Enhancing the Role of ICT in Doctoral Research Processes [1 ed.] 1522570659, 9781522570653

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Enhancing the Role of ICT in Doctoral Research Processes Kwong Nui Sim Victoria University of Wellington, New Zealand

A volume in the Advances in Library and Information Science (ALIS) Book Series

Published in the United States of America by IGI Global Information Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA, USA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: [email protected] Web site: http://www.igi-global.com Copyright © 2019 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark.

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Names: Sim, Kwong Nui, 1983Title: Enhancing the role of ICT in doctoral research processes / Kwong Nui Sim, editor. Description: Hershey, PA : Information Science Reference, [2019] | Includes bibliographical references. Identifiers: LCCN 2018014730| ISBN 9781522570653 (hardcover) | ISBN 9781522570660 (ebook) Subjects: LCSH: Doctor of philosophy degree. | Dissertations, Academic--Research--Methodology. | Graduate students--Supervision of. | Internet in higher education. | Information technology. | Communication and technology. Classification: LCC LB2386 .E64 2019 | DDC 378.2--dc23 LC record available at https://lccn.loc. gov/2018014730

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Titles in this Series

For a list of additional titles in this series, please visit: https://www.igi-global.com/book-series/advances-library-information-science/73002

Exploring the Relationship Between Media, Libraries, and Archives Collence Takaingenhamo Chisita (Harare Polytechnic, Zimbabwe & University of South Africa, South Africa) and Alexander M. Rusero (Harare Polytechnic, Zimbabwe) Information Science Reference • ©2019 • 263pp • H/C (ISBN: 9781522558408) • US $175.00 Literacy Skill Development for Library Science Professionals S. Thanuskodi (Alagappa University, India) Information Science Reference • ©2019 • 410pp • H/C (ISBN: 9781522571254) • US $175.00 Innovative Applications of Knowledge Discovery and Information Resources Management Susan Swayze (The George Washington University, USA) and Valerie Ford (ISP Global Communications LLC, USA) Information Science Reference • ©2018 • 325pp • H/C (ISBN: 9781522558293) • US $195.00 Library and Information Science in the Age of MOOCs Anna Kaushik (University of Kota, India) Information Science Reference • ©2018 • 309pp • H/C (ISBN: 9781522551461) • US $185.00 Changing the Scope of Library Instruction in the Digital Age Swati Bhattacharyya (University of California – Riverside, USA) and K Rama Patnaik (Indian Institute of Management Bangalore, India) Information Science Reference • ©2018 • 286pp • H/C (ISBN: 9781522528029) • US $175.00 Handbook of Research on Innovative Techniques, Trends, and Analysis for Optimized ... Victor X. Wang (Grand Canyon University, USA) and Thomas G. Reio Jr. (Florida International University, USA) Information Science Reference • ©2018 • 445pp • H/C (ISBN: 9781522551645) • US $275.00 Measuring the Validity of Usage Reports Provided by E-Book Vendors Emerging Research ... Aiping Chen-Gaffey (Slippery Rock University of Pennsylvania, USA) and Heather Getsay (Slippery Rock University of Pennsylvania, USA) Information Science Reference • ©2018 • 130pp • H/C (ISBN: 9781522532385) • US $135.00 For an entire list of titles in this series, please visit: https://www.igi-global.com/book-series/advances-library-information-science/73002

701 East Chocolate Avenue, Hershey, PA 17033, USA Tel: 717-533-8845 x100 • Fax: 717-533-8661 E-Mail: [email protected] • www.igi-global.com

Hereby, I would like to dedicate this edited book, especially to: • •

My beloved family – my parents, my two younger brothers, my sister in law and my two little nephews who live happily in Malaysia; My academic workplace – Centre for Academic Development at the Victoria University of Wellington, New Zealand.

“Reading is a conversation. All books talk but a good book listens as well.” – May this edited book be a spark of inextinguishable thoughts in this field.

List of Reviewers David Bolton, West Chester University, USA Thom Cochrane, Auckland University of Technology, New Zealand Ben Daniel, University of Otago, New Zealand Dawn C. Duke, University of Surrey, UK Tony Harland, University of Otago, New Zealand Maggie Hartnett, Massey University, New Zealand E. Alana James, DoctoralNet, Ireland Swapna Kumar, University of Florida, USA Lynnette Lounsbury, Avondale College of Higher Education, Australia Stephen Marshall, Victoria University of Wellington, New Zealand Jenny McDonald, Independent Researcher, New Zealand Paula Mildenhall, Edith Cowan University, Australia Ricardo Morais, Universidade Católica Portuguesa, Portugal Adon Moskal, Otago Polytechnic, New Zealand Maria Northcote, Avondale College of Higher Education, Australia Nicola Parkin, Flinders University, Australia Peter Rawlins, Massey University, New Zealand Tom C. Reeves, The University of Georgia, USA Ambyr Rios, Texas A&M University, USA Jennifer Rowland, Macquarie University, Australia Rachel Spronken-Smith, University of Otago, New Zealand Sarah Stein, University of Otago, New Zealand Radhika Viruru, Texas A&M University, USA Peter Whiteford, Victoria University of Wellington, New Zealand

Table of Contents

Preface................................................................................................................. xvi Acknowledgment............................................................................................... xxii Introduction......................................................................................................xxiii Section 1 ICT Use in the Doctoral Research Process Chapter 1 Reconceptualizing Postgraduate Research: An Online Blended Learning Approach.................................................................................................................1 Maggie Hartnett, Massey University, New Zealand Peter Rawlins, Massey University, New Zealand Chapter 2 Using ICTs to Check Plagiarism in PhD Research Works in Nigeria: Prospects and Challenges......................................................................................24 Floribert Patrick C. Endong, University of Calabar, Nigeria Chapter 3 Knowledge Visualization for Research Design: The Case of the Idea Puzzle Software at the University of Auckland................................................................46 Ricardo Morais, Universidade Católica Portuguesa, Portugal Ian Brailsford, University of Auckland, New Zealand Chapter 4 Big Data and Doctoral Research: Opportunities, Challenges, and Cautions........67 Richard C. Berry, Intersect Australia, Australia Lucy Johnston, University of Newcastle, Australia



Section 2 ICT Use to Support Doctoral Study Chapter 5 Digital Higher Degree Research (HDR) Scholarly Support and Community Building................................................................................................................85 Jennifer Rowland, Macquarie University, Australia Chapter 6 Doctoral Platforms and Apps for Professional Development and Student Support................................................................................................................108 E. Alana James, DoctoralNet Ltd, Ireland Chapter 7 Digitalization of Higher Degree Research (HRD) and Its Benefit to Postgraduate Researchers....................................................................................133 Joseph Stokes, Dublin City University, Ireland Rachel Keegan, Dublin City University, Ireland Mark Brown, Dublin City University, Ireland E. Alana James, DoctoralNet, Ireland Section 3 Online Practices in Doctoral Study Chapter 8 Don’t Be a Ghost Who Drops Grades in Blackboard: Findings From a Program Evaluation of an Online Doctoral Program in the United States.........154 Ambyr Rios, Texas A&M University, USA Radhika Viruru, Texas A&M University, USA Burhan Ozfidan, Texas A&M University, USA Chapter 9 A Framework for E-Mentoring in Doctoral Education.......................................183 Swapna Kumar, University of Florida, USA Melissa L. Johnson, University of Florida, USA Nihan Dogan, University of Florida, USA Catherine Coe, University of Florida, USA



Chapter 10 The Use of ICT in Researcher Development......................................................209 Sam Hopkins, University of Surrey, UK Erin A. Henslee, Wake Forest University, USA Dawn C. Duke, University of Surrey, UK Conclusion......................................................................................................... 234 Compilation of References............................................................................... 239 About the Contributors.................................................................................... 269 Index................................................................................................................... 276

Detailed Table of Contents

Preface................................................................................................................. xvi Acknowledgment............................................................................................... xxii Introduction......................................................................................................xxiii Section 1 ICT Use in the Doctoral Research Process Chapter 1 Reconceptualizing Postgraduate Research: An Online Blended Learning Approach.................................................................................................................1 Maggie Hartnett, Massey University, New Zealand Peter Rawlins, Massey University, New Zealand The professional inquiry (a researcher training and development course) was introduced into the Master of Education program at Massey University, New Zealand in 2014 as a practitioner-based alternative to the research thesis pathway. In contrast with traditional, independent, time intensive models of postgraduate research supervision, the authors developed and implemented an innovative blended learning model of postgraduate research training and development to ensure the growing demand of future, predominantly distance, students would be met. The online, blended model developed and discussed here within the discipline of Education has the potential to be utilized across different disciplines and postgraduate programs including those at doctoral level. In its fifth year of delivery, the online community has grown from nine students and seven specialist academic advisors in the first cohort to 45 students and 27 academics in the current offering, ensuring an accessible and equitable research learning experience for all students.



Chapter 2 Using ICTs to Check Plagiarism in PhD Research Works in Nigeria: Prospects and Challenges......................................................................................24 Floribert Patrick C. Endong, University of Calabar, Nigeria The proliferation of plagiarism in African universities has rationalized the adoption of various strategies to mitigate or eradicate it. In Nigeria particularly, computerassisted approaches such as the Turnitin software have been appropriated to tackle this challenge. Many Nigerian universities have adopted Turnitin to ameliorate the quality of PhD research produced in their faculties. Although lauded in many quarters, this recourse to ICTs to check plagiarism has seen multiple challenges, some of which include poor anti-plagiarism policies, fallible anti-plagiarism software, and the Nigerian factor, among others. Using observations and secondary sources, this chapter critically explores these challenges. The chapter provides a conceptual definition of plagiarism and plagiarism detection systems; it shows how plagiarism is affecting PhD research in Nigerian universities and explores the place of ICTs in anti-plagiarism policies adopted by Nigerian universities. The chapter ends by examining the prospects and challenges of using ICTs to mitigate PhD student plagiarism in Nigeria. Chapter 3 Knowledge Visualization for Research Design: The Case of the Idea Puzzle Software at the University of Auckland................................................................46 Ricardo Morais, Universidade Católica Portuguesa, Portugal Ian Brailsford, University of Auckland, New Zealand This chapter presents a case of information and communication technology use in doctoral research processes. In particular, it presents the use of the Idea Puzzle software as a knowledge visualization tool for research design at the University of Auckland. The chapter begins with a review of previous contributions on knowledge visualization and research design. It then presents the Idea Puzzle software and its application at the University of Auckland. In addition, the chapter discusses the results of a large-scale survey conducted on the Idea Puzzle software in 71 higher education institutions as well as its first usability testing at the University of Auckland. The chapter concludes that the Idea Puzzle software stimulates visual integrative thinking for coherent research design in the light of Philosophy of Science. Chapter 4 Big Data and Doctoral Research: Opportunities, Challenges, and Cautions........67 Richard C. Berry, Intersect Australia, Australia Lucy Johnston, University of Newcastle, Australia



This chapter explores opportunities and challenges that are presented to doctoral candidates (and indeed all researchers) through access to big data. The authors consider what big data is and what it is not, and how working with big data differs from traditional research design and analysis. They provide examples of the opportunities that big data offers in terms of the combination of diverse data sets, sources, and types and how it can provide new perspectives on inter-disciplinary challenges. They also highlight some of the challenges for the use of big data, both for the individual researcher and for institutions. The authors advocate for the need to embrace these challenges but without foregoing data integrity and the expert use and interpretation of data. Section 2 ICT Use to Support Doctoral Study Chapter 5 Digital Higher Degree Research (HDR) Scholarly Support and Community Building................................................................................................................85 Jennifer Rowland, Macquarie University, Australia In this chapter, the development of a digital support system for higher degree research (HDR) student training and development is conversed in the context of the young faculty of medicine at Macquarie University in Sydney, Australia. First, the case and the issues that need to be addressed in providing digital support to the HDR cohort are discussed. Then, the development of the digital platform is presented. Finally, an overall reflection is made with respect to the effectiveness and future directions in implementing the digital platform with a focus on developing a scholarly community of support for the faculty’s higher degree research students, supervisors, and the wider research community. Chapter 6 Doctoral Platforms and Apps for Professional Development and Student Support................................................................................................................108 E. Alana James, DoctoralNet Ltd, Ireland Using the experience derived across multiple universities, this chapter endeavors to discuss how ICT can play a role in the larger evolution of higher education, as well as with helping doctoral students complete their research and writing requirements. Practitioner research underpins the discussion of two rounds of research centered on ICTs role in equalizing disparity in financial and social capital between students and taking those solutions to scale. The first round (2012 – present) focuses on the ICT suite of services as they develop, and the second (2015 – present) investigates



how, and in what ways, the interdependence between the Deans’ office and the subscription business play a part in student adoption and usage. The findings suggest that a willingness to develop interdependent solutions between ICT developers and postgraduate studies will be instrumental in bringing services for doctoral students to scale. Chapter 7 Digitalization of Higher Degree Research (HRD) and Its Benefit to Postgraduate Researchers....................................................................................133 Joseph Stokes, Dublin City University, Ireland Rachel Keegan, Dublin City University, Ireland Mark Brown, Dublin City University, Ireland E. Alana James, DoctoralNet, Ireland Graduate Schools offer supports to enhance and improve the graduate skills development of their postgraduate research community not only in their research but also in preparing them for their future careers. The European University Association Council for Doctoral Education has identified the digitalization of doctoral education as necessary to the future to fully globalize the graduate school offerings. This vision is aligned, for example, to several of the objectives in Dublin City University 20172022 Strategic Plan. Online supports go towards the development of DCU as a global university allowing us to attract, and to provide aid to, research students who are studying primarily outside of Ireland. The same structured support also benefits staff who are involved in the life cycle of a research student. Therefore, it is important to assess the needs of our graduate researchers in terms of online supports and to provide them with such tools to ascertain if their needs can/are being met. Hence, this chapter begins this journey by determining what online resources our doctoral community use to move their studies forward and then follows on to measure the value of one resource “DoctoralNet,” which offers comprehensive support to such students. This chapter discusses surveyed material, yielding a positive message that our doctoral education requires such digital resources to meet their (students’) educational needs. Section 3 Online Practices in Doctoral Study Chapter 8 Don’t Be a Ghost Who Drops Grades in Blackboard: Findings From a Program Evaluation of an Online Doctoral Program in the United States.........154 Ambyr Rios, Texas A&M University, USA Radhika Viruru, Texas A&M University, USA Burhan Ozfidan, Texas A&M University, USA



This chapter presents the results of a program evaluation conducted to assess the effectiveness of an online doctoral program in educational leadership at a Research One University from the perspective of its students. Feedback was sought from over 80 currently enrolled students. The study focused on three aspects of the program, namely faculty social and cognitive presence. Recent changes to the program that address these areas include the creation of a thematic group model that clusters students based on academic interests over the last 2 years of the program, extensive revisions to coursework, the adoption of a problem-based dissertation model, and the use of social media and an online community portal to promote student engagement. The results indicate that although students had encountered positive experiences in all three areas, online doctoral students continue to need focused individual mentoring in order to experience success. Chapter 9 A Framework for E-Mentoring in Doctoral Education.......................................183 Swapna Kumar, University of Florida, USA Melissa L. Johnson, University of Florida, USA Nihan Dogan, University of Florida, USA Catherine Coe, University of Florida, USA The upward trend in online graduate degrees, the mobility of graduate students, and the increase in the number of dissertations completed at a distance from universities poses several challenges for faculty who supervise research virtually, students being mentored virtually, and institutions invested in the quality of doctoral education. At the same time, emerging communication technologies present new opportunities for mentoring approaches that build upon those used in traditional on-campus environments. Based on qualitative research with 29 graduates who completed their dissertations at a distance, this chapter presents a framework for the e-mentoring of research and dissertations that encompasses strategies and support at the institutional, mentor, small group, and mentee levels. Chapter 10 The Use of ICT in Researcher Development......................................................209 Sam Hopkins, University of Surrey, UK Erin A. Henslee, Wake Forest University, USA Dawn C. Duke, University of Surrey, UK This chapter provides a case study highlighting the importance of ICT use in researcher development, exploring both training pedagogy and ICT skill development, utilizing the authors’ experience of managing and delivering ICT-based researcher development across a wide range of disciplines for researchers, including part-time and distance researchers who conduct their research away from campus. Participant feedback and examples of best practice will be highlighted alongside potential challenges



to encourage readers to confidently utilize a wide variety of ICT in order to create innovative researcher development material to best support the next generation of researchers. Conclusion......................................................................................................... 234 Compilation of References............................................................................... 239 About the Contributors.................................................................................... 269 Index................................................................................................................... 276

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THE ROLES OF ICT IN DOCTORAL EDUCATION The Overview The use of Information and Communication Technology (ICT) has grown enormously in the last decade with computers and smart devices becoming indispensable in tertiary students’ study practices. The roles of ICT in supporting doctoral study has been long accepted as key. It is believed that ICT should help PhD students to complete their study in doing background research for the thesis, conducting datagathering and analysis activities, time/project management, scheduling, accessing/ organising resources, communicating and writing the thesis; that is, in all phases of research and in the best possible ways (e.g., Onilude & Apampa, 2010). Much research into the use of ICT to support doctoral supervision/study, however, focuses on the benefits of using ICT, such as information searching, virtual communicating and online learning. One area receiving limited exploration is the support for ICT use in the doctoral research processes. There is scant research in the literature that provides insights into the actual ICT use/practices during the process of carrying out doctoral supervision and/or undertaking doctoral study. While ICTs are prominent in educational practices at all levels, skills/understandings for using them well in academic settings cannot be assumed. This is especially the case for PhD students who are expected to make use of various ICT throughout their research process (e.g., preparation phase, fieldwork phase, analysis phase, and write-up phase). In fact, current studies indicate that PhD students continue to adopt repeated patterns of educational practices incorporating limited ICT use (Sim, 2016), even though the use of ICT has grown enormously in the last 10 to 20 years.

The “Fit” of This Book With the current interest in ensuring success of students and completion of PhD degrees being closely related to high quality supervision (e.g., McCallin & Nayar,

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2012), there are opportunities to fine tune understandings and practices about how to support students in their use of ICT for their doctoral research. This is particularly when there is an expectation and/or assumption that PhD students will make use of various ICT throughout their research process as mentioned earlier. The purpose of this book is to explore doctoral supervisors’ and PhD students’ conceptions of the role and place of ICT skills in supervision and doctoral study. It identifies the variety of ways that supervisors and students perceive the role played by ICT and/or adopt ICT within the doctoral research processes. Current literature revealed that most of the PhD students seemed to regard their doctoral research as a full time job but they generally only engaged with basic built-in software applications in their daily research practice. At the same time, it seems that ICT use is rarely being discussed during and/or after supervision meetings. Also, there is no indication of difference among the students at any stage of their doctoral research in terms of the level of their engagement with ICT, and in the use of computer applications as well as documents by the students despite their different discipline backgrounds. In other words, there is a strong sign that while ICT is playing a dominant role in doctoral study, ICT use to support research practice is limited. This is particularly obvious when ICT use appears to be overlooked during doctoral supervision. While ICT use is adopted daily by both PhD supervisors and students, it is seemed not to be as crucial to their research practice as was expected. It looks as if current perceptions in research literature about the importance of ICT for research practice may need to be questioned. This book hopes to highlight the implications of ICT use for research and for practices of academics/supervisors and PhD students, concerning the learning environment and support provided in/through the doctoral research supervision/ process. This will then lead to developing guidelines that inform the research literature, policy, practice and staff/student professional development related to enhancing ICT skills within doctoral study processes.

The Target Audience A range of studies have focused on doctoral study and supervision more broadly and provided evidence of the need for supportive and well-planned ICT environments, so that staff and student learning about ICT use is facilitated effectively. These include research on doctoral supervision, research skills and professional development practice in New Zealand and Australia (e.g., Denholm & Evans, 2007; Rath, 2008). Some Australian studies on ICT skills and use have focused on postgraduate students (e.g., Dowling & Wilson, 2017), and there are some UK studies on students, though not necessarily doctoral students (e.g., Oliver, 2011). Further, it is assumed and expected that university academics and students are using ICT in their daily academic xvii

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practices, particularly during doctoral research processes. However, in one of the current research projects (Sim, 2018), it shows that most supervisors do not think it is their responsibility to promote ICT use during supervision and the PhD students do not see the significance of effective and efficient use of ICT either. These bases (viz., literature about the nature of supervision; ICTs in tertiary teaching and learning; and ICT use by doctoral students as part of their research processes) provide the context of this book. This book aims to stimulate a discussion among academics and researchers working in the fields of doctoral research processes, doctoral study and doctoral supervision in various disciplines. In addition, this book provides insights and support executives concerned with the use of ICT in this educational technology field.

Organisation of This Book The book commences with a detailed introduction chapter which depicts the relationship between digital technologies and doctoral research. The chapter describes the roles of digital technologies in education generally followed by in the doctoral education specifically. It then defines two significant constructs in the doctoral education domain: the use of technology in knowledge production as well as in knowledge representation. In conclusion, the author asks important questions about what doctoral education is, and what it is for, in the light of the tensions and debates due to the introduction of technology. The book is then organised into three sections with 10 chapters. A brief description of each of the sections as well as the individual chapters follows. Section 1 consists of four chapters demonstrating the use of ICT in the doctoral research process. Chapter 1 introduces an innovative online blended learning model of postgraduate research training and development to ensure meeting the students’ needs, predominantly the potential growing of distance students. The chapter sets the scene for future possibilities of adopting this model across different disciplines and programmes within the authors’ university in New Zealand and beyond. According to the authors, the model underpinning the professional inquiry course provides future opportunities to research on online blended communities of learning to support the research process. Chapter 2 provides a conceptual definition of plagiarism and its detecting system. The author argues the issue of plagiarism, which is affecting PhD research in Nigerian universities, by exploring the place of ICTs in anti-plagiarism policies adopted by the local institutions. The chapter ends by stating the challenges of using ICTs to mitigate the plagiarism issue among PhD students in Nigeria. Chapter 3 presents the use of the Idea Puzzle software as a knowledge visualisation tool for research design at a university in New Zealand. The authors examined the usability of the software at the university alongside with xviii

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71 other higher education institutions worldwide. The findings showed that the software stimulates visual integrative thinking for coherent research design with regard to Philosophy of Science. Section 1 finishes with Chapter 4 which reviews opportunities and challenges that are presented to PhD students through Big Data access. Examples are illustrated in the aspects of the opportunities as well as the challenges of such access respectively. The chapter concludes that Big Data affords opportunities despite of all the anticipated challenges. Section 2 entails with three chapters illustrating the use of ICT to support doctoral study. Chapter 5 converses about the development of a digital support system for PhD students at a young faculty of medicine in one of the Australian universities. The issues in this development are identified before an overall reflection is made with respect to the efficiency and effectiveness of the system in order to support not only the PhD students but also the supervisors as well as the wider research community. The author is confident that such development has a positive potential of producing an online resource that offers support to doctoral study communities. Chapter 6 discusses how ICT plays a role in helping PhD students to complete their study. The author grounds her arguments based on two rounds of studies focusing on the use of ICT suite for services as well as the interdependence between the Deans’ office/the subscription business’ role in student use of ICT. The findings reveal that the willingness to develop such interdependent solutions will contribute to the usage scale among PhD students. Chapter 7 addresses the support offered in Ireland to enhance and improve the graduate skills development in conjunction with the vision of the Digitalisation of Doctoral Education. A study is carried out to determine what online resources doctoral community use to move their studies forward including the use of DoctoralNet. The findings showed that doctoral education requires such digital resource to meet educational needs. Section 3 comprises of three chapters exemplifying online practices in doctoral study. Chapter 8 analyses the results of a programme evaluation conducted to assess the effectiveness of an online doctoral programme at an American university. The results express the need of having focused individual mentoring for PhD students in order for them to experience success in an online doctoral programme. The chapter also raises the questions, “How does the invisibility of technology mind set hinder/ help integration of future technologies in an online environment?” and “What is the next invisible player in the online learning continuum?” Chapter 9 offers a framework for e-mentoring in doctoral education derived from a study run in an American university. The authors contend about the significance of e-mentoring especially for PhD students who are at a distance. The proposed framework encompasses several elements of effective mentoring of doctoral research processes in all environments, specifically in the virtual environments. Chapter 10 details a case study which emphasises the importance of ICT use in researcher development, exploring both xix

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training pedagogy and ICT skill development. Not only to develop the ICT skills needed for research, the authors also stress about the skills to engage with the whole range of ICT tools. The authors advocate for agile support in utilising a wide range of ICT for researchers. The book ends with a comprehensive overview of ICT roles in doctoral education as the conclusion chapter. The chapter is centred around the idea of people effecting change in the doctorate in through and about technological activity. The author claims that technology is an integral part of learning about and undertaking research, and vice versa but technology is influencing, determining and changing doctoral education.

In Summary ICT is now woven deeply into the fabric of teaching and learning processes in higher education (e.g., Henderson et al., 2015). This is particularly true for PhD students for whom ICT are essential within day-to-day research practices. Diverse perspectives on efficiency and effectiveness in academic practices held by doctoral supervisors and PhD students could be barriers for optimal use of ICT. The lack of awareness of the intended graduate outcomes concerning ICT integration held by doctoral supervisors and students, indicates that they may not be as prepared for a future academic or research-related professional career. This book reflects the recommendation made by Marshall and Shepherd (2016) to have a “generalisable pedagogical framework” (p. 38) that will go beyond the use of specific technologies to provide a model for selecting ICT tools and approaches that support doctoral study and supervision processes. In short, this book will help promoting a deeper conversation about the roles of ICT in doctoral education.

REFERENCES Denholm, C., & Evans, T. (Eds.). (2007). Supervising doctorates down under. Keys to effective supervision in Australia and New Zealand. Camberwell, UK: ACER Press. Dowling, R., & Wilson, M. (2017). Digital doctorates? An exploratory study of PhD candidates’ use of online tools. Innovations in Education and Teaching International, 54(1), 76–86. doi:10.1080/14703297.2015.1058720 Henderson, M., Selwyn, N., Finger, G., & Aston, R. (2015). Students’ everyday engagement with digital technology in university: Exploring patterns of use and ‘usefulness’. Journal of Higher Education Policy and Management, 37(3), 37–41. doi:10.1080/1360080X.2015.1034424

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Marshall, S., & Shepherd, D. (2016). E-Learning in tertiary education. Highlights from Ako Aoteraroa projects. Wellington, New Zealand: Ako Aotearoa. Retrieved from https://akoaotearoa.ac.nz/download/ng/file/group-4/e-learning-in-tertiaryeducation-highlights-from-ako-aotearoa-research.pdf McCallin, A., & Nayar, S. (2012). Postgraduate research supervision: A critical review of current practice. Teaching in Higher Education, 17(1), 63–74. doi:10.10 80/13562517.2011.590979 Oliver, M. (2011). Technological determinism in educational technology research: Some alternative ways of thinking about the relationship between learning and technology. Journal of Computer Assisted Learning, 27(5), 373–384. doi:10.1111/ j.1365-2729.2011.00406.x Onilude, O. O., & Apampa, O. R. (2010). Effects of information and communication technology on research and development activities: The FIIRO experience. Retrieved from http://www.webpages.uidaho.edu/~mbolin/onilude-apampa.htm Rath, J. (2008). Developing research supervision skills: Understanding and enhancing supervisor professional development practice in the Aotearoa New Zealand context. Final RHPF Report. Retrieved from https://akoaotearoa.ac.nz/projects/developingresearch-supervision-skills Sim, K. N. (2016). An investigation into the way PhD students utilise ICT to support their doctoral research process (PhD thesis). University of Otago. Retrieved from http://hdl.handle.net/10523/6263 Sim, K. N. (2018). ICT Use in the Doctoral Research Process: Whose Call? Paper presented in 2018 Quality in Postgraduate Research, Adelaide, Australia.

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Acknowledgment

Although I take full responsibility for this edited book, I would like to acknowledge the following individuals who contributed valuable support and assistance in the completion of this collaborative project on time: All the selected chapter authors for their depth of knowledge in this research area, availability, and their good-natured support for the whole process; All the reviewers for their generous and unequivocal support in providing ideas, feedback, and advice in each chapter; Both the invited authors – Professor Martin Oliver for the introduction and Dr Sarah Stein for the conclusion – for their unerring support by generously giving their time and expertise to enhance the quality of this book; The proof reader – Fiona Stuart for her enthusiasm, immense knowledge, and above all, her untiring assistance in helping me during the editing phase of this book. In an effort to avoid the inadvertent omission of any individual, I would like to acknowledge everyone around me who has directly or indirectly inspired this project. I thank each and every one of them for their encouragement and endorsement. Receiving all this support is a blessing. I will be forever grateful to all the aforementioned individuals. The Editor, Kwong Nui Sim Victoria University of Wellington, New Zealand

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DIGITAL TECHNOLOGIES AND DOCTORAL RESEARCH Introduction This chapter explores some of the ways in which new technologies – particularly digital technologies – relate to developments in doctoral education. First, some context is provided in terms of the wider relationships between digital technology and education, before moving to look specifically at doctoral education itself. Then, two more specialised areas of doctoral work are considered, dealing with the production and representation of knowledge. Throughout, these discussions are related to the chapters that form the remainder of the book, providing context for the work that follows.

Digital Technologies and Education Technology has become a ubiquitous part of higher education. Arguably it has been years since it made sense to talk about “blending” digital and embodied learning (Oliver & Trigwell, 2005), since it is now so hard to imagine any contemporary educational practice that is not in some way defined by its relationship to the digital. Indeed, technology has always been important to conversations about education; arguments about the relationships between technology and learning can be traced back at least as far as Plato (Oliver, 2016). Even though this issue can be seen as part of a longstanding discussion, the increasing number of digital devices that students own, and the growing prevalence of students’ digital connectivity, has driven scholars across the world to ask important new questions about how technology affects education, the spaces where learning is undertaken, and questions such as what happens when learning is represented in terms of “big data” (Becker et al., 2017). Even in settings that have been denied the level of resources enjoyed by leading Western universities, students have proved themselves capable of resilient and innovative forms of engagement with their

Introduction

studies via mobile devices (Czerniewicz, Williams & Brown, 2009). Given the ubiquity of these experiences, Jandrić et al. (2018) recently proposed that since “we are increasingly no longer in a world where digital technology and media is separate, virtual, ‘other’ to a ‘natural’ human and social life” (p. 893), it may be time to start discussing education in terms of the “postdigital”, to recognise the increasingly complex inter-relationships between technologies, bodies, practices, discourse, knowledge and so on. This level of complexity makes it both important and difficult to specify how technology and education intersect – a problem that becomes even more acute in relation to doctoral research. In schools, and even in universities, the volume of data available sometimes makes it possible to stand back and look for patterns, simplifying the complexities of specific cases in order to create general claims about what people might typically do with technology. A meta-analysis of literature about online learning (Means, Toyama, Murphy, Bakia, & Jones, 2010), for example, was able to make some cautious claims about the small but positive effects of what they described as blended conditions for teaching US. K-12 children. Even this review, however, warned against over-simplifying the patterns the authors discerned in the data, and explicitly outlined the risks of generalising from the small body of available evidence. They noted that it would be a mistake to assume that this small positive difference was “caused” by the media, for example, pointing instead to the way in which teachers tended to be more thoughtful when designing blended instruction, and the extra time that children spent studying. These words of caution are important to keep in mind, but it remains difficult to see whether they hold true in Higher Education or doctoral study. As education advances and specialises, the size of the available evidence base available dwindles even further, which makes it increasingly difficult to make credible claims about typical or even prevalent uses. At the level of doctoral study, the current evidence base is so small and so fragmented that a completely different approach is required to make sense of this phenomenon. It is this challenge that this book addresses.

Digital Technologies and Doctoral Education Although many doctoral programmes share common features, the diversity and specialisation involved in doctoral study, and the challenges of getting any kind of evidence about individuals’ independent study, make it very difficult to draw generalisations about technology use in this context. Moreover, until recently, many educational institutions paid little attention to doctoral study, simply relying on assumptions about students’ technology use in this context (Sim & Stein, 2016). Doctoral programmes differ considerably in terms of national requirements (e.g., whether or not they are assessed by an oral examination, which could be public or xxiv

Introduction

private) and also academic purpose. In the UK, US, and Australia, for example, there is evidence of a marked growth in the provision of professional doctorates over the last two decades, including doctorates in Education (EdD) such as the one discussed in Chapter 8, the Doctor of Business Administration (DBA), Doctorates in Clinical Psychology (DClinPsy), and so on. There are also closely related developments that provide pathways that lead students into doctoral study, such as the Professional Inquiry in masters-level postgraduate research programme described in Chapter 1. These new kinds of programme have seen an increase in the use of structured, taught elements, as well as the assessment of professionally-relevant contributions to knowledge as an important feature of doctoral work. Online doctoral programmes have also flourished. Historically, these programmes have been held back by concerns about their credibility, and the unfortunate association of online education with funding scams and fake degrees, as well as with more legitimate concerns about whether online provision provided adequate support for specialist areas of academic development such as laboratory expertise (Adams and deFleur, 2005). As Bengtsen observed (2016), the face-to-face encounter has become a taken-for-granted element of doctoral pedagogy; it has been granted a position of primacy, being assumed to be more authentic and more effective than mediated alternatives. However, as noted in Chapter 10, mediated approaches to study have been shown to be viable, and capable of supporting students who need more flexibility in order to take part in doctoral study, even if new pedagogies are needed when working with students and researchers at a distance. Chapter 7 shows how an online portal for doctoral study was able to support part-time as well as full-time students. There is a growing body of research that demonstrates that the challenges of sustaining rich interpersonal interactions amongst distributed cohorts of doctoral students can in fact be overcome, particularly within the context of more structured doctoral education (Crosta, Manokore, & Gray, 2016). Chapter 9 describes how online supervision practices have developed over the last decade in an online doctoral programme offered by a research university in the US. This included the use of a range of technologies to support individual and small group interactions, from email to video conferencing and screensharing. Chapter 8 describes further how important it is for students learning online for the technology to be used not just to deliver materials, but in a way that makes the students visible, to each other as well as to tutors and supervisors; and likewise how important it is for the success of the programme for instructors to be visible to their students, even if they are working in a facilitative way. Chapter 10 describes how new pedagogies were developed to help manage students’ experience of “off-campusness” that can result from the adoption of flexible, individualised provision, as well as the ways technology was able to support the development of virtual researcher communities.

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The kinds of diversification described above might suggest a fragmentation of doctoral work; however, in parallel, there have been other developments that promote convergence and consistency, at least within Europe. Policy developments have led to greater standardisation of characteristics of doctoral education, such as quality assurance procedures, the specification of skills, procedures for assessment, and other such rules and regulations (Kehm, Freeman & Locke, 2018). As described in Chapter 9, this kind of development is important for all involved in doctoral education, including students, supervisors, the institution, and also peers within the cohort. Clear expectations help everyone; they become particularly important when there are unfamiliar elements to the programme, such as online learning. For example, Chapter 1 shows how in taught components of doctoral programmes, online discussion fora, can be important mechanisms for the provision of feedback on work, whether that feedback comes from academics or peers; but that the various participants can only take up their roles if they know that this is expected of them. However, the importance of clarity about expectations is certainly not unique to online programmes. As discussed at the opening of this chapter, digital technologies have even permeated doctoral programmes that might superficially appear to be face-to-face (Bengtsen, 2016); technology is now so embedded within the everyday life of doctoral work that it may be impossible to separate it from “normal” doctoral provision. Bengtsen noted, for example, how even around archetypal face-to-face supervisory discussions, work is typically shared, read, revised, returned, and corresponded about using digital resources such as electronic copies of files, emails, and so on. Even during the meeting itself, computers are often used to refer to evidence or analysis, sections of writing, information about conferences or journals, institutional regulations, and other digital resources. As a result, even what might be assumed to be the prototypical personal encounter is commonly permeated by all kinds of technological activity. There may therefore be less difference, and more consistency, between “innovative” and “traditional” programmes of doctoral study than is initially apparent. Perhaps as a result of these elements of standardisation, evidence has begun to accumulate that provides at least a descriptive account of contemporary doctoral study practices, and in some cases, allows the development of theory to explain the issues students face – a point echoed in Chapter 6. For example, Sim and Stein (2016) were able to find evidence about students’ use of technology to search for references or analyse data sets, as well to establish that doctoral students were using technology when looking for information, for writing, and for correspondence with supervisors and peers. Other work has generated evidence about the use of technology to support various processes integral to doctoral education. Esposito (2015), for example, used a survey

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of doctoral students at institutions in the UK and Italy to develop a mapping of their activities to the tools that they used to achieve these (Table 1). Similar ideas are visible in Chapter 10, where Hopkins, Henslee and Duke show how different configurations of technology can be used to support training, writing, networking with peers, mentoring and also wider engagement with different potential audiences, and in Chapter 7, Stokes et al. discuss how a dedicated online platform supported students’ training and development. Esposito’s (2013) analysis also underscores the points made earlier about the continuity between different programmes. Her anaylsis showed that these uses were not limited to those pursuing doctorates by distance learning; they were common practices for doctoral students and early career researchers in very different institutions and across different kinds of programmes. Indeed, her respondents were keen not to be singled out as exceptional or unusual cases, but wanted instead to be acknowledged as normal, even typical; they wanted to be seen as “neither digital or open. Just researchers” (Esposito, 2013). This sense of doctoral students as complex human beings who weave together various digital and embodied practices, rather than as simple taxonomic categories or “types” of students, points to the kinds of struggle that students can encounter as they learn to negotiate their way through the messy experiences of doctoral work. Chapter 6 discusses, for example, how challenges in areas such as work-life balance, independence, personal development, and lack of support can lead to doctoral student disengagement, to the point where 50% may consider dropping out of the programme at some point. This draws attention to the importance of supporting doctoral students in the development of opportunities for new experiences, building their capacity to undertake scholarly work, and also in dealing with the emotional challenges that they face whilst pursuing their studies. As Rowland has shown in Chapter 5, it is possible to support students as they face these challenges, even when they are not primarily Table 1. Technologies used to support doctoral activities Doctoral Activities

Focus

Tools/Venues

Updating

Searching for relevant materials

Google Scholar, Twitter, discipline-specific databases, Facebook

Networking

Seeking research bonds for future collaboration

Email, Facebook, research-focused SNs

Disseminating

Building reputation

Academia.edu, LinkedIn, Twitter, blogs

Discussing research issues

Increasing self-confidence

ResearchGate, LinkedIn groups, Skype

Pursuing personal development

Expanding knowledge and firsthand experiences

MOOCs, YouTube

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campus-based. Given that there is growing research evidence that doctoral students have a greater prevalence of mental health problems than other kinds of students, or even other highly educated individuals in society (Levecque et al., 2017), it is increasingly important that these issues are taken seriously. Another important element of Esposito’s work is that it draws attention to the ways in which the experiences of doctoral students are connected to, not separated from, the experiences of other people in academic careers – particularly early career researchers. The transitions out from doctoral study into postdoctoral positions are known to be difficult (Locke, Freeman, & Rose, 2018), and technology has a role to play at the point of exit as well as through the body of the programme. As Locke et al.’s work shows, many elements play roles during this transition, but there is clear evidence that ongoing institutional support is important. This might include ongoing library access (including access to the digital collection), use of the institutional email account, and so on. Not all of these factors are technological, and even those that are may have more to do with the researcher’s claims to identity (through institutional affiliation) than actual research. Nevertheless, use of institutional systems has material and symbolic consequences, in terms of access to resources and credibility, at this point of transition out of doctoral study and towards subsequent careers.

