institution data, 576 curriculum data, 577 financial impact, 577 professional development, 577 staff demographic attributes, 576 staff perceptions, 577 Instructional design, 498, 504 Instructional strategies, 118 Instructivist, 250 Instructor, 407, 408 Intellectual property (IP) policy, 422 Intelligent tutoring systems, 293 Intensive block courses, 110 Interactive learning, 104 Interactive learning mode, 113, 121 Interactive online whiteboard, 119

Index Intercultural education, 85 Interculturalism, 85 Interdisciplinary, 551 Internal quality system, 601 International Council of Distance Education (ICDE), 608 International Organization for Standardization (ISO), 560 Internet, 246, 248, 249, 251 Internet access, 87 Internet connectivity, 81, 84, 87 Internship, 379, 382 Interpersonal skill, 363 Intrinsic motivators, 487, 496–497 Iron triangle, 319

J Jisc eLearning Quality Standards, 602 Joint cross-institutional courses, 110 Just-in-time learning experience, 219

K Key performance indicators, 580 King’s College London (KCL), 111 Knowledge co-construction, 119, 120 Knowledge maps, 332–334 Knowledge worker, 550

L Labour market, 469, 472 Lagging innovators, 99 Leadership, 96, 100, 105, 298 Learner activity, 513 Learner-centered approach, 258 Learner-content interaction, 249 Learner engagement, 228, 239 Learner-instructor interaction, 249 Learner-learner interaction, 249 Learning 2.0, 271 Learning achievements, 504 Learning activities active mode, 113 constructive mode, 113 embodied experiences, 116–121 ideas and construct knowledge, 115, 116 interactive mode, 113 passive mode, 113 roles, 114 technological affordances and pedagogical strategies, 114 visualize invisible phenomena, 114, 115

Index Learning analytics (LA), 53, 83, 88, 293, 504, 583 BCW policy categories, 194 (see also Behavior change wheel (BCW)) benefits and challenges, 194, 195 data, ethics and privacy, 195 data, 189 definition, 188 design principles, 196, 197 educational data mining, 188 frameworks, 188, 189 insights, 190 intervention functions, 192 virtual universities, 196 Learning Analytics–Learning Design (LA-LD) Framework, 189 Learning and teaching, 94–96, 98, 100, 101, 104, 105 Learning content, 534 Learning design, 95 Learning ecosystem, 473, 474 Learning engagement, 241 Learning management system (LMS), 45, 112, 133, 166, 167, 170, 171, 174, 175, 179, 180, 188, 257, 284, 286, 291, 292, 294, 295, 297–299, 318, 321, 322, 324, 325, 329, 332, 341, 366, 505, 575 active engagement, 289 adoption and use, 299 analytics, 293 assessment, 290 Blackboard, 284 business, 284 Canvas, 284 first generation, 290 foundational requirement, 289 functionality, 287, 291 hierarchical architectural elements, 288 initial LMS designs, 285 learning resources, 289 LMS Market Share, 285 mobile access, 292 modern LMS, 286–290 Moodle, 284 pedagogical experience, 291 reality of, 294 role of, 290 second generation, 291 server-based AI features, 292 single system model, 285 traditional LMS, 290 use of, 290 Learning platform, 292, 294, 296, 298–299

647 Learning theories, 204–211, 219–221, 229–231, 233, 237, 241 cognitive, 240 grounded in, 240 in-depth knowledge about, 239 knowledge of, 241 lack of consistency in, 241 reflect on pedagogy and, 240 Lecture attendance, 482 Lecture-based learning, 110 Lifelong and lifewide learning, 28 Lifelong learning, 453, 455, 458 Linguistic barriers, 85 Logic models, 579

M Machine learning (ML), 120, 405, 506, 519, 553, 562 Marker-based systems, 531 Market logic, 173 Massive Multiplayer Online Games (MMOGs), 534 Massive Multiplayer Online Roleplaying Games (MMORGs), 534 Massive open online course (MOOC) 45, 104, 210, 459, 466, 470, 477, 549 Memes, 268 Metaverse, 528, 535 Metropoles, 556, 557, 565 Micro-credentials, 110, 453–462 analysis, 452 Australasia, 459, 460 award and non-award programs, 452 COVID-19 pandemic, 452 creation, 455 definition, 452, 467 digital certification and badges, 473 employer-university partnership model, 458 funding models, 460 global profiles, 454 in-house certifications, 458 models, 456–459 national employability metrics, 462 online space, 452 operating structures, 460 partnership model, 471 professional credentials, 468 recognition, 453 (see also Short courses) short form credentials, 466 stakeholder engagement, 457 student demographic, 453, 454 student profile, 453 success measures, 460–462 virtual university, 474