Technology and Knowledge Production Much of the previous discussion has considered the ways in which technology can contribute to the process of studying for a doctorate. A common issue with this kind of discussion, however, it that it suggests technology is somehow optional, that it would be possible to do doctoral work without it. However, arguably, technology needs to be understood as being integral to, not “other” than, the very process of knowledge production that lies at the heart of doctoral study. It would be a mistake to assume that doctoral research is conducted first; and then, like a separate step, technology is added, perhaps to mediate supervision or to disseminate the findings. This kind of assumption ignores decades of research in fields such as the philosophy of technology, and science and technology studies, which makes the case that technology is and always has been an integral part of creating new knowledge. Bengtsen (2014) described, for example, Comenius’ account of “the character of the school as a physical place with specific material objects and spaces … full of luring and enchanting things” (p. 179), recognising how these material artifacts motivate and sustain knowledge work. Latour and Woolgar’s (1979) classic ethnographies of laboratory work demonstrated some decades ago that knowledge is not rarefied, but is instead the accumulation of sociomaterial processes of testing, inscribing, producing, writing, and so on. These sociomaterial processes of knowledge work are visible in the following chapters, although they are perhaps less prevalent than xxviii

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the discussions about how doctoral programmes can be mediated. In Chapter 10, for example, Hopkins, Henslee, and Duke describe how the material and social infrastructure of writing retreats was redistributed using technology to support researchers working at a distance; this helps to demonstrate how moving “online” does not efface the need for material and embodied practices, although it did allow them to be reshaped and dispersed. Analyses such as Latour and Woolgar’s laid the foundation for later proposals about the importance of creating “centres of calculation”, physical sites that bring together the specialised instruments and inscription devices that knowledge work requires (Latour, 1990). As Barad has argued, we cannot develop knowledge, and cannot know the world, without using material instruments: Time isn’t an abstract idea for Einstein; time is what we measure with a clock. . . . Ideas that make a difference to the world don’t fly about free of the weightiness of their material instantiation. To theorise is not to leave the material world behind and enter the domain of pure ideas where the lofty space of the mind makes objective reflection possible. Theorising, like experimenting, is material practice. (Barad, 2007, p. 55) Doctoral work, therefore, also needs to be understood as material practice, something that draws on specialised instruments and inscription devices, which might include petri dishes, desktop computers, medieval manuscripts, digital audio recorders, places such as fieldwork sites or of course analogue texts such as books and printed journals. As shown in Chapter 4, it can also include newer digital resources, such as “big data”, which allow new kinds of knowledge production to be undertaken. Technology can also take a role in the design of the research process itself, not just provide material to be worked with. The Idea Puzzle software described in Chapter 3, for example, shows how software that incorporates codified knowledge about research processes can share the task of decision making with students. Such specialist software can form part of the infrastructure that institutions provide, shaping the way in which doctoral research is undertaken.

Technology and Knowledge Representation As relationships between technologies and the production of doctoral knowledge have developed, ways of representing and sharing doctoral knowledge have also shifted. Of course, this too is a well-established phenomenon; as Wilson has argued (2012), changes in modality have always had epistemic consequences, even if continuity has been provided by the aspects of embodiment and materiality that have never been digitised. xxix

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Revisiting Esposito’s (2015) mapping of technologies and processes (Table 1) shows the range of different technologies that are now used to carry representations of doctoral knowledge. Outputs can be created and shared digitally, whether produced by specialist analysis software such as SPSS or NVivo, or as material that students seek for or create themselves, such as blog posts, tweets, and podcasts. This in turn has raised questions about how these different forms of knowledge might be represented in the thesis itself – something that, as Borg and Davis (2012) suggest, poses challenges for students, supervisors and examiners: The dissertation is contingent, changing and changeable. While supervisors may expect their students to produce a dissertation that resembles the one they wrote themselves, changes both in the available technologies and in the kinds of knowledge the dissertation is expected to represent are having a significant effect on its form as well as its content. (p. 13) Some of these challenges are fundamental to the credibility of the award: If the doctorate is to be judged as an original contribution to knowledge, it is necessary to confirm that the contribution is indeed original, particularly in settings where plagiarism is widespread. Chapter 2 describes, for example, how the ubiquitous use of the Internet has contributed to the use of “cut and paste” approaches to composition in Nigerian universities, but also how technology has been used to reveal these practices so that the academic integrity of the submitted work can be confirmed. Importantly, however, this work is not just about policing students: it is also about educating them, so that they understand why plagiarism is an issue, and can use detection tools to review and improve their own work. Technology here is not simply either a cause or a means of surveillance, but becomes something that informs what it means to make credible claims to knowledge. Given the proliferation of new technologies for analysis and representation, it is hardly surprising that experimental forms of the doctorate have blossomed in recent years. Although tried and tested theses formats remain well suited to many topics, and there are well established mechanisms to archive and disseminate them, there are other topics which rely on digital data or analysis where the printed form may be a poor fit (Andrews & England, 2012). Stansbie (2012), for example, describes the experimental doctoral study presented as a website as http://www.zeppelinbend. com, and talks through the choices made in structuring the site so that the required elements of a doctoral thesis remained visible and examinable. As Gourlay has argued (2012), so long as contributions to knowledge require intertextual references and complex propositional arguments, verbal texts will remain important, whilst

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the allusive and metaphorical character of more visual texts may render them less stable and less amenable to critique. As Brabazon and Dagli (2010) note, “claims for artistic quality are not a technique for marginalising academic protocols”. The consequence of this is the growing prevalence of hybrid doctoral theses, in which the more conventional academic text is accompanied by other material. This might include, for example, material, visual, or sonic components such as a data set stored on digital media, or a record of a performance. The written text, however, remains central to the credibility of doctorates, and to the maintenance of standards - something that is vital both to the credibility of institutions’ awards but also the equitable treatment of students. There is nothing enlightening, democratic or empowering about individualising scholarly standards. The consequences of such a statement on examination and examinations are deeply worrying. If a doctoral researcher “re-interprets” requirements (regulations?) for an exegesis, then what precisely are examiners examining. . . ? Without clear criteria and internationally validated regulations, examiners are offering much more personal, opinionated and unanchored judgements about doctorates. (Brabazon & Dagli, 2010)

Conclusion At first impression, it might seem as if the introduction of technology to doctoral education has changed it fundamentally; a more cautious assessment would be that technology has always been part of knowledge work, and that there are a great many points of continuity between current doctoral education and the ways in which these processes have operated in the past. As with so many issues, neither impression quite captures the situation; collapsing into an either/or binary would be naïve and unhelpful. Doctoral education has changed fundamentally, at exactly the same time that it has remained unchanged in many important aspects. The configurations of people, things, and places that are required to undertake doctoral study have changed, both in terms of the process of studying and the work of generating original contributions to knowledge. Aspects that might once have been undertaken informally, such as networking and personal development, are increasingly considered to be part of the process of studying for a doctorate, and in some cases are directly targeted by training or other interventions. Other aspects have also changed that have nothing directly to do with technology, such as broadening out the kinds of knowledge valued in doctoral study to include professional contributions. However, at the same time, the purpose and standards of doctoral education have persisted, and if anything, have been made more consistent.

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The introduction of technology has served to highlight tensions and debates that mark out important questions about what doctoral education is, and what it is for. The remaining chapters in this book explore these developments, documenting the ways in which technology is now part of doctoral research. Martin Oliver University College London, UK Martin Oliver is Professor of Education and Technology at the UCL Institute of Education, where he has served as Graduate Tutor and is now head of the department of Culture, Communication and Media. His research draws on social and philosophical approaches to explore the design and use of technologies, primarily in Higher Education. One strand of this work addresses the experiences of doctoral students, and the ways in which they incorporate technology into their day-to-day study practices.

REFERENCES Adams, J., & DeFleur, M. H. (2005). The acceptability of a doctoral degree earned online as a credential for obtaining a faculty position. American Journal of Distance Education, 19(2), 71–85. doi:10.120715389286ajde1902_2 Andrews, R., & England, J. (2012). New forms of dissertation. In The Sage handbook of digital dissertations and theses (pp. 31-46). London: Sage. Barad, K. (2007). Meeting the universe halfway: Quantum physics and the entanglement of matter and meaning. Durham, NC: Duke University Press. doi:10.1215/9780822388128 Becker, S. A., Cummins, M., Davis, A., Freeman, A., Hall, C. G., & Ananthanarayanan, V. (2017). NMC horizon report: 2017 higher education edition. Austin, TX: The New Media Consortium. Bengtsen, S. (2014). Into the heart of things: Defrosting educational theory. In P. Gibbs & R. Barnett (Eds.), Thinking about higher education (pp. 175–191). Springer International Publishing. doi:10.1007/978-3-319-03254-2_12 Bengtsen, S. S. (2016). Doctoral supervision: Organisation and dialogue. Aarhus: Aarhus University Press. Borg, E., & Davis, S. B. (2012). The thesis: Texts and machines. In The Sage handbook of digital dissertations and theses (pp. 13-30). London: Sage.

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Brabazon, T. & Dagli, Z. (2010). Putting the doctorate into practice, and the practice into doctorates: Creating a new space for quality scholarship through creativity. Nebula, 7(1-2), 23-43. Crosta, L., Manokore, V., & Gray, M. (2016). From an online cohort towards a community of inquiry: International students’ interaction patterns in an online doctorate program. Journal of Interactive Online Learning, 14(2), 45–57. Czerniewicz, L., Williams, K., & Brown, C. (2009). Students make a plan: Understanding student agency in constraining conditions. Research in Learning Technology, 17(2), 75–88. doi:10.1080/09687760903033058 Esposito, A. (2013). Neither digital or open. Just researchers: Views on digital/open scholarship practices in an Italian university. First Monday, 18(1). doi:10.5210/ fm.v18i1.3881 Esposito, A. (2015, September). PhD researchers using social media: Trajectories of academic identities. Paper presented at SRHE Digital Universities Network, London, UK. Gourlay, L. (2012) Media Systems, Multimodality and Posthumanism. In The SAGE Handbook of Digital Dissertations and Theses (pp. 85-100). London: Sage. Hawkes, D., & Yerrabati, S. (2018). A systematic review of research on professional doctorates. London Review of Education, 16(1), 10–27. doi:10.18546/LRE.16.1.03 Jandrić, P., Knox, J., Besley, T., Ryberg, T., Suoranta, J., & Hayes, S. (2018). Postdigital science and education. Educational Philosophy and Theory, 50(10), 893–899. doi:10.1080/00131857.2018.1454000 Kehm, B. M., Freeman, R., & Locke, W. (2018). Growth and diversification of doctoral education in the United Kingdom. In J. C. Shin, B. M. Kehm, & G. A. Jones (Eds.), Doctoral training for knowledge society: Global convergence or divergence? (pp. 94–110). Dordrecht, The Netherlands: Springer. doi:10.1007/978-3-319-89713-4_7 Latour, B. (1990). Drawing things together. In M. Lynch & S. Woolgar (Eds.), Representation in scientific practice (pp. 19–68). Cambridge, MA: MIT Press. Latour, B., & Woolgar, S. (1979). Laboratory life: The social construction of scientific facts. Beverly Hills, CA: Sage. Levecque, K., Anseel, F., De Beuckelaer, A., Van der Heyden, J., & Gisle, L. (2017). Work organisation and mental health problems in PhD students. Research Policy, 46(4), 868–879. doi:10.1016/j.respol.2017.02.008

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Locke, W., Freeman, R. P. J., & Rose, A. (2018). Early career social science researchers: Experiences and support needs. London: Centre for Global Higher Education. Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2010). Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies. Washington, DC: U.S. Department of Education, Office of Planning, Evaluation, and Policy Development. Oliver, M. (2016). Open access, freedom and exclusion. In M. Deimann & M. Peters (Eds.), The philosophy of open learning: Peer learning and the intellectual commons. Oxford, UK: Peter Lang Publishing. Oliver, M., & Trigwell, K. (2005). Can ‘blended learning’ be redeemed? E-Learning and Digital Media, 2(1), 17–26. Sim, K. N., & Stein, S. (2016). Reaching the unreached: De-mystifying the role of ICT in the process of doctoral research. Research in Learning Technology, 24(1), 30717. doi:10.3402/rlt.v24.30717 Stansbie, L. (2012). Establishing the cybertextual in practice based PhDs. In The Sage handbook of digital dissertations and theses (pp. 390–404). London: Sage. doi:10.4135/9781446201039.n23 Wilson, A. (2012) How changes in representation can affect meaning. In The Sage handbook of digital dissertations and theses (pp. 427-441). London: Sage.

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Section 1

ICT Use in the Doctoral Research Process

1

Chapter 1

Reconceptualizing Postgraduate Research:

An Online Blended Learning Approach Maggie Hartnett Massey University, New Zealand Peter Rawlins Massey University, New Zealand

ABSTRACT The professional inquiry (a researcher training and development course) was introduced into the Master of Education program at Massey University, New Zealand in 2014 as a practitioner-based alternative to the research thesis pathway. In contrast with traditional, independent, time intensive models of postgraduate research supervision, the authors developed and implemented an innovative blended learning model of postgraduate research training and development to ensure the growing demand of future, predominantly distance, students would be met. The online, blended model developed and discussed here within the discipline of Education has the potential to be utilized across different disciplines and postgraduate programs including those at doctoral level. In its fifth year of delivery, the online community has grown from nine students and seven specialist academic advisors in the first cohort to 45 students and 27 academics in the current offering, ensuring an accessible and equitable research learning experience for all students.

DOI: 10.4018/978-1-5225-7065-3.ch001 Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Reconceptualizing Postgraduate Research

INTRODUCTION This chapter describes an innovative blended learning model of postgraduate research training and development. The Professional Inquiry (a researcher training and development course) was introduced into the Master of Education programme at Massey University, New Zealand, in 2014 as a practitioner-based alternative to the traditional research thesis pathway. Information available at the time suggested that approximately 60% of all Master of Education students would opt for the professional inquiry, highlighting an increasing demand for postgraduate research supervision. However, limited availability of academic staff to supervise postgraduate research via time-intensive, traditional mechanisms necessitated a new and innovative approach to research training and development to meet the growing demand of future, predominantly distance, students. In response to this challenge, an innovative team-based, blended model of digitally-facilitated postgraduate research training was developed. The chapter begins with a brief discussion of the wider societal and educational context in which the development of the professional inquiry is situated. This is followed by a discussion of the small but growing body of online/ distance postgraduate research literature that helped to inform the development of the course. Next, the general nature of blended learning is discussed and is contrasted with the innovative blended learning model of postgraduate research training and development created for this course. A detailed description of various aspects of the course follows, along with how the characteristics of the design align with strategies known to support online research supervision and postgraduate student satisfaction. The chapter concludes with challenges that arose from course implementation, solutions to identified issues and suggested areas for future research.

BACKGROUND The use of digital technologies has become ubiquitous in today’s world. Few areas of everyday life are unaffected by the introduction of technology, including education. Within the Higher Education (HE) sector, the availability and uptake of distance and online courses has been steadily growing. The latest report from the US (Seaman, Allen, & Seaman, 2018) shows that in 2016 almost one in three (31.6%) higher education enrolments were for distance education courses. This is up from one in four (25.9%) in 2012 and has occurred at a time when overall enrolments in US higher education declined by 3.8%. The tertiary education sector within New Zealand has also seen the proportion of equivalent full time students (EFTS) in courses delivered using technology (that includes web-supported, web-enhanced, and web-based) gradually increase from the 2005-2009 period to the 2010-2014 2

Reconceptualizing Postgraduate Research

period, regardless of the level of qualification (Guiney, 2016). At postgraduate level, the predominant mode of delivery is blended (defined as a mix of face-to-face and online learning in this context). Given the rapid advances in digital technologies, the line between traditional and distance learning environments is blurring (evident in the different terminologies used in the previous paragraph), with similar technologies being used to support learning in both environments. This has led to the emergence of flexible/blended and mixed modes of learning that Bates (2015) conceptualised as a technology-based learning and teaching continuum from face-to-face (with no technology use) to fully online (distance) where there is no classroom or on-campus teaching. E-learning has been a commonly used term to describe anything on this continuum (though it has fallen out of favor in recent years) that incorporates digital resources and some form of technology-mediated communications in the learning process (Nicols, 2008). Therefore, it is important to define what is meant by online (distance) learning as it relates to the context of this chapter. Online learning has its roots in distance education. The term fully online is used by Bates (2015) to distinguish distance courses where students must have access to the internet via an appropriate digital device to undertake a course of study. Ally (2008) also highlighted that there are many definitions of online learning that reflect the diversity of practice and technologies in use. He went on to define it in the following way: The use of the internet to access materials; to interact with the content, instructor, and other learners; and to obtain support during the learning process, in order to acquire knowledge, to construct personal meaning, and to grow from the learning experience. (p. 5) Given the range of terminology, the authors have adopted the fully online term from Bates (2015). The authors chosen terminology encompasses the holistic definition of the learning experience offered by Ally (2008) and incorporates the fully online distinction used by Bates that makes cognizant the distance context of courses. In other words, fully online learning in this chapter is taken to be a form of distance learning mediated by technological tools, where learners are geographically separated from the instructor and the main institution.

Online Postgraduate Research Research interest in postgraduate education has grown over the last few decades (Andrew, 2012). Most of the available research has focused on the research processes and supervisory practices of the doctoral experience (Albion & Erwee, 2011; Ali 3

Reconceptualizing Postgraduate Research

& Kohun, 2007; Andrew, 2012; Butcher & Sieminski, 2006; Evans, 2005; Evans & Green, 1995; Harrison, Gemmell, & Reed, 2014; Orellana, Darder, Pérez, & Salinas, 2016). Until relatively recently, there has been limited research related to the distinctiveness of postgraduate distance offerings (Nasiri & Mafakheri, 2015). Once again, the research that is available predominantly addresses doctoral research (Tweedie, Clark, Johnson, & Kay, 2013). While the primary focus of this book is the role of ICT skills in doctoral study, there is a case for exploring innovations at master’s level for two reasons. Firstly, digital technologies also play an increasingly important role in masters research and supervision and can help to inform the field of postgraduate research supervision more broadly. Secondly, students undertaking doctoral studies often do so after completing a master’s degree (particularly in the social sciences) and their ICT experiences at master’s level can influence their expectations and experiences at doctoral level. Therefore, research highlighting the distinctiveness of online supervision practices at master’s level is also needed. The limited amount of research relating to master’s level postgraduate research that explores supervision practices and students experiences has tended to investigate on-campus students (see for example, C. Anderson, Day, & McLaughlin, 2008; de Kleijn, Mainhard, Meijer, Pilot, & Brekelmans, 2012; Drennan & Clarke, 2009; Pilcher, 2011). However, studies do exist whose primary focus is on the experiences of online and distance research masters students (Budash & Shaw, 2017; Harrison, et al., 2014; Rogerson-Revell, 2015; Ross & Sheail, 2017). A number of online postgraduate supervision challenges (at both doctoral and master’s level) have been identified within the literature (Nasiri & Mafakheri, 2015) that have been shown to impact on the satisfaction of the student experience (de Kleijn, et al., 2012; Harrison, et al., 2014). From a review of the literature, Nasiri and Mafakheri (2015) identified a range of challenges distinctive to postgraduate online education supervision. Challenges included: the influences of spatial and temporal distance (known as the dislocation effect) that can lead to a tentative supervision relationship; increased workload for supervisors due to student expectations that they will be constantly available; and issues with digital literacy skills for both students and staff which can by exacerbated by the part-time nature of study. Feedback is a further problematic issue because of the reduction in frequency, timing, and quality in an online context (Harrison, et al., 2014). The type of feedback and support required by research students in an online learning context not only encompasses research-specific content, but also requires supervisory staff to be “closely involved in discussions regarding the research programme requirements and ethics, and needs to stay fully updated and prepared for timely guidance on administrative and pastoral issues” (Nasiri & Mafakheri, 2015, p. 1965). Clarity of information, clear

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course structure, as well as relevant and useful resources are also necessities as they enable students to make accurate judgements about the scope and standard of assessed work required (Harrison, et al., 2014). Online research students may have few opportunities for interaction with their peers thereby reducing opportunities for mutual feedback and guidance and increasing dependence on the supervisor (Crossouard, 2008) rather than fostering a sense of agency (C. Anderson, et al., 2008). This, in turn, can lead to poor integration of students into the research culture of a department particularly in situations where students are studying on a part-time basis (Drennan & Clarke, 2009) and result in students’ lacking confidence in their research capabilities (Carpenter, 2012). A range of strategies has emerged from the literature to address the issues identified with online postgraduate research supervision. Suggestions include: using a variety of feedback approaches, in terms of speed, length and depth, to increase communication and address issues of dislocation (Nasiri & Mafakheri, 2015); providing opportunities for formative feedback which encourages students to engage more widely with content (Rogerson-Revell, 2015); setting deadlines and timelines to balance workload and providing a structured/guided approach to the curriculum and course content (Harrison, et al., 2014; Nasiri & Mafakheri, 2015); and developing approaches that increase peer interaction and opportunities for collaborative learning, mutual guidance and support (Drennan & Clarke, 2009; Nasiri & Mafakheri, 2015; Rogerson-Revell, 2015; Wisker, 2007). Offering an induction at the beginning of the research journey is considered a crucial precursor to the ongoing success of online students (Wisker, 2007), and administrative support to reduce the workload of academics and provide pastoral care for students is another way in which both academic and students needs can be accommodated (Nasiri & Mafakheri, 2015). Furthermore, group supervision (or a blended supervision approach) enables students to be involved in the research culture of the discipline by providing “regular opportunities to participate in a forum where theoretical perspectives, methodological questions and practical know-how of the craft of research were being discussed at a level where they felt comfortable to contribute” (Dysthe, Samara, & Westrheim, 2006, p. 312). While this set of strategies provides a useful starting point, Nasiri and Mafakheri (2015) argued that due to the limited research in the field there “still exists a gap in the literature on how to develop appropriate and effective strategies to address these challenges” (p. 1964). What is clear from the discussion so far is that customisation for online delivery is needed which goes beyond a simple translation of on-campus supervisory practices to the online environment. Considerations such as student expectations, the quality and quantity of online communications, and specific skills and knowledge needed by both staff and students need to be addressed explicitly in order to create a successful online research programme.

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Blended Learning Approach Defining blended learning is problematic because there are multiple meanings and various terminologies in use (Sharpe & Oliver, 2013). Blended learning can also be referred to as hybrid or mixed-mode learning indicating that there is no one model that encompasses all ideas and approaches (Caner, 2012). For example, Allan and Seaman (2003) defined blended learning as a “course that is a blend of the online and face-to-face. Substantial proportion of the content is delivered online, typically uses online discussions, typically has some face-to-face meetings” (p. 6). Garrison and Vaughan (2008) stated that blended learning “is the thoughtful fusion of face-to-face and online experiences” (p. 3). In other words, blended learning is generally considered to be a coherent design approach that integrates the strengths of synchronous face-to-face teaching with asynchronous online learning to provide more engaging learning experiences for students (Garrison & Vaughan, 2008). Researchers tend to agree that the combination of in-person and online modalities is not sufficient by itself but that effective blended learning adopts pedagogical approaches that add value to overall learning experiences and outcomes (Caner, 2012; Garrison & Vaughan, 2008; Means, Toyama, Murphy, & Baki, 2013). Picciano (2009) goes further and developed the “blending with purpose” (p. 14) multimodal model that posits that pedagogy should determine the approaches taken which can be achieved using multiple modalities. The multimodal model identifies six central pedagogical objectives, approaches and activities including content, the social/emotional environment, dialectic/questioning, assessment, collaboration, and reflection. When done effectively, blended learning offers the potential for increased flexibility and pedagogical innovation (Garrison & Vaughan, 2008). The increased flexibility that blended courses offer are particularly suited to mature-age, part-time students with work and family commitments (Holley & Dobson, 2008). The distinctiveness of physical and online learning spaces can encourage the use of different pedagogical strategies in each with the flexibility of the online space enabling differentiation in teaching and learning practices (Wu, Tennyson, & Hsia, 2010). Blended learning has been linked to pedagogical innovation (Hastie, Hung, Chen, & Kinshuk, 2010). For example, blended learning can offer opportunities to transform teaching practice to more learner-centered approaches that emphasise knowledge construction, interaction and discussion, collaboration, resource exploration, self-review, and reflection (Lameras, Paraskakis, & Levy, 2008; Vaughan, 2007).

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THE PROFESSIONAL INQUIRY RESEARCH COURSE It’s clear from the discussion so far that mixing in-person and online experiences is a common feature of blended learning. However, the post-graduate professional inquiry course, the focus of this chapter, conceptualises blended learning more broadly, and in a more nuanced way, than the integration of traditional modes of teaching (and research supervision) with new types of technology (Caner, 2012). The blended learning model adopted to underpin this course aligns with the blending with purpose multimodal model of blended learning (Picciano, 2009). In this context, blended learning is considered holistically as blending lecturer-led teaching with academic research guidance; blending research specific content with more generic content; blending lecturer-directed learning with more self-directed learning; blending structure and guidance with more flexible pathways; and blending independent and co-operative learning opportunities. Using this model allows for core research content to be taught across all student-specific research projects thereby sharing teaching time, academic expertise, and resources while promoting collaboration and teaching presence (Vaughan, Cleveland-Innes, & Garrison, 2013). Massey University in New Zealand has a tradition as a major provider of quality distance education that spans over 50 years (Prebble, 2010). Originally, Massey was the predominant provider of distance, correspondence education to students located within New Zealand and overseas. With changes in technology, the range of courses and qualifications available via online, blended and distance delivery has expanded. This broader offering includes courses and programs, at both undergraduate and postgraduate levels, across colleges and departments within the university including those offered by the Institute of Education. Providing flexible learning opportunities for postgraduate students is important, as many students are mature-aged, study parttime, and are working professionals with multiple family and social commitments that requires them to balance multiple identities (Nasiri & Mafakheri, 2015; RockinsonSzapkiw, Spaulding, & Lunde, 2017). This is the case with students undertaking the professional inquiry course as their research component of the Master of Education qualification. The predominant mode of delivery is online via the university’s online learning management system (LMS) Moodle. In contemporary workplaces, which are more inquiry oriented, and evidence based, educators need to be both consumers and producers of research. Traditional models of students conducting research via thesis has tended to have them work primarily in isolation, with only their supervisors to consult with. In designing the professional inquiry course, the authors were cognizant that the majority of students were seeking to develop applied, “real-world” research skills applicable in their own educational contexts.

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To achieve this, the authors designed and developed a course curriculum that blends components of a taught course with academic guidance (i.e., supervision), provided by an advisor who is an expert in the student’s chosen area of research, at five pre-determined points throughout the course. Figure 1 shows the five points at which the advisor interacts with their student, in addition to course assessments and activities associated with the awarding of digital badges. The advisory role is innovative, as it has been conceptualised as distinctly different from existing models of research supervision. The content advisory role (academic content specialist) complements the taught components, which consist of specific topics that are common to all student professional inquiry research that all students complete under the guidance of the lecturers who teach the course (the authors). The pedagogical design and approach adopted is facilitated within a rich, digitally enabled environment which supports the formation of a diverse, supportive and inclusive community (T. Anderson, 2008) of postgraduate students and experienced academics that promotes scholarly, ethical, and collaborative practice within an applied professional learning context (Picciano, 2009). This approach has contributed to an active and thriving postgraduate culture with students engaging online with the course lecturers, their specialist content advisor and other students throughout the academic year. While students are engaged in individual research projects, conversations with their advisor, including questions designed to probe what students understand (Picciano, 2009), are shared among the wider community, encouraging understanding of the broad nature of educational research. As such, students are immersed in rich learning experiences that expose them to a broad range of pedagogical approaches and research concepts that promote active student engagement, interactivity, and collaborative learning (Dysthe, et al., 2006). The nature of the interactions in this community also model effective online collaboration; important in contemporary workplaces where people are often separated geographically, and where a wider range of digital modes of communication have become ubiquitous. The development of an effective blended community of learning has also helped to reduce the sense of isolation many students experience when undertaking research at a distance (Ross & Sheail, 2017). The course itself comprises an introduction and orientation module that functions as an induction for students (Wisker, 2007). The orientation module incorporates an initial, introductory video, a course overview, suggested timetable, assessment requirements (including rubrics and exemplars), and general course resources. Online modules that cover literature review development, ethics, data collection and analysis, and report writing, each of which contain academic readings, multimedia

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Figure 1. The professional inquiry overview

resources, and online activities, make up the remainder of the course content. Multiple technologies and media were utilised in the development of the content (Picciano, 2009). Asynchronous communication channels are available to students via discussion fora, and a private messaging dialogue feature within Moodle. Weekly communication updates to students ensure they are cognizant of current activities and approaching deadlines. Advisors are also kept informed of upcoming, scheduled activities via a dedicated asynchronous forum, which, in addition to providing student-related information, offers technical guidance for academic staff unfamiliar with some of the more advanced features of the LMS. Since the introduction of this blended learning model of researcher training and development, the online community has grown from nine students and seven specialist academic advisors in the first cohort, to 45 students and 27 academics in the current offering ensuring an accessible and equitable research learning experience for all students (Keppell & Riddle, 2012).

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ISSUES, CONTROVERSIES, AND PROBLEMS Since its inception, the course has undergone a number of revisions and further development in response to feedback raised by students and advisors. Similar to previous research, notable issues that required attention included: addressing problems for students who found it difficult to make progress, which necessitated inclusion of explicit course structure, timely feedback, as well as relevant and useful resources (Harrison, et al., 2014); academic’s professional development needs, including digital literacy needs (Nasiri & Mafakheri, 2015); and managing the significant administrative requirements associated with the course (Nasiri & Mafakheri, 2015).

Student Progress The professional inquiry requires students to make steady progress throughout the academic year in order to complete the course in the time available (i.e., two semesters running from February to late October). After teaching the professional inquiry course for the first time, the authors quickly realised that the majority of students in the first cohort found it challenging to manage the different phases and processes required to complete their research project. This was despite the fact that students had completed all the prerequisites (including a research methods course) to enroll in this course which constituted the final part of their degree. This issue was apparent when students struggled to meet deadlines for formative feedback (e.g., annotated bibliography) or failed to allow sufficient time to complete compulsory tasks (e.g., gaining ethics approval for their project) and, in some cases, resulted in students failing to complete or withdrawing from the course. An important reason why students found it difficult to plan effectively was because they had little or limited prior knowledge of the steps necessary to, for example, write a literature review or gain ethics approval, essential parts of the research culture of the discipline (Dysthe, et al., 2006).

Academic Professional Development As the number of academics acting as advisors grew with each cohort, it became apparent that academics’ knowledge and experience of the traditional supervisory role associated with a thesis were acting as a barrier to understanding and engaging with the innovative blended learning model adopted here. While general resources were provided for students and academics within the online site, it became clear that academics were too time poor to access the necessary information about the structure of the course and the role of the advisor contained within the course introduction (at that time). This lack of engagement with the necessary information 10

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resulted in some academics relying on their existing knowledge of supervision (i.e., thesis supervision). This caused some confusion for students who were unclear when to engage with the course lecturers and when to engage with their advisor. This situation was exacerbated by the lack of prior knowledge and experience academics needed to engage with the more advanced features of the Moodle LMS. In some cases, academic staff had only a basic working knowledge of the LMS, as this was all that they required for their online teaching to that point.

Administrative Requirements As the online community of students and academics grew, the administrative requirements associated with keeping track of multiple processes also grew. For example, ensuring that each student developed a manageable, investigable research question that guided their inquiry that was approved by their advisor, required a tracking process to ensure this was achieved by all students. Similar processes were required for other aspects of the course including the ethics approval procedures that required all students to gain ethics approval before any approach to potential research participants or data collection could commence. Burgeoning administrative needs also included tasks requiring advisors’ input. Examples included the need to ensure advisors provided formative feedback for students’ annotated bibliography in a timely manner. Marking of formally assessed work, undertaken by the advisors, needed to occur within a set timeframe and late marking followed up. These administrative requirements were the responsibility of the course coordinator (the first author) which left little time for other, more important, roles such as student pastoral care and the creation of a supportive social and emotional learning environment (Picciano, 2009)

SOLUTIONS AND RECOMMENDATIONS In response to the issues raised above, a number of changes and innovations have been implemented which are discussed below. These changes are part of an ongoing iterative development cycle that is responsive to student and staff feedback. As will be appreciated, developing and implementing a new model of postgraduate research supervision takes time, care, and ongoing evaluation.

Student Progress In order to support students’ progress through the research process, access to clear instructions, processes, and timelines for conducting their research were provided (Harrison, et al., 2014; Nasiri & Mafakheri, 2015). While structural and procedural 11

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aspects of the research process are usually clear to supervisors, postgraduate students are frequently only given general guidelines of the nature of quality research and how it can be achieved. To counter this lack of specificity in traditional supervision arrangements, the professional inquiry course is explicitly structured. For example, to address the issue of lack of student progress, the authors introduced a suggested timetable to help scaffold students’ advancement through the course. Rather than make this fixed, the authors made it clear to students that the timelines were recommended based on experience with previous cohorts. Students were able to progress more quickly through the assigned activities if they wished, thereby blending structure and guidance with more flexible pathways. Apart from formerly assessed work (i.e., literature review and final research report), other deadlines were recommended rather than fixed (e.g., ethics approval, data collection approval). Weekly video updates, provided during the course, ensure students are aware of approaching deadlines and consequences for not meeting those dates (e.g., not gaining ethics approval at the suggested time will delay the commencement of the research). Associated with clear instructions, processes and timelines is the provision of relevant and useful resources to support the student in the research process. In traditional supervision arrangements, the provision of guidance on such things as methodology, reviewing the literature, as well as collecting and analysing data often occurs during less structured conversations between individual students and their supervisors, frequently leading to variable support for students. In contrast, the professional inquiry course provides extensive resources throughout the multiple modules of the course, designed to support students through the various aspects of conducting research. For example, the literature review unit incorporates academic readings, videos and other resources to aid students in understanding the purpose for and process of conducting a literature review. Included in the module is a link to the university’s online library research skills unit designed and managed by library staff. To help students appreciate the importance of systematically reading and creating notes from academic readings, the authors have created an Endnote library containing all course readings, as a starting point. Resources focused on how to write in the social sciences and examples of previous assignments are provided, in addition to a structural template for the final report. One of the key decisions made by the authors was that, as much as possible, all communication would go through the LMS site rather than private channels (e.g., email or similar). LMS communications range from public forum conversations, to private discussions between advisors, students, teaching and support staff via the Moodle dialogue feature. In this way, all course related conversations can be efficiently accessed by core staff. This is particularly important given the number of academics involved with each student. For example, at the commencement of the course, the advisor assists the student to fine tune their research focus and research 12

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question. The lecturers then review these conversations when working with students in the various modules of the course. Given that different staff teach the various modules, it is important that conversations relating to, for example, the ethics of a given project are available to the course lecturers, as are conversations in the data collection and analysis, and final report writing modules. Another area that assists students to make regular progress is the adequate provision of feedback (Nasiri & Mafakheri, 2015). Feedback can come from a variety of sources, including course lecturers, peers, advisors, and the students themselves. Within this course, various feedback mechanisms have been adopted and the most straightforward of these uses discussion fora. Various fora have been created in the modules for students to share aspects of their work and receive feedback from the course lecturers, advisors, and other students. For example, in the ethics unit, students review aspects of their project against the university’s code of ethical conduct for research with human participants. While all students were introduced to ethical principles and guidelines as part of their prerequisite research methods course, this was their first opportunity to apply that learning. To assist students with this process, the authors have developed a set of ethical traffic lights that identify areas with little or no ethical risk (green), areas where further consideration of ethical risk needs to be undertaken (amber), and areas that cannot be undertaken within the professional inquiry (red). Students are able to self-assess their project using the traffic lights, and then discuss the ethical considerations associated with their project in an open forum. Students may be unclear whether a particular aspect of their project warrants some ethical mitigation. Reviewing the work of other students, through online discussions, can often clarify these situations, for themselves and other students who read the posts. In this way, the activity enables students to evaluate and synthesise their new learning (Picciano, 2009). Accordingly, students receive timely feedback through the self-assessment process, the peer-review process, and from the course lecturers. There are similar fora in the other modules of the course where students similarly share aspects of their work or ask questions from classmates and lecturers. Through these fora, students increasingly interact with their peers, engaging in collaborative learning through mutual support and guidance. In addition, it helps students to develop confidence in their growing research knowledge and skills. Engagement in these fora was high across the cohort throughout the duration of the course. In part this was because meaningful engagement in some of the fora was required before students could receive their digital badge (e.g., the professional inquiry focus, and their ethics badges). But in other fora, students either engage by posting questions, answering questions from their classmates, or simply reading postings from other students, a form of legitimate peripheral participation (Lave & Wenger, 1991).

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In addition to the two assignments where students receive summative feedback from their advisors and the course moderators, there are two formative assessment opportunities built into the course (see Figure 1). These opportunities do not contribute towards the final course grade but they do provide feedback to the students with the aim of encouraging students to reflect on their work with the aim of improvement (Picciano, 2009). The first of these occurs during the first module of the course where students produce an annotated bibliography. It was clear to the authors that students required explicit, detailed advice and guidance for complex tasks such as synthesising literature. In response to this need, the formative task requiring students to develop an annotated bibliography was refined to include a small literature synthesis exercise. Including a practice literature synthesis was considered necessary, as the authors had observed over several years that many students found literature synthesis difficult and, in some cases, did not understand what was required. Students received formative feedback on their annotated bibliography and synthesis activity from their specialist advisor, which provided scaffolding for the development of their formally assessed literature review. The second formative assessment opportunity occurs towards the end of the course when students submit their draft report for feedback. A course moderator (i.e., an experienced research supervisor) reviews the draft report and provides feedback on areas including structural issues, clarity, and writing style, rather than the content associated with the particular focus of the student’s inquiry. Both of these formative assessment opportunities are optional but highly recommended. To further encourage student progress, the authors chose to use innovative technologies with the introduction of digital badges. Digital badges scaffold the research process (supporting the blending of lecturer-directed learning with more self-directed approaches), modelling alternative forms of assessment, and recognising key research competencies such as the development of research questions, data gathering methods, and ethics approval. In this way, students’ progress through their research journey can be mapped and key competencies acknowledged, giving students a sense of direction and achievement (Gibson, Ostashewski, Flintoff, Grant, & Knight, 2013). Breaking down larger tasks into smaller more manageable ones, while still keeping a sense of the gestalt, helps students learn how to conduct practitioner research within increasingly busy and complex workplaces. It also models digital badges use (McDaniel & Fanfarelli, 2016). In practice, four digital badges are awarded at key points along the research trajectory (see Figure 1). Each badge signals that the student has demonstrated competence in an important component of the research process that ultimately contributes towards the successful completion of the research project. Digital badges have been established in the LMS and are awarded automatically on the successful completion of the associated step. For example, when an advisor approves their 14

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student’s focus for the professional inquiry (i.e., research question) the advisor signals this by approving the student’s submission. This then acts as a trigger to automatically award the badge. Students need to collect all four badges before commencing their data collection.

Academic Professional Development Similar to other research (Dysthe, et al., 2006), the development of an online community of learning focused on research is integral to the success of this course. This development has helped to strengthen the postgraduate research community, bringing together larger numbers of students and academics in research discussions. In this way, the role of the “teacher”, or teaching presence (Garrison, Anderson, & Archer, 2000) in the online environment is shared as students and academics take turns to lead and contribute to discussions. In addition to providing structure for the students, a scaffolded approach was also adopted when working with the advisors. As a result, the course design actively facilitates staff participation in a different model of supervision. This participation benefits both experienced and inexperienced academic supervisors. The approach has encouraged collegiality and collaboration among staff and provides a mentoring structure for academics new to the course similar to that noted in other research (Maor, Ensor, & Fraser, 2016). Staff feedback indicates that working within the professional inquiry model is beneficial because they are able to focus specifically on their area of research expertise rather than the process of research. The scaffolding within the course design consciously optimises staff time and expertise while ensuring a consistent, high quality experience for students and maximises their chances of successful completion in a timely fashion. Given that, for most, the role of advisor in the professional inquiry course is quite different from their previous experiences of supervision, staff acting as advisors require support to fully understand the design of the course and their role within it. This is done through a set of advisor-specific resources, in the form of a dedicated module within the professional inquiry LMS site. The module includes detailed information about the structure of the course, the role of the advisor, explicit information about the pre-determined points of contact with students, ethical guidelines, and guidance on assignment marking, clear expectations of the level and nature of feedback, marked assignment examples, and step-by-step instructions and protocols for downloading and marking student work. A dedicated forum, not visible to students, was also established where the lecturers teaching the course can communicate with advisors about upcoming requirements and answer any questions. To help manage advisors’ workloads (Nasiri & Mafakheri, 2015), five specific points of contact between the advisor and their student are integrated into the course design (see Figure 1). The first of these is at the start of the course where 15

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the advisors work with the student to fine tune the research question. Advisors are also involved in the provision of formative feedback on the annotated bibliography (second input). The third input from the advisor is marking the literature review (assignment one). The fourth input requires the advisor to provide guidance and support to the student as they develop and refine their data collection instrument and data analysis approach. Advisors then step back from working with the students, leaving the supervision of undertaking the research, the analysis of the data, and the writing of the final research report to the course lecturers. The intent here is that when the advisors mark the final research report—their fifth and last input—they have a degree of separation and independence from the student’s work as required by university policy. To support advisors’ judgements when marking assignments and to ensure consistency and equivalence, all assignments are moderated. In some instances, one of the course lecturers undertakes this, but in most instances, this is completed by one of a team of specialist moderators with extensive experience as research supervisors. To support the moderation team, a set of resources are provided including annotated examples of moderated assignments. There is also a private moderators’ forum, where discussion occurs between the moderators and lecturers related to students’ assignments. Any assignments where the marks may need to be adjusted are reviewed by another member of the moderation team. To aid with the tracking and organisation of the marking process, the marking workflow tool in Moodle is used. Given that engaging with the technology is an issue for some academic staff, professional learning sessions were developed and offered to both new and experienced advisors. These sessions occur early in the year with staff from different campuses able to attend in person or via web conferencing. These sessions provide an opportunity for the course lecturers to outline how the course differs from traditional models of supervision as well as technical information to assist with engagement with technical features of the LMS used to host the course (e.g., marking workflow, digital badges). In addition to upfront professional learning, just-in-time learning opportunities are provided via the advisors’ forum. These typically take the form of informational posts that provide a step-by-step guide on how an advisor needs to, for example, complete the marking process or sign off a student’s data collection approach.

Administrative Requirements A further area identified above that supports successful postgraduate supervision is the provision of administrative support (Harrison, et al., 2014; Nasiri & Mafakheri, 2015). In 2017, a teaching assistant was employed to take over the administrative work associated with the growing course. The teaching assistant is now responsible for 16

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ensuring that processes that require tracking in the case of both students and advisors occur in a timely manner and any issues that arise are brought to the attention of the course lecturers. In many cases, the teaching assistant has become the first point of contact for both students and advisors, freeing up the course lecturers to concentrate on teaching and creating a supportive, collaborative learning environment conducive to students conducting research for the first time (Picciano, 2009).

FUTURE RESEARCH DIRECTIONS This chapter has discussed in depth an innovative online, blended learning model that has enabled Massey University to offer an effective, structured, and supported research-weighted route to Master of Education completion for a relatively large cohort of students each year. The model was developed using a broad, inclusive definition of blended learning, a focus on digital facilitation within the course, and developing a learning community. While the model is currently used in one programme in one discipline at the university, it has the potential to be utilised across different disciplines and postgraduate programs within Massey and beyond. With that in mind, future research opportunities include undertaking a formal review of the professional inquiry that systematically explores the experiences of staff and students involved in the course, with the view to identifying key characteristics of the design that support learning outcomes for students. In addition to investigating the professional inquiry in its current form, research that examines the adoption of the online blended model across different disciplines to determine whether it can be established within other cohort driven postgraduate qualifications, including the taught component of doctoral programs, is needed. Such research would provide evidence of the efficacy of the model in addition to adding to the small body of empirical research currently available on online and distance research supervision including that at master’s level (Ross & Sheail, 2017).

CONCLUSION Traditional models of students conducting research via thesis have tended to have students work primarily in isolation, with only their supervisors for consultation. These models are time intensive for academic staff and can often lead to a “dislocation effect” and a tentative supervision relationship. A growing need for research-capable practitioners and a limited availability of academic staff to supervise postgraduate research necessitated a new and innovative blended approach to research training and development to meet the growing demand of future, predominantly distance, 17

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students. The professional inquiry course was designed primarily to help practicing educators develop the skills necessary to critically examine existing research and undertake new research relevant to their own educational contexts. Not only do students develop the practical skills to effectively conduct research, they also develop a greater awareness of the importance of ethics in the research process, and a more nuanced understanding of how existing research might apply to their own setting. Higher levels of researcher capacity, particularly amongst practitioners, will be increasingly important within a growing inquiry oriented and evidence based work culture (Cochran-Smith & Lytle, 2009). The model underpinning the professional inquiry course sheds new light on how research may be conducted within discourse rich, online, blended communities of learning that draw on a wider group of academics and students to support the research process.

REFERENCES Albion, P. R., & Erwee, R. (2011). Preparing for doctoral supervision at a distance: Lessons from experience. In C. D. Maddux (Ed.), Research highlights in technology and teacher education 2011 (pp. 121-128). Chesapeake, VA: Society for Information Technology & Teacher Education (SITE). Ali, A., & Kohun, F. (2007). Dealing with social isolation to minimise doctoral attrition: A four stage framework. International Journal of Doctoral Studies, 2, 33–49. doi:10.28945/56 Allen, I. E., & Seaman, J. (2003). Sizing the opportunity: The quality and extent of online education in the United States, 2002 and 2003. Retrieved from http:// sloanconsortium.org/publications/survey/sizing_the_opportunity2003 Ally, M. (2008). Foundations of educational theory for online learning. In T. Anderson (Ed.), Theory and practice of online learning (2nd ed.; pp. 3-31). Academic Press. Retrieved from http://www.aupress.ca/index.php/books/120146 Anderson, C., Day, K., & McLaughlin, P. (2008). Student perspectives on the dissertation process in a masters degree concerned with professional practice. Studies in Continuing Education, 30(1), 33-49. doi:10.1080/01580370701841531 Anderson, T. (2008). Toward a theory of online learning. In T. Anderson (Ed.), Theory and practice of online learning (2nd ed.; pp. 45–74). Edmonton, Canada: AU Press. Retrieved from http://www.aupress.ca/index.php/books/120146

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Andrew, M. (2012). Supervising doctorates at a distance: Three trans‐Tasman stories. Quality Assurance in Education, 20(1), 42–53. doi:10.1108/09684881211198239 Bates, A. W. (2015). Teaching in a digital age. BC Open Textbooks. Retrieved from https://opentextbc.ca/teachinginadigitalage/ Budash, D., & Shaw, M. (2017). Persistence in an online master’s degree program: Perceptions of students and faculty. Online Journal of Distance Learning Administration, 20(3). Butcher, J., & Sieminski, S. (2006). The challenge of a distance learning professional doctorate in education. Open Learning: The Journal of Open, Distance and e-Learning, 21(1), 59-69. doi:10.1080/02680510500472239 Caner, M. (2012). The definition of blended learning in Higher Education. In P. S. Anastasiades (Ed.), Blended learning environments for adults: Evaluations and frameworks (pp. 19–34). Hershey, PA: IGI Global. doi:10.4018/978-1-4666-09396.ch002 Carpenter, J. (2012). Researchers of tomorrow: The research behaviour of generation Y doctoral students. Information Services & Use, 32(1/2), 3–17. doi:10.3233/ISU2012-0637 Cochran-Smith, M., & Lytle, S. L. (2009). Inquiry as stance: Practitioner research for the next generation. New York, NY: Teachers College Press. Crossouard, B. (2008). Developing alternative models of doctoral supervision with online formative assessment. Studies in Continuing Education, 30(1), 51–67. doi:10.1080/01580370701841549 de Kleijn, R. A. M., Mainhard, M. T., Meijer, P. C., Pilot, A., & Brekelmans, M. (2012). Master’s thesis supervision: Relations between perceptions of the supervisor– student relationship, final grade, perceived supervisor contribution to learning and student satisfaction. Studies in Higher Education, 37(8), 925-939. doi:10.1080/03 075079.2011.556717 Drennan, J., & Clarke, M. (2009). Coursework master’s programmes: The student’s experience of research and research supervision. Studies in Higher Education, 34(5), 483–500. doi:10.1080/03075070802597150 Dysthe, O., Samara, A., & Westrheim, K. (2006). Multivoiced supervision of Master’s students: A case study of alternative supervision practices in higher education. Studies in Higher Education, 31(3), 299–318. doi:10.1080/03075070600680562

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Evans, T. (2005). Crossing boundaries between learning and research: Doctoral programs at a distance. In ODLAA 2005: Breaking down boundaries: international experience in open, distance and flexible education. Proceedings of the 17th ODLAA conference (pp. 115-121). Adelaide, Australia: ODLAA. Evans, T., & Green, B. (1995, November). Dancing at a distance? Postgraduate studies, ‘supervision’, and distance education. Paper presented at the Australian Association for Research in Education Conference, ACT, Australia. Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2), 87–105. doi:10.1016/S1096-7516(00)00016-6 Garrison, D. R., & Vaughan, N. (2008). Blended learning in higher education: Framework, principles, and guidelines. San Francisco, CA: Jossey-Bass. Gibson, D., Ostashewski, N., Flintoff, K., Grant, S., & Knight, E. (2013). Digital badges in education. Education and Information Technologies, 20(2), 403–410. doi:10.100710639-013-9291-7 Guiney, P. (2016). E-learning provision, participation and performance: Learners in tertiary education. Wellington, New Zealand: Ministry of Education. Retrieved from http://www.educationcounts.govt.nz/publications/icth Harrison, R., Gemmell, I., & Reed, K. (2014). Student satisfaction with a webbased dissertation course: Findings from an international distance learning master’s programme in public health. International Review of Research in Open and Distance Learning, 15(1), 182-202. Hastie, M., Hung, I.-C., Chen, N.-S., & Kinshuk. (2010). A blended synchronous learning model for educational international collaboration. Innovations in Education and Teaching International, 47(1), 9–24. doi:10.1080/14703290903525812 Holley, D., & Dobson, C. (2008). Encouraging student engagement in a blended learning environment: The use of contemporary learning spaces. Learning, Media and Technology, 33(2), 139–150. doi:10.1080/17439880802097683 Keppell, M., & Riddle, M. (2012). Distributed learning spaces: Physical, blended and virtual learning spaces in higher education. In M. Keppell, K. Souter, & M. Riddle (Eds.), Physical and virtual learning spaces in higher education: Concepts for the modern learning environment (pp. 1–20). Hershey, PA: IGI Global. doi:10.4018/9781-60960-114-0.ch001

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Lameras, P., Paraskakis, I., & Levy, P. (2008). Conceptions of teaching using virtual learning environments: Preliminary findings from a phenomenographic inquiry. In Proceedings of the 6th International Conference on Networked Learning (pp. 218225). Lancaster, UK: Academic Press. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge, UK: Cambridge University Press. doi:10.1017/CBO9780511815355 Maor, D., Ensor, J. D., & Fraser, B. J. (2016). Doctoral supervision in virtual spaces: A review of research of web-based tools to develop collaborative supervision. Higher Education Research & Development, 35(1), 172–188. doi:10.1080/07294 360.2015.1121206 McDaniel, R., & Fanfarelli, J. (2016). Building better digital badges. Simulation & Gaming, 47(1), 73–102. doi:10.1177/1046878115627138 Means, B., Toyama, Y., Murphy, R., & Baki, M. (2013). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teachers College Record, 115(3), 1–47. Nasiri, F., & Mafakheri, F. (2015). Postgraduate research supervision at a distance: A review of challenges and strategies. Studies in Higher Education, 40(10), 1962–1969. doi:10.1080/03075079.2014.914906 Nicols, M. (2008). E-learning in context - #1. ePrimer series. Retrieved from Ako Aotearoa website: http://akoaotearoa.ac.nz/project/eprimer-series/resources/files/elearning-context-1-eprimer-series Orellana, M. L., Darder, A., Pérez, A., & Salinas, J. (2016). Improving doctoral success by matching PhD students with supervisors. International Journal of Doctoral Studies, 11, 87–103. doi:10.28945/3404 Picciano, A. G. (2009). Blending with purpose: The multimodal model. Journal of Asynchronous Learning Networks, 13(1), 7–18. Pilcher, N. (2011). The UK postgraduate Masters dissertation: An ‘elusive chameleon’? Teaching in Higher Education, 16(1), 29-40. doi:10.1080/13562517.2011.530752 Prebble, T. (2010). From a distance: 50th jubilee of distance learning. Palmerston North, New Zealand: Massey University. Rockinson-Szapkiw, A., Spaulding, L. S., & Lunde, R. (2017). Women in distance doctoral programs: How they negotiate their identities as mothers, professionals, and academics in order to persist. International Journal of Doctoral Studies, 12, 49–72. doi:10.28945/3671 21

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Rogerson-Revell, P. (2015). Constructively aligning technologies with learning and assessment in a distance education master’s programme. Distance Education, 36(1), 129–147. doi:10.1080/01587919.2015.1019972 Ross, J., & Sheail, P. (2017). The ‘campus imaginary’: Online students’ experience of the masters dissertation at a distance. Teaching in Higher Education, 22(7), 839854. doi:10.1080/13562517.2017.1319809 Seaman, J. E., Allen, I. E., & Seaman, J. (2018). Grade increase: Tracking distance education in the United States. Oakland, CA: Babson Survey Research Group. Retrieved from http://www.onlinelearningsurvey.com/highered.html Sharpe, R., & Oliver, M. (2013). Designing for learning in course teams. In H. Beetham & R. Sharpe (Eds.), Rethinking pedagogy for a digital age designing for 21st century learning (2nd ed.; pp. 341–367). New York: Routledge. Tweedie, M. G., Clark, S., Johnson, R. C., & Kay, D. W. (2013). The ‘dissertation marathon’ in doctoral distance education. Distance Education, 34(3), 379-390. do i:10.1080/01587919.2013.835778 Vaughan, N. (2007). Perspectives on blended learning in higher education. International Journal on E-Learning, 6(1), 81–94. Vaughan, N., Cleveland-Innes, M., & Garrison, D. R. (2013). Teaching in blended learning environments: Creating and sustaining communities of inquiry. Edmonton, Canada: AU Press. Retrieved from http://www.aupress.ca/index.php/books/120229 Wisker, G. (2007). Supervising postgraduates: Internationally, and at a distance. In P. Wilcox, H. Jones, M. Sumner, & E. Berrington (Eds.), Connections: Sharing the learning space (pp. 23–28). Brighton, UK: Falmer Press. Wu, J.-H., Tennyson, R. D., & Hsia, T.-L. (2010). A study of student satisfaction in a blended e-learning system environment. Computers & Education, 55(1), 155–164. doi:10.1016/j.compedu.2009.12.012

KEY TERMS AND DEFINITIONS Advisor: An academic staff member with subject specific expertise who provides advice and guidance to a student at predetermined points within the professional inquiry course. Blended Learning: A mix of lecturer-led teaching with academic research guidance, research specific content with more generic content, lecturer-directed 22

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learning with more self-directed learning, structure and guidance with more flexible pathways, and independent and co-operative learning opportunities in an online environment. Digital Badges: A digital indicator of an accomplishment, skill, or competency within the course. Online Learning: The use of the internet to access resources to interact with course content, academics, and other learners and to obtain guidance and support throughout the learning process in order to construct knowledge and meaning. Online Learning Community: A group of people who share mutual academic goals and purposes. Communication, collaboration, and a sense of belonging are important attributes of a thriving online community. Professional Inquiry: A postgraduate researcher training and development course designed to develop students’ skills and abilities to become consumers and producers of research. Research: A systematic exploration of a specified question. Supervisor: The traditional model of research supervision where a student works independently from other students with advice and guidance provided by an individual or small group of academic experts.

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Chapter 2

Using ICTs to Check Plagiarism in PhD Research Works in Nigeria: Prospects and Challenges Floribert Patrick C. Endong University of Calabar, Nigeria

ABSTRACT The proliferation of plagiarism in African universities has rationalized the adoption of various strategies to mitigate or eradicate it. In Nigeria particularly, computerassisted approaches such as the Turnitin software have been appropriated to tackle this challenge. Many Nigerian universities have adopted Turnitin to ameliorate the quality of PhD research produced in their faculties. Although lauded in many quarters, this recourse to ICTs to check plagiarism has seen multiple challenges, some of which include poor anti-plagiarism policies, fallible anti-plagiarism software, and the Nigerian factor, among others. Using observations and secondary sources, this chapter critically explores these challenges. The chapter provides a conceptual definition of plagiarism and plagiarism detection systems; it shows how plagiarism is affecting PhD research in Nigerian universities and explores the place of ICTs in anti-plagiarism policies adopted by Nigerian universities. The chapter ends by examining the prospects and challenges of using ICTs to mitigate PhD student plagiarism in Nigeria.

DOI: 10.4018/978-1-5225-7065-3.ch002 Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Using ICTs to Check Plagiarism in PhD Research Works in Nigeria

INTRODUCTION It is an accepted premise that plagiarism is a serious threat to quality research. It has since been a highly intolerable tradition in the literary, political, entertainment, digital, and research community; and has thus given rise to multiple methodologies and movements aimed at mitigating or totally eradicating it. However, since the advent of the Internet and the World Wide Web, plagiarism has proliferated to the extent of constituting a veritable plague in African universities. In effect, the proliferation of the new information and communication technologies has made original ideas increasingly rare, as most people tend to simply reproduce their contemporaries’ ideas, presenting them (these ideas) as their own. The Internet has thus facilitated the tradition by students of copying and pasting online material into their assignments or research works. Conscious of this sad reality, academic institutions and research bodies across the world have adopted various paradigms to check or sanction cases of plagiarism in their institutions. Most scientific journals around the world have, for instance, resorted to various technology-assisted approaches to check plagiarism in their research productivity. In line with this ethicist movement, specific and very relevant software (notably Dupli Checker, PaperRater, Copyleak, Plagiarisma, Plagium, PlagScan, i-Theticate, i-Paradigm and Turnitin among others) are today deployed by most scientific journals, publishers, and other research institutions in the evaluation process of research papers submitted to them for review and publication. Similar trends occur in most universities where plagiarism software – particularly Turnitin – are increasingly popularised and deployed in the evaluation of school assignments and Master and PhD dissertations. New ICTs have thus, not only been one of the principal factors lying at the root of plagiarism; they have equally constituted a strategic tool to combat various forms of plagiarism in research works. Like their counterparts from other parts of the world, Nigerian universities have, in recent times, embraced ICTs as an elixir of both student’s and lecturer’s plagiarism. In line with this, PhD theses produced in many Nigerian universities are, as a matter of principle, screened to check similarity index (for plagiarism check), with the use of computer software. Such ICT-assisted screening is a pre-requisite for the vetting and acceptance of students’ theses by universities. The similarity index check evidently follows the reasoned myth, which stipulates that the recrudescence of plagiarism in a given educational institution is liable to tarnish the reputation not only of the school, but equally of its products (students who graduate from it). Unchecked plagiarism across several institutions in a country has the potential to discredit a national education system (PlagiarismAdvice.org, 2014). Despite these laudable efforts towards academic integrity, PhD student plagiarism continues to be a serious concern in many Nigerian universities; this, for reasons 25

Using ICTs to Check Plagiarism in PhD Research Works in Nigeria

which are connected to poor anti-plagiarism policies (adopted by universities), endemic corruption, the “Nigerian factor”, the prevalence of computer illiteracy among a large section of PhD students and the prevalence of digital divide among other factors (Idiegbeyan-Ose, Nkilo & Osinulu, 2016; Olutola, 2016; Orim, 2014; Oyewo & Uwem, 2016). Using secondary sources and critical observations, this chapter critically explores the prospects and challenges of the use of ICTs to combat plagiarism in PhD research works produced in Nigerian universities. It aims specifically at answering the following research questions: what is plagiarism and how is it affecting PhD research productivity in Nigerian universities? How can new ICTs be used to combat the phenomenon? To what extent have these ICTassisted methodologies been popularised in Nigerian university milieus to combat PhD student plagiarism; and what are the prospects and challenges of applying ICT driven methods to combat PhD student plagiarism in Nigerian universities? To answer these research questions, the present chapter is divided into four main parts. The first part provides conceptual clarification, which gives attention to two key terms used in the discourse, namely plagiarism and plagiarism detection systems. The second part focuses on showing how plagiarism is affecting PhD research in Nigerian universities. The third part explores the place of ICTs in the policies adopted by Nigerian universities to tackle plagiarism in PhD research works, and the last part is devoted to the prospects and challenges of using ICTs to combat PhD student plagiarism in Nigeria.

CONCEPTUAL FRAMEWORK For the sake of ensuring clarity of presentation in the subsequent sections of this discourse, it will be very helpful to provide a number of definitional illuminations on two key terms used in the chapter. These terms include student plagiarism and ICT assisted plagiarism detection systems.

Student Plagiarism The term plagiarism is derived from the Latin word “plagiarus” (which means a kidnapper, a stealer or the abductor of a child or a slave); and the Greek word “plagion” which means kidnapper (Abdusalam, 2017; Clarke, 2006). It is commonly used to refer to the act of intentionally or unintentionally appropriating another person’s ideas, language, processes, results, or statements without obtaining permission or without giving due credit. In other words, it means “stealing” somebody’s works, ideas or words, by using them as if they were your own. In line with this, the International

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Using ICTs to Check Plagiarism in PhD Research Works in Nigeria

Center for Academic Integrity (2013) more clearly construes the plagiarism as a situation in which Someone uses words, ideas, or work products attributable to another identifiable person or source without attributing the work to the source from which it was obtained in a situation in which there is a legitimate expectation of original authorship in order to obtain some benefit, credit, or gain which need not be monetary. (cited from Plagiarismadvice.org, 2013, p.1) As earlier mentioned, plagiarism has become an issue in different domains or industries which are not necessarily related to the academia, namely the media, the entertainment industry, and the political arena. Thus, various contextualised definitions of the term have been formulated. In the context of music making, for instance, Atilano (2017) construes plagiarism as the act of copying the melodies of other songs, stealing the lyrics, ripping off the guitar riff, mimicking the drum pattern, emulating the song structure, and so on. Similarly, with reference to politics, Labossiere (2016) extends the semantic sphere of the term plagiarism to include the common practice in political speech writing of using the services of speechwriters without acknowledging them. He noted that if one considers the premise that plagiarism occurs when someone tries to claim that substantial words and ideas are his own, when in reality they belong to someone else, it could be inferred that, when a politician (or spouse) delivers a political speech that was written by someone else (a speechwriter) as if he or she was presenting his or her own words and ideas, then he or she is just plagiarising. Unless, of course, he or she engages in proper citation practices. Based on this conception, Labossiere (2016) contended that almost all political speeches are acts or products of plagiarism. In the context of academia, plagiarism – particularly student plagiarism – can vary remarkably from one educational institution to another as well as from one culture to another. However, a common feature in the definitions given the term by critics in this context is that the act to plagiarise is connected with research misconduct and is mostly committed during paper, assignment, PhD and Master’s dissertation writing. In tandem with this, student plagiarism commonly involves at least one of the following practices: 1. Submitting another person’s work word-for-word as one’s own, 2. Using large portions of text from a single source without alterations, 3. Changing the key words and phrases and maintaining the essential content of a source in a paper,

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Using ICTs to Check Plagiarism in PhD Research Works in Nigeria

4. Paraphrasing from different sources and making the content fit together seamlessly, 5. Borrowing generously from one’s previous publications without citation (self-plagiarism), 6. Combining perfectly cited sources with copied passages – without citation – in one paper, 7. Mixing copied passages from several different sources without citation, 8. Citing non-existent sources or providing inaccurate information about sources, 9. Using the “aggregator” technique consisting in citing properly but producing a paper which contains almost no original work, 10. Relying excessively on the wording and/or structure of a source (Abdusalam 2017; Turnitin, 2012). Authors such as Fasola (2017) have extended typologies of plagiarism to include the two acts of fabricating and falsifying results or data during research processes. However, discussions in the present discourse will hinge essentially on the abovementioned conceptions of student plagiarism.

Plagiarism Detection Systems Plagiarism detection systems could simplistically be defined as instruments deployed to check plagiarism. However, given the complexity of plagiarism as a phenomenon, the term “plagiarism detection systems” has variously been conceptualised. According to Kraus (2016), plagiarism detection systems are the ensemble of approaches deployed in an institution to create a fair environment for academic publication as well as to acknowledge authors’ research works properly. Mozgovoy (2007) on the other hand defines the term more in line with the use of ICT. To him, it is the process of using computers to check or reveal cases of plagiarism. The deployment of such a process entails a technical revision of common definitions of plagiarism as the appropriation of another person’s language, ideas, or processes without proper citation/attribution. As he noted, most of these definitions are not perfectly apt in the context of a computer-assisted plagiarism detection system. In such a context, one may think that it is not indispensable to hinge on an exact definition of “plagiarism” since: Computer-detectable plagiarism is something almost self-evident; at least, documentation of many systems often just claim that plagiarism is detected without providing precise specification of the system’s capacities. On the highest level of abstraction, it is almost true, but there are additional details on the lower levels. (p. 13) 28

Using ICTs to Check Plagiarism in PhD Research Works in Nigeria

Obviously, a computer can detect plagiarism if and it treats a number of documents as similar. So, in this case, the term “plagiarism” is used as a synonym to the term “similarity” (p.13). Such similarity is calculated by means of a certain file-file comparison function. Plagiarism detection can be done in two principal ways, namely manually or digitally (that is through computer assisted methods). Manual detection of plagiarism involves the use of several techniques or clues, some of which include: • •

• •

•

Looking out for strange formatting features such as unusual font size and unexpected line breaks and text blocks among others. Checking level of consistency in citation style. Some plagiarised works often contain a mixture of old and updated editions of citation styles. Suspicion should rise when the text contains only older references when much of it (the text) is about thinking that is more current. This may strongly indicate that the plagiariser copied from an old text. Analyse writing style to determine level of consistency. A text where there are remarkable variations in level of language and style of writing presents common features observable in plagiarised works. Examining the content proper, to see how consistent the paragraphs are, with respect to the topic discussed in the paper and the style of writing used by the author. Most plagiarised works are structured with glued paragraphs which sometimes do not really flow and which are not crafted according to the same style of writing (Mozgovoy, 2007). Analyse sentence structure to check whether the text may have been tweaked by paraphrasing software. This will entail checking inappropriate terminologies related to the subject context as well as unusual strings of words (“word salads”) which do not make sense (Rogerson, 2017).

The weaknesses identified above are not absolute indexes of plagiarism. However, the clues provided are working ways of manually detecting possible plagiarism in the work of students. If manual approaches to checking plagiarism appear tedious, subjective, and unsystematic, computer assisted ones are generally less energy and time consuming. As notes by Birkic Celjak et al. (2010), computer assisted approaches to plagiarism detection have the advantage of the possibility to review a very large volume of papers stored in repositories within a relatively short period of time, this to check similarity and rapidly provide a report to ascertain the originality of non-originality of a file. Computer assisted methods of detecting plagiarism include the use of software and other digital tools to check plagiarism. Some of the software deployed in this context include Dupli Checker, PaperRater, Copyleak, Plagiarisma, Plagium, 29

Using ICTs to Check Plagiarism in PhD Research Works in Nigeria

PlagScan, i-Theticate, i-Paradigm, and Turnitin among others. The software provides users information on whether and how much of the contents of a paper are identical or copied from another source. In other words, they help check similarity index. According to Kraus (2016), this similarity check could be done according to four main parameters: 1. Lexical features which could be N-grams or Word-grams. 2. Syntactic features which are bigger portions of text such as chunks, sentences or POS. 3. Semantic features which exceed the actual content of the text under examination, and consider abstract concepts rather than concrete words. 4. Stylometric features which is determined by the statistics on the previously mentioned features. They involve issues such as frequency of words, synonyms, average sentence length, and average paragraph length among others. There are many ways employable to categorise computer assisted plagiarism detection systems (Chew & Blackey 2010; Evans, 2006; Kraus 2016; Mozgovoy 2007). Kraus (2016) identified three categories namely Extrinsic, Intrinsic, and Cross lingual plagiarism detection systems. An Extrinsic plagiarism detection system checks similarity between a screened text and relevant source documents. In other words, it is a file-to-file detection system. Intrinsic detections system aims to detect suspicious sections of a screened text without an external source. Here the query document is not examined vis-à-vis another document. Cross lingual plagiarism detection systems aim to detect plagiarism across languages. This chapter will mainly hinge on computer assisted plagiarism detection.