648 Microsoft HoloLens, 531 Microsoft O365 platform, 215 Microsoft Office 365 Teams, 277 Microsoft Teams, 215, 216 Mixed reality (MR), 536, 555 active engagement with, 538 applications, 533 barrier removal, 539 challenges, 539 classification, 529 collaboration, 537 content in context, 536 feedback, 538 future directions, 540 impact, 533 limitations, 540 pedagogical affordances, 533 presence and immersion, 537 simulation, 530 3D content and relationships, 536 visualisation of the invisible, 536 Mobile, 292 Model of Strategic Capability, 189 Modularized learning, 26 Modularity, 468 Molecular Workbench, 116 Moodle, 277, 284 Moodle LMS quiz, 321, 324 Mozilla Hubs, 535 MUD Object Oriented (MOOs), 534 Multimedia learning, 114 Multimedia program, 252 Multimodal analytics, 195 Multi-User Dungeons (MUDs), 534 Multi-user Virtual Environments (MUVEs), 534

N Nano Gold model, 115, 117 Nanoscience, 115 Nanotechnology, 116 National Institute of Open Schooling, 442 Natural language processing, 195 NetLogo, 116 Networked learning, 248–251, 253 Network effects, 296 Network society, 550 Neural networks, 562 Newton’s equation of motion, 116 New Zealand e-Learning guidelines, 606, 607 New Zealand Qualification Authority (NZQA), 470 Next Generation Internet (NGI), 559

Index Non-English language, 429 Northampton, 94–101, 103, 105 Nudging, 586

O O365, 216 Object based assessment, 334–336 Oculus Quest hardware, 535 OER for Specific Languages, 425 OLC Quality Scorecard, 602, 604 On-demand examination system, 442 Online, 4–10, 12, 14, 18, 621, 622, 625–632, 634, 635, 637, 638 Online community, 155, 159 Online courses, 111 Online education, 25, 102, 439 Online industry projects, 382–383 Online interactive oral assessment (OIOA), 336–338 Online learning, 94, 167, 175, 205, 207, 209, 494 Online learning environments, 110, 408 Online pedagogy, 171 Online Program Enablement (OPE), 300 Online Program Management (OPM), 300 Online/remote placements, 383–384 Online study models, 111–113 Online teaching, 102, 148, 150, 153, 154, 156–159, 161 Online TEL, 45 Online WIL simulation, 384–385 Online writing applications, 277 Open, Online, Flexible and Technology Enhanced Learning (OOFAT) framework, 605 Open badges, 466 Open education, 434 Open educational practices (OEP) affordances of, 426–427 approaches and initiatives, 421 building practitioners’ capacity, 424 capacity building, 423–424 definition, 421 global south to global north, 429 institutional guidelines creation, 427 learners, 428 open pedagogy, 424–425 policy development, 421–423 principles, 420 social justice and inclusion, 425–426 top-down leadership, 427 virtual universities, 427

Index Open educational resources (OER), 248, 420, 436 See also Open educational practices (OEP) Open Grants Program, 422 Open learning, 436 Openness, 434–439, 442–444 Open pedagogy, 421, 424 Open policy, 422, 428 Open scholarship, 436 OpenSimulator environment, 535 Open University, United Kingdom (OUUK), 434 Open University UK (UKOU), 470 Organizational strategy, 26 Organization theory, 47, 49 Originality collaboration, 402 computers, 404–406 plagiarism, 401, 402 Otitis media, 563

P Padlet, 112 Participatory learning, 112 Passive learning mode, 113 Paywalls, 435 PebblePad, 211, 213–215, 221 Pedagogical approaches, 248, 249, 251, 254, 255, 257–259, 440 Pedagogic transformation, 94, 96–99, 102–104 Pedagogy, 315 design, 229 knowledge of, 234 Peer and collaborative assessment (PCA) academic preparedness and programmatic assessment, 364 benefit, 355 case study, 361 certification and accreditation purposes, 357 complementary approach, 357 course design, 359 design, 354 elements, 367–368 external accreditation, 370 factors, 358, 369 goal, 355, 356 individual student capabilities, 358, 359 innovative practice and scholarship, 368–370 institutional-level support, 356 learning intentions, 356 learning purposes, 357