PLAGIARISM AS A POPULAR CULTURE AMONG POSTGRADUATE STUDENTS IN NIGERIAN UNIVERSITIES Plagiarism has become a serious problem with high potential to adversely affect the quality of research in school institutions across the globe. In the Nigerian context, the phenomenon has caught the attention of a great number of critics and higher education ideologues (Adeleye & Adebamowo, 2012; Archibong 2013; Okonta & Rossouw, 2013), who, for the most part have advocated its prompt mitigation/eradication in the Nigerian university system. The prevalence of the plagiarist culture among faculty members and students has caused many Nigerian universities to embark on various movements integrating disciplinary and preventive measures against plagiarists. In 2013 for instance, the Universities of Calabar and Ekitti and the Federal University of Agriculture, Abeokuta (FUNAAB) sacked and/or demoted an impressive number 30

Using ICTs to Check Plagiarism in PhD Research Works in Nigeria

of their faculty members, including professors and senior lecturers, on allegations of plagiarism. Similar disciplinary measures were taken by the Delta State University in August 2017, against 17 lecturers culpable of plagiarism. Egregious instances of sanctions taken against students (plagiarists) have been rarely reported in recent research. However, a number of studies have revealed that student plagiarism is a very sad reality in most, if not all, Nigerian universities (Fasola 2017; Idiegbeyan-ose, et al., 2016; Olutola 2016; Orim 2014, 2015). This is revealed by the fact that major essay competitions organised in Nigerian universities always see the submission of plagiarised contributions. It is on record, for example, that in the 2017 Mike Okwonkwo National Essay Competition, 27 out of the 796 essays submitted for the contest were not original – they were plagiarised (Abdusalam, 2017). In line with the myth that student plagiarism has been accentuated these last years in Nigerian universities, the online magazine Nairaland (2017) reviewed a number of recent cases of PhD students who, in collaboration with their supervisors, plagiarised the works of both endogenous and exogenous peers. In view of these incidences, Nairaland surmised that final project writing by undergraduate students as well as Master and PhD dissertations are “an academic rape” in Nigerian universities, thanks to the awful phenomenon of plagiarism. A number of recent empirical studies have sought to illustrate the prevalence of plagiarism in Nigerian universities. One of these studies conducted in 2011 by Adebayo revealed that 63.6% of students in Nigerian universities are liable to paraphrase sources without proper citation (cited in Olusola, 2016). A similar study conducted by Orim (2014) revealed that Nigerian-born postgraduate students in both nationally based and foreign universities are engaged in plagiarism for reasons which range between lack of awareness (by students) to lack of concern (by Nigerian universities) over the issue of plagiarism. In effect, based on a survey of postgraduate students in 39 Nigeria-based universities and two UK-based universities, Orim found out that high percentages of Nigerian post-graduate students admitted to having indulged in plagiarism at least once in the preparation of assignments and research papers. From these results, it shows that 40% of the postgraduate students enrolled in Nigeria-based universities indulged in the act of plagiarism from a lack of awareness. It is important at this point to highlight that most Nigerian postgraduate students have a myopic or perverted understanding of the concept of plagiarism as well as its ethical implications. This has made them engage in plagiarism more or less ignorantly but reprehensibly. Such costly ignorance could principally be blamed on the Nigerian educational system, particularly the nature of university programs which do not integrate research ethics as a main course right from the early years of training in the university. Some universities offer courses in research methodology in the first

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year of their Master’s programs. Nevertheless, research ethics (as a key feature of these courses) is hardly existent in these courses. To make matters worse, many of the faculty members who supervise Master and PhD students during the writing of their dissertations similarly have incomplete or distorted conceptions of plagiarism. Such wrong or misleading conceptions of the phenomenon has resulted in some of them, on countless occasions, to commit plagiarism in their publications, thereby serving as “bad” role models for postgraduate students or junior faculty members (International School of Management, 2014; Fasola 2017). Research showing that faculty members have the same myopic or incomplete conceptions of plagiarism as the students they supervise (Orim 2014) is enough evidence or justification to envisage a scenario wherein supervisors are incompetent to properly guide their supervisees towards avoidance of plagiarism in their research works. PhD research supervision has thus, in some cases, been comparable to the biblical allusion of a blind man leading another blind man on a pit-ridden way, as far as research ethics are concerned. Another key factor responsible for a high incidence of plagiarism among postgraduate students in Nigerian universities is that courses or class exercise related to scholarly writing are not cardinal features of capacity building in many of these universities. Student evaluation at the undergraduate level illustrates this, as it is mainly based on examination rather than paper writing, resulting in many students lacking the basic skills to engage in scholarly writing. They gain admission into postgraduate programs with such deficiencies, and are most often vulnerable to the temptation to plagiarise at the slightest opportunity. Furthermore, the Nigerian factor (alarming moral corruption in Nigerian society) has made key academic arbiters in Nigerian universities to be hypocritical about the fight against plagiarism. It is not uncommon to find situations in which plagiarism is condemned only in the open (in theory) but encouraged or tolerated in practice. Abdusalam (2017) reviewed various instances in which some university lecturers were disciplined or dismissed because they “dared” to exposed acts of plagiarism committed by their colleagues. The prevalence of the Nigerian factor has subtly pushed many postgraduate students not to give serious thought to the culture of plagiarism and its possible adverse implications. This has particularly followed the truism that a myriad of cases of well-known plagiarists have survived in their school system un-reproached if not subtly celebrated. The culture of tolerance in favor of plagiarisers has contributed in no small measure in naturalising the phenomenon of plagiarism, making it the norm among students and teachers (Kalawole, Eyong & Arikpo, 2016; Olusola, 2016). As noted by Uloma and Chinyere (2013), many faculty members and students engage in plagiarism due to the lack of real enthusiasm among university administrators or governing bodies to sanction plagiarists.

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Using ICTs to Check Plagiarism in PhD Research Works in Nigeria

There are many factors responsible for the high prevalence of plagiarism among postgraduate students in Nigerian universities. Some of these factors will be discussed in the subsequent section of this discourse, devoted to challenges to the use of ICT assisted approaches to plagiarism in Nigerian universities.

USING ICT ASSISTED STRATEGIES TO TACKLE PLAGIARISM IN PHD RESEARCH IN NIGERIAN UNIVERSITIES As earlier mentioned, the urge to mitigate or eradicate plagiarism has pushed universities across the world to embark on various innovative tactics. One of these tactics has been the conception of ICT assisted initiatives or projects aimed at tackling the challenge of plagiarism. These initiatives and projects have structurally varied from one country to the other and from one institution to the other. However, the common orientation has been that tools, like software, be popularised to check plagiarism among other digitally driven approaches. In the UK for instance, the project PlagiarismAdvice.org has been operating since 2002 as a national strategy to allow UK higher and further education institutions to check the authenticity of student work. In line with the project, UK universities and colleges were given access to the Turnitin similarity check software, free of charge for an initial three years (PlagiarismAdvice.org, 2014). Similar anti-plagiarism strategies were deployed in Asia. In 2009, Pakistan’s Higher Education Commission (HEC) designed a plagiarism eradication strategy involving the popularisation of the Turnitin software in over 127 universities and colleges in the country. The use of such ICT assisted methods were complemented by relevant policies and practices in university campuses (Higher Education Commission, 2012). In a bid to be abreast of latest anti-plagiarism concepts, the majority of Nigerian universities have embraced – at least theoretically – the culture of using software among other relevant ICTs to check the authenticity of students’ research works. In 2012, the Committee of Vice Chancellors of Nigerian Universities (CVCNU) introduced this culture (the use of the Turnitin text matching software in particular) as a strategy aimed at raising awareness about the unethical nature and academic implications of plagiarism in Nigerian universities. In addition to this, some universities have developed anti-plagiarism software to ameliorate the quality of research produced by postgraduate students in their institutions. One direct consequence of this plagiarism policy has been that the authenticity of Master’s and PhD dissertations are now scrupulously checked by special university boards/units before students can proceed to their defenses or oral examinations. Sanctions are expected to reduce the acts of plagiarism among postgraduate students. The institution of this software assisted plagiarism detection in the PhD research process has represented a detection-cum33

Using ICTs to Check Plagiarism in PhD Research Works in Nigeria

punitive method which, though plagued by a number of irregularities, has to an extent produced some dividends. Firstly, the adoption of the software has revolutionised plagiarism detection methods in Nigerian universities. Prior to the adoption of this software, plagiarism detection was mainly undertaken using manual tools. As such, successful detection of plagiarised thesis depended on the intuition or flair of the supervisor or examiner. In line with the manual plagiarism detection systems that prevailed in Nigerian universities, PhD theses were mostly crosschecked vis-à-vis sources, supervisors and examiners. Supervisors and examiners had to depend on their familiarity of the student being examined, or cross-examine theses with previously generated papers by the candidate to check levels of consistency in style and language, among other tactics. With the advent of the software assisted method for detecting plagiarism, an amount of sanity has been realised in the domain of PhD research. Secondly, the plagiarism policy has in itself contributed to sensitising many Nigerian PhD students in about the need to achieve originality in their dissertations. Nigerian students are now increasingly aware that their dissertations are be subjected to plagiarism checks and that a very nominal similarity index is required for their works to be eligible for examination. This implication of the policy has somehow pushed most PhD students to give a second thought to the need to be original in the writing of their dissertations.

PROSPECTS OF USING ICTS IN THE FIGHT AGAINST PHD STUDENT PLAGIARISM IN NIGERIAN UNIVERSITIES A number of factors directly or tacitly militate for the popularisation of ICT assisted methods to check or combat plagiarism in Nigerian universities. Three of these factors include (i) the remarkable globalisation of anti-plagiarism movements, (ii) Nigerian universities’ struggle to rebrand themselves visibly to attract prestige and national and international students, and (iii) the existence in Nigeria of various academic arbiters which, of recent, have manifested strong enthusiasm to promote research ethics and academic integrity.

Globalisation of Anti Student Plagiarism Movements The necessity to combat plagiarism by the most reliable means, including recent digital technologies, has become the talk of the town in most – if not all – countries. This is partially attributable to the heavy mediatisation and globalisation of recent movements designed across Europe, America and beyond, to sanction plagiarists in the political, media, and entertainment worlds as well as in academia. In effect, high 34

Using ICTs to Check Plagiarism in PhD Research Works in Nigeria

profile cases of plagiarism in America, Germany, Romania, Greece, and even Nigeria have caused the resignation of important and powerful politicians and the termination of renowned professors’ tenure contracts in many countries of the world. In 2012 for instance, Germany saw the resignation of its Minister of Defense and Education minister, both on allegation of plagiarised PhD theses. A similar case was reported in Romania where, on allegation of plagiarism, the Prime Minister of the country found himself compelled to resign from his position (PlagiarismAdvice.org, 2014). More recently, Melanie Trump, the wife of the US President Donald Trump, saw herself wrapped in plagiarism scandals after copying sections of Michelle Obama’s speech. Another monumental example happened recently in Nigeria, with President Mohammadu Buhari’s plagiarism of various words used by Obama, The high profile cases highlighted above have not only served as global indexes to show the level of prevalence of plagiarism, but also created increased enthusiasm of relevant stakeholders in Nigerian academia to deal with plagiarism. It is therefore not a surprise that various Nigerian universities have, since 2012, embarked on diverse moves to mitigate plagiarism through the dismissal and demotion of culpable faculty staffs and the adoption of systematic plagiarism checkers in the vetting process of Master’s and PhD dissertations. One could see that Nigerian universities have, in their own ways, tried to attune their modus operandi with the global razzmatazz against plagiarism. This trend may be friendly to the use of flexible and adapted technologies to combat plagiarism in the research works produced by PhD’s.

Nigeria’s Universities Constant Struggle to Launder Their Image The remarkable struggle or moves to launder their image in both national and international markets may push Nigerian tertiary institutions to increasingly resort to anti-plagiarism to certify and ameliorate the quality of research produced in their faculties. It is increasingly axiomatic that repeated cases of plagiarism among students or faculty members are liable to tarnish the image of a university (Hallak & Poisson 2007, PlagiarimAdvice.org, 2014). Conscious of this fact, in the last 3 years Nigerian universities have embarked on various rebranding strategies aimed at visibly shaping international observatories’ perception of their faculties as the national opinion about their services. The fight against plagiarism has thus been envisaged by some universities within the country as one of the tools to rebrand themselves and potentially attract more prestige and students from the country and abroad. Using his institution (Ibrahim Badamasi Babaguida University- IBB) as a case study, Turnitin administrator Kabir Adamu opined that the use of antiplagiarism software to certify Master’s and PhD dissertations produced in IBB

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Using ICTs to Check Plagiarism in PhD Research Works in Nigeria

University is liable to help the institution “gain global recognition academically”. Such a development “will be beneficial to the student[s] and the university” (Cited in Vanguard 2017, para 8).

The Creation of Academic Integrity Institutions In the Nigerian educational landscape, the existence of bodies such as the Committee of Vice Chancellor (CVC), the International School of Management, and the Center for Academic Integrity, Research, and Anti-Plagiarism augurs well for the popularisation of anti-plagiarism schemes in Nigerian universities. In effect, to promote an environment conducive for academic integrity in Nigerian universities, the CVC instituted the use of Turnitin to check plagiarism in students’ dissertations and many other activities. It is hoped that instituting this technology in the process of examining Master’s and PhD students is just one of the positive steps made in favor of cultivating academic integrity in Nigerian universities.

CHALLENGES OF THE USE OF ICTs IN COMBATING PHD STUDENT PLAGIARISM In spite of its revolutionary effects, the use of computer assisted approaches to check plagiarism in postgraduate research is faced with a great number of challenges. In this section, attention is particularly given to four of these challenges, namely the limitations of the Turnitin software, poor academic integrity policies in Nigerian universities, the Nigerian factor, and wrong conceptions of plagiarism and academic integrity by some members of Nigerian Academia.

Pitfalls of the Turnitin Software Although Turnitin has so far been instrumental in checking for plagiarism of postgraduate students at Nigerian universities, the software has shown some serious limitations. In effect, Turnitin merely helps to determine similarity index and not really check for plagiarism in the strict sense of the term. The point being made here is that the software simply compares the contents of the paper being screened with those of a multitude of sources in view of highlighting any similarity between the two. It does not actually detect plagiarised passages but similarity. Because of this, the software has sometimes detected properly cited and plagiarised passages of submitted manuscripts, thereby providing more or less misleading reports on the status of those manuscripts. Many users of the software have seen most of the

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Using ICTs to Check Plagiarism in PhD Research Works in Nigeria

direct citations appearing in their works flagged as plagiarised, irrespective of the fact that they endeavored to properly attribute these citations (Kalawole et al., 2016; Tela, 2017). In addition, it has been condemned that Turnitin is essentially online based. The external sources on which the software depends for its similarity check do not include non-digitised sources. This means any material which is plagiarised from sources that exist only in print cannot, in theory, be detected. This is a serious limitation in the Nigerian context given the fact that most Nigerian “publishers” are more printers than publishers. Thus, Nigerian publications have a negligible online presence. With this, works plagiarised from Nigerian publications are theoretically liable to be undetected by the software. Another factor evidencing the fallibility of Turnitin is that the software sometimes does not flag paraphrased passages copied from external sources as plagiarised. This has encouraged many postgraduate students to deploy paraphrasing and rework highlighted sections of their manuscripts. Meanwhile, it remains clear that there is a clear dichotomy between paraphrasing and originality. Paraphrasing a passage undoubtedly changes the structure of sentences but not the idea expressed in the passage. Therefore, through paraphrasing, some postgraduate students have been beating the Turnitin software. This factor and many others contribute to making Turnitin in particular, and other plagiarism detection approaches deployed in Nigerian universities fallible, as far as detecting plagiarism is concerned. The reduced efficiency of Turnitin clearly gives credence to aphorisms which view the plagiarism detection process principally as something which should involve human expertise more than anything. Technology, in this process, can only assist man but not do the part of man. The answer to PhD student plagiarism therefore lies in well-conceived processes and procedures rather than on technology alone.

The Nigerian Factor The Nigerian factor could be defined as the widespread tendency by Nigerians to snobbishly and unrepentantly get involved in naturally reprehensible acts just because the extreme moral decay reigning in the country and popular culture permit it. It is the culture of thinking that anything (ethically questionable) can go on in Nigeria because the country’s ethical system is inherently corrupt. As defined by many sociologists, it is Nigerians’ defeatist attitudes in the face of the profound moral decay eating into the socio-cultural fabric of the country (Endong, 2017; Idowu, 1999). The Nigerian factor has the potential to affect ICT-assisted plagiarism detection systems in various ways. One of these ways has to do with the culture of laisser faire in plagiarism detection systems used in Nigerian universities. Many Nigerian postgraduate students may interpret the reign of corruption in the Nigerian system

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Using ICTs to Check Plagiarism in PhD Research Works in Nigeria

as an index indicating that they will always have their way even in the event of high similarity index between their work and online sources. The Nigerian factor has made plagiarism in some academic quarters to be disregarded or not dealt with as a serious academic plague. As earlier mentioned, the Nigerian factor has caused some anti-plagiarist members of Nigerian academia to be stigmatised, negatively profiled, and even unjustly sanctioned by their colleagues. In the same line of thought, Orim (2014) reported cases of university lecturers and supervisors who have hypocritically sidelined student plagiarism from the academic reality of their universities, thereby downplaying the assumption that plagiarism is a serious popular culture among students and faculty members in Nigerian institutes of higher learning. All these indexes are liable to console plagiarising students and make them think that the environment is conducive to plagiarism.

Poor Academic Integrity Policies Anti-plagiarism strategies applied in Nigerian universities are mainly based on detecting and punishing cases of outright plagiarism. Very little emphasis is placed on prevention through the popularisation of clearly formulated codes of ethics and the introduction of better re-orientation and capacity building programs directly connected to scholarly writing and research ethics in the early years of university education. In effect, most Nigerian universities have mainly instituted plagiarism check as a panacea for reducing plagiarism in postgraduate work, apparently overlooking the fact that many postgraduate students whose works are to be subjected to this check lack basic knowledge on academic writing and research ethics. As earlier mentioned, many have a myopic conception of issues like plagiarism or intellectual propriety (Orim 2014; Interational School of Management 2014). What makes the situation worse is that some universities do not have clearly enunciated policies on plagiarism – that is, clearly written codes which plainly define plagiarism. With this, some postgraduate students operate in total obscurity as far as knowing the research ethics is concerned. It goes without saying that under such circumstances, supervisors and university administrators are bound to see more plagiarised works, as the tendency or temptation for students to get involved in plagiarism is higher when they have suspicious, limited, or no knowledge of what plagiarism is all about (Olutola, 2016). In view of this, the International School of Management (2014) contended that: Some of the lecturers and administrators of Nigerian universities think there is no case of plagiarism on their campuses. This sounds very ridiculous but logically true; it is a confirmation of the cliché ‘where there are no rules, there also is no offense’. If there are no clearly defined rules for citation and referencing, then it is right to 38

Using ICTs to Check Plagiarism in PhD Research Works in Nigeria

say there is no plagiarism. Plagiarism is only a violation of a set of ‘known’ rules and regulations (that is, honest and correct citation and referencing) for academic writing. (p. 8) According to most experts, prevention is supposed to be an indispensable and complementary aspect of the use of computer software to check plagiarism. Detection as a method is likely to be very effective if and only if it is associated with prevention. As noted by Larsson and Henson (2013), a strategy based principally on detection and punishment has only short-term effects as it most often pushes students to be originality conscious only during their university program or when examined. They likely give a modicum of attention to originality just to have their dissertation or research works vetted and accepted by the university. Thus, academic integrity becomes a value which is only shared temporarily by the students as, after completion of their postgraduate programs, they tend to jettison this laudable culture. Another aspect of anti-plagiarism policies prevailing in Nigerian universities is the fact that the use of software to check similarity index is essentially confined to the treatment of PhD and Master’s dissertations. Other research papers written by students during course work are mostly not checked. Meanwhile, it is not uncommon for these research papers to be the product of various forms of plagiarism. In view of this, it may be argued that the use of ICTs in the struggle to reduce plagiarism in postgraduates’ research works is only partial. Such an anti-plagiarism policy neglects another important aspect of PhD student’s training as researchers and knowledge generation agents. All the points presented above are pointing to the fact that there is an imperative to review the academic integrity policies applied in most Nigerian universities. Detection and punishment approaches to combating plagiarism should be intelligently complemented with prevention strategies. Such strategies should include initiatives organised from the early stages of students’ university education. They should include the introduction of relevant capacity building and orientation programs related to scholarly writing and research ethics, right from the first year of undergraduate education. This may enable students develop necessary research skills and embrace good scholarly values early in their university training.

Questionable Conceptions of Plagiarism by Some Members of Nigerian Academia A sad reality in some Nigerian universities is that many faculty members – among which should be counted supervisors of Master and PhD dissertations – have an incomplete or myopic conception of plagiarism. One easily notices that plagiarism is popularly and quasi-essentially perceived as copying someone’s work or idea without given credit or presenting another person’s work as one’s own. Other subtle 39

Using ICTs to Check Plagiarism in PhD Research Works in Nigeria

forms of plagiarism, like copying large portions of a source which has been cited and paraphrasing large portions of text without attribution of the ideas it contains, are not really viewed by some members of Nigerian academia, including supervisors and students, as an unethical culture (International School of Management, 2014; Olutola 2016; Orim 2014). A module prepared for a postgraduate orientation program organised on January 7th at the University of Nsuka, Nigeria, for instance, questionably recommended that for students to avoid plagiarism, they have to “print out [online materials], read them, understand the main idea[s] of the writer and put those ideas in [their] own word[s]” (Ukwuoma 2018, p.4). What is to be ploblematised here is the fact that the recommendation is somehow misleading. As earlier argued, paraphrasing the ideas of someone else does not change them or add anything to knowledge. If presented as one’s own, they remained plagiarised. The incomplete knowledge about plagiarism is fuelling ignorant perpetration of plagiarism not only by faculty members, but equally by students. It will not be surprising that some postgraduate students get involved in certain forms of plagiarism thanks to the kind of guidance or orientation they receive from their supervisors or lecturers.

CONCLUSION Plagiarism is one of the most serious forms of unethical culture in the domain of research which, in the last few years have been accentuated thanks to the Internet and the proliferation of various software – such as Google paraphrasing and translation software. In view of the adverse impact of the phenomenon on the quality of PhD research produced in universities and tertiary institution, most, if not all, universities and research institutes have adopted various paradigms to mitigate it. Most of these universities and research institutes have resorted to ICT assisted plagiarism checkers such as Turrnitin for the purpose, among other academic integrity policies. In Nigeria, computer assisted approaches such as the Turnitin software have most recently been appropriated to tackle the challenge. Many universities in the country have adopted Turnitin to ameliorate the quality of PhD research produced in their institutions. Although lauded in many quarters, this recourse to ICTs to check plagiarism has resulted in multiple challenges including poor plagiarism detection policy, the digital divide, corruption, and computer illiteracy among PhD students, and the Nigerian factor among others. Using observations and secondary sources, this chapter has critically explored these challenges. The chapter has provided a conceptual definition of plagiarism and plagiarism detection systems, showed how plagiarism is affecting PhD research in Nigerian universities, and explored the place of ICTs in the policies adopted by Nigerian universities to tackle plagiarism 40

Using ICTs to Check Plagiarism in PhD Research Works in Nigeria

in PhD research works. In addition to these achievements, the chapter has examined the challenges of using ICTs to combat PhD student plagiarism in Nigeria. Major challenges in the use of ICTs to check plagiarism in PhD’s include the defects of the plagiarism software used in Nigerian universities, the poor academic integrity policies applied in Nigerian universities, the Nigerian factor, and the digital divide which continue to prevail among postgraduate students. In view of the issues analysed above, the following recommendations have been formulated: 1. Nigerian universities should invest more in the acquisition of better antiplagiarism technologies. As mentioned in above, Turnitin – which is the sole resort of Nigerian universities in matters of plagiarism check – is seriously defective as it mainly checks similarity between students’ works and files/ materials published on online platforms. The program does not include works published exclusively in print in its databases. These software defects have facilitated a situation wherein students use “well calculated” models of paraphrasing; translating and synthesising to re-express other people’s ideas and thus beat the Turnitin software. Efforts should therefore be made towards acquiring better calibers of software which go beyond the paradigm of merely checking similarity index. 2. While working toward the development of more sophisticated ICT-assisted plagiarism checkers, Nigerian universities should use Turnitin in conjunction with manual methods of plagiarism checking enunciated in conceptual framework section of this discourse. A popular but faulty assumption in Nigerian universities’ approaches to PhD student plagiarism has been that the new ICTs are too sophisticated and quasi-infallible vis-a-vis ensuring originality in students’ works; meanwhile, in view of the defects highlighted above, it will be more axiomatic not to blindly thrust ICTs. The mobilisation of Turnitin and other anti-plagiarism software is just a step towards the right direction and not the unique elixir of the problem. 3. In line with the necessity to combine Turnitin with other approaches Nigerian universities should consider using preventive methods that combat PhD students plagiarism through upfront strategies such providing better education and orientation to students before they begin writing their dissertations and seminars. This can be done through well designed course work and specifically designed seminars. It will be expedient to organise these plagiarism-related programmes as ways to include faculty members who supervise post-graduate works. This will be strategic given that research has demonstrated that many supervisors have an insufficient – or a rather perverted – notion of plagiarism

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Using ICTs to Check Plagiarism in PhD Research Works in Nigeria

and are therefore rarely efficient in checking plagiarism in their supervisees’ research works (Endong 2018; International School of Management 2015 Orim 2014). 4. Another very vital recommendation in the use of ICTs to fight plagiarism in PhD students’ will be to extend the use of Turnitin or any other plagiarism checker to the examination and evaluation of students work from the first assignments they submit as requirement to pass courses integrated into their doctoral programs. This will definitely help make the fight against plagiarism an omnipresent feature in the training of students and may cause the latter to be more – nay optimally - sensitised on the issue of plagiarism and the need for originality in their works. The tendency has been for ICTs to be used to check plagiarism exclusively in the students’ PhD theses, while neglecting all the other research works conducted during course work by students. Meanwhile, it is not uncommon for students to develop pro-plagiarism cultures during this stage of their doctoral training, cultures which are later reflected in their PhD theses.

REFERENCES Abdusalam, A. (2017). 5 times Nigerian lecturers were sanctioned over plagiarism. EDECELEB. Retrieved from https://educeleb.com/5-times-nigerian-lecturerssanctioned-plagiarism/ Adeleye, O. A., & Adebamowo, C. A. (2012). Factors associated with research wrong doing in Nigeria. Journal of Empirical Research on Human Research Ethics; JERHRE, 7(5), 15–24. doi:10.1525/jer.2012.7.5.15 PMID:23324199 Archibong, I. A. (2012). Forms of dishonesty among academic staff and the way forward. Canadian Social Science, 8(6), 39–43. doi:10.3968/j. css.1923669720120806.1057 Atilano, J. R. (2017). Plagiarism in music is stealing. Inquire Net. Retrieved from http://entertainment.inquirer.net/223986/plagiarism-music-stealing Birkic, T. (2016). Report: Analysis of software for plagiarism detection in science and education. Zagreb: University of Zagreb. Chew, E., & Blackey, B. (2010). e-Plagiarism detection at Glamorgan. World Academy of Science and Technology, 70, 195–199.

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Clarke, R. (2006). Plagiarism by academics: More complex than it seems. Journal of the Association for Information Systems, 7(2), 91–121. doi:10.17705/1jais.00081 Endong, F. P. (2018) The concepts of academic humility and seniority syndrome in research: A critique of research traditions in Nigerian universities. In J. J. Jeyesekar & P. Saravanan (Eds.), Handbook of research on scientific methods and measurements (pp. 223-231). IGI Global. Evans, R. (2006). Evaluating an electronic plagiarism detection service. Learning in Higher Education, 7(1), 87–99. doi:10.1177/1469787406061150 Fasola, O. (2017). Ethical violations in research: Prevention rather than cure. Retrieved December 28, 2017, from https://www.acu.edu.ng/documents/seminar/ FASOLA.pdf Hallak, J., & Poisson, M. (2007). Corrupt schools, corrupt universities: What can be done? New York: Institute for Educational Planning & UNESCO. Higher Education Commission. (2012). Plagiarism eradication system. London: Higher Education Commission. Idiegbeyan-ose, J., Nkilo, C., & Osinulu, I. (2016). Awareness and perception of plagiarism of postgraduate students in selected universities in Ogun State. Library Philosophy and Practice, 1322, 1–26. International Center for Academic Integrity. (2013). Why integrity? Retrieved December 27, 2017 from http://www.academicintegrity.org/icai/integrity-1.php International School of Management. (2014). Academic integrity and culture sensitivity: Hints and pathway to the future. Lagos: Center for Academic Integrity, Research and Anti-Plagiarism. Kalawole, S. O., Eyong, E. I., & Arikpo, S. V. (2016). Pitfalls of turnitin in detecting percentage of plagiarism among graduate students’ theses in University of Calabar, Cross River State, Nigeria. International Journal of Advanced Studies in Ecology, Development and Sustainability, I(2), 90–96. Kraus, C. (2016). Plagiarism detection – state-state-of-the-art systems (2016) and evaluation. Retrieved from https://arxiv.org/abs/1603.03014 Labossiere, M. (2016, July 21). Politics and plagiarism. Talking Philosophy: The Philosopher Magazine. Retrieved from http://blog.talkingphilosophy.com/?p=9727

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Larsson, K., & Henson, H. (2013). Anti-plagiarism strategies: How to manage it with quality in large scale thesis production. International Journal of Educational Integrity, 9(2), 60-73. Mogozgovoy, M. (2007). Enhancing computer-aided plagiarism detection (Unpublished Master’s thesis). University of Joensuu. Retrieved from https://core. ac.uk/download/pdf/15167022.pdf Nairaland. (2017). Plagiarism by Professor Mathew Aremu of Federal University Wukari – Politics - Nairaland. Retrieved from http://www.nairaland.com/3022465/ plagiarism-professor-mattew-aremu-federal Okonta, P., & Rossouw, T. (2013). Prevalence of scientific misconduct among a group of researcher in Nigeria. Dev World Biotech, 13(3), 1–14. doi:10.1111/j.14718847.2012.00339.x Olutola, F. O. (2016). Towards a more enduring prevention of scholarly plagiarism among university students in Nigeria. AJCJS: African Journal of Criminology and Justice Studies, 9(1), 83–97. Orim, S. M. (2014). An investigation of plagiarism by Nigerian students in higher education (Unpublished PhD thesis). Coventry University, Coventry, UK. Orim, S. M. (2015, June). Student plagiarism: Do we care? University World News, 371. Retrieved from http://www.universityworldnews.com/article. php?story=20150609151735677 Oyewo, A. E., & Uwem, U. S. (2016). Information literacy, research, scholarship and publication: Comparative of PhD students in Nigerian and South African universities. IFLA Columbus, 11, 1–15. PlagiarismAdvice.org. (2013). A national strategy for addressing student plagiarism. Retrieved from http://plagiarismadvice.org Rogerson, A. (2017). A troubling new way to evade plagiarism detection software (And how to tell if it is being used). Reactions Watch. Retrieved from http:// retractionwatch.com/2017/04/26/troubling-new-way-evade-plagiarism-detectionsoftware-tell-used/ Tela, A. Y. (2017, October). Turnitin also needs Turnitin. Daily Trust. Retrieved from https://www.dailytrust.com.ng/turnitin-also-needs-turnitin.html Turnitin. (2012). Defining plagiarism: The plagiarism spectrum. Retrieved from https://www.amsacs.org/pdf/Turnitin_WhitePaper_PlagiarismSpectrum.pdf

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Ukwoma, S. C. (2018). Turnitin: Software for plagiarism check. Workshop for postgraduate students of the University of Nigeria, Nsukka, Nigeria. Uloma, D. O., & Chinyere, N. I. (2013). Dealing with the plague of plagiarism in Nigeria. Journal of Education and Practice, 4(11), 102–107. Vanguard. (2017, October). IBB employs plagiarism checkers to certify research works. Vanguard. Retrieved from https://www.vanguardngr.com/2017/10/ibb-varsityemploys-plagiarism-checker-certify-research-works/

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Chapter 3

Knowledge Visualization for Research Design:

The Case of the Idea Puzzle Software at the University of Auckland Ricardo Morais Universidade Católica Portuguesa, Portugal Ian Brailsford University of Auckland, New Zealand

ABSTRACT This chapter presents a case of information and communication technology use in doctoral research processes. In particular, it presents the use of the Idea Puzzle software as a knowledge visualization tool for research design at the University of Auckland. The chapter begins with a review of previous contributions on knowledge visualization and research design. It then presents the Idea Puzzle software and its application at the University of Auckland. In addition, the chapter discusses the results of a large-scale survey conducted on the Idea Puzzle software in 71 higher education institutions as well as its first usability testing at the University of Auckland. The chapter concludes that the Idea Puzzle software stimulates visual integrative thinking for coherent research design in the light of Philosophy of Science.

DOI: 10.4018/978-1-5225-7065-3.ch003 Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Knowledge Visualization for Research Design

INTRODUCTION This chapter describes a knowledge visualisation tool – the Idea Puzzle software – for the overall design of a research project. The Idea Puzzle framework was created by the first author in 2007, in response to doctoral candidates’ scepticism that they could share the same course on research design despite their heterogenous disciplinary background. The tool is based on Philosophy of Science to allow a visual overview of a research project beyond restricted notions of research design as method or fieldwork. Between 2007 and 2017, the first author presented the tool in 231 seminars, having received 1004 responses to an online anonymous feedback questionnaire. In 2009, the first software version of the tool was made available online, being licensed to higher education institutions (HEIs) since 2012. In 2016, the Academy of Management Learning and Education (4.235 5-Year Impact Factor) considered the Idea Puzzle software “a very useful tool for research across a multitude of disciplines, not only for PhD students as they learn about all of the elements of research project design, but also for reviewers and research project teams” (Parente & Ferro, 2016, p. 645). In 2017, the second author conducted the first usability testing of the Idea Puzzle software at the University of Auckland which subsequently led to the acquisition of its licence by the School of Graduate Studies. Congruent with such a chronological line, this chapter begins with a review of previous contributions on knowledge visualisation and research design. It then presents the Idea Puzzle software and its application at the University of Auckland. The chapter follows with an analysis of the issues identified in the large-scale survey and usability testing mentioned above, and concludes with solutions for the issues identified and suggestions for future research.

BACKGROUND In recent years, there has been an unprecedented interest in the visualisation of academic research processes (Meyer, Höllerer, Jancsary, & Leeuwen, 2013). Previous contributions have focused, among others, on the practical visualisation of scientific knowledge (Worren, Moore, & Elliott, 2002), on the complementarity between visual formats (Eppler, 2006), on the disciplinary background of visualisation research (Eppler & Burkhard, 2007), on the visualisation of conceptual frameworks (Leshem & Trafford, 2007), and on the pitfalls of visualisation (Bresciani & Eppler, 2015). Taken together, such contributions have shed light on the origins, differences, and implications of visual representations in academia. However, previous research on visualisation has neglected the overall design of a research project as a crucial stage of scientific practice. In the words of Meyer 47

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et al. (2013), “we should aim at actively making use of the potential of visual representations to enable better research processes and results. This starts at the stage of designing projects” (p. 536). Such a research gap is relevant because the overall design of a research project is more complex than that of its constituent parts, requiring holistic and immediate visualisation as a complement to linear and sequential verbalisation (Meyer et al., 2013). The purpose of this chapter is therefore to present a visual decision-making tool – the Idea Puzzle software – that supports the overall design of a research project. In the words of Parente and Ferro (2016), it is “a support tool to assist PhD students and researchers in the process of designing research projects through a focus on three central dimensions of research that are collectively represented by a triangle” (p. 643). Parente and Ferro (2016) further emphasise the visual dimension of the Idea Puzzle software as follows: Our students repeatedly commented that using Idea Puzzle contributed significantly to their understanding of the meaning of the multiple and interrelated dimensions of the research project process. In addition, they applauded the functionality of having an automatic evaluation of their input into each section/piece of the triangle allowing them to control the development of the project design, as well as to decide which points they should invest more time into to build the final “puzzle” (i.e., visual representation) of their research project. (p. 644) The two following sections thus review previous contributions on knowledge visualisation and research design, with a particular emphasis on the jigsaw puzzle metaphor and on Philosophy of Science, respectively.

Knowledge Visualisation According to Eppler and Burkhard (2007), “the emergent field of knowledge visualisation examines the use of visual representations to improve the management of knowledge on all levels” (p. 112). An example of a knowledge visualisation format is the visual metaphor whose main feature is the dual function of a) positioning information graphically to organise and structure it; and b) conveying an implicit insight through the characteristics of the metaphor employed. Eppler (2003) argues that visual metaphors are powerful templates for experts to communicate their knowledge with non-experts, giving the example of philosophers of science such as Aristotle, Hume, Ockham, Popper, and Wittgenstein. In particular, Aristotle regards the metaphor as a tool of cognition which “provides rapid information and is to the highest degree instructive” (Eppler, 2003, p. 82). Concrete examples of metaphors include Ockham’s Razor, Hume’s Fork, Popper’s Bucket, 48

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and Wittgenstein’s Ladder. Interestingly, Wikipedia considers Ockham’s Razor the only scientific law of Philosophy of Science named after a person. Visual metaphors for knowledge transfer or creation may be natural objects such as an iceberg or human-made objects such as a jigsaw puzzle. The invention of the jigsaw puzzle is attributed to John Spilsbury, a London cartographer and engraver who commercialised sawed pieces of wood with the shape of national boundaries for the teaching of Geography (Hannas, 1972). The jigsaw puzzle is therefore a human-made object created for educational purposes. It is not surprising, therefore, that the jigsaw puzzle metaphor is recurrently employed in academia to research phenomena as diverse as curricular integration (Pearson & Hubball, 2012), environmental uncertainty (Sarasvathy, Dew, Read, & Wiltbank, 2008), and mobile application development (Danado & Paternò, 2014). Eppler (2006) compared systematically visual metaphors with three other types of mapping methods – concept maps, mind maps, and conceptual diagrams – recommending their combined uses in four didactic steps. In particular, the author recommends empty conceptual diagrams for joint in-class concept development, mind maps for in-class individual note taking, concept maps for individual reviewing at home, and visual metaphors for joint in-class summaries. In terms of systematic comparison between the two joint in-class mapping methods, Eppler (2006) considered conceptual diagrams appropriate for concise overviews, structuring of a topic into systematic building blocks, and application to a variety of situations in the same manner, whereas visual metaphors are appropriate as a mnemonic aid, to draw attention and curiosity, and to facilitate understanding by triggering functional associations. In terms of drawbacks, conceptual diagrams may be difficult to understand without knowledge of the category meanings, do not provide mnemonic help, and do not foster creativity or self-expression, whereas visual metaphors cannot be easily modified, may trigger wrong associations, and may be misunderstood. From such a discussion, it is possible to conclude that the Idea Puzzle software employs a jigsaw puzzle metaphor in the shape of a triangle, with 21 jigsaw pieces (Figure 1). The jigsaw pieces, in turn, represent five systematic building blocks – theory, method, data, rhetoric, and authorship – which can be difficult to understand without knowledge of their category meaning. The following section thus reviews the development of the Idea Puzzle framework since 2007 in the light of previous contributions to research design.

Research Design According to Creswell (2009), research design is a plan or proposal to conduct research which “involves the intersection of philosophy, strategies of inquiry, and 49

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Figure 1. Idea Puzzle framework Source: www.ideapuzzle.com

specific methods” (p. 5). The author proposes a conceptual framework for research design which, correspondingly, adopts the format of a triangle based on the three elements described above – philosophical worldviews, selected strategies of inquiry, and research methods. Such a triangular view of research design is thus restricted to method at the level of philosophical stances, research strategies, and techniques for data collection and analysis. An even more restricted view of research design is espoused by Leshem and Trafford (2007) and Layder (2013), who equate research design with fieldwork. Brinberg and McGrath (1985) also propose a triangular conceptual framework for research design – the Validity Network Schema – under the assumption that “research involves three interrelated but analytically distinct domains: the conceptual, the methodological, and the substantive” (p. 15). Such a view of research design is

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more holistic than the one by Creswell (2009), since it relegates method to one of the three elements of empirical research. The triangular format of the Idea Puzzle framework (Parente & Ferro, 2016) is based on Brinberg and McGrath’s (1985) Validity Network Schema. In particular, the left side of the Idea Puzzle triangle corresponds to the conceptual domain (theory), its bottom side to the methodological domain (method), and its right side to the substantive domain (data). In the words of Parente and Ferro (2016), “each side of the Idea Puzzle triangle corresponds to one of the three dimensions that every empirical research project should ideally include: ontology (data), epistemology (theory), and methodology (method)” (p. 643). Huff (2009) took an even more holistic view of research design than Brinberg and McGrath (1985) by proposing a conceptual framework with six major decisions: ontology/epistemology, discipline/profession subfield, literature review, policy/ practice, model(s)/explanation/theory, and method(s)/context. In particular, the author claimed that “specific research design decisions in the areas listed (and others as well) must help you depart from what is currently known to your audience, while staying close enough to their interests that your contribution is recognised and valued” (Huff, 2009, pp. 86-87). Such a view emphasises the importance of relevance for academic audiences. Van de Ven (2007) similarly emphasised the importance of relevance, but for both academic and non-academic audiences through the Aristotelian notion of rhetoric. The Idea Puzzle framework (Parente & Ferro, 2016) thus follows Van de Ven (2007) by adding rhetoric to theory, method, and data, as a fourth building block in the upper inner part of the triangle. At the individual level, Huff (2009) emphasised the importance of personal and professional experience for research design. She describes such an association as follows: “It has taken me a long time to discover how my ‘ordinary’ life could or should inform my academic life. Gradually, I have drawn more explicitly on experience outside of academia” (Huff, 2009, p. 22). In similar fashion, the Idea Puzzle framework (Parente & Ferro, 2016) considers the alignment between the research design and the author’s personal and professional experience as a fifth building block – authorship – visually represented in the lower inner part of the triangle. The Idea Puzzle framework thus adds two elements – rhetoric (Van de Ven, 2007) and authorship (Huff, 2009) – to the triangle between theory, method, and data suggested by Brinberg and McGrath (1985). In the light of Philosophy of Science, such five elements generally correspond to epistemology (theory), methodology (method), ontology (data), rhetoric (axiology of the audience), and authorship (axiology of the author). The Idea Puzzle framework was created by the first author in 2007, in the context of an interdisciplinary course on research design for doctoral candidates of Mathematics, 51

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Engineering, and Social Sciences. In that particular course, doctoral candidates were sceptical that they could share the same lectures given their contrasting scientific disciplines. As the sole facilitator of the course, the first author reassured the participants that Philosophy of Science (Riggs, 1992) was common to any scientific discipline and created the Idea Puzzle framework of 21 decisions (Parente & Ferro, 2016) to engage them in a common research design challenge. The feedback was positive and the first author received invitations by methodological teachers, deans of Graduate Schools (GSs), research deans, and even university rectors to lecture the same framework to interdisciplinary audiences in other universities and countries. The point of departure for the Idea Puzzle framework were five methodological decisions – “philosophical stance”, “research strategy”, “data collection techniques”, “data analysis techniques”, and “quality criteria” – inspired by the macro structure of John Creswell’s (1998) book on five approaches to research design. Such a macro structure is path-dependent in the sense that prior decisions (e.g., philosophical stance) limit the range of options available for subsequent decisions (e.g., research strategy). Such a funnelling logic can, however, be applied to other than methodological decisions. In fact, the first author soon realised that teaching research design without reference to the theoretical and empirical context of the research project was pedagogically counterproductive. He therefore added five theoretical decisions and five empirical decisions to the initial five methodological decisions. The five theoretical decisions help doctoral candidates focus a literature review in terms of two “keywords”. Such two keywords limit, in turn, the range of “streams of thought” to review. Experts within the streams of thought will suggest, in turn, avenues for future research (“research gap”), thus legitimising a certain “research question or hypothesis”. The result of such a funnelling sequence of theoretical decisions is a synthesis of the current answer to the research question or the hypothesis (“state of the science”). The five empirical decisions, on the other hand, help doctoral candidates focus their discussion of evidence in terms of a “unit analysis” at a certain “level of analysis”. Such unit and level of analysis will be documented, in turn, with qualitative or quantitative data (“nature of data”), based on primary or secondary sources (“origin of data”). The result of such a funnelling sequence of empirical decisions is a set of one or more examples of the unit of analysis (“sample”). Taken together, the 15 theoretical, methodological, and empirical decisions can be visualised as a triangle that reflects the dilemmatic nature of the research process (McGrath, 1981) and the need for a permanent interplay between the research question or hypothesis, the research strategy, and the empirical sample (Brinberg & McGrath, 1985). In particular, recent topics are expected to involve fewer streams of thought and exploratory research questions, thus requiring qualitative research strategies and small samples. Mature topics, on the other hand, generally involve 52

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larger numbers of streams of thought from which operationalisable hypotheses can be deduced and tested with quantitative research strategies, thus requiring large samples for inferential statistics. The triangle reminds doctoral candidates that methodology can only be decided in relation to epistemology and ontology (Tsang, 2016). In other words, it helps doctoral candidates integrate method, theory, and data, with a focus on theory development (original contribution to knowledge) rather than research methods per se (Hillman, 2011). On the other hand, such an integration of method, theory, and data through 15 decisions helps doctoral candidates realise that an empirical research project involves a sample of data, but also of theory and method (Mullins & Kiley, 2010). In terms of academic writing, the triangle provides food for thought for the structuring of the literature review (theory), the methodological section (method), and the discussion of evidence (data) in academic texts such as research proposals, dissertations, and articles. In addition to the 15 decisions on theory, method, and data, the first author included in the Idea Puzzle framework three decisions on rhetoric, following Van de Ven’s (2007) notion of engaged scholarship that is rigorous for academic audiences and relevant for society. The three rhetoric decisions of the Idea Puzzle framework are “pathos”, “logos”, and “ethos”, following Aristotle’s trilogy on rhetoric. Such three types of arguments are expected to raise awareness of the emotions, logic, and credibility conveyed by the conclusions of an academic text. In terms of emotions, it is relevant to consider the academic, public, and commercial interest of the research project as well as its ethical and political implications. In terms of logic, it is important to acknowledge the difference between inductive, hypothetic-deductive, and abductive reasoning. The credibility of an academic text largely results from the disclosure of theoretical, methodological, and empirical limitations. The three rhetoric decisions of the Idea Puzzle framework thus provide inspiration for the conclusions of an academic text, namely in terms of research and practical implications, in spite of theoretical, methodological, and empirical limitations. The final three decisions of the Idea Puzzle framework are authorial in the sense that the author is conceptualised as part of the system of 21 dilemmatic decisions (McGrath, 1981). In particular, the author’s CV and future career is regarded as an accumulation of three interdependent, intangible and irreversible assets – “wisdom”, “trust”, and “time” – following Pierre Bourdieu’s (1986) notions of cultural, social, and economic capital, respectively. Wisdom includes the author’s education as well as personal and professional experience which will benefit the research project (Huff, 2009). Trust is a restricted notion of networking, since it only refers to persons that will be mentioned in the acknowledgements of an academic text. Time refers to

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the author’s availability for the research project since part-time working regimes are usually associated with lower completion rates (Council of Graduate Studies [CGS], 2007). The overall Idea Puzzle framework of 21 decisions thus emphasises the need for integration of theory (epistemology), method (methodology), and data (ontology) – the triangle mentioned by Parente & Ferro (2016) – as well as rhetoric (axiology of the audience) and authorship (axiology of the author) – upper and lower inner parts of the triangle, respectively – for a coherent research design in the light of Philosophy of Science (Morais, 2010). Such an integrative and holistic view of research design follows previous calls for more integrative thinking in general (Kallio, 2011) and doctoral training in Philosophy of Science in particular (Abrahamson, 2008). Such a systemic perspective of the research process (Brinberg & McGrath, 1985) based on dilemmatic decisions (McGrath, 1981) complements the chronological visualisation of the research process as a sequence of project tasks such as literature review, data collection, and data analysis (Bryman, 2012). In the terminology of the Vitae Researcher Development Framework (Careers Research and Advisory Centre [CRAC], 2010), the Idea Puzzle framework emphasises the need for problem solving skills for research design (cognitive skills) as a complement to project planning and delivery skills for research planning (research management skills). The following section reviews the development of the Idea Puzzle framework into a software and its application at the University of Auckland.

THE IDEA PUZZLE SOFTWARE AT THE UNIVERSITY OF AUCKLAND In 2008, the first author established a public limited company – Idea Puzzle – to visually support integrative research design based on Philosophy of Science through a dedicated website and software based on the Idea Puzzle framework. The initial funding included, among others, seed capital from a public limited company – Crivo – that specialises on university spin-offs and individual researchers licensing intellectual property from applied research. The main research and development expenses of Idea Puzzle are the continuous investment in the Idea Puzzle website and software as well as in free seminars lectured by the first author in HEIs. In 2009, the beta version of the Idea Puzzle software was made available for free at the respective website. In 2012, the University of Porto became the first HEI ever to acquire the license of the Idea Puzzle software, having renewed it annually ever since.

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In its current version, the Idea Puzzle software asks 21 questions, helps answer them, and allows the self-evaluation of each answer. The sequence of 21 questions follows a funnel logic to help focusing a research design. The output of the Idea Puzzle software is a research design with an overall score and a visual jigsaw puzzle based on the 21 answers and the respective self-evaluation. The estimated time to complete a research design is of one working day, ideally six months after enrolling in a PhD. The main benefits of the Idea Puzzle software are the coherent design and defence of a research project from the point of view of Philosophy of Science (Morais, 2010). To date, the Idea Puzzle software has helped design more than 4000 research projects worldwide. In 2017, the University of Auckland agreed to purchase a university-wide license of the Idea Puzzle software. Since it was not created exclusively for the University of Auckland it was necessary to make it work for its constituencies. This was achieved through: a) communications to supervisors and doctoral candidates; b) a mandatory induction day for new doctoral candidates in the first few months of candidacy; c) the “Writing the full thesis or research proposal” workshop; and d) the “Organizing and writing the literature review” workshop. In particular, the Idea Puzzle software has been explained to the doctoral candidates and supervisors through university-wide communications, emphasising that it complements rather than replaces regular academic support. After the induction day, participants are sent an email message thanking them for their involvement during the day and mentioning that they can delve more deeply into the question of what doctoral level research is (one of the key discussion themes during the induction) for their own project, by registering at the Idea Puzzle website. In addition, doctoral candidates are advised to watch the 25-minute YouTube presentation by the first author to help them understand the software’s purpose. This approach ensures that all new doctoral candidates get to know about the Idea Puzzle software and its contextualisation within their doctoral induction to the university. In addition, two core Doctoral Skills Programme (DSP) workshops have been redesigned – “Writing the full thesis or research proposal” and “Organizing and writing the literature review” – to accommodate the Idea Puzzle software. In particular, each student has to submit a fully developed thesis proposal before final confirmation into the doctoral programme around nine months after enrolment. While the required length and format of the written proposal will vary from faculty to faculty, they all go through independent review by a departmental or school postgraduate committee and each student has to meet with the committee members to discuss and (if required) defend it. And, in essence, all proposals irrespective of academic discipline, need to demonstrate that: there is a coherent thesis question or problem

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(that will likely yield an original contribution); the doctoral candidates are aware of the key debates in the literature pertinent to their topic; the methodological approach is appropriate for the task at hand; and that, overall, the document is coherent, well written, scholarly, and persuasive. With the availability of the Idea Puzzle software to help doctoral candidates think through the key questions about their research (and hopefully spark conversations with their supervisors about ontology and epistemology), the DSP thesis proposal workshop has been altered to focus more on the university’s requirements (timelines, resources, financial support for attending academic conferences, and conducing fieldwork etc.) and examining recent thesis proposals donated by doctoral candidates in their second and third years as University of Auckland exemplars. So, the faceto-face workshop deals with the practicalities of producing a written document to meet the university’s expectations with the proviso that the Idea Puzzle software is the recommended mechanism to develop the content for their full thesis proposal. On the other hand, people attending the “Organizing and writing the literature review” workshop are asked to watch the first eight minutes of the 25-minute YouTube presentation by the first author, where he overviews the “theory” set of questions embedded in the Idea Puzzle software. The pre-workshop information thus instructs participants as follows: Dr Morais discusses the literature review: key words, key debates in your research as well as methodology and data collection. His talk is 25 minutes; the opening eight minutes deal with the literature review (the first five theoretical questions of the puzzle). Watch the whole presentation if you have time before the workshop but, if nothing else, please view the first part where he discusses the literature review and think about the questions he poses. We will build on these in the workshop. This preparation not only directs doctoral candidates to the Idea Puzzle software as a resource for the entirety of the doctorate but in the short-term it helps focus the workshop discussions and activities to a higher level of abstraction rather than getting bogged down in minutiae of each person’s literature review. Facilitators ask participants to explain their two key concepts or key search terms in smaller groups and give a lay person’s explanation of not only who were (or are) the seminal researchers for their topic, but why these researchers, and their associated works, are important. These conversations are to help participants create a narrative or overarching purpose for their literature review, from which they can start identifying sub-section headings for the review and then start writing. As a result, DSP organisers now expect doctoral candidates to acquire generic project management skills with DSP workshops (CRAC, 2010), deeper integrative

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thinking with the Idea Puzzle software (Abrahamson, 2008; Kallio, 2011), and further feedback from their supervisors and methodology teachers. The Idea Puzzle software thus fills in a gap in the DSP, but it requires an introduction to its purpose in the context of the doctorate. This and other issues are discussed in the following section.

ISSUES, CONTROVERSIES, AND PROBLEMS Between 2007 and 2017, the first author tested the Idea Puzzle framework in 231 seminars with doctoral candidates, supervisors, and methodological teachers. Such seminars, usually with a duration of one hour, provided face-to-face questions and feedback to the Idea Puzzle framework and were supplemented by an online anonymous feedback questionnaire emailed as a link to the participants after the seminar. In April 2018, the first author presented the analysis of the first 1004 responses to the online anonymous feedback questionnaire at the 13th Quality in Postgraduate Research Conference in Adelaide, Australia (Morais, 2018). The response rate was 15.5%, from a total of 6487 seminar participants from 71 HEIs in 15 countries: Austria, Belgium, Chile, Denmark, Estonia, Finland, Germany, Lithuania, Portugal, Slovakia, Spain, Sweden, Switzerland, UK, and USA. The online anonymous feedback questionnaire included eight quantitative closed questions to be rated in a scale of 0 to 10. The average rating per closed question was the following: 1. 2. 3. 4. 5. 6. 7. 8.

Achieving the objectives of the seminar – 8.7. Contents of the seminar – 8.8. Suitability of the teaching method – 8.7. Study materials – 8.0. Interaction with the participants – 8.2. Lecturer’s knowledge of the topic – 9.5. Clarity of teaching – 9.1. Total evaluation of the seminar and the lecturer – 8.8.

According to such results, the Idea Puzzle framework is primarily associated with new knowledge and clarity. Such feedback may reflect, in turn, the emphasis of the Idea Puzzle framework on Philosophy of Science rather than research methods, on dilemmatic decisions rather than sequential tasks, and on sampling as a matter of data, but also theory and method. The online anonymous feedback questionnaire also included three qualitative open questions on the best points of the seminar, suggestions for improving the seminar, and topics that could justify other future seminars. The qualitative analysis 57

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of the respective responses generated the following alphabetical index of themes of interest for the participants in the 231 seminars: academic writing, assessment, comparative studies, data analysis, data collection, digitalisation, interdisciplinarity, impact, literature review, Philosophy of Science, project management, Psychology of Science, research cases, research ethics, research focus, research methods, research teams, science communication, Sociology of Science, specialisation, supervision, thesis defence, theory development, and visualisation. Philosophy of Science was mentioned by 92 respondents as topic that could justify other future seminars (9.1% of the 1004 respondents). Visualisation was mentioned as one of the best points of the annual seminar “How to design your PhD” at the European Institute for Advanced Studies in Management (EIASM) in Brussels: “The Idea Puzzle is very helpful in terms of organising one’s research stand and helps visualise the work that is yet to be done.” Based on the facilitation of the 231 seminars and the analysis of the 1004 responses to the online anonymous feedback questionnaire, the first author concluded that: 1. The notion of research design tends to be restricted to method (rather than theory, method, data, rhetoric, and authorship). 2. The notion of sampling tends to be restricted to data (rather than theory, method, and data). 3. Doctoral candidates struggle to find a balance between focus and quality (more theory than they can synthesise, more methods than they can implement, and more data than they can process in the course of a three-year doctorate). 4. Supervisors and methodology teachers tend to convey sequential (e.g., research question first), boundary (e.g., qualitative vs. quantitative) and conflating (e.g., research strategy and technique) myths. 5. Cross-cultural face-to-face seminars are a powerful vehicle for theory development and testing. 6. Online anonymous feedback is more sincere than face-to-face seminars. 7. Online anonymous feedback reveals a wide range of research training gaps from the point of view of doctoral candidates, starting with Philosophy of Science. In August 2017, the second author conducted a usability testing of the Idea Puzzle software at the Libraries and Learning Services of the University of Auckland, together with a colleague with expertise in digital learning resources. Five international doctoral candidates were individually interviewed while they engaged with the Idea Puzzle software for about 1-2 hours over lunch time. The doctoral candidates were from South and East Asia, South America, and Europe (four of whom were from non-English speaking backgrounds). One interviewee really 58

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liked the Idea Puzzle software and wished it had had the opportunity to use it right after enrolling as a doctoral candidate. Three interviewees were very supportive of the Idea Puzzle software and promptly answered its questions. One interviewee, however, was very ambivalent about the purpose of the Idea Puzzle software in the context of the doctorate. Navigation feedback from the doctoral candidates included the lack of an explicit conclusion after answering the 21 questions and the impossibility to export the research design in Word format. Deeper feedback included the unclear purpose of the Idea Puzzle software and its integration with the rest of the doctorate. The DSP coordinators (including the second author), on the other hand, were initially concerned that the four European languages available at the Idea Puzzle website – Portuguese, English, Spanish, and French – would be insufficient for non-European users. Such concerns were partially alleviated, however, when one interviewee demonstrated familiarity with the four languages. The following section presents the solutions implemented to address each of the issues identified in the usability testing at the University of Auckland as well as general recommendations based on the large-scale survey described in this section.

SOLUTIONS AND RECOMMENDATIONS One of the suggestions of the 19-page usability testing report from the Libraries and Learning Services of the University of Auckland was the creation of an introduction to the Idea Puzzle software at the Idea Puzzle website (Morais & Brailsford, 2018). As a result, a new home page was created (“Introduction to the software”), including a short text and a video introduction. The introduction states the problem (lack of doctoral training on Philosophy of Science, integrative thinking, timely completion, and focus); the solution (visual decision-making tool for an integrative research design based on Philosophy of Science, including output, examples, benefits, and practicalities); as well as the time and timing required to complete a research design (one working day, ideally six months after enrolling in a PhD). In terms of usability, the following updates were made in the Idea Puzzle software: 1. The 21 jigsaw pieces of the triangle are now triggered by the user’s mouse to pop-up an alternative text stating their category (theoretical, methodological, empirical, rhetorical or authorial) and the respective question. 2. The word implicit was removed from all questions. 3. The help button has now darker and larger font than other buttons.

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4. The philosophical terminology was removed, except in the help to answer question 6 (philosophical stance). 5. The examples in the help are now more prominent. 6. The examples now include five disciplines (Design, Engineering, Management, Medicine, and Psychology). 7. The help and examples now open in a new page of the Internet browser to be more visible. 8. A button “previous” has been added before “next”, being deactivated in question 1. 9. The button “next” was deactivated in question 21 to prompt users to conclude the process of answering the 21 questions with the buttons “preview” and “print PDF”. 10. A button “convert PDF” was added to the software menu to allow the sharing of the research design with supervisors and methodology teachers in Word format. In terms of recommendations, this section builds on the results of the large-scale survey presented in the previous section to suggest higher plurality of contents in doctoral curriculum (Gonzalez-Ocampo et al., 2015). In particular, doctoral candidates need to be aware of holistic (Huff, 2009) rather than restricted notions of research design (Creswell, 2009) to account for the integration of theory, method, data, rhetoric, and authorship in their research projects. In addition, they might benefit from an extended notion of sampling that applies to data as well as to theory and method (Mullins & Kiley, 2010). Particularly delicate is the struggle of doctoral candidates to find a balance between focus (McGrath, 1981) and quality (Brinberg & McGrath, 1985). In this respect, this section suggests a period of divergent reading in the first six months after enrolling in a PhD and convergent thinking thereafter. Correspondingly, doctoral candidates are invited to diverge with mind and conceptual maps (Eppler, 2006) in the first six months, and converge with the Idea Puzzle software thereafter (Parente & Ferro, 2016). A related issue is that doctoral candidates should not be asked by supervisors or methodology teachers a research question or hypothesis as necessarily the first step in research design because it tends to be deduced from the literature review (Bryman, 2012). This is not to say, however, that research is just a sequence of project tasks such as the literature review because it also involves dilemmatic decisions (McGrath, 1981). In addition, it is important to clearly separate philosophical stances, research strategies, and techniques for data collection and analysis (Creswell, 1998) and to remain open about the pros and cons of qualitative, quantitative, and mixed 60

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research designs (Creswell, 2009; McGrath, 1981). Finally, more research training on Philosophy of Science is needed for doctoral candidates (Abrahamson, 2008) so that a PhD may indeed mean that one has become a Doctor of Philosophy no matter the discipline of graduation. In line with these recommendations, the following section suggests avenues for future research.

FUTURE RESEARCH DIRECTIONS Although this chapter presents the results of a large-scale survey and a usability testing, further feedback is necessary to validate the impact of the Idea Puzzle software on its users. As a first step in that direction, the online anonymous feedback questionnaire has been updated to include two quantitative and two qualitative questions on the utility and usability of the Idea Puzzle software for the participants in the respective seminar. In similar fashion, further usability testing such as the one conducted at the University of Auckland is necessary to better understand the contribution of the Idea Puzzle software to the research of doctoral candidates. This is particularly important given the continuous investment on the Idea Puzzle website and software as a result of user feedback and technological development. By the end of 2018, approximately 630 new University of Auckland doctoral candidates will have been exposed to the Idea Puzzle software through the induction day. The two authors will then be able to calculate the proportion of new candidates who voluntarily took up the offer to register at the Idea Puzzle website. In addition, it will be possible, with ethics approval, to invite users to attend focus groups that explore how they used the Idea Puzzle software, especially to develop their full thesis proposal and write a draft literature review chapter. Another avenue for future research is the study of the Idea Puzzle software as a knowledge visualisation tool. In this respect, it will be important to assess possibilities such as different colours or shades to further clarify its three levels of synthesis: a) three sides of the triangle; b) five categories of decisions; and c) 21 key decisions. A related issue is whether the jigsaw puzzle metaphor employed by the Idea Puzzle software may be complemented with other mapping methods (Eppler, 2006). These questions provide interesting avenues for future research. In particular, it will be important to conduct both qualitative and quantitative studies of users’ reaction to the Idea Puzzle software in the context of their doctorate. More importantly, it will be relevant to assess if the use of the Idea Puzzle software increases completion rates above their institutional average.

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CONCLUSION The Idea Puzzle software stimulates visual integrative thinking (Kallio, 2011) for coherent research design in the light of Philosophy of Science (Morais, 2010). In particular, it provides a framework of three domains (Brinberg & McGrath, 1985), five major categories (Epper, 2006), and 21 dilemmatic decisions (McGrath, 1981) for a more holistic (Huff, 2009) and visual (Meyer et al., 2013) integration of research design. For that purpose, it employs a visual metaphor which is known for its cognitive and instructional virtues as well as for its adoption by several philosophers of science (Eppler, 2003). The particular visual metaphor of the Idea Puzzle software is the jigsaw puzzle, a human-made object which was invented for educational purposes (Hannas, 1972). A large-scale survey in 71 HEIs based on an online anonymous feedback questionnaire following 231 seminars in 15 countries inspired recommendations for doctoral education. In particular, the adoption of the Idea Puzzle software in HEIs suggests the need for more Philosophy of Science and Knowledge Visualisation in doctoral curriculum (Gonzalez-Ocampo et al., 2015). In addition, the first usability testing of the Idea Puzzle software at the University of Auckland allowed the identification of issues and respective solutions concerning its utility and usability. Correspondingly, the main suggestions for future research are the need for more studies on the utility and usability of the Idea Puzzle software as a knowledge visualisation tool for the overall design of a research project.

ACKNOWLEDGMENT The authors would like to acknowledge Dr David Oliva Uribe, Head of the European University Association – Council for Doctoral Education, and Associate Professor Caroline Daley, Dean of Graduate Studies at the University of Auckland, for meeting the first author in Brussels; Dr Liz Sowden, Learning Adviser at the University of Auckland, for conducting the usability testing with the second author; the five participants in the usability testing for their constructive feedback on the Idea Puzzle software; Dr Helen Ross, Manager of the School of Graduate Studies at the University of Auckland, for acquiring and renewing the first license of the Idea Puzzle software in Australasia; and the participants in the 13th Quality in Postgraduate Research Conference in Adelaide, Australia for their constructive feedback on earlier versions of this chapter.

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REFERENCES Abrahamson, E. (2008). 22 things I hate: Mini rants on management research. Journal of Management Inquiry, 17(4), 422–425. doi:10.1177/1056492608324093 Bourdieu, P. (1986). The forms of capital. In J. Richardson (Ed.), Handbook of theory and research for the Sociology of Education (pp. 241–258). New York, NY: Greenwood. Bresciani, S., & Eppler, M. (2015, October). The pitfalls of visual representations: A review and classification of common errors made while designing and interpreting visualisations. SAGE Open, 1–14. Brinberg, D., & McGrath, J. (1985). Validity and the research process. Beverly Hills, CA: Sage Publications. Bryman, A. (2012). Social research methods (4th ed.). Oxford, UK: Oxford University Press. Careers Research and Advisory Centre. (2010). Vitae researcher development framework. Retrieved from https://www.vitae.ac.uk/vitae-publications/rdf-related/ researcher-development-framework-rdf-vitae.pdf/view Council of Graduate Schools. (2007). Ph.D. completion and attrition: Analysis of baseline program data from the Ph.D. completion project. Washington, DC: Council of Graduate Schools. Creswell, J. (1998). Qualitative inquiry and research design: Choosing among five traditions. London: Sage Publications. Creswell, J. (2009). Research design: Qualitative, quantitative, and mixed methods approaches. Thousand Oaks, CA: Sage Publications. Danado, J., & Paternò, F. (2014). Puzzle: A mobile application development environment using a jigsaw metaphor. Journal of Visual Languages and Computing, 25(4), 297–315. doi:10.1016/j.jvlc.2014.03.005 Eppler, M. (2003, July). The image of insight: The use of visual metaphors in the communication of knowledge. Paper presented at the I-KNOW ’03 Conference, Graz, Austria. Eppler, M. (2006). A comparison between concepts maps, mind maps, conceptual diagrams, and visual metaphors as complementary tools for knowledge construction and sharing. Information Visualization, 5(3), 202–210. doi:10.1057/palgrave. ivs.9500131 63

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Eppler, M., & Burkhard, R. (2007). Visual representations in knowledge management: Framework and cases. Journal of Knowledge Management, 11(4), 112–122. doi:10.1108/13673270710762756 Gonzalez-Ocampo, G., Kiley, M., Lopes, A., Malcolm, J., Menezes, I., Morais, R., & Virtanen, V. (2015). The curriculum question in doctoral education. Frontline Learning Research, 3(3), 23–38. Hannas, L. (1972). The English jigsaw puzzle: 1760-1890. London: Wayland Publishers. Hillman, A. (2011). Editor’s comments: What is the future of theory? Academy of Management Review, 36(4), 607–609. doi:10.5465/AMR.2011.65554604 Huff, A. (2009). Designing research for publication. Thousand Oaks, CA: Sage Publications. Kallio, E. (2011). Integrative thinking is the key: An evaluation of current research into the development of adult thinking. Theory & Psychology, 21(6), 785–801. doi:10.1177/0959354310388344 Layder, D. (2013). Doing excellent small-scale research. London: Sage Publications. doi:10.4135/9781473913936 Leshem, S., & Trafford, V. (2007). Overlooking the conceptual framework. Innovations in Education and Teaching International, 44(1), 93–105. doi:10.1080/14703290601081407 McGrath, J. (1981). Dilemmatics: The study of research choices and dilemmas. The American Behavioral Scientist, 25(2), 179–210. doi:10.1177/000276428102500205 Meyer, R., Höllerer, M., Jancsary, D., & Leeuwen, T. (2013). The visual dimension in organizing, organisation, and organisation research: Core ideas, current developments, and promising avenues. The Academy of Management Annals, 7(1), 489–555. doi: 10.5465/19416520.2013.781867 Morais, R. (2010). Scientific method. In A. Mills, G. Durepos, & E. Wiebe (Eds.), Encyclopedia of case study research (Vol. 2, pp. 840–842). Thousand Oaks, CA: Sage Publications. Morais, R. (2018, April). The Idea Puzzle framework: 21 decisions to focus a research design. Paper presented at the 13th Quality in Postgraduate Research Conference, Adelaide, Australia.

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Morais, R., & Brailsford, I. (2018, April). Usability testing and research software: The case of the University of Auckland and the Idea Puzzle software. Paper presented at the 13th Quality in Postgraduate Research Conference, Adelaide, Australia. Mullins, G., & Kiley, M. (2010). ‘It’s a PhD, not a Nobel Prize’: How experienced examiners assess research theses. Studies in Higher Education, 27(2), 369–386. Parente, C., & Ferro, L. (2016). Idea Puzzle (www.ideapuzzle.com), created by Ricardo Morais. Academy of Management Learning & Education, 15(3), 643-645. Pearson, M., & Hubball, H. (2012). Curricular integration in pharmacy education. American Journal of Pharmaceutical Education, 76(10), 1–8. doi:10.5688/ ajpe7610204 PMID:23275669 Riggs, P. (1992). Whys and ways of science: Introducing philosophical and sociological theories of science. Carlton, Victoria: Melbourne University Press. Sarasvathy, S., Dew, N., Read, S., & Wiltbank, R. (2008). Designing organisations that design environments: Lessons from entrepreneurial expertise. Organization Studies, 29(3), 331–350. doi:10.1177/0170840607088017 Tsang, E. (2016). The philosophy of management research. New York, NY: Routledge. doi:10.4324/9781315463216 Van de Ven, A. (2007). Engaged scholarship: A guide for organisational and social research. Oxford, UK: Oxford University Press. Worren, N., Moore, K., & Elliott, R. (2002). When theories become tools: Toward a framework for pragmatic validity. Human Relations, 55(10), 1227–1250. doi:10.1177/ a028082

KEY TERMS AND DEFINITIONS Conceptual Framework: An analytical tool that depicts a certain phenomenon parsimoniously. Integrative Thinking: A synthesis of lower-level elements that integrates and reformulates them into a coherent new whole. Jigsaw Puzzle: A human-made object with the educational purpose of assembling jigsaw pieces with different shapes to convey an overall picture. Knowledge Visualization: A visual representation that allows the transfer and creation of knowledge between two or more persons.

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Philosophy of Science: An academic discipline that studies the logic of scientific discovery and justification for the acquisition of original knowledge. Research Design: A draft that integrates theory, method, data, rhetoric, and authorship for subsequent implementation of academic research. Research Software: A computer-based application that converts inputs into outputs to support the user in one or more research tasks. Usability Testing: A face-to-face session in which interviewers register the reactions of interviewees as they interact with a certain website or software. Visual Metaphor: A visual representation that maps knowledge with the support of an analogy from the natural or human-made world. Visual Representation: A mode of communication based on holistic and immediate visuals rather than linear and sequential verbalization.

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Big Data and Doctoral Research:

Opportunities, Challenges, and Cautions Richard C. Berry Intersect Australia, Australia Lucy Johnston University of Newcastle, Australia

ABSTRACT This chapter explores opportunities and challenges that are presented to doctoral candidates (and indeed all researchers) through access to big data. The authors consider what big data is and what it is not, and how working with big data differs from traditional research design and analysis. They provide examples of the opportunities that big data offers in terms of the combination of diverse data sets, sources, and types and how it can provide new perspectives on inter-disciplinary challenges. They also highlight some of the challenges for the use of big data, both for the individual researcher and for institutions. The authors advocate for the need to embrace these challenges but without foregoing data integrity and the expert use and interpretation of data.

INTRODUCTION Consider this thought experiment: How you would attempt to identify those people who do not default on credit-card payments. With concerns about personal debt and loan sharks, this ability would benefit not just credit-card companies but also broader society. Social scientists might consider retrospective comparison of DOI: 10.4018/978-1-5225-7065-3.ch004 Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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groups of individuals who have and have not defaulted on payments in order to identify differences between them. They may also conduct prospective studies that follow the activities of individuals with credit cards to see who does and does not default on payments. In the former, what variables would they compare between the defaulters and non-defaulters? In the latter, what aspects of the individuals’ lives would be tracked and how many people would have to be followed, and for how long, to get a large enough sample of defaulters and non-defaulters to give the study sufficient power? Whichever of these approaches, or any of the myriad of other possible approaches is taken, it is likely that it would be a long time, if ever, before such researchers identified that the purchase of anti-scuff furniture pads is a strong predictor of not defaulting. Identification of this factor is an example of the power of Big Data (Shaw, 2014). The ease and reduction in cost of storing large quantities of data, and the huge improvements in the accessibility and discoverability of online data sets has led to a previously inconceivable increase in the availability of data which has had, and will continue to have, profound impacts on research, including doctoral research. Doctoral candidates are no longer restricted to using data that they are able to collect, store, and process in the limited timeframe and resources of their doctoral candidature. Instead they will have access to national and international datasets and networks of researchers that will facilitate not only access to more data, but also comparisons across time and location, and have opportunities for developing previously unconceived of research collaborations. Data from doctoral candidates’ research will also add to the global collective, where it can be made available to others such that the impact and influence of that data may be wider than that which might be achieved by the doctoral candidate working in isolation. For example, the interRAI project (www.interrai.org) looking at the lifestyle and health of the elderly has a standard 250 question survey administered to all participants in over 30 participating countries, including Australia and New Zealand. Doctoral candidates working on this project accordingly have access to a much larger data set than would ever have been possible prior to the developments in data storage, analysis, and in the ease of international communication and data transfer. This increased access to data has had, and will continue to have, a positive impact on doctoral research, its advancement of knowledge, and impact on society. At the same time, however, this increased data access and storage will lead to differences in the doctoral research experience. The traditional doctoral thesis was an isolated piece of research with data collection, collation, and analysis often done by the candidate under supervision. The candidate typically took responsibility for the research design, the nature of the data collection, and for data integrity. Using Big Data may constrain the candidate’s control over these aspects, perhaps resulting in more emphasis on collaboration (including virtual collaboration), and inter-disciplinarity. 68

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In embracing Big Data, institutions have needed to enhance various data analytics training for doctoral candidates as seen below. At the same time, institutions should also consider whether measures need to be taken to ensure that skills acquired in the traditional thesis model not be lost through a change in data paradigm.