649 macro-level view, 365–368 online environment, 356 outcomes, 369 participatory research, 356 possibilities, 368 research, 368 rubrics, target employability skills, 362, 364 skills, 363 staff and university systems, 360 stakeholders, 357 student preparedness, 364, 365 summative contribution, 357 TBA strategy, 361, 362 teaching teams, 360 technology, 365 Peer assessment, 355, 358 Peer Observation of Teaching (PoT), 148 collegiality, 155 developmental model, 149 evaluation model, 149 honesty and openness, 152 longitudinal approach, deep reflection, 153 observation of teaching, 160 online PoT, 150–152, 154 pandemic online pivot, 153 pedagogy, in listening environment, 152 peer review model, 149, 157 presence of teaching, 154 relationship building, 158 sharing and listening to stories of practice, 151 support and guidance, 155–156 teaching practice, 158 trust, 155, 159 virtual university, 150, 156–157, 160 Performance Indicator, 610 Permeability, 565 Personalization, 548, 549, 555 Personalized guidance, 586 Personalized learning, 494–495, 503, 504, 507, 519 Personal Learning Environment (PLE), 271 Physical opportunities, 192 Placement-based approaches, 378 Placements, 381, 383, 384 Plagiarism, 401, 402, 555 Platform, 294–297, 367 PLuS Alliance’s online programs, 111 Podcasts, 272–275 Policies/procedures, 35, 360, 368, 396, 598, 607 Policy development, 421–423 Political communities, 558

650 Practical inquiry model, 255 Predictive modelling, 585–586 Privacy, 195 Procedural justice, 86 Process organization theory, 47 Professional development, 364, 584 Professionalism, 610 Professional representation systems (PRS), 457 Programmatic approach, 359 Programmatic assessment approach, 359, 364 Project, 379, 382–384 Project-based learning approaches, 518 Project Rewire, 174–177, 180, 181 Proofs of concept, 564 Public discourse, 549, 551 Public-private partnership model, 458 Python programming, 471

Q Qualitative data, 581 Qualitative feedback, 576 Quality, 60–66, 69, 71–73, 621, 623–625, 627, 630, 632–634, 636–638 Quality as sense-making, 66, 67 Quality enhancement model, 94, 96–98, 102–104, 590 Quality matters, 602, 604, 606 Quantitative data, 581 Quasi-legal system, 401

R Rapid decision making, 100 RealTimeWWII, 273 Reciprocal engagements, 557 Recognizing barriers, 82–85, 89 Reed’s law, 296 Reflection, 149–153, 155, 158, 160 Regional students, 82 Regulations-based decision-making, 50 Reliable AI, 561 Remote labs, 341–343 Remote students, 81–84 Remote workstation facility, 514 Residential universities (RUs), 554 Resource constraint, 319 Responsive personal communications, 516 Responsive-reactive enhancements, 103 Reverse tutelage, 557 Rheingold, H., 552 RIPPLES model, 33–37 Risks, 467, 472–474, 519, 554, 555, 559

Index RMIT University COVID pivot, 177–178 Project Rewire, 174–177 Robustness, 561 Rubric, 362, 363 Rules-based decision-making, 50

S Safety, 560 SAMR model, 320–322, 333, 336, 338, 340–342, 345, 346 Scaffolded active learning, 259 Scaffolded degree, 467, 471 Scaffolded learning activities, 289 Scalability, 317, 540 Scalable, 319, 321, 341, 346 Sceptical and resistant, 99 Sceptical but obliging, 99 Scholarships, 84, 86 Seamless learning, 271, 274, 276, 278 Security, 318, 553 Self-/intra-team peer assessment, 361, 364, 365 Self/peer assessment project, 366 Self-directed learning, 112, 229, 232, 253 Self-explainability, 560 Self-policing, 399 Self-reflexive approach, 557 Self-regulated learning, 229, 520 importance of, 241 Sense-making, 65–67, 69, 71, 72, 95 Sense of community, 88 Sentiment analysis, 588, 589 Server-based AI features, 292 Service learning, 385 Short courses class central list, 468 disruptive threat, 469 higher education degree program, 466 opportunities, 470–472 private and global providers, 468 risks, 472–474 skills development, 467 workplace skills, 466 Short form credentials, 466 Simulated equipment, 341–343 Simulated lab equipment, 343, 345 Simulated laboratory, 341 Simulation, 377, 382, 384, 386, 387, 530, 532, 535, 538, 540 Singularity University, 560 Situated learning, 253 Skillnet Networks, 459