DEFINING BIG DATA In considering Big Data and its potential impact on doctoral (and indeed other) research, it is important to begin with a clear definition of what Big Data is, what it is not, and importantly to clarify the distinction between large datasets and Big Data. Being LARGE is not enough for a data set to qualify as BIG data. While Big Data does also take advantage of all the opportunities afforded by large datasets, it also offers something more than “just more”. While the origins of the term Big Data are unclear, it may have been used as early as the late 1990s (Diebold, 2012). However, it took another decade or so for the term to take on broad usage. While Big Data can be difficult to capture in a simple definition, a common understanding comes from the leading technology advisory company Gartner. According to them, Big Data involves deriving insights from data this is some unwieldly combination of the “three Vs” (Gartner, 2018): “Volume” refers to the size of the data. Data set volume is usually measures in bytes, while Big Data sets are often in terabyte or petabyte scale. A terabyte can hold approximately 166 hours of high definition video, or 1,300 hours of high quality audio, or 160,000 images taken with a 20 megapixel camera. A petabyte is 100 terabytes and one petabyte can hold roughly 5 years’ worth of satellite imagery data from NASA’s Earth Observing System (at 46 mbps) (Bunn, 2011). So Big Data sets are certainly large in volume. It is not, however, the only important factor that needs to be considered (Jagadish, 2015). “Velocity” refers to the speed at which data is generated, like, for example, the 300 million Facebook photos uploaded per day (Zephoria Digital Marketing, 2018). Or the sequencing of the entire human genome – initiated in the 1980s by the groundbreaking Human Genome Project – that took 13 years to complete the first full sequence of the entire human genome (NIH, 2012), but with today’s modern sequencers and computing technology would take approximately a day. “Variety” refers to the data source. Data we seek to use in research are often “structured” data. They are well ordered, of a single predictable type, and lend themselves to storing cleanly in columns, rows, tables, and matrices. However, much of the information in the world (up to 90% according to one source: van den Hoven, 2001) is considered to be “unstructured”. Text information found in documents, blogs, websites, or social media sites is usually not uniform in things like length, 69

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syntax, topic, or even language; nor it is organised in well-defined structure that is easy to search, quantify, compare and contrast. Similarly, audio or video recordings are not easily classified or organised in a way that allows rapid analysis. High variety usually refers to including text or other unstructured data sources. However, many Big Data projects seek to include data from a multitude of sources, each of which adds some additional potential for insight into the data. Another aspect of highvariety is therefore the inclusion of data from multiple sources. While the three Vs form a common theme in definitions of Big Data, several other Vs have also been proposed to form part of the definition of Big Data, the common ones being including the veracity, variability, and value of data (Gandomai & Haider, 2015). To put it more succinctly, Big Data is inherently large, volatile, and messy. So much so that it stretches the limits of both computers and humans, requires innovative new approaches to be developed for handling Big Data sets, and forces analysts and researchers out of the comfort zone of their traditional data analysis methods. Volume is important because working with such large data sets creates some significant challenges. The complete data set may well be much too large to store on one computer, meaning it will most likely need to be stored in a data centre or possibly in a distributed fashion across multiple computers or data centres. Moving such large data from one location to another may be very slow and require special software or infrastructure, and processing large data sets may also need something far more powerful than a single laptop or desktop computer, which is where specialised computing facilities become important. Velocity too creates significant computational challenges. Often manually retrieving and analysing data of this velocity is out of the question, and therefore specialised programs must be developed to automate these aspects. Velocity is especially problematic when the most up-to-date information possible is needed, which requires continually analysing the data in real-time. However, velocity can also be a problem in certain research fields where timeliness of data processing is important. A good example is the increasingly important area of personalised medicine, where results from an individual may need to be compared against a much larger pool of data from a wider population and returned to the patient within hours or days. Perhaps the most profound difference, and the most challenging for established researchers is variety, the third of the Vs. Processing and categorising unstructured data is often complex and time consuming (not to mention tedious), often requiring special, sophisticated tools and techniques for automating analysis, such as machine learning algorithms and natural language processors. Additionally, different data sources are likely to have different ways of organising, categorising, and coding data. This leads to further

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problems when attempting to combine data from different sources, which is another common characteristic of Big Data. Defining Big Data highlights some of the challenges for doctoral candidates. To take the opportunities afforded, these candidates need to have access to suitable infrastructure and training in Big Data management. This will be a particular challenge for current doctoral candidates who have not previously been exposed to such types of data. Over generations of doctoral candidates, however, this will likely change as Big Data analytics is added to undergraduate and masterate curricula. A final consideration in defining Big Data is consideration of when data becomes Big Data; how do you know if the data you are working with is Big Data? Perhaps, unsurprisingly, there is no simple answer; it is not possible to point to a single threshold or data size, type, or velocity to define a clear benchmark for clearly identifying Big Data. One concept for a Big Data threshold is that a data set enters the realm of Big Data when any combination of its Volume, Velocity or Variety (or any other aspect that makes it difficult to work with) becomes difficult to handle in context (Jagadish, 2015). This “3V tipping point” could be reached when using “traditional” technologies (such as a database or data warehouse) is no longer able to produce intelligence in a timely fashion (Gandomai & Haider, 2015). Some would say it could also be reached when a data set outgrows the limit of a spreadsheet program, if that is what the researcher, analyst, or data owner is familiar with and currently uses to process their data (Jagadish, 2015). An interesting feature of the 3V tipping point concept is the implication that there will always be Big Data-like challenges, and that what constitutes a Big Data challenge varies between researchers and organisations. The ever-increasing availability of new data sources and need to process and understand data in shorter timeframes have continually pushed the need for new approaches and technologies in computing. Therefore, while many consider the term Big Data to be a passing buzzword, it seems likely that the underlying types of problems posed by Big Data, and the need for new technologies and platforms will continue. Data sources are continually evolving and becoming more voluminous, complex and accessible. Increasingly sophisticated instruments (e.g., satellites and microscopes; (Kramer, n.d.a; n.d.b), can output greater volumes of data in shorter spaces of time. There has been an explosion of the number and type of data sensors (e.g., mobile phones (Zhang et al., 2012). And more recently research, government and private industry data sets are now openly accessible (e.g., data.gov.au). A key for those supporting doctoral (and other) researchers is to assist with the tricky navigation between challenge and opportunity, providing researchers and end-users not only with the right tools but also the confidence in the product of such analyses.

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THE OPPORTUNITIES AND THE CHALLENGES The prevalence and ability to access this data has enormous potential to make new and exciting discoveries. The power and impact of Big Data comes not only from the joint development of large-scale research projects or the combination of datasets from international collaborations; it comes from the ability to combine existing datasets, both structured and unstructured, from a multitude of sources. These datasets were not designed with combination in mind and have no a priori agreements about the structure or the manner of collection of the data – what would previously have been considered to be an anathema to doctoral research. The biggest research gains may not be in larger datasets per se but rather in the linkages between different datasets, bringing together data to reveal insights that may never have been thought of or considered in the design and collection of any individual project. Where previously links between datasets containing different information in different formats collected for different purposes could not be reliably made, Big Data techniques now allow for such. Society’s challenges are becoming more and more complex. The exciting research opportunities to address the challenges that Big Data afford must be grasped. The opportunities for all researchers offered by Big Data are exciting, but a number of important questions need also to be considered to ensure that the power of Big Data is used to produce high quality research. A key lies in rigorous use of the data analytic tools available to researchers. The quality and impact of any and all research findings is based on the appropriateness of the research methodology and the rigour by which the research is conducted and analysed. The introduction of new research techniques and methodologies requires caution to ensure that these tools are used properly and add value to research programs – data does not answer research questions without the skill of the expert researcher. A cautionary tale from a Harvard University adjunct professor illustrates these risks (Harvard School of Public Health, 2012; https://www.hsph.harvard.edu/news/ press-releases/cell-phone-data-malaria/). Based in Rwanda, the researcher - an expert in communication engineering networks and systems - used mobile phone usage data to develop models of the daily and weekly commuting patterns of villagers. Noticing patterns in the data, it was hypothesised that these commuting patterns were related to the onset of communicable diseases. Linking his data set with that from the Ministry of Health, utilising the power of Big Data methods to link disparate datasets, revealed a powerful effect. Villager movement dropped prior to cholera outbreaks such that the magnitude of an outbreak could be predicted from the decrease in movement. Wow! On closer inspection, however, it turned out that the model was not predicting cholera outbreaks at all, but rather pinpointing the location of flooding which limits movement and increases risk of illness outbreaks. 72

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“Ultimately, all this analysis with supercomputers was identifying where there was flooding—data that, frankly, you can get in a lot of other ways” (Shaw, 2014). Without knowledge of the context in which data were collected and the research questions being asked, highly sophisticated data analysis on huge quantities of data can be fundamentally flawed. Computers are good at spotting patterns in data but they don’t have the contextual knowledge to draw out causation from correlation. What is needed is a combination of researcher skills (asking the right questions and interpreting the findings) and what the computer is good at (computation and statistics). One alone is not sufficient. It is also important to recognise the human superiority in understanding patterns in data – expert researchers must be looking at and interpreting the data patterns revealed through sophisticated computational modeling to glean the most from those data. Put more simply, researchers need to be taught how to use Big Data techniques appropriately. Big Data and data mining techniques allow for discovery, for combining data sets with no pre-planning for such, and for answering questions not conceived of when the data sets were collected or not possible to be considered from one data set or one type of data alone. This leads us to consider perhaps an even more fundamental question – whether the emergence of Big Data might change the nature of how we conceive of research and research questions. The traditional approach to research, including doctoral research, is to identify a pertinent research question (often derived from a theoretical basis or framework) and consider how it might be tested, and then, as necessary, collect, access and collate relevant data to address these questions. The availability of Big Data techniques might invert this process with researchers starting with access to datasets and asking what they might find in them and especially in the links between them. Rather than looking at data to answer a research question, the researchers are looking at data to find the question to ask, or indeed the question that the discovered data patterns answer. Hey et. al. (2009) referred to this as the fourth paradigm of science – exploratory science which is data intensive and involves statistical exploration and data mining (as cited in Kitchin, 2014). What impact might this shift from a curiosity-driven to a data-driven research agenda have? Knowledge-driven approaches collect data to meet a specific purpose. Data sets tend to be small, with a sample of data selected in some way from a wider population. Similarly, the number of analysis techniques applied tend may be the minimum number required to support or refute a hypothesis. Data-driven approaches often reuse data that has been collected for another purpose. The size of these data sets may be enormous, potentially covering an entire population rather than a sample. In order to uncover interesting associations, a large range of analysis techniques may be applied to any given data set, possibly with very little human direction. Consider how a hypothetical research question might be addressed by traditional knowledge-driven or data-driven approach: “There has been a recent rapid increase 73

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in the number of blue-tongue lizards found dead on farmland in Australia and researchers from the environmental sciences are trying to identify why”. A knowledgedriven approach to this problem might be to first identify known or suspected risk factors based on expert opinion and previous knowledge and then sample farmland for evidence of such factors (e.g., toxic fertilisers). A data-driven approach might be to obtain and compare satellite images of farmland before, during, and after the period of high deaths to monitor changes in known blue-tongue habitat zones, such as human interference or bush fires, and to use acoustic monitoring stations to monitor predator populations combining this with other possibility related sources such as rainfall and temperature data or land zoning data (identifying changes in possible pollutants). Is one approach better than another? In this case that might be assessed not by whether the correct cause of the lizard decline is identified (both approaches will likely achieve this) but how rapidly that cause is identified and how many blue-tongue lives are saved. Some proponents have argued that Big Data is capable of replacing established scientific methods and processes, calling for widespread adoption of Big Data analysis at the expense of other approaches (Anderson, 2009), while others have suggested caution, proposing that while Big Data can prove a useful means to uncover insights and correlations (the what), it is not at all suited to describing why; why questions being the realm of deep exploration and subject matter experts (Kitchin, 2014). Each approach might provide insight into the hypothetical problem and many other research questions, but in general each approach is best suited to different types of research question. Knowledge-driven research tends to be best suited to in-depth exploration of a defined topic, especially when there is a considerable amount of prior theoretical and empirical knowledge on the subject, which is necessary to propose a hypothesis to be tested. The data-driven approach is especially useful for exploring data sets to gain insights, which are then used to inform theories that can be analysed in a deductive manner - a “reconfigured version of the scientific method” (Kitchin, 2014, p. 6). The exploratory nature of data-driven research, combined with potentially large, messy, and interconnected data sets involved means that there is a high-degree of overlap between data-driven research and Big Data - both may encounter similar problems in dealing with high-volume, high-velocity high-variety data sets, and therefore require the same techniques and technologies to work with. Research methodology training has long led doctoral candidates to select specific research methods to tackle their specific research question. The recent increase in accessibility of Big Data and associated data analytical techniques to doctoral candidates raises the importance that they also have the skills to select an appropriate research framework – knowledge-based or data-driven – for their research question. Such skill must be incorporated into relevant research methodology training.

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History is littered with serendipitous research findings – penicillin, the microwave oven, post-it notes – but society needs curious minds as research, policy, education, and business leaders. Leaders in doctoral research education – supervisors, departments, institutions, Deans – must develop approaches, including computational skill development, that enable doctoral students to take advantage of the huge opportunities provided by Big Data without losing the basics of fundamental scientific enquiry; our focus needs to be on a balanced approach. Data-driven (Big Data) approaches are not appropriate to answer all research questions; other approaches must not be cast aside in the enthusiasm for the opportunities afforded by Big Data. Before embarking a Big Data research project, due care must be taken to consider whether the methodology fits with the desired outcomes, as in the cautionary tale at the beginning of this section. Another example where the success of using Big Data can be questioned is Google Flu Trends (GFT) (Ginsberg et al., 2009). An automated method to determine the prevalence and onset of influenza was created by examining 5 years’ worth of search engine log data. This automated approach could predict the onset of flu in an area 1-2 weeks ahead of the US Centre for Disease Control (CDC) obtained by the more traditional methods. Early detection of influenza pandemics could allow public health systems to respond earlier and more accurately to flu outbreaks (for example by focusing resources such as medical supplies and vaccines, or raising awareness through the media), which could have life-saving consequences. Six years later, however, researchers reported that GFT had significantly overestimated influenza rates for 100 out of 108 weeks between 2011 and 2013 (Lazer, Kennedy, King, & Vespignani, 2014). In the worst case, influenza predictions by GFT were about twice as high as rates published by the CDC. In fact, simple forward projection using 2-week lagged CDC data were found to be a better predictor of flu prevalence than GFT (Goel, Hofman, Lahaie, Pennock, & Watts, 2010). Google eventually ceased flu statistics and today GFT only provides historical data. So what when wrong? Researchers have suggested a number of factors that contributed to GFT’s failure to predict influenza prevalence, including that GFT was detecting winter, not influenza, that media attention from GFT led to over-reporting of flu symptoms and through Google’s use of suggested search terms (Lazer et al., 2014; Goel et al., 2010; Butler, 2013; Cook, Conrad, Fowlkes, & Mohebbi, 2011).

USING BIG DATA As noted above, fundamentally different computational skills are required for the analysis of big datasets than for the analysis of small datasets. We are not talking about adding a few more data points to a t-test or linear regression analysis. Analysis 75

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of Big Data sets requires fundamentally different techniques to be applied. The pervasive, all-encompassing nature of Big Data renders some tried and tested statistical techniques invalid, many of which assume the data being tested is a carefully curated sample of a larger population. In stark contrast, Big Data sets may be the entire population rather than a sample, they are almost always messy, seldom abide by assumptions of bell-shaped distributions, and may contain text, images, audio, or some other unstructured data that does not easily lend itself to conventional analysis. Data mining – the identification of relevant variables and their inter-relationships – and a myriad of other techniques such as machine learning, neural networks, network analysis, text, and image processing supplement, or in some cases replace, the traditional role of statistics. Further complicating the analysis is the pressing problem of how researchers deal with acquiring, storing, and visualising the incredible volume of the data and the speed at which it is generated. Even a 1% randomised sample of Twitter data, for example, generates unconceivable numbers of bytes/tweets per hour. The challenge facing doctoral candidates seeking to capitalise on the opportunities afforded by Big Data is not limited to the steep curve involved in learning new methods of analysis, but also the more fundamental issues of using advanced technology such as supercomputers and cloud computing to conduct that analysis in the first place. Perhaps most significantly, given the pace at which new analysis techniques are emerging, is the risk of errors induced by inappropriate use of techniques. Computers blindly follow the programmed algorithms, even if those algorithms are developed by people unfamiliar with the research questions being asked (such that the wrong questions are rightly answered) or by people who lack appropriate computational expertise to develop appropriate search algorithms (such that the right question is wrongly answered). The development of algorithms (research questions) and their interpretation still needs to be led by expert researchers, theory, and knowledge. Big Data, and Big Data analytical techniques, open wide many large doors for researchers but societal questions are problems solved by expert interpretation by knowledgeable experts; data analytic techniques cannot operate without a skilled driver. Notably, and highlighting the risks that arise here, the areas where the availability of Big Data might have the most profound effects – in medicine and public health and in social sciences – are those areas in which researchers, including both doctoral candidates and their supervisors, do not typically have the relevant computational skills to access and analyse Big Data. Institutions must ensure the availability of such training to enable effective and meaningful use of big datasets. Given the lack of computation skills amongst social science researchers, there is a tendency for those with the computational skills (e.g., engineers, physicists) to apply these skills to new research areas. The opportunities for inter-disciplinary collaborations abound, but disciplinary expertise cannot be forsaken; those with the computational skills cannot 76

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explore without the guidance of those with content knowledge. Equally, advancement and enhanced impact of research in the social sciences, public health, and so on are predicated on researchers in these domains embracing and upskilling in advanced computation skills and/or working in partnerships with skilled data analysts.

THE BIG DATA WORKFLOW The Big Data processing pipeline involves data acquisition, information extraction and cleaning, data integration, aggregation and representation, modelling and analysis, and interpretation (Jagadish, 2015) Big Data research projects can source data from a wide variety of locations. The challenges involved (and hence the tools and techniques applied) strongly depend on the characteristics of the underlying data source. Consideration needs to be given to a number of factors, only some of which are similar to questions asked in traditional data collection. For example, should data be captured continuously, which allows for real-time information but additional complexity, or should it be a snapshot of a defined time period? Should raw data be filtered at the point of capture to reduce the amount of information and simplify data transfer and storage requirements, although potentially limit the ability to reuse this information later for other purposes? How will raw data be validated to ensure complete and secure transfer from point of capture to point of analysis? Data coming in from many sources cannot usually be directly used in analysis. First, it must be cleaned and processed into a form that can be more easily used in subsequent analysis - a process often referred to as “data wrangling” or “data munging”. This cleaning might involve removing unwanted metadata or data, adding additional metadata (e.g., data source, time, and date of collection, and the software used for extraction), identifying and dealing with missing data, and converting data from one format into another. For example, consider a project extracting Twitter data that contains the hashtag #Research. Cleaning would include separating tweets containing multiple hashtags into separate lines, adding a username identifier to each, and adding metadata describing details such as the tweet location. Data sets also need to be checked for error. Given the size and complexity of the data set, verification and validation of the data can be a significant task itself that may need to be automated, for example, by designing scripts to assess the accuracy of data acquired or processed by other scripts. In order to be analysed, all data eventually needs to be stored somewhere, and determining how to store data in a data-intensive research project can be a very important decision. Even in a relatively simple case where the data comes from a single source, there are a large number of ways to store this data, and the method 77

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chosen can affect the speed and ease of later analysis. Big Data sets often include a lot of unstructured data such as text, images, or audiovisual recordings to be analyzed. Such data needs to be stored so it can to be readily and quickly analysed, for example, using a NoSQL (Not only SQL) database. These databases are structured in way that can make storage and analysis of such unstructured data much easier but are not currently taught. Infrastructure location and costs also need to be considered. Should data be stored on-site, or in a cloud-based system (for example Amazon Web Services or Microsoft Azure). In many cases processing the data will require significant computing power, and it is often advantageous to have computing power “close” to where the data is stored, in order to avoid bottlenecks that occur when having to transfer data across the internet. Large scale cloud computing systems provide for both data storage and computing but are costly. While cloud-based storage offers advantages in terms of easy access and back-up reliability, in small countries such as New Zealand and Australia this storage is often offshore, which can raise questions over data security and sovereignty. Once captured, cleaned, and securely stored, data analysis and modelling can occur. As we noted earlier, data-driven research often involves characteristically different types of data than more traditional methods, and it may not be possible to apply the same types of analysis scaled up. For example, many commonly used statistical tests rely on inherent assumptions being met, such as sampling from a wider population, or normal distribution of values. In contrast, Big Data often involves massive data sets that are messy and noisy, and may be constantly updated, which means traditional methods may not be suitable. A wide range of analysis tools have emerged to meet this need such as machine learning algorithms, neural networks, and network analysis. Often these tools are complex and require significant computing resources, which also invokes the need for specialist infrastructure, such as parallel computing, specialist technical staff, and/or data analytical courses for doctoral candidates so they can analyse the data. The final stage of the analysis pipeline is to interpret the results of the analysis with the aim of drawing robust and meaningful conclusions. A key element to this stage is data visualisation. Being able to represent data in a readily digestible form has now become almost an art form of itself, and generating visualisations can pose a significant computing challenge, especially where the data is dynamic and such visualisations must be frequently updated. In addition, due to the size and complexity of the data sets, presentation of a single (or small number) of images does not allow the user to investigate the full depth of the data set. The current generation of visualisation tools therefore commonly allows interactive exploration of the data (either preset or ad hoc) and often used web-based architectures to allow the processing to be run on a powerful server or cluster. Again this raises the need for specialist infrastructure, technical staff, and training. 78

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Once data has been analysed it is critical that researchers return to the purpose of the research. The primary aim of many Big Data projects (especially those that could be classified as “data-driven”) is to uncover insights in the data. It is ideally suited to exploration and uncovering associations within the data rather than deep exploration of the underlying reasons why an association exists. Kitchin (2014) quoting Porway (2013) noted that “As data scientists, we are well equipped to explain the ‘what’ of data, but rarely should we touch the question of ‘why’ on matters we are not experts in.” (p. 5). These “why” questions typically require on subject matter experts, which is part of the reason why such Big Data research projects are interdisciplinary endeavours.

FROM LITTLE THINGS BIG THINGS GROW Big data analytics is a big field. Which skills are required depends on the specifics of the doctoral research project in question. The geomorphologist investigating coastal erosion needs the ability to combine satellite imagery with tide gauge data to form a sophisticated simulation, while the social scientist investigating political opinions expressed on social media require skills in text mining, sentiment analysis, and visualisation. In addition to the range of skills possibly required, doctoral research projects vary enormously in scale from the lone PhD candidate working on a smallscale project in near isolation to large-scale international collaborations with interdisciplinary teams. This variance utterly rules out a “one-size-fits-all” approach to supporting doctoral research candidates in their Big Data endeavors, creating quite the challenge for universities. One the one hand, the potential opportunities and rewards of Big Data research are clear; on the other hand, the costs of nurturing and developing these skills may be considerable and, in places, meet with resistance. Nevertheless, a number of simple strategies may go a long way to supporting Big Data research. First and foremost, exposing doctoral research candidates to the concept of Big Data and the associated rewards and pitfalls is a necessary first step. While the concept of Big Data is seeping into the social consciousness, an understanding of how it can be applied in research and the practicalities of what is involved remain sorely lacking. Short seminars and training courses may help to alleviate this issue by raising awareness and knowledge of Big Data amongst PhD students and their supervisors. Second, technical proficiency - in particular an understanding of computer programming - is inescapable for any Big Data analysis (as well as many other forms of modern research). Wixom and colleagues (2014) advised that development of Big Data skills should become a key focus in academia at both undergraduate 79

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and graduate levels. The spectacular rise of organisations and events that focus on teaching programming and other eResearch skills to researchers (such as Software Carpentry - https://software-carpentry.org/ and ResBaz - https://resbazblog. wordpress.com/) attest to the broad need in academia. While not a complete solution in itself, participation in these events alone may be sufficient to start an entrepreneurial doctoral candidate on a path of self-learning. Furthermore, most universities provide a range of undergraduate courses in Big Data related topics that could be made accessible to doctoral candidates needing more in-depth knowledge. Thirdly, establishing cross-disciplinary teams must be considered for research projects that exceed the limits of even a technically skilled student. Again, universities themselves often have a broad talent pool that be utilised here given the right opportunities to mix skills need with skills availability. This includes both academic collaborators as well as support from professional staff in university IT departments. Indeed, some universities have gone so far as to establish centres specifically dedicated to support cross-disciplinary computationally intensive research, for example the Centre for Big Data Research in Health (https://cbdrh.med.unsw.edu.au/about-us) at the University of New South Wales, and the Centre for Translational Data Science (https://sydney.edu.au/data-science/) at the University of Sydney. Fourth, Big Data analysis has progressed far faster outside academia than within it. Industry partners may therefore provide a viable means for obtaining the required skills, either on a consulting basis or through industry placement of doctoral candidates. Finally, while many Big Data projects require significant (and expensive) computing systems, many governments – including both Australian and New Zealand governments – have made considerable investments into computing infrastructure for research use. However, awareness of these facilities, combined with lack of knowledge on how to use them, has meant that uptake is often limited to certain fields (astronomy, climate science, theoretical chemistry etc.). Campaigns to raise awareness of these facilities and their use may go a long way alleviating this imbalance. In short, a number of simple strategies focused on raising awareness, fundamental training, and fostering collaboration between the research specialists, computer scientists, statisticians, and software engineers may go a surprisingly long way to supporting Big Data research.

THE FUTURE Above all, some caution must be exercised to ensure appropriate and meaningful use of these data. In addition to the questions posed above and the discussion on the provision of research training and support for use of big data, there are additional challenges posed by these changes in how research is conducted in terms of data 80

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protection (see for example, https://dataprivacylab.org/projects/wa/index.html), privacy, and intellectual property protection. As mentioned above, storage of Big Data raises issues in regard to privacy especially when the data set can be accessed by many researchers independently of the others. Use of login credentials is a safeguard but if a credentialed individual were to download data into private storage it is then outside the scope of system protections. Privacy leakage may also be an issue, particularly when different facets of sensitive data are released separately, which unintentionally may enable somebody to piece together private information from the different releases. At the very least, doctoral candidates need to receive training around data protection, security, and the ethical conducting of research and research dissemination. It is important to note that doctoral candidates – those at the beginning of their research and leadership careers – are an important group for whom research leaders must ensure protection and not exploitation through the provision of, and access to, big data. Big Data affords exciting opportunities which doctoral candidates, leaders of the future, must grasp but in doing so must be aware of the challenges and responsibilities that they are grasping. HE PAI TE TIROHANGA KI NGA MAHARA MO NGA RAA PAHEMO ENGARI KA PUTA TE MAARAMATANGA I RUNGA I TE TITIRO WHAKAMUA It’s fine to have recollections of the past but wisdom comes from being able to prepare opportunities for the future

REFERENCES Anderson, C. (2009, July). The End of Theory: The Data deluge makes the scientific method obsolete. Wired Magazine. Retrieved from https://www.wired.com/2008/06/ pb-theory/ Bunn, J. (2011). How big is a Petabyte, Exabyte, Zettabyte, or a Yottabyte? Retrieved from http://highscalability.com/blog/2012/9/11/how-big-is-a-petabyte-exabytezettabyte-or-a-yottabyte.html Butler, D. (2013, February). When Google got flu wrong. Nature, 494(7436), 155–156. doi:10.1038/494155a PMID:23407515 Cook, S., Conrad, C., Fowlkes, A. L., & Mohebbi, M. H. (2011, August). Assessing Google flu trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic. PLoS One, 6(8), 1–8. doi:10.1371/journal.pone.0023610 PMID:21886802 81

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Diebold, F. X. (2012). A personal perspective on the origin(s) and development of ‘Big Data’: The phenomenon, the term, and the discipline, Second Version (PIER Working Paper No. 13-003). Penn Institute for Economic Research, Department of Economics, University of Pennsylvania. doi:10.2139srn.2202843 Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. doi:10.1016/j.ijinfomgt.2014.10.007 Gartner. (2018). IT Glossary: What Is Big Data? Retrieved from http://www.gartner. com/it-glossary/big-data Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012–1014. doi:10.1038/nature07634 PMID:19020500 Goel, S., Hofman, J. M., Lahaie, S., Pennock, D. M., & Watts, D. J. (2010). Predicting consumer behavior with Web search. Proceedings of the National Academy of Sciences of the United States of America, 107(41), 17486-17490. doi: 10.1073/ pnas.1005962107 Hey, T., Tansley, T. S., & Tolle, K. (2009). The Fourth Paradigm: Data-Intensive scientific discovery. Redmond, WA: Microsoft. Jagadish, H. V. (2015). Big data and science: Myths and reality. Big Data Research, 2(2), 49–52. doi:10.1016/j.bdr.2015.01.005 Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data and Society, 1(1), 1–12. doi:10.1177/2053951714528481 Kramer, H. J. (n.d.a). Landsat-1 to 3: Landsat-1 to Landsat-3. eoPortal Directory. Retrieved from https://directory.eoportal.org/web/eoportal/satellite-missions/l/ landsat-1-3 Kramer, H. J. (n.d.b). Landsat-8: Landsat-8/LDCM (Landsat Data Continuity Mission). eoPortal Directory. Retrieved from https://directory.eoportal.org/web/ eoportal/satellite-missions/l/landsat-8-ldcm Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: Traps in Big Data analysis. Science, 343(6167), 1203–1205. doi:10.1126cience.1248506 PMID:24626916 NIH National Human Genome Research Institute. (2012). A brief history of the human genome. Retrieved from https://www.genome.gov/12011239/a-brief-historyof-the-human-genome-project 82

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Shaw, J. (2014, March-April). Why “Big Data” Is a big deal: Information science promises to change the world. Harvard Magazine. Retrieved from https:// harvardmagazine.com/2014/03/why-big-data-is-a-big-deal Tanner, L. (2010). Declaration of open Government. Department of Finance, Australian Government. Retrieved from http://www.finance.gov.au/blog/2010/07/16/ declaration-open-government/ van den Hoven, J. (2001). Information Resource Management: Foundation for knowledge management. Information Systems Management, 18(2), 1–4. doi:10.12 01/1078/43195.18.2.20010301/31281.12 Wesolowski, A., Eagle, N., Tatem, A. J., Smith, D. L., Noor, A. M., Snow, R. W., & Buckee, C. O. (2012). Quantifying the Impact of Human Mobility on Malaria. Science, 338(6104), 267–270. doi:10.1126cience.1223467 PMID:23066082 Wixom, B., Ariyachandra, T., Goul, M., Gray, P., Kulkarni, U., & Phillips-Wren, G. (2011). The current state of business intelligence in academia. Communications of the Association for Information Systems, 29(16), 299–312. Zephoria Digital Marketing. (2018). The top 20 valuable Facebook statistics. Retrieved from https://zephoria.com/top-15-valuable-facebook-statistics Zhang, S., Wu, Q., van Velthoven, M. H., Chen, L., Car, J., Rudan, I., ... Scherpbier, R. W. (2012). Smartphone versus pen-and-paper data collection of infant feeding practices in rural China. Journal of Medical Internet Research, 14(5), e119. doi:10.2196/jmir.2183 PMID:22989894

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ICT Use to Support Doctoral Study

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Chapter 5

Digital Higher Degree Research (HDR) Scholarly Support and Community Building Jennifer Rowland Macquarie University, Australia

ABSTRACT In this chapter, the development of a digital support system for higher degree research (HDR) student training and development is conversed in the context of the young faculty of medicine at Macquarie University in Sydney, Australia. First, the case and the issues that need to be addressed in providing digital support to the HDR cohort are discussed. Then, the development of the digital platform is presented. Finally, an overall reflection is made with respect to the effectiveness and future directions in implementing the digital platform with a focus on developing a scholarly community of support for the faculty’s higher degree research students, supervisors, and the wider research community.

INTRODUCTION The Faculty of Medicine and Health Sciences (FMHS) at Macquarie University was launched in 2014 and presents a blue-sky environment for developing quality systems for training researchers within health disciplines hosted by five core departments: Clinical Medicine, Biomedical Sciences, Health Professions, Health Systems and Populations, and the Australian Institute of Health Innovation. Researchers range DOI: 10.4018/978-1-5225-7065-3.ch005 Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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in experience from entry-level Master’s students completing a Master of Research Program (MRes), to doctoral researchers (PhD) and clinical scholars. The purpose of this chapter is to discuss the development and implementation of a new digital platform to provide a virtual postgraduate research student community to support our diverse and geographically dispersed students and supervisors.

WHAT IS THE ISSUE? Effective support of higher degree research students has become a central priority over recent times. This is underpinned by numerous reports outlining the changing nature of the research sector, with increasing competition for funding and a larger emphasis on building skillsets that can enhance employability for doctoral graduates beyond the confines of the academic sector (Edwards & Roy, 2017; Frick, 2018; McGagh et al., 2016). Coupled with this, students routinely confront a number of challenges during their candidature that may impact their mental health and wellbeing (Pyhältö Toom, Stubb, & Lonka, 2012). Heavy workload, hard study pace, and the experience of little feedback are all documented to impact student experiences (Pyhältö, Stubb, & Lonka, 2009). This issue is compounded in science, technology, engineering, and mathematics (STEM) disciplines, where pressure is also applied in relation to complex hierarchical structures that become more apparent as students shift from undergraduate into higher-degree-research training (Rowland, 2017). HDR study tends to be more apprenticeship-like in nature, involving greater independence to pursue individually tailored and self-driven research investigations (Lum, 2018). Supporting students in the HDR space incorporates some unique challenges beyond those confronted at the undergraduate level. The difficultly in providing this support is compounded by the fact that students are working on diverse research topics and tend to originate from a wide variety of backgrounds: nationality; culture; or language. Similarly, students will enter into HDR training from a variety of educational backgrounds. A faculty of medicine, such as FMHS, may host engineers, social scientists, linguists, clinical professionals, allied-health practitioners, biomedical scientists, and more. At the time of writing, the FMHS student cohort originates from 21 countries comprising six continents. Indeed, “diversity, as opposed to consistency, acting as the norm within the postgraduate student population” (Eckersley et al., 2016, p. 5). Encouraging community engagement to promote wider interest in research disciplines for the purpose of attracting funding and community support is well documented (Syed & Palermo, 2010). It is also well documented that communities of support can promote collaborative practices, improve prolongation of the PhD experience, and prevent dropouts/failure to complete (Pyhältö et al., 2009; Pyhältö 86

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et al., 2012). Similarly, the issues that many HDR students face are less prominent in students that identify as being part of a community of peers or of similarly focused professionals (Golde, 2005). However, promoting community building in the FMHS has proven challenging in the context of students pursuing disciplines that comprise extensive laboratory activities, special research facilities, or that work in dispersed environments like clinics and geographically dispersed hospital or lab environments. Traditional practices in medical-research-focused academic departments have involved more face-to-face training, professional, and networking activities. Nonetheless, medical researchers are notoriously time poor, and FMHS student engagement in HDR-specific training and networking activities tends to be intermittent and brief. It is well known that greater frequencies of communication interactions promote a deeper sense of community (Dawson, 2006), thus a mode of interaction beyond direct workshops and social events was needed. Importantly, an avenue was sought through which to engage FMHS HDR students, which might improve their engagement with their wider department and faculty. It was decided, thus, to build a digital community to provide not only a means of information sharing, but also as an avenue to promote community connectivity amongst the faculty’s HDR students. Social media has become a core component of developing wider research communities of practice, publicising research, accessing and sharing content (Päivi & Asta, 2017; Taylor 2013), however, few studies have presented within-institution approaches. Reports have stated that data sharing can promote collaboration in medical research (Olfson, Wall, & Blanco, 2017) and it was evident at the outset that FMHS HDR students would benefit from some kind of digital networking space.

CURRENT SUPPORT AVAILABLE HDR students in FMHS may access professional training via programs delivered across campus by various providers. This includes a range of programs coordinated by the Learning Skills team from the Dean HDR’s office, focused on professional development, managing their candidature, and communications practices. In addition to these established programs, students are often offered ad hoc training and workshops provided within the individual departments, which are tailored to their disciplinary focus. Nonetheless, given the young age of the faculty, the five departments combined host only approximately 150 HDR students at the time of writing, which might be considered equivalent in size to a more established single department alone. An established student-led, peer-mentoring program is also very active in the HDR space, however, again, FMHS students that are engaged in intensive research work often fail to attend the activities promoted. Engagement of 87

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HDR students beyond their own department, or indeed their own research group, has been difficult to promote given conflicting practical schedules required to complete their research work. One consistent face-to-face requirement is an annual HDR student review held by each department, where students are required to formally present their project and outline their progress with regard to their candidature. This coincides with departmental interviews of both supervisors and students by panellists whose work is unrelated to that being pursued. Nonetheless, little explicit HDR network-building support is currently in place within the faculty. The delivery of online courses is now an established practice in the academic community, provided either completely via digital interfaces or in blended learning strategies (Bonnici, Maatta, Klose, Julien & Bajjaly, 2016; Lai, 2015). However, few reports have outlined a faculty or department creation of online digital communities focused on supporting higher degree research students, despite clear benefits that arise from community building (Dawson, 2006) and virtual learning environments (Barajas, 2002). Thus, the approach taken in building an online community for FMHS research postgraduate students is described here.