Index Slack, 277 Small fast team (SFT), 177 Smart learning, 548, 564 Smart surveillance applications, 404 SnapChat, 268 Social comparison, 269 Social constructivism, 230, 256, 533 Social equity, 80–82, 89 Social inequity, 80, 81, 89 Social influence, 269 Social justice, 80–82, 86, 89 Social justice and inclusion, 425–426 Social learning communities, 30 Social logic, 181 Social media, 45 blogs, 274 concerns and drawbacks, 269–271 evolution, 266 Facebook, 267 FlipGrid, 277 future directions, 278, 279 Instagram, 267 learning management systems (LMS), 277 memes, 268 Microsoft Office 365 Teams, 277 online writing applications, 277 pedagogical applications, 272 personal learning environment (PLE), 271 podcasts, 275 slack, 277 Snapchat, 268 social navigation, 268 social tagging, 266 TikTok/Douyin, 268 twitter, 267 user generated content, 266 virtual universities, 275 VoiceThread, 277 WeChat, 268 WhatsApp, 268 wikis, 274 Youtube, 267 Social navigation, 268 Social networking, 264, 267, 268 Social networks, 88, 267–270 Social norms, 555 Social opportunities, 192 Social responsibility, 560 Social tagging, 266 Socio-cultural barriers, 80, 82 Sociocultural learning theories, 119 Socio-economic status (SES), 82–84, 86, 89

651 Sociomaterial, 168, 170–172, 180, 181, 183, 377–381 Socio-technical computing, 562 Socio-technical systems, 562 Socio-technical tools, 562 Socio-technological challenges, 194 Software as a service (SaaS), 286, 292 Southern Cross University's (SCU) 6x6 model, 111 Speech recognition, 87 Stackable, 468, 470, 473 Stakeholders, 60, 62, 66, 68, 70–73, 94 Standardization, 555–560 State logic, 173, 181 Strategic alignment, 579–581 Strategic capability, 584 Student, 166–168, 170, 173, 174, 176–180, 182 Student-centered approach, 254 Student-centered learning, 441 Student data, 574 extra-curricular activities, 575 learning outcomes, 576 online engagement data, 575 program-level surveys, 575 progression, retention, and completion, 576 socio-demographic data, 574 study attributes, 574 subject and educator perceptions, 574 support services, 576 Student demographic, 453, 454 Student development, 24 Student driven context, 215 Student engagement, 188, 194 Student Evaluation of Teaching (SET) surveys, 574 Student management system (SMS), 457 Student preparedness, 364, 365 Students’ learning processes, 110 Summative assessment, 357 Superhydrophobic surfaces, 115 Supporting Higher Education to Integrate Learning Analytics (SHEILA) framework, 190 Sustainable Development Goal, 435 Sustainable digital infrastructure, 550 Swarm-based AI, 562 Swedish E-Learning Quality Model, 604

T Task-based simulations, 518 Teacher-centered model, 110 Teachers for Teachers for Tertiary (T4T4T), 44

652 Teaching and learning 24/7 accessible, 131 digital capabilities, 133–135 digital integration, 135–137 learning management system, 133 process, 142 Teaching practice, 95, 96, 98, 99, 102, 103, 105 Team-based assessment (TBA), 356, 361, 362 Team-based learning, 95 Teamwork critical thinking, 369 interpersonal skills, 365 skills and behaviors, 361 Technical skill, 363 Technological, pedagogical and content knowledge (TPACK), 111 Technological innovation, 30 Technological modularity, 296 Technological transformation, 550 Technology, transformative model of teaching, 167–170 Technology acceptance model (TAM), 171, 173 Technology affordances, 320 Technology-enabled learning, 25, 246, 255, 259, 287, 434, 437 Technology-enhanced learning environments, 597 Technology enhanced learning (TEL), 4–5, 7–8, 17, 18, 44–46, 132, 134, 139, 140, 622, 623, 625–630, 636, 637 higher education, COVID, 53–55 organizational and individual factors for, 170–174 Technology-enhanced learning (TEL), 205, 220, 221, 548 cognitivism and, 209 connectivism and, 210, 211 constructivism and, 210 learning theories, 209 Technology-enhanced models, 110 Technology-reinforced compliance approaches, 409 TELAS Framework, 157 Telepresence, 530, 538 TEL quality frameworks, 603 TEL quality system ACODE TEL Framework, 599 assessment, 608–610 baseline standards, 604 elements, 598 expectations and requirements, 599 fully online standards, 602 future directions, 612