SELECTING A PLATFORM: MOODLE A tailored community site was developed through the Moodle Learning Management System that Macquarie University currently employs (https://moodle.com/), which is rebranded as “iLearn”. Learning and teaching support to operate within this platform is provided across campus. Students who have come from undergraduate programs at Macquarie University are established users of iLearn, as all undergraduate units are delivered with iLearn-based support (https://www.mq.edu.au/iLearn/about. htm). Through this platform, unit convenors typically deliver recorded lectures through the Echo360 video platform (https://echo360.com), which is equipped in most lecture spaces at Macquarie university, and provided as a block/link on each unit’s iLearn page. Although those involved in teaching and learning in the undergraduate space at Macquarie should be familiar with the iLearn platform, in the HDR space a number of HDR supervisors, Early Career Researchers (ECRs) that undertake HDR supervision, and students entering the postgraduate research programs at Macquarie that have come from other institutions, both local and abroad, may be unfamiliar with this platform. Thus it was clear from the outset that training would need to be provided if iLearn was going to be used to develop a HDR research community.

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WHICH COMMUNITIES REQUIRE SUPPORT? Two units were created to support HDR students and supervisors in FMHS: MRes and PhD. A summary of the main focus of support for these two communities is outlined here.

The Master of Research Community Unit A community unit was needed to support the Master of Research (MRes) Students and Supervisors. The program straddles two modes of delivery: coursework in year 1, and a research project leading to a thesis in Year 2. The 2-year program aligns to the Australian Qualifications Framework levels 8 and 9 (Australian Qualifications Framework Council, 2013). The first year of the MRes program represents a transitional period, given that students are at an early stage in their research training, moving from heavily directed, reactive, undergraduate learning to research-project focused, proactive, autonomous learning that reflects more of an apprenticeship model (Lum, 2018). In this first year, students are required to complete eight coursework units, of which two are required to be taken from the student’s host faculty (Communications and Research Frontiers), and the remaining six may be selected from a broad range of offerings from any of the five faculties in the university. This facilitates the tailoring of each student’s program in a bespoke fashion to best suit their perceived research interest. Students are encouraged to undertake research rotations in resident research groups in order to gain broad experience, and identify the most suitable group to join in Year 2 of the program, which is then focused primarily on delivering a research thesis. Students may choose to exit after this first year of the Master of Research with an undergraduate qualification, a Bachelor of Philosophy award (BPhil). During Year 2 of the program, students are required to deliver seven tasks, three of which will impact their final grade, and all of which are focused on aiding the delivery of a quality, externally examined thesis of approximately 20,000 words. Given the complexity of the program and its novelty in the Australian postgraduate research training landscape, clear information relating to the program, as well as the provision of ongoing support, is needed for both students and supervisors. Students and supervisors alike had been struggling to source up-to-date information from the university website, which was sometimes lacking the most recent information relative to the FMHS program. The MRes program was only introduced at Macquarie in 2013, rendering it the first Australian university to align with the “Bologna model”. As such, research leaders that were trained in different pathways, such as the Honours degree to PhD model, need information and guidance to ensure that their students deliver the required tasks, in particular during Year 2 of

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the program. Similarly, when students directly approach supervisors with interest in joining their research group, the potential supervisor requires a clear understanding of program guidelines and easy access to up-to-date, faculty-specific information. This is particularly important when supporting students in selecting study plans for the coursework year, or identifying whether a student may be able to enter HDR training at a later time point in the program where proof of equivalent experience should be presented. The short Year 2 project is like a sprint, as the students have a minimum time to design, launch, complete, and deliver a short research investigation, that might often be used as the pilot study leading to doctoral research (Lum, 2018). Both supervisors and students require clarity on program time points and access to key resources to ensure that the student delivers what is required and on time. Similarly, given the established plagiarism checking software in use by Macquarie University, an approach was sought to include Turnitin dropboxes for some of the assessments (Joyce, 2018) in order to track the student’s written tasks and be able to intervene where questionable communication practices were evident. Ensuring that all students in this program pursue their research work with academic integrity and good practice is a key focus that underpins the ethos of the research that takes place in the faculty.

PhD Community Unit A community unit was needed to support PhD students that are trained in line with the Australian Qualifications Framework level 10 (Australian Qualifications Framework Council, 2013). Ongoing support is provided over the 3-year program from various providers on campus to aid students and supervisors to progress over the long doctoral project. Workshops are encouraged, as are mentoring networks, ongoing training and development to improve professional practice, and development of marketable skills to aid students beyond their doctoral studies. Emphasis is placed on building skills in not only conducting their research, but building their own awareness of the marketability of the skillsets that they develop in the process of pursuing their doctoral studies (McGagh et al., 2016). Although doctoral students are more embedded in their research groups by this stage of training, students can, and do, encounter challenges and setbacks during their candidature, thus it is critical that they are aware of the support networks available to them outside their research groups. The inclusion of information pertaining to mental health and wellbeing, key contacts that can provide professional and pastoral support, and the current student networks that are active on campus (MedSoc and HDR Mentors), are crucial to helping doctoral students navigate the PhD marathon without suffering major personal or professional setbacks.

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CONSIDERATIONS IN DEVELOPING THE UNITS Whilst the Moodle/iLearn platform is widely utilised at Macquarie University, the interface is usually relatively bland and mostly represented by drop down menus of curated links, files, and activities for each week of the semester. However, for an HDR community unit, a more engaging interface was sought. At this stage, the professional staff from the Macquarie University Learning Innovation Hub were petitioned to create a button-based theme, where students would navigate through the portal by clickable buttons presenting an image and text outlining the key content that they would access. The community was split into MRes and HDR doctoral students, and the platforms were designed to suit the needs of each of these cohorts separately. There is some redundancy in this process which increases the workload to make common announcements for both student populations compared to individual email notifications. Nonetheless, this separation facilitates more specialised support and a collated, easy-reference record of announcements over time, where comparatively these are often lost in busy email inboxes. Furthermore, The MRes Community Unit permits the integration of first-year, student engagement together with the second-year research students, thus creating a cohesive community environment over a degree program straddling two teaching modes: coursework and a one-year research project. This is particularly beneficial for the first-year MRes students, as they can view information pertaining to what is expected in the second year of the degree program, and both have access to announcements and information provided for the overall program.

Main Sections The main sections included in each of the community units—Master of Research and PhD—are listed in Table 1. Each section is provided as a clickable button. The Master of Research Unit has an entry point where three buttons are shown as a menu to enter the appropriate section: BPhil/MRes Year 1; MRes Policy/HDRO; or MRes Year 2 (Figure 1). The main clickable buttons in the units were linked to pages that provide curated content and links out to important information. A key contacts gallery, with support information for each person, is provided for ease of access to further support for HDR students. Each department hosts their own page on the sites, whilst announcements and forums actively promote discussion. The faculty’s postgraduate student society is represented on the portal and provides a social component to the interface to encourage student community building.

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Table 1. Main sections presented in the FMHS community units Master of Research Community Unit BPhil/MRes Year 1

MRes Policy/ HDRO

MRes Year 2

PhD Community Unit

Announcements

Announcements

Announcements

Forum

Forum

Forum

Support, Training and Development

Support, Training and Development

Support, Training and Development

Key Contacts

Key Contacts

Key Contacts

Resources

Resources

Resources

Ethics and Permissions

Ethics and Permissions

Ethics and Permissions

Unit Selection

MedSoc

MedSoc

FMHS Units

Department of Biomedical Sciences

Department of Biomedical Sciences

FSE Units

Australian Institute of Health Innovation

Australian Institute of Health Innovation

FoHS Units

Department of Health Professions

Department of Health Professions

FBE Units

Department of Health Systems and Populations

Department of Health Systems and Populations

FOA Units

Department of Clinical Medicine

Department of Clinical Medicine

Research Opportunities

Transition to PhD

Scholarships

FMHS = Faculty of Medicine and Health Sciences, FSE = Faculty of Science and Engineering, FoHS = Faculty of Human Sciences, FBE = Faculty of Business and Economics, FOA = Faculty of Arts.

MASTER OF RESEARCH: UNIQUE SECTIONS REQUIRED Given the practicalities discussed here, the key sections provided in the Master of Research Community Unit relate to the year-level, thus the unit was split into Year 1 and Year 2 entry points. The landing page for the unit includes a button to enter Year 1, Year 2, or MRes Policy, which is provided in a single policy page with links to the relative information provided on the Macquarie University web portal (Figure 1; Table 1).

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Figure 1. The entry point for the Master of Research unit on iLearn

Year 1 The MRes Year 1/BPhil program page hosts thirteen clickable button links, of which seven are unique to this page. The common pages will be discussed separately (vide destra), and the unique pages and their content explained here.

Unit Selection This page is unique to the BPhil/MRes Year 1 community pages, outlining the coursework component of the program. The structure of the program, and recommendations for unit selection are outlined here. Although most BPhil/Year 1 students are already enrolled by the time they commence the program, this section proves useful for guiding potential unit changes as they proceed. Similarly, potential supervisors or academic advisors may use this resource page to guide prospective students.

Faculty-Specific Pages Macquarie University hosts five separate faculties: the Faculty of Business and Economics (FBE), Faculty of Arts (FoA), Faculty of Human Sciences (FoHS), Faculty of Science and Engineering (FSE), and Faculty of Medicine and Health Sciences (FMHS). All of these faculties offer units that may be selected by students enrolled 93

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in MRes Year 1, however they enrol under the guidance of departmental directors of higher degree research, and if relevant, potential future supervisors. As such, each faculty has been assigned a page here to outline the most typical or recommended units offered by each, and also to provide guidelines for entry, given that many units require a prior knowledge in the discipline to take MRes Year 1-level training.

Transitioning to Year 2 Most of the students completing the first year of the MRes program will continue on to the research project in Year 2. Thus, a collection of information on research opportunities and processes for transitioning are presented on this page. Although a university-funded MRes scholarship is typically applied for by students planning to pursue the second year of the program, other alternative relevant scholarship opportunities are also listed here.

Year 2 The MRes Year 2 program page hosts 13 clickable buttons, of which eight are unique to this page. The common pages will be discussed separately (vide destra), and the unique pages and their content explained here.

Department-Specific Pages The Year 2 of the MRes program is run directly from each individual department, and currently requires seven key assessment tasks to be completed throughout the year. Each of the five departments have been provided with a page to provide information to their Year 2 project students, which is specific to their own program. Each department provides bespoke program guides, timelines, and details of their tasks and links to individual student dropboxes where assessments may be submitted and checked via Turnitin. The MRes Advisor for each department and the faculty director of the program can access these dropboxes to review assessments and distribute to examiners.

Embedding Assessment Tasks In 2018, four out of the seven assessment tasks required in Year 2 of the MRes program are required to be submitted via Turnitin dropboxes linked to their department’s page on the community unit. This requirement is intended to promote student engagement with the unit, and Turnitin assessment allows the review of submissions for plagiarism. Plagiarism checks provide an opportunity to identify students at risk earlier in their 94

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candidature, and to thus provide support and development opportunities for these students to improve their writing practice as they progress through their research training.

Transition to PhD Most of the students completing the MRes program will explore and pursue ongoing doctoral studies in the same research group, or within the university. Although a large number of scholarships are offered in-house by Macquarie University on a competitive basis, a collection of information on alternative scholarship opportunities and processes for entering into doctoral training are presented on this page. A curated list is regularly updated which covers a range of scholarships that are specifically tailored to the disciplines that are researched in the faculty.

PHD COMMUNITY UNIT: UNIQUE SECTIONS The key sections provided in the PhD Community Unit reflect the stage of professional development and growing independence of the doctoral researcher. Thus the unit’s main page is presented with 13 clickable button links (Table 1). There is only one unique section in this unit, when compared to the MRes Year 1 and 2 pages—scholarships.

Scholarships Although the vast majority of students enrolled in this unit have already secured scholarships for their doctoral studies, links are provided here to search wider scholarship options. A curated list of useful travel awards, as well as information about the Macquarie University Postgraduate Research Fund (PGRF) is provided. This fund is a competitive award for current Macquarie HDR students to attend conferences, or to fund research travel.

COMMON SECTIONS Several sections are common to MRes Year 1, MRes Year 2, and PhD. These are listed here with an explanation of their purpose and relative content, including similarities and differences between the units.

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Announcements All those registered to the units can make relevant announcements that are distributed to registered email addresses and the announcement stored on a page that can be reviewed later. This is intended to provide a curated list of relevant HDR announcements for the FMHS HDR student body, which replaces the usual approach of individual emails. Supervisors and students can receive announcements to their inbox individually or in a digest format. The types of announcements made between the units varies due to the nature of the longer doctoral program – three years – as compared to the MRes Year 2 which is a one-year project. PhD students are encouraged to participate in workshops provided across campus to develop their skills. This is particularly important for students that join the university as they commence their PhD, having not completed the research training and theory offered during the MRes program. Many of these students come from non-native English speaking backgrounds, and although they may have passed entrance criteria to join the program, ongoing support to develop their scientific writing skills is provided both within the faculty and by the HDR Learning Skills team on campus. Comparatively, given the brief nature of the MRes program and dominance of domestic enrolments, these students are not encouraged to participate in workshops, but rather focus on completing their often closely-guided research investigations. Some exceptions to this apply where students may require extra support in their MRes Year 2, and in these cases, the students will be individually advised by their supervisor or departmental advisor for the MRes program.

Forum Each unit hosts a standard forum, where unit-wide discussions can take place. The intention is for this to serve as a community discussion place, however, little engagement has yet taken place in this section. Involvement in discussion boards is an ongoing focus for coursework unit convenors (Xia, 2013), however, finding a motivating factor in a community of diverse postgraduate researchers proves challenging. How this forum may be employed for the FMHS HDR cohort in the future will be reviewed in time. We may encourage student blogging, which may serve not only as a discussion point, but also to provide reflective feedback on the student experience within the faculty, thus an integrated learning tool (Goh, 2016).

Support, Training, and Development Each student-cohort page (MRes Year 1, MRes Year 2, and PhD) carries a page link for “Support, Training, and Development”, where a collection of information 96

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is curated that is applicable to skills development and research support. Although much of the information is overlapping, there is a subtle difference to each page. The relative MRes Year 1 page includes support provided for undergraduate students, given that this group is completing an undergraduate-level coursework year. The MRes Year 2 page includes support provided by the HDR office tailored to research postgraduates, as well as information relating to developing teaching skills, as many students in this cohort will begin tutoring undergraduate practical classes once reaching this level of qualification to develop teaching skills and to obtain casual income. More information relating to publishing and research strategy is included from Year 2 onwards. The PhD community unit page mirrors that of MRes Year 2, however, more emphasis is placed on PhD students to participate in workshops and courses throughout their candidature to build their research practice and communication skills. All of the pages in this category include information relating to mental health and wellbeing to ensure that students are aware of the support available on campus when needed.

Key Contacts Each student cohort page (MRes Year 1, MRes Year 2, and PhD) carries a page link for “Key Contacts”, which hosts a photo gallery of the staff involved in supporting their research training. This includes the Associate Dean for HDR, MRes Director, and HDR Administrator for the faculty; the departmental directors of HDR and their administrators; research support personnel from the HDR office; department and faculty-assigned research librarians; and representatives for the research student society “MedSoc”. Clicking on a key contact’s photo will lead the user to a page including a description of their role in relation to research training in FMHS, as well as their contact information.

Resources All cohort pages include a “Resources” page, which begins on the MRes Year 1 page with information about available study spaces and book recommendations relating to scientific communication, student orientation to the iLearn space, and useful online resources relative to the research experience. The MRes Year 2 resources page builds on this to also include links to slide stacks from faculty and university commencement programs and workshops tailored to support students in their assessment-task preparation. Given the structure of the Year 1 coursework component of the MRes program, this support is not as relevant for this cohort, however Year 1 students may “look forward” to year 2 to review this content also. Given that both MRes Year 2 and doctoral students are firmly situated within the 97

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HDR space, they are provided here with information related to writing a thesis, managing wellbeing as a research student, statistics and numeracy support, and book/digital resource recommendations.

MedSoc All units carry a page assigned to the medical research students’ society, “MedSoc”, which is established as a part of the university’s clubs and societies organisation. MedSoc organises events and activities throughout the year to provide both professional and community support for all of the HDR students in FMHS. The executive of this society has been assigned a page in each unit where they can provide key information for the student body. Similarly, these students announce their activities through the main announcements pages in each unit. One of the key activities currently being delivered by MedSoc is a careers seminar series called “Beyond Academia”, focused on promoting career options for doctoral graduates (Frick, 2018; McGagh et al., 2016). Guest speakers that have moved beyond the academy after completing their graduate studies are invited to give a one-hour seminar every two months, which is filmed and the resultant video of their talk uploaded to VIMEO. These videos are embedded to the MedSoc page, together with show notes summarising the main points of the talks.

DIGITAL RESOURCES Whilst training and information sessions for FMHS HDR students are still delivered in face-to-face lectures and workshops, these are being included to both community units, often by video-recordings rather than ECHO video capture, due to technical challenges. Faculty research activities and seminars tend to take place in the FMHS in-house seminar rooms that are not equipped for lecture capture via the ECHO system. Instead, a VIMEO account was created where video recordings of the lectures can be uploaded and embedded to the community unit, together with slides and, if relevant, audio and notes, so that students can easily retrieve key information that has been embedded into the community unit. Digital resources do not detract from the requirement of face-to-face support, but rather provide ongoing resources that students may revisit or access if unable to attend any given event. The intention here is to create an evergreen curated collection of tailored resources that are accessible via the platform. Some examples of the content recorded include: guest lectures, commencement programs, training talks, and workshops. This content can also provide resources for research communication training and self-driven professional development. 98

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SOCIAL MEDIA AND NETWORKING As these community units grow, it is anticipated that students will be able to form deeper community links, with a clearer perspective of opportunities and events. Encouraging deeper participation in a digital community is intended to prepare students to become a part of the wider research community. Social media is increasingly used by researchers (Jaring and Bäck, 2017) and medical professionals (Chan, Trueger, Roland, & Thoma, 2018) to share and promote their work, and to professionally network with peers (Tran & Lyon, 2017). Similarly, global collaboration networks can overcome some of the instability that has arisen with greater competitiveness in research funding opportunities and promote productive collaboration and data sharing (Olfson et al., 2017). Therefore, we encourage all HDR students to develop online profiles that can promote their research interests and professional profiles. Twitter is increasingly employed by researchers to promote their academic output, funding successes, and achievements of their research teams, as well as provide commentary of the state of the art of their discipline, and to network with other researchers (Lamb, Gilbert, & Ford, 2018; Haustein et al., 2013). In its current guise, these community units thus encourage social media engagement through Twitter, with hashtags for each unit—#FMHSHDR and #FMHSMRES—that are shown in a feed displayed on the relative community pages. This works further to provide an outward-facing profile (Twitter content) for potential new students to the FMHS HDR programs to engage and review the type of scholarly communications underway between FMHS postgraduates on social media.

COMMUNITIES FOR WELLBEING At the time of writing, exactly half of the 116 HDR PhD students hosted in the faculty are domestic, and half international, whilst seven out of 45 MRes students are international. Thus, particularly in the PhD community, the development of a community of peers in the HDR space is of great importance and may help international PhD students to deal with common problems that they encounter, including language problems, cultural differences, and personal matters (Son & Park, 2014). Similarly, mental health and wellbeing issues are well documented in graduate education (Evans, Bira, Gastelum, Weiss, & Vanderford, 2018), where a call has been made to encourage intervention strategies to address this issue. The development of this type of local online community is critical for time-poor research students who may not be able to attend face-to-face mentoring network activities. Whilst the FMHS faculty is engaging widespread mental-health first-aid training for research support teams and supervisors, information about online resources provided 99

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by the university, as well as wider research support communities, is provided by these community units. Similarly, the curated content provided by these community units ensures equity in availability of support for research students who would otherwise be unable to attend lectures, training sessions, or workshops.

WIDE ENGAGEMENT AND CONTENT ACCESS Apart from official email announcements that must generally be mediated by a faculty HDR administrator, any staff member or student registered to the site can make announcements to all those registered to the units (all HDR students and supervisors). Thus, this represents an informal opportunity for network building and generalised communication in the HDR space between all those with an interest in supporting HDR students. Similarly, these portals and their capacity to hold key content minimises resource outlay required to deliver annual training to the HDR student audience. Although concerns have been raised that free availability of digital lecture content can reduce engagement in training (Davis, Hodgson, & Macaulay, 2012), access to key lectures, training sessions, and curated digital content can also facilitate more tailored face-to-face training sessions that are fewer in number and more interactive and focused on consolidating understanding, rather than lecturing (Prunuske, Batzli, Howell, & Miller, 2012). Furthermore, research supervisors can easily access information as needed relating to supporting the students under their direction. Supervisor engagement is evidenced by the unit views recorded over the first year of unit implementation for both MRes and PhD cohorts (Figure 2). More unit views are recorded for supervisors at the beginning of the academic year, in February, when most new students enrol in HDR training. Comparing this to the student engagement plotted over the first year of community unit implementation, no clear trend is evident, however, a fluctuating interaction with the unit throughout the year is seen for PhD students (Figure 2, A). A more detailed review of this engagement in relation to major events in the faculty and the components of the unit that are more frequented will be analysed at the end of 2018, as the first six month period represented the set-up stage of the units, where much content was being added. This shift between 2017 and 2018 is clearly shown in Figure 2, B, which shows that a much higher number of student views are seen for MRes students in 2018 compared to 2017, coinciding with the introduction of online Turnitin dropbox submissions for the Year 2 assessment tasks. Overall, the unit-view numbers are reassuring in that both supervisors and students are engaging with the units online (Figure 2), however a clearer perspective will be drawn from surveys and interviews that are intended to be performed from late 2018, together with a comprehensive review and 100

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Figure 2. Unit Views over the first year of delivery of the PhD Community Unit (A) and MRes Community Unit (B), from July 2017 to July 2018. Data is shown for Students (●) and Supervisors (♦). Supervisors were not enrolled in the PhD unit until August 2017. MRes Year 2 assessments were required to be submitted via drop boxes from January 2018. At the time of writing, approximately 91 users were enrolled in the MRes Community Unit and 235 in the PhD Community Unit.

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reflection on the efficacy of these units and how they can be improved. Nonetheless, it is anticipated that engagement with the online units will grow over time as more curated content is added to the community portals and more students and staff become aware of the growing resource and thus choose to employ it more frequently.

CHALLENGES AND ISSUES Several issues have impacted the adoption of this platform in FMHS. First, the relative social and human social factors and dynamics that influence the use of the units are challenging to manage and manipulate (Barajas, 2002). This type of organisational change has been better approached in the MRes unit, where assessment tasks for year 2 of the program are integrated, via Turnitin dropboxes (Joyce, 2018), thereby ensuring that active research students utilise the portal. This approach cannot engage doctoral students in the PhD Community unit, however. It is anticipated that as the information curated in the units grows, more students will engage with the units to access this content. Similarly, key stakeholders, such as research supervisors, research librarians, and departmental representatives, are encouraged to engage with the unit to distribute information for HDR students in the faculty where possible. Another concern is that the information made available on this unit is not outward facing, with the exception of the content presented via the Twitter feed, thus making it challenging to engage potential future students to join the HDR programs in the FMHS faculty. Nonetheless, the easy access to up-to-date content via these community units facilitates easier development of content for a separate outward facing HDR web page that is currently under development.

SUMMARY This chapter has presented a detailed overview of two community units designed and launched for HDR students in the young Faculty of Medicine and Health Sciences at Macquarie University. At the time of writing, the units have been operational for approximately twelve months, and discussions continue regarding the next stages of development. This portal represents the first stage in providing improved blended learning and digital support opportunities for the FMHS HDR community in the faculty’s HDR training program, which encompasses both the Master of Research and PhD programs. Through this medium, the faculty seeks to promote improved scholarly community engagement throughout the HDR cohort.

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Given that the majority of students in the faculty are focused on researching in STEM disciplines, the development of networking skills represents a key component of their training as they are largely pursuing resource-heavy investigations that usually rely on collaborative practice to achieve desired outcomes (Wagner & Leydesdorff, 2005). Not only are these students also shifting from teaching-centred to learningcentred training, they are also shifting from the focus on individual to focus on community (Dawson, 2006). This evolution is reflected in the changing structure of postgraduate research training, which has moved from an apprenticeship model to a small business model, where students are more likely to be supported by a research team, operating on group grants (Rowland, 2017). Thus the type of community support sought through the provision of these units is intended to compliment this shift and to aid student professional development through network building. The most critical challenge in the development of this digital community has been to promote engagement of the students, as well as the researchers and administrators who contribute to the delivery of research student training and development. The units are clearly being accessed by both students and supervisors, serving the purpose of providing a safe information repository for students and supervisors involved in FMHS HDR programs. However, approaches should be developed to boost the community potential of the sites moving forward and ongoing development and practical initiatives are required to promote the use of this platform across the wider faculty. At present, this comprises building the availability of useful content across the units, in particular, video lectures and digital resources. Despite the challenges of promoting deeper engagement, there is clear benefit from the development of these scholarly community units to support HDR students (Pyhältö et al., 2009; Pyhältö et al., 2012). They have been well received by both faculty staff and students, as well as colleagues from other faculties on campus. Overall, the MRes and PhD community units to support the HDR students in FMHS hold great potential to provide an online resource that offers curated key content for scholarly support, and community building for time-poor research-active students.

ACKNOWLEDGMENT The development of these community units would not have been possible without the support of the team of Lilia Mantai, Alper Yuceozsoy, and Fidel Fernando at Macquarie University Learning Innovation Hub. The ongoing feedback and encouragement of the current HDR Committee was also key to proceeding with the development of these units. Thank you to Mark Connor, Mark Hancock, Danè Turner, Andrew Georgiou, Rosie Garner, Janaki Amin, Stuart Graham, and Viviana Bong.

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Evans, T. M., Bira, L., Gastelum, J. B., Weiss, L. T., & Vanderford, N. L. (2018). Evidence for a mental health crisis in graduate education. Nature Biotechnology, 36(3), 282–284. Retrieved from https://www.nature.com/articles/nbt.4089#supplementaryinformation. doi:10.1038/nbt.4089 PMID:29509732 Frick, L. (2018). Next Gen PhD: A guide to career paths in science. Higher Education Research & Development, 37(1), 222–224. doi:10.1080/07294360.2017.1360131 Goh, P. S. (2016). Using a blog as an integrated eLearning tool and platform. Medical Teacher, 38(6), 628–629. doi:10.3109/0142159X.2015.1105947 PMID:26558420 Golde, C. M. (2005). The role of the department and discipline in doctoral student attrition: Lessons from four departments. The Journal of Higher Education, 76(6), 669–700. doi:10.1353/jhe.2005.0039 Haustein, S., Peters, I., Sugimoto Cassidy, R., Thelwall, M., & Larivière, V. (2013). Tweeting biomedicine: An analysis of tweets and citations in the biomedical literature. Journal of the Association for Information Science and Technology, 65(4), 656–669. doi:10.1002/asi.23101 Jaring, P., & Bäck, A. (2017). How researchers use social media to promote their research and network with industry. Technology Innovation Management Review, 7(8), 32-39. Retrieved from http://www.timreview.ca/sites/default/files/article_PDF/ JaringB%C3%A4ck_TIMReview_August2017.pdf Joyce, D. (2007). Academic integrity and plagiarism: Australasian perspectives. Computer Science Education, 17(3), 187–200. doi:10.1080/08993400701538062 Lai, K.-W. (2015). Knowledge construction in online learning communities: A case study of a doctoral course. Studies in Higher Education, 40(4), 561–579. doi:10.1 080/03075079.2013.831402 Lamb, C. T., Gilbert, S. L., & Ford, A. T. (2018). Tweet success? Scientific communication correlates with increased citations in Ecology and Conservation. PeerJ, 6, e4564. doi:10.7717/peerj.4564 PMID:29666750 Lum, J. (2018). Supervising a master’s/honours: A project management approach to researcher development. In S. Carter & D. Laurs (Eds.), Developing research writing: A handbook for supervisors and advisors (pp. 111–118). London: Routledge. McGagh, J., Marsh, H., Western, M., Thomas, P., Hastings, A., Mihailova, M., & Wenham, M. (2016). Review of Australia’s research training system. Retrieved from www.acola.org.au

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Olfson, M., Wall, M. M., & Blanco, C. (2017). Incentivising data sharing and collaboration in medical research: The s-index. JAMA Psychiatry, 74(1), 5–6. doi:10.1001/jamapsychiatry.2016.2610 PMID:27784040 Päivi, J., & Asta, B. (2017). How researchers use social media to promote their research and network with industry. Technology Innovation Management Review, 7(8). Prunuske, A. J., Batzli, J., Howell, E., & Miller, S. (2012). Using online lectures to make time for active learning. Genetics, 192(1), 67–72. doi:10.1534/genetics.112.141754 PMID:22714412 Pyhältö, K., Stubb, J., & Lonka, K. (2009). Developing scholarly communities as learning environments for doctoral students. The International Journal for Academic Development, 14(3), 221–232. doi:10.1080/13601440903106551 Pyhöltö, K., Toom, A., Stubb, J., & Lonka, K. (2012). Challenges of becoming a scholar: A study of doctoral students’ problems and well-being. ISRN Education, 12. doi:10.5402/2012/934941 Rowland, J. (2017). Managing research groups and projects:Effective management practices for academic researchers. Practical Academic. Son, J.-B., & Park, S.-S. (2014). Academic experiences of international PhD students in Australian higher education: From an EAP program to a PhD program. International Journal of Pedagogies and Learning, 9(1), 26–37. doi:10.1080/183 34105.2014.11082017 Taylor, M. (2013). How virtual science communities are transforming academic research. Retrieved from https://www.elsevier.com/connect/how-virtual-sciencecommunities-are-transforming-academic-research Tran, J. A., & Lyon, C. Y. (2017). Faculty use of author identifiers and researcher networking tools. College & Research Libraries, 28(2). doi:10.5860/crl.78.2.16580 Wagner, C. S., & Leydesdorff, L. (2005). Network structure, self-organisation, and the growth of international collaboration in science. Research Policy, 34(10), 1608–1618. doi:10.1016/j.respol.2005.08.002 Xia, J. C., Fielder, J., & Siragusa, L. (2013). Achieving better peer interaction in online discussion forums: A reflective practitioner case study. Issues in Educational Research, 23(1), 97–113. Retrieved from http://www.iier.org.au/iier23/xia.html

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KEY TERMS AND DEFINITIONS BPhil: Bachelor of philosophy. HDR: Higher degree research. FMHS: Faculty of Medicine and Health Sciences at Macquarie University, Sydney, Australia. STEM: Science, technology, engineering, and mathematics. iLearn: The Macquarie University brand name for the Moodle learning management system. PhD: Doctor of philosophy. MRes: Master of Research, a two-year, research-training program at Macquarie University that comprises a coursework year and a research year.

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Doctoral Platforms and Apps for Professional Development and Student Support E. Alana James DoctoralNet Ltd, Ireland

ABSTRACT Using the experience derived across multiple universities, this chapter endeavors to discuss how ICT can play a role in the larger evolution of higher education, as well as with helping doctoral students complete their research and writing requirements. Practitioner research underpins the discussion of two rounds of research centered on ICTs role in equalizing disparity in financial and social capital between students and taking those solutions to scale. The first round (2012 – present) focuses on the ICT suite of services as they develop, and the second (2015 – present) investigates how, and in what ways, the interdependence between the Deans’ office and the subscription business play a part in student adoption and usage. The findings suggest that a willingness to develop interdependent solutions between ICT developers and postgraduate studies will be instrumental in bringing services for doctoral students to scale.

AUTHOR’S NOTE Nassim Nicholas Teleb (2012) takes academics to account in the way research writes about innovation. This author agrees with his premise that, to some extent, we are guilty of rewriting history when we take the back and forth, step-by-step process of innovation and synthesise or theorise about it. Discussing the topic of ICT in DOI: 10.4018/978-1-5225-7065-3.ch006 Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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doctoral education is to discuss a view of an innovative moment in the evolution of education. As an experimenter in this realm, this research is written from the dual role of an academic interested in putting words to the shifts in practise, while also a driver of change as the business owner producing the service being investigated. In agreement with Teleb, that it is in fact backwards to the real world of innovation within digital learning to write about ongoing changes, because theory lags innovation, this chapter reports first on the development of the ICT data and then discusses its potential ramifications to postgraduate centred teaching and learning. Throughout this chapter, I stay true to experience from both the position of practitioner research into doctoral teaching and learning and my leadership as CEO. This chapter first tells the story that explains the work, then moves through the data to what was learned through those experiences, and only then to the lessons and implications to practise that stand out. As a solo academic/edupreneur, the author hopes that the story engages others in a way that a strict research format may not.

INTRODUCTION This chapter endeavors to discuss how ICT can play a role in the larger evolution of higher education as well as with helping doctoral students complete their research and writing requirements. It starts with a short discussion of this authors’ experience as a supervisor of over 50 students who completed degrees in Education or Business, graduating from online programmes in the United States. These doctoral students were “nontraditional”: from diverse backgrounds, usually older, all at a distance from campus, engaging in education that was heavily influenced by instructional design as well as professorial understanding or personality. Then it goes on to outline lessons learned through two rounds of ongoing action learning/research which took place from 2012-2018. Finally, the chapter ends with a view of a potential future and a discussion of recommended next steps. A Vice Provost at a university we support told me that, “the most important answer for a university of our size to answer is how we can equalise the difference between students, especially those who have less social and financial capital, and how we can take those solutions to scale” (Benedik, 2017). The underlying question that serves as a focus for the research discussed in this chapter cycles back to that problem by asking, “What kinds of technology will be part of the solution and how should it be delivered?” As author, I stand between two roles in this document: author and researcher on the one side, and founder and CEO of the company that provides the Software as a Service (SaaS) to universities that has been studied. Thus, I am a new type of practitioner, academically trained and previously employed educator, now discussing 109

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ICT teaching and learning as owner of the business that interacts with Deans and Provosts responsible for Postgraduate Studies. Three premises underlie this chapter and the beliefs about ICT for doctoral education and constitute the wider picture of doctoral education upon which it is based: 1) that postgraduate education is unique in many ways and therefore students deserve ICT that respects that unique environment, 2) that doctoral professional development and ICT have not kept up with changes in the potential of technology, forcing universities to either repeat what they have always done by regularly hosting seminars that may be attended only by a few, or to adopt undergraduate retention and completion technology which only partially hits the mark for doctoral students, and 3) the research in this chapter draws no distinction between the “professional” doctorate and the PhD when it comes to what professional development and support are needed, as it has been the experience of this researcher that during the end game of viva or final defense, few examiners draw any real distinction when discussing the rigor and requirements of the final dissertation or thesis. Something should also be said about the modern relationship of the graduate school or department within the university. These offices are often decentralised playing a supportive role, collaborating with programmes, tracking credits, setting up professional development services, all with little or no budget for programing. Deans report that time is tight, budgets are slashed, and they are constantly battling challenges to ensure all their students have access to quality supervision for their research and proper support to overcome the issues they do not understand (Canales, 2017). Given these tensions, the modern Graduate Dean is simultaneously charged with improving the doctoral experience and increasing completion rates at a time when their daily work frequently is filled with more mundane tasks (James, 2017a).

Practice Disrupted by Technology Higher education is not the first major, global industry to have been disrupted by technology. Two tertiary experiences may offer a few insights. Like healthcare, universities have seen their role in the education of students devolve from being a single source of information to being one of many. Just as the modern consumer goes to the web to investigate a healthcare issue prior to seeing a doctor, students investigate multiple online and printed resources to uncover what needs to be done prior to discussions with their committee chairs or supervisors1. As with healthcare, the proliferation of resources on the web creates a higher potential for those that are searching to receive poor or inadequate information and the networks that develop provide a greater opportunity for adequate support (Christensen, Grossman, & Hwang, 2009; James, 2017c, Kayyali, Knott, & Van Kuiken, 2013). This results in the need for greater interdependence between universities and the technology 110

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providers’ resources, in a similar fashion to ways in which clusters of healthcare providers offer holistic or supplemental resources to clients (Baker, Wagner, Singer, & Bundorf, 2003). Eighteen years ago, faculty at the John Hopkins University School of Nursing wrote something that is mirrored in higher education today… Healthcare delivery is being transformed by advances in e-health and by the empowered, computer-literate public. Ready to become partners in their own health and to take advantage of online processes, health portals, and physician web pages and e-mail, this new breed of consumer is slowly redefining the physician/patient relationship. Such changes can effect positive results like improved clinical decisionmaking, increased efficiency, and strengthened communication between physicians and patients. First, however, physicians and the organisations that support them must fully understand their role in the e-health revolution. Both must advance their awareness of the new consumers and their needs and define specific action items that will help them realise the benefits of e-health. (Ball & Lillis, 2000, p. 1.) ICT in doctoral education is also influenced by corporate issues. As the need for business resources to be used at scale by virtual team members, and those working occasionally at a distance from the main plant or office increased while budgets decreased, online reusable training and communication packages developed. Several concepts drove change in this arena: modular on-demand content delivery, selfdirected learning, the personalisation of the user experience to specialised groups, and repeatable micro-learning content (and bots) humanising content delivery (Dobrovolny, 2007; Tannenbaum, Mathieu, Eduardo, & Janis, 1991; Wentling, et al., 2000). Currently the use of ICT in doctoral education is dynamic and precludes any view to stay accurate for long. The understanding of those who purchase and use ICT both as professors and students changes every year, and because of that increased understanding, new demands are addressed with new tools. Higher education does not exist in its own bubble, so every new tool or experience in the life of online consumers translates into higher expectations of what universities should be able to do in as part of the postgraduate experience. These circumstances move practitioners through stages from uninterested in ICT’s use, to early adopters, to an increasing understanding of the technology enhanced environment and onwards to developing our own expertise with new tools (Karahanna, Straub, & Chervany, 1999). This chapter is limited to what is being done in doctoral professional development (inclusive of the research and writing process, research design, the processes of

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efficient research, personal well-being and transferrable skills), and the results as of early 2018. It closes with a discussion of possible next steps and future research. Theoretical implications for future research surface as ICT moves forward with new tools. Lessons are learned that indicate future avenues to be explored. PhD research appears hampered with its reliance on socialisation theory to explain the tensions in doctoral retention and completion. This results in educators responding in a similar fashion to the urban designer who built sidewalks only to be dismayed when people cut across the grass. Students don’t necessarily follow the paths as laid out for their socialisation, therefore a new model of student quality experience (QX) in relation to ICT tools is suggested (James, 2017a). Finally, these topics are of wider significance than just teaching and learning practises for doctoral students. Indeed, they are bigger than the lessons and changes to theoretical and philosophical studies in higher education. Digital learning and the ways it will impact equality in education and scale are the roots to which this chapter endeavors to return. It closes discussing what has been learned and what is needed in future research before higher education can fully capitalise on those lessons.

THREE ROUNDS OF RESEARCH: USING ICT FOR PROFESSIONAL DEVELOPMENT Ten years of this author’s career was spent supervising and mentoring EdDs and Doctor of Management students through to successful research and dissertation completion while teaching at US universities. All the students were enrolled in online programmes and so, presumably, were comfortable with ICT. On the other hand, they were consistently outliers and non-traditional students, frequently recently released from the military, often over 55 years of age, and working full time while pursuing doctoral degrees. Their goals were to launch themselves as leaders in their communities, more often than not because they desired an academic career. The standards for completion were high and the learning curve to meet them was strenuous. As online education came under greater scrutiny by the certifying agencies, the standards at viva, or final defense, rose to an extremely arduous level. The ICT focused on in this chapter is a suite of services aimed at making the job of graduate schools, supervisors, and young researchers easier because it offers a digital environment for students who need support. Since postgraduate offices are stressed with deadlines, instructors and professors face competing demands as they teach or do their own research and writing, it is the raison d’être of this ICT suite to aid not only student skill development and efficient completion, but

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to provide softer supports such as fostering transition from postgraduate education to workforce, providing motivational aids and tools for work-life balance (James, 2015). It has been demonstrated that the more students wrap their research into the rest of their lives the more efficiently they finish (Eisenbach, 2013). Currently this amounts to the following: •

•

•

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A university level website platform that students sign onto using their university email. These portals contain: all the pedagogical content, selfassessment tools, and linkage to other resources at the same time they link the student to their university through personalised graduation videos, messages from Deans, etc. Opt-ins tools that can be chosen when the student desires extra and more personalised support, contained on the university portal or accessed through the app on their own, sent to the student privately on demand. These include a 30-day writing challenge, a 30-day work-life balance challenge, and motivational emails to name the most popular. Webinars and the international groups in which those conversations are embedded are a favorite for many students giving them not only new or refresher knowledge but membership in a bigger network. These require very specialised IT and the company partners with an outside firm for their production. Boxed sets of video, written works, slides and other kinds of content focused on popular topics such a literature review, academic writing, critical thinking, etc.

Of the 50+ students who completed while working with this author at universities, it was recognised that, no matter how the university set up their pedagogical design, students faltered during: 1) research design, or 2) academic writing, and/or 3) critical thinking and the ability to build a consistent and cogent argument from their data. There appeared to be a never-ending list of what they needed to improve before they passed. The question became, “Why can’t technology help with this?” Therefore, the ICT suite of services first focused on helping students finish their research and write it up in a manner that met or surpassed basic standards. It’s context expanded, as the ICT subscriptions were sold to a broader range of universities, to include how ICT can be a benefit to Deans and perhaps the university. Practitioner research, specifically Action Learning and Action Research, have proven useful in developing programmes for underserved populations of students at-risk in both the United States and Ireland (James, 2005, 2006a, 2006b). Because of the cycle of first taking an overview of current literature, responding to it by

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testing new action, measuring and then reflecting on the outcomes of that action, these methods proved useful in other circumstances in developing “least intrusive” educational solutions (Mitra, Dangwal, Chatterjee, & Bisht, 2005). In other words, in developing practices which helped the at-risk student while not disrupting the rest of the existing educational practises (James, 2017b). For this reason, action learning/ research was employed during and throughout the development and implementation of the ICT studied here. Round one began in 2012 with a personal search into what was needed by students, on demand, to teach them the subtleties that only become important while completing research. The design of the ICT was based on the idea that part of the retention/completion problem in graduate education is that while courses teach the basics, a safety net is needed for some students who need more guidance. This is especially true when graduate education is to be delivered at scale, and universities want to ensure greater percentages of success. In 2012 the first ICT platform began to be developed and was tested as to whether students found it useful. Data from those early tests amounted to qualitative interviews about the ICT sandwiched into university educational practice as professor colleagues and I performed the role of the students’ committee chair or supervisor. The first round of research continues through to today and with each new cohort and university ICT usage data helps us refine our understanding of what students are looking for, how that meshes (or not) with what universities think they should have. As technology evolves it also changes the ways in which student expect to access professional development. Round two began in 2015 when it became apparent that the decentralised structure common in postgraduate education had an impact on campus level decisions, kicking off the second, congruent round of research into how best to blend ICT practises with the ongoing educational structure. A Graduate School may have little budget of their own, with key stakeholders to whom they are responsible in every graduate programme, causing some Deans to be conservative as they have numerous divergent perspectives on education, each needing to sign off on new developments. These offices are embedded in the general chaos of the full university as well. At least in the US and Ireland (the countries included in this study), situations in which Graduate Deans focus include: business model restructuring, loss of financial support and/ or greatly increased diversity across the doctoral student population, new faculty with little training in supervision, new IT services being implemented, etc. (James, 2017b). Data from this round are largely unpublished qualitative exchanges, gathered during educational meetings. Round three of this research began earlier this year (2018) and will address whether and to what extent ICT tools can help the university address the more pressing issues of retention and completion of students are the next issues to be addressed. 114

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ROUND ONE RESULTS: MOVING BEYOND PEDAGOGY TOWARD ADDRESSING STUDENT FRUSTRATIONS The best pedagogy in the world does not work if no one uses it. Therefore, the first round of research focused on the transference of pedagogical ideas from the classroom to the research and development of the ICT. Findings here are derived from usage data and student surveys. This round taught us: 1) the difference between class or web design for use at will rather than guided, 2) to manage the restraints inherent in, but not across, different platforms or components, and 3) how to begin meeting student frustrations rather than merely adding more pedagogy.

Design for Use at Will ICT needs to be structured in a manner that attracts as high a percentage of users/ use as possible. Yet at the same time students are unlikely to proceed through a portal or app in a step by step manner, therefore creating questions as to the ways in which to attract them to the content they need. While the learning management systems (LMS) of today still largely imitate the face to face classroom, instructors know that student users jump around at will within the systems, looking ahead, skipping sections etc. Instructional designers integrate assessment and discussion at key points to force the likelihood that most students will follow the original pedagogical steps. As other authors have said, every new development in ICT involves a complex relationship between instruction, the institution, the learner, the transference of data, and centralised or decentralised learning approaches (Siemens, Gašević, & Dawson, 2015). Because the suite of tools discussed here was developed by teachers, the first iterations did not vary that much from the step-by-step model in most classrooms. Developed at the same time Khan Academy began, and with a similar set of experiences from its founder (Khan, 2011), doctoral professional development was mostly instructional, micro learning content, delivered in a mix of short video explanations, written content, and checklists. Within the first two years of production it became obvious that students on a web portal jump around through some inner set of interests, and rarely follow the pedagogical pattern that underpins the original design. We quickly learned that students always attend at will and/or for their own personal reasons when it comes to professional development. Therefore, it is critical that tools focus on the needs of the students as they express them. You can’t “make them” come. With no control over student adoption, the research question becomes, “How do you encourage attendance on issues students report to be most critical?”

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The first lesson in our first round of research then became addressing the holistic aspect of student retention, that of work-life balance. By 2014 the ICT development concerns became how to offer student support to both academic and wellness issues in as efficient a manner as possible. The development staff were driven by the question of “what do students need and want” equally to what we thought they needed to have. Data demonstrated that useful tools, delivered quickly and easily, was what students were looking for. As the platform developed, addressing both concerns, usage tripled. The first year Dublin City University students piloted the platform usage was under 20%, by year two it approached 50% and in year three we saw spikes of almost 70%.

Working Within Platform Constraints The second lesson was that the ICT platform constrains presentation. For example, some websites become a standard tool, but when access is desired out of the office, a phone app is used instead and we may be irritated by a difference in options or layout. During this study, analytic data show that while long content is rarely read on a phone, students attend webinars on every kind of device, sometimes switching during the presentation or recording. We noticed that once a user signed up for a series, it was delivered by email, and components might be accessed at different times. As one student told us: I enjoy the interaction and connecting with the wider community of researchers. I hate missing a session but am so relieved that I can catch up later. I also listen back to previous videos to keep myself focused and motivated. Wonderful stuff!!! Your lively upbeat manner makes it all seem so possible. Thank you for creating hope. Support, needs to be personal as much as is possible, either through webinars or written support delivered by a known entity, a person with a picture, name, and personality. Each of these considerations houses a different set of ICT design problems. A successful example of working within constraints to develop useful ICT for doctoral students are the automations© or self-assessments. As survey tools became more widely available as open source extensions, self-assessments2 could be developed to help a doctoral student unpack the full range of criteria against which their ideas would be judged by their supervisors. When the online selfassessments were developed it became clear that students are much more resilient to the written word than they are to the personal interview, for as one student told us, “your Automation© tool helps the student both emotionally and logically.” Students demonstrated a willingness to take on a complex series of criteria in the completeness in which they filled out the self-assessment and their comments afterward. The tool 116

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helped them analyse the strength and weakness in their topic and design ideas. As another commented: The self-assessments are really useful. The fact that the process is step by step and logical contributes to this. I have finished my data collection and just wanted to check myself. I think that for someone starting the process the additional information really guides them. Universities are beginning to imbed these automations or self-assessments into courses or required meetings with their committee chair. It is hoped that by requiring their use, they will prove useful in increasing the quality of communication between student and supervisor. When going over the resulting pdf, whenever the student believes their idea meets the specific criteria and the supervisor disagrees, the conversation immediately advances to cover the subtleties of the embedded issues in research design. Ultimately, the relationship is improved, the student progresses, and the supervisor increases their understanding how the student is viewing their work.

Addressing Doctoral Student Frustrations Before a graduate student disengages they frequently stall out. Within that group some disengage (current literature suggests 50%) which seems a huge waste of human resources and lost potential leadership (Christensen, Horn & Johnson, 2008; Council of Graduate Schools, 2010; Gardner, 2008). Who is responsible for doctoral disengagement? Responses from research have studied several options: the student (Kniffin, 2007; Mewburn, 2011), their supervisor (Barker, 2011), the programme they attend (DeShields, Kara, & Kaynak, 2005; Preston, 2014). Or is completion and advancement through doctoral degrees the responsibility of everyone in “the village” that comprises higher education? (Maher, & Macallister, 2013; Tinto, 2010). Research has investigated doctoral disengagement for 50+ years (Bess, 1975; Tinto, 1975), largely pinning the challenges as one of socialisation. Provocatively, Susan Gardner, (2008) coalesced those tensions into five variables which, from the students’ point of view, are most likely to cause disengagement: ambiguity, worklife balance, independence, development, and support. In a subsequent and ongoing survey of doctoral students internationally (current n = 369), doctoral students reported significant percentages of regular and pernicious challenges. Work-life balance accounted for distress two or more days a month for 54% of those surveyed, causing them to consider disengagement. Lack of support ranked second for almost the same number of students (52%), and ambiguity caused frustration with that same frequency for more than 30% of the respondents (James, 2018).

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These challenges respond to the reasons over 50% consider dropping out and contribute in a significant way to how doctoral students experience their education. We feel that developing integrated means to address these frustrations should be forefront of any discussion about ICT in doctoral education. A simple formula was developed which we hope to test in the third round of research: If frustration causes disengagement, building systems that address the frustrations should increase retention and completion.

Meeting Doctoral Students’ Needs for Support, Ambiguity, and Work-Life Balance During webinars discussing the five frustrations and support, our data show that 95% of the doctoral students attending responded that they didn’t have enough of it. However, few could add specifics of what else was needed. They commented on the difficulty they had accessing people on campus, etc. Yet Dean’s offices offer multiple means of support, leading us to ask, “What causes students to feel lack?” Our hypothesis is that there cannot be too much redundancy in support offerings. It appears no two people access a website in the same way and without multiple points of access, content may not be found when needed. Instrumental and pedagogical processes needed to meet students where they are and point them onward. To do so we found the same message needed to be repurposed in many ways and at many places in the academic discussion. For example, redundant support in research design would include: 1) Step by step procedural discussions, 2) webinars that unpack particular subtleties known to cause failure, 3) examples of others work critically analysed, 4) writing groups focused on what goes where and the logic needed in each section, 5) boxed set groups of resources for those who know they need to figure out a section but don’t know where to start, and 6) open forum events where students could ask questions. To develop ubiquitous support there is a never-ending list of potential redundancy in order for students to find patterns that work for them, fortunately that is not difficult in digital learning. As one student explained their experience of these supports: I want to post on the community discussion, a kudo to you. I am going to start calling you our DM student’s ‘ankle link.’ I heard a minister talk about the 400 meter relay race in this summers’ Olympics. Evidently the African American girl who led her team to the Gold, called her position on the team the ankle link because she is the one that has go make up the distance for those that have fallen behind. These tools do that for us only in a way where they teach us what it is we need to do to be successful in these doctoral programs. For me when everything was falling through the cracks and it didn’t seem like I would make it, These tools made a way forward. 118

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For so many of us you are supplying the missing link that leads to our success. As Curtis Mayfield said, ‘Keep on Pushing.’ (JB, US Doctor of Management)3 Ambiguity generally begins to be voiced after a doctoral student takes their ideas to their supervisors and leaves unsure if and to what extent they understand what was said. A famous PhD comic called this the “Aura of Logical Distortion,” and portrays the student as understanding their professor during a discussion, but quickly feeling lost (Cham, 2012). Several possibilities arise: 1) their supervisor speaks in a language that they don’t understand, 2) the discussion or comments stem from criteria they have never encountered before, or 3) the critique they receive impacts them on a personal level, playing into fears and insecurities and they feel diminished. The maps, milestones and self-assessments are standalone tools that allow them to reinvestigate their ideas in a neutral environment where they can ask questions. My biggest realisation after going through your site’s content was how lost I was in my dissertation work—and how serious it is due to the impending completion deadline. I am delighted with the service I have had since joining. (BLS, US Engineering student) Online international groups became centers of understanding, especially good at addressing the ambiguity in academic writing or in helping students move on when stalled in finishing their thesis (ABD). As one student commented to a Professora Emerita: Thank you, Maria, this was brilliant! I am finally seeing the light. You have a gift to explain frustrating concepts in a clear way. You inspire me every week. I can be really frustrated and stressed when attending on of your online groups and afterward I want to jump right back into my work. (Analien, South African Archeology student) Students voted a resounding, “Yes!” to the idea of using the 30-day challenge format to improve skills. For instance, the following data refer to the writing challenge. Provoking challenge! I scheduled the challenge on my Google Calendar thus I almost did it on-time (in 31 days). Thank you ladies for these great videos. (ED, UAE Engineering student) Thank you for a great challenge. I committed to writing and ended up going for hours each day. I was able to advance the stages of my proposal. Thank you!! (KM, US Nursing student)

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Work-life balance and student wellbeing are tricky to address. Hundreds of registrations for webinars indicated interest in topics inclusive of these issues. Roughly 54% of the 369 students who had taken the five frustrations survey reported severe issues (more than twice a month) with work-life balance issues, to a severity they considered dropping out of their programmes. Issues of work life balance, like wellness in general and mental health specifically, are strategically harder to address than writing because each contains issues of psychology, time management, planning, self-worth, etc. We had had a great response to the 30-eay writing challenge. Would a similar pattern work for work-life? The first iteration began to directly offer support through a webinar on the topic, followed by tools from positive psychology and training in time management. The resulting 30-day work-life balance challenge will be fleshed out during 2018 with more material and checklists, but to date 33 people have taken the challenge resulting in one comment as to its helpfulness. Other tools work tangentially on wellbeing. The 365 motivational emails continue to be successful with 300+ students having subscribed over time. They generally receive a 35-40% open rate, which considering their frequency, seem outstanding. The 365 emails are Amazing! I needed this article to foster increase in my Self! Wonderful and on time. (CM, Irish Business student)

THE SECOND ROUND: FITTING DIGITAL PROFESSIONAL DEVELOPMENT INTO THE CAMPUS How do all these tools integrate with a university setting? How many use them? What can be said from research on their efficacy? What are the next research steps? It is still early days. The suite of tools is used in twelve universities in three countries. Dublin City University has the longest use, having piloted the suite through the last three years of development with 100 students in business, nursing, education, and human development. When the topics of the webinars were published to the entire university during 2016-17, there was a 25% increase in users outside of the original group, many from the STEM degrees. It was originally thought that students at a distance needed the tools, but when a shift in university personnel necessitated one programme pulling out of the original pilot, that number of students were brought online from the full time on campus population. Statistics showed no difference in usage, on campus or off, full time or part time.

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Usage and Barriers 2017 saw three universities in the United States: Morgan State, West Virginia University and Texas A&M at Corpus Christi take on the suite of tools for their entire PhD student population. In addition, Texas A&M International, Calpoly San Luis Obispo, and Texas A&M Qatar all opened sites for their Master’s Students. Percentages of use vary from 15% to 65%; however, consistently the webinars and international online community find the largest population of use at the start. Then, as students attending live sessions hear about the tools available and through student trial and error, use of the opt-in website and self-assessments grows. Integration of new ICT tools takes time within a university structure. Experience shows that during the first year the Deans pretty much allow the online system to run on its own, as they see what happens. When it becomes clear that students are adopting the tools and stories begin to filter back about use and value, the Deans encourage use through their own notifications, etc. Growth develops in alignment with the interdependence of the institution and the technology. Communication between Deans and the service increases interdependence and results in specific elements of support being included in the next terms offerings. As an example, during 2017, comments from university personnel resulted in a series on how to get published, transferable skills for PhDs obtaining nonacademic employment, and an upgrade in academic writing aids for Master’s students. Webinars were repurposed as boxed sets and all proved students’ interest through downloads. ICT can support students through change in university personnel or other stress experienced at the university level. A partner university saw 35% of their students lose their committee chairs due to professors moving to other employment. Fortunately, while the students looked for new people to fill that role they were not without professional development guidance. What is not known is what causes variation in usage between and within a cohort of students. Are some simply more prepared or have better supervisors and therefore don’t need the tools? Perhaps some are more adept at digital tools and so are more likely to be the early adopters of an ICY solution? Perhaps others don’t read their emails or app notifications and opportunities pass them by? There are many opportunities here for future research. Barriers to usage are untested in any broad sense. At the student level there appears to be a requirement for constant communication so that the student knows which tool to use when they need it. Comments come in from students that they don’t think many others in their cohort know the tools exist. The tech support that is requested is usually from students unable to sign on, primarily because they have forgotten how to access the platform due to irregular use. Successful onboarding

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plagues the development and distribution of any new tool. One successful strategy has been to close webinars with a short discussion where other tools related to the topic can be found. This results in an increase in the use of those tools during the immediate week. The same is true when university partners include ICT links to tools in newsletters, on their website with orientation videos, or through emails from the graduate studies office. What is noticeable is that year-to-year usage has increased as the doctoral professional development suite of tools has integrated with the Postgraduate office. Much depends though on a solid start with orientation in the fall. During the 20172018 university year at one university, communication between the postgraduate office and the company drifted causing a much slower uptake by students. When Graduate offices regularly post upcoming webinars and other notifications, students understand the partnership and both usage and testimonial data increase. Another barrier to adoption is lack of understanding of the potential of these tools by supervisors. Occasionally students mention a professor that recommended they test out what is available, but, largely, knowledge of the tools is limited and requires regular attention during faculty meetings. Deans of Programmes or Head of Research to some degree display uncertainly about the impact or value of ICT both in general and specific to this suite of tools. As one Dean told us: At our institution I don’t think we are intentional about socialisation. If we think about it at the program level, we just think it happens. When students fail to engage we don’t ask ourselves what may be wrong with our set up that contributes to this outcome. I think the clarity of your conceptual model and the personalisation of your tools will overcome those barriers. (Anonymous, 2017) Further integration with the professoriate on campus would create a deeper working relationship. One professor suggested the self-assessment tools could become part of the university standards for meetings with supervisor. It appears that the longer the suite of tools are used, the more likely professors hear good things from students, the greater the increase in willingness to incorporate online and app solutions with the library and writing center that traditionally work on campus. Data demonstrate that students who use the sites typically are comfortable using a variety of types of tools. At the point where there were only 1800 users, registrations for webinars and groups for five universities totaled 1522 during the autumn semester of 2017. Academic writing resources received equal notice with 1357 investigations. This resulted in one Dean commenting on the dramatic increase his figures represented from those of some on-campus efforts. Interdependence has a role to play here as well, with Deans and Programme Chairs suggesting new

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content and then publicising it for their students. Popular topics get repackaged and replayed as boxed sets or opt-ins, constantly enlarging the area of influence for every good idea. Several barriers that used to exist have been overcome, some of which were financial. Tiers of service options exist but keeping costs low and charging a maintenance fee according to the number of students allows the university to be more likely to offer the services widely across campus. While cost can often be a barrier in education, in the long run outsourcing professional development is economically a strong idea. A university would need a full-time employee to provide an equal level of service; a software company can deliver them at a fraction of the cost. Another barrier was hesitancy by the university to engage in a new business model. Data made it clear that collaboration produces the best results when a university see the tools as their own. As an example, several universities have uploaded their graduation videos as the login to these services and help design the next season’s webinar offerings.

Five Lessons to Date 1. Whether acknowledged or not, there is an interdependence between technology, faculty, and students. The best leadership puts ICT on the team as a player that increasingly helps students past the hurdles they face. For example, a usage increases from 50-76%, gives the students the best chance of finding the tools they need. 2. Interdependence will also result in the swiftest development of new tools. It requires the ICT company, the university, and the students to communicate ideas. Porter and Perns (2017) reported that since we can’t know the experience from the student’s point of view, they need to be included in discussions with vendors as new ICT is developed. 3. Redundancy in tools and efforts is required. As seen in doctoral students’ responses to the five frustrations, the personal interacts with the academic in a doctoral degree and the student’s success. Redundant platforms and contexts for communication add exponentially the likelihood the student will find the support they need on time. Some will come to an office, others will go online, and many will do whatever occurs to them. 4. Wellness and work-life balance cannot be ignored. Our data has demonstrated that students disengage for purely personal reasons, and their choice as to what kinds and styles of help they seek are equally individual. Campuses tell us that they are paying greater amounts to provide mental health services for their graduate population.

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5. Engagement comes in stages, the first merely a demonstration of interest such as opening emails. Voluntary ICT usage needs to be measured in stages.

Beyond Student Experience (UX) to QX (Quality Experience) The Internet of Things (IoT), and digital learning both have influence and drive change in issues of teaching and learning (Tianbo, 2013). This is a recent development and not one academia has caught up with everywhere. From the vantage of a new professorship at an online university in the mid-1990s, it seemed shocking when a fellow administrator calmly mentioned that the students could jump around through a course by their own design, so [there is] no real control (Epp, 2004). From a “bricks and mortar context” this was unheard of because if the students immediately could see where the course was taking them they were empowered to sidestep, circumvent, or derail premade plans at will, something teachers were vigilant to prevent. That was the basis on which online education embraced the theoretical development of constructivism and connectivism, as well as the concept of the personal learning environment (Duffy, Lowyck, & Jonassen, 2012; Siemens, 2005, Wilson, et al, 2006). Modular on-demand training, self-directed learning, the personalisation of training and indeed the whole IoT have influenced the pace of knowledge transfer and students everywhere are coming to expect 24/7, 365 support. Common sense dictates that universities should track what students do, where they go, and what precedes success or failure, but as this research has shown, how students reach out for support is subject to their personal circumstances. Thus their options and the solutions they are offered need to be both wide and redundant. As the breadth of ICT grew so did the expectation of personalised experiences and the subsequent decrease in patience with systems that seem clunky, slow, or non-intuitive. Referring once again to the urban designer who puts in lovely square corner sidewalks, only to find that users cut the corners, marketing professionals designing web experiences quickly found that tracking users and meeting them there with a personalised message created success. ICT is the only way universities can do this to scale. Doctoral students want to move faster, they try to proceed straight to the research outcome, and frequently come up against a necessary corner they have by-passed. As ICT design develops, practitioners and Deans will see indications of these gaps and be able to fill them with popup guideposts, and notification, etc., to guide them onward. Doctoral students should be aided in efficient processes to help them avoid feeling they need to cut corners. ICT needs to meet them on the new walkway and provide guideposts when they might need them. Merged with the standard university pedagogy across various programmes, ICT in this case, offers the potential for improving the overall practise because the multiple redundancies required are more easily produced by on demand technology. 124

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Looking back to the quotation that grounds this research – how to best meet the needs of those who have less social and financial capital and how to do that at scale? Excellent ICT design provides the foundation for quality student experiences in digital tools, while research with ICT designers and Graduate Schools or Postgraduate offices investigating whether and to what extent their solutions are addressing key outcomes such as retention and completion will be key in the future.

Five ICT Design Elements to Student QX Table 1, below merges these ICT pedagogical developments with the five elements of UX design as discussed by Garrett (2000). Columns one and two are the original five elements. The third column considers future levels of quality ICT development Table 1. Morville and Garrett user experience guidelines redesigned for doctoral education

Surface

Skeleton

Structure

Scope

Strategy

Visual design

Appealing and welcoming visual design imitates wider app or websites context such as use of university logo, colours, web interface for media, etc., so that it seems familiar. All visual elements drive home a “you can do this” message and motivational design.

Information design

Redundant access points allow doctoral students access to multiple ways to search and find. Redundancy is needed in content across user experience to catch students who missed a previous pathway. Maps and milestones guide the pedagogy with self-assessment tools such as surveys and checklists throughout. Adult learners appreciate audio, video, as well as written content and content groups, etc. The outcomes of this skeleton need to be useful, desirable, and credible (Morville, 2004).

Interaction design information architecture

Pedagogical design is constructivist or connectivist, facilitating tasks by driving focus on the connection between subtle concepts that are part of bigger doctoral issues, such as what goes where in the dissertation or thesis. This is done through linkages across content areas. Content also provides continuity goals across academic writing, critical thinking, maintaining well-being, etc. The structure must be findable, accessible, and usable (Morville, 2004).

Functional specs and content requirements

Professional development as it relates to dissertation or thesis completion, transferable skills obtained during study, academic writing, critical thinking, student well-being and motivation etc., is the topic of this chapter, but these ideals could be applied to any other aspect of doctoral student need. Content needs to be available across as many platforms as possible. How students perceive the value of the content is central to design (Morville, 2004).

User needs and objectives for the technology

To what extent the content meets the needs of students is measured by regular data collection. Examples of data points include usage, the incorporation of student ranking or voting on the usefulness of content, etc., as well as testimonials. The final outcome considers whether and to what extent ICT tools aid in the decrease of percentage of students frustrated during doctoral research work (Gardner, 2008).

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for doctoral students. The proposed student QX design incorporates the educationally validated UX honeycomb as part of skeleton, structure and scope (Brindley, Crawley, & Peters, 2017; Garrett, 2000; Morville, 2004).

Foundations for Action The future of ICT for doctoral students includes constant upgrades to the standards in the chart above as they develop while working with new universities, improving the scale and scope of professional development services offered through the ICT they use, and the continual development of new tools as other contexts develop them. An example of recent developments includes two universities, Texas A&M and South Dakota State, who will work to include aspects of the tools in the platform and app in their internal professional development certificate programmes. The end goal needs to be seamless from the students’ point of view, allowing the university to maintain their brand specifics while providing more support than they could afford to develop on their own. This chapter provides insights into both sides of the ICT/educational practitioner fence but, as mentioned earlier, it is early days and development needs to be agile. Nevertheless, those foundational ideas seem to suggest the following:

Future Research Needed Recent budgetary and governmental pressures force the need for student-yearcosts and the losses due to students’ disengagement need to be quantified by each institution. Research requires solid baseline data. Universities can’t decide what is a reasonable expense for new ICT to save students from disengaging if they don’t understand the long-term costs of losing students midway through their doctoral careers. Within that discussion, the subject of return on investment of ICT offerings needs to be critically considered, as the efficiencies in scale offered by technology should far outstrip the temporary inconveniences that come with change. Most educators have only qualitative evidence as collected by CRM systems and stories they hear during their own practise on which they base their understanding of the wider student population’s experience. To increase success at scale, a University should regularly survey the frustrations experienced by their doctoral students and develop multiple means for those to be overcome. ICT can help, but so will changes in program design or management. ICT vendors and solutions, when viewed as interdependent forces within a student’s UX, can work together to build QX. Every campus needs to gather as much qualitative and quantitative evidence as possible as teaching and learning practises include ICT. So much is not yet understood such as:

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1. The role in ICT adoption of ICT comfort by the student user (Childs & van Oostveen, 2017). 2. The role of design in the student user experience (Brindley, et al., 2017). 3. The role in ICT adoption of other student considerations such as age, work, culture, need to reskill, distance from campus, etc. (James, 2015). And, most importantly, to what extent can ICT aid universities in improving retention and completion? To date, an unrealised research potential is for academically driven ICT companies to work with universities, perhaps in an action research context, to continue to develop the relationship between research and teaching and learning in a digitally enhanced environment. Once again relying on earlier lessons learned in healthcare… The traditional dichotomy between research and practise invariably results in having to make difficult choices between the two, or trying to balance them in some way in order to keep both academic and corporate ‘sponsors’ happy. The real challenge is not so much balancing the two as achieving a closer fusion and synthesis between them (Bate, 2002, p. 478).

CONCLUSION ICT for doctoral students is in an evolutionary stage. To the extent that university Provosts, Deans, and staff are willing and able to create an atmosphere of interdependence with those who design and produce technology solutions, their ideas will benefit not just their own campuses but the evolution of new standards within doctoral education. The lessons learned in first round of ongoing action learning/research, centred on the development of appropriate ICT for doctoral students, include: 1) the difference between class or web design for use at will rather than guided, 2) to manage the restraints inherent in, but not across, different platforms or components, and, 3) how to begin meeting student frustrations rather than merely adding more pedagogy. When circumstances forced us to widen the research to include the interactions with and support of the Graduate School Office to develop the best possible professional development, findings in our second round showed that questions regarding usage have many spokes to consider. These include the degree of communication and interdependence between the university and the ICT used, the level of redundancy in the tools, whether wellness and work-life balance are considered, and ways in which engagement is scaffolded or cyclic. A reworking of the Morville (2004) and Garett (2004) to suit doctoral professional development was suggested. 127

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As the Provost mentioned at the beginning of this chapter told us, the challenge that kept him up at night was how to provide adequate services at scale for students who came from families with less social or financial capital. ICT’s economy of scale allows students to have use of tools not previously available and outside of what can be developed by each campus separately and may begin to provide answers to this pressing problem. What is needed is research into who is most likely to use it, to what extent does it alleviate the frustrations experienced by students, and how can the universities mold it to their best advantage so that their retention and completion go up?

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Porter, D., & Perns, K. (2017, October). Rethinkg our approach to the online learning ecosystem: Learning resources, learning experiences, recognition of learning. Paper presented at the ICDE World Conference, Toronto, Canada. Preston, J. (2014). Online doctoral programs: Can the produce the business scientists and leaders needed for the 21st century? International Journal of Leadership and Change, 2(1), 39–47. Siemens, G. (2005). Connectivism: A learning theory for a digital age. Retrieved from http://www.elearnspace.org/Articles/connectivism.htm Siemens, G., Gašević, D., & Dawson, S. (2015). Preparing for the digital university: A review of the history and current state of distance, blended, and online learning. Ontario, CA: Athabasca University. Taleb, N. N. (2012). Anti-fragile: Things that gain from disaster. New York: Random House. Tannebaum, S., Mathieu, J., Eduardo, S., & Janis, C.-B. (1991). Meeting trainees’ expectations: The influence of training fulfillment on the development of commitment, self-efficacy and motivation. doi:10.1037/0021-9010.76.6.759 Tianbo, Z. (2013, January). The internet of things promoting Higher Education revolution. Paper presented at the Xplore 2012 Fourth International Conference on Multimedia Information Networking and Security, Nanjing, China. Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125. doi:10.3102/00346543045001089 Tinto, V. (2010, October). Enhancing student retention: Lessons learned in the United States. Paper presented at the National Conference on Student Retention, Dublin, Ireland. Wentling, T. L., Waight, C., Strazzo, D., Jennie, F., La Fleur, J., & Kanfer, A. (2000). The future of e-learning: A corporate and an academic perspective. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.110.7680&rep=rep1&t ype=pdf Wilson, S., Liber, O., Johnson, M., Beauvoir, P., Sharples, P., & Milligan, C. (2006). Personal learning environments: Challenging the dominant design of educational systems. Retrieved from http://hdl.handle.net/1820/727

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ENDNOTES

1



2 3

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US vs European language differ in Master’s and Doctoral education in the world. This article tends to be inclusive of graduate vs postgraduate or dissertation vs thesis, or supervisor vs committee chair, mentor or other names for this role at the doctoral level. ©Automations were the first proprietary tool developed by DoctoralNet Ltd. All student quotes are direct and used with their permission, having been gathered during the course of doing business online from 2013-2018.

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Chapter 7

Digitalization of Higher Degree Research (HRD) and Its Benefit to Postgraduate Researchers Joseph Stokes Dublin City University, Ireland

Mark Brown Dublin City University, Ireland

Rachel Keegan Dublin City University, Ireland

E. Alana James DoctoralNet, Ireland

ABSTRACT Graduate Schools offer supports to enhance and improve the graduate skills development of their postgraduate research community not only in their research but also in preparing them for their future careers. The European University Association Council for Doctoral Education has identified the digitalization of doctoral education as necessary to the future to fully globalize the graduate school offerings. This vision is aligned, for example, to several of the objectives in Dublin City University 20172022 Strategic Plan. Online supports go towards the development of DCU as a global university allowing us to attract, and to provide aid to, research students who are studying primarily outside of Ireland. The same structured support also benefits staff who are involved in the life cycle of a research student. Therefore, it is important to assess the needs of our graduate researchers in terms of online supports and to provide them with such tools to ascertain if their needs can/are being met. Hence, this chapter begins this journey by determining what online resources our doctoral community use to move their studies forward and then follows on to measure the value of one resource “DoctoralNet,” which offers comprehensive support to such students. This chapter discusses surveyed material, yielding a positive message that our doctoral education requires such digital resources to meet their (students’) educational needs. DOI: 10.4018/978-1-5225-7065-3.ch007 Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Digitalization of Higher Degree Research (HRD)

INTRODUCTION At the 2018 Quality in Postgraduate Research Conference held in Adelaide Australia, Dr. Kwong Nu Sim, Victoria University of Wellington, highlighted the outcomes of a pilot study that examined how supervisors support their PhD students, and how PhD students use Information and Communication Technology (ICT) to support and advance the doctoral research process (Sim, 2018). During this conference, a Special Interest Group discussion on supervision noted that supervisors are not equipped to fully support their students in the new technological era. This concern is echoed in Europe with the European Commission highlighting of the crucial importance of the training of higher education staff in areas of digitalisation “including innovation in pedagogy and the use of technology” (European Commission, 2016, p. 4). At the same time, it is acknowledged that “[n]ew generations of doctoral candidates will increasingly be familiar with modes of blended learning with online content” (EUA, 2016) and as such will expect to avail of supports and services provided by the university through a variety of modes and media. For universities, these changes require leadership to consider the impact of digitalisation on their mission and visions, therefore requiring strategic focus. Doctoral education plays a crucial role here, and the 10th Annual Meeting of the European University Association-Council for Doctoral Education (EUA-CDE) in 2017 highlighted