Index governance, 607 policy and procedure, 599 political and symbolic quality dimensions, 600 professionalism, 610 TEL framework, 605 virtual university, 600 Textual analysis of student-written passages, 588 3D content and relationships, 536–537 3D multiuser virtual environments (MUVE), 338–341 3D printing, 339 Three-dimensional representation, 534 3D virtual reality, 118 TikTok, 268 Time constraints, 516 Time management, 363 Time of learning, 439 Top-down leadership, 427 Total Quality Model (TQM), 607 Tragedy of the horizon, 483 Transformative model of teaching, 167–170, 181 Transition, 166–168, 171–173, 179–181, 183 Transparency, 49–51, 53 as a problem and a solution, 50–51 definition, 49–50 of financial performance data, 51 and institutional behavior, 51–52 and technology, 52–53 Trust, 149, 150, 152, 153, 155, 156, 158–160, 553, 560 Trustworthiness, 370 autonomy, 560 deep learning, 562, 563 digital transformation, 561 interoperability, 560 operational systems, 561 requirements, 561 socio-technical systems, 560 standardization, 560 swarm-based AI, 562 Trustworthy ethics, 561 Teaching-to-the-test (TTTT), 314 TU Delft Strategic Framework 2018-2024, 422 21st century skills, 308, 310, 311, 313–315, 321, 337, 346, 485, 564 Twitter, 267, 273

U Unbundling of education, 468 Unique learning and teaching model, 95 Universal design for learning (UDL), 87

Index University AI-based auditing, 553 background briefing, 549 conceptions, 549 University learning environments, 287 University of Melbourne, 552 University of New South Wales (UNSW), 111 University of Northampton, 96–98 Upskill, 466, 471, 474 User experience, 27 User experience design, 507 User generated content, 266

V Validity, 318 Value-added content, 472 Verification/authentication technologies, 409 Verification, 553 Vertex Cover problem, 563 Victoria University’s (VU) block model, 111 Videoconferencing system, 119 Video technology, 115 Virtual campus, 316 Virtual classes, 513 Virtual collaborations, 119 Virtual education, 246–248, 250, 252–255 Virtual higher education, 54 Virtual immersive environments, 338–341 Virtual infrastructures, 452 Virtual labs, 341, 343 Virtual learning, 189, 247 Virtual learning environments (VLE), 45, 95, 112, 113, 121, 246–248, 256, 257, 457, 552 See also Learning management system (LMS) Virtual learning experiences, 112 Virtual organization theories, 47–48 Virtual reality (VR), 339, 377, 384, 531–533, 555 Virtual tour, 120 Virtual universities, 46, 122, 188, 192, 421, 423, 425–429, 438 assessment approach, 441 costs of education, 443 curricular flexibility, 440 entry requirements, 438 learning resources, 441 openness, 443 pedagogical approach, 440 recognition of credentials, 442 study location, 439

653 technology use, 440 time of learning, 439 Virtual university, 17, 25, 80–83, 85–88, 102–104, 204–206, 208–212, 214, 216, 219–221, 251, 366, 452, 453, 461, 462, 472, 474, 596, 597, 600, 608–613, 620–624, 626, 627, 629–632, 634–638 bureaucratic dimension, 62 collegial dimension, 64 gamification, adaptive and conditional learning, 14–15, 633–635 learning theories, 8–9 learning theories and application of TEL, 626–629 LMS (see Learning management system (LMS)) new and emerging forms of assessment, 629–631 online assessment, 11–14 openness plays, 10–11 policy and governance models, 5–6 political dimension, 63 quality, benchmarking, learning and educational analytics, 636–637 quality assurance, benchmarking, 16–17 sense-making quality for, 65–71 social media, 9–10 supporting staff and students, 7–8, 624–626 symbolic dimension, 63 systemic dimension, 64 transparency, 622 virtual learning environment, 7 Virtual university (VU), 552, 554 Virtual video classroom, 112 Virtual work integrated learning, 343–346 Virtual World, 338, 340, 528–530, 534, 535, 537 Visualization of the invisible, 534, 536 VoiceThread, 212, 213, 221, 277

W Wash back, 314 Web 2.0, 271 Weblog, 272 WeChat, 268 Western Governor’s University (WGU), 70 Western Governors Virtual University, 552 WhatsApp, 268 Whiteboard, 119 Whole Earth Lectronic Link (WELL), 552 Wicked problem, 394–396 Widening access, 82, 86, 87 Widening participation, 81, 85, 86

654 Widen participation, 81, 466 Wikis, 264, 266, 272, 274, 275, 278 Woolf University, 560 Work, 376–380, 382–386, 388, 389 Workforce development, 469 Work-integrated learning (WIL), 89, 376–383, 385–389 placement, 387 typology, 381–382 virtual modes, 386 Work–life commitments, 112 Workplace, 376, 378, 379, 382–384, 386–388

Index Work placements, 89 Work-readiness, 378 World Reference Levels (WRLs), 455 World Wide Web Consortium (W3C), 318

Y Youtube, 267

Z Zone of Proximal Development, 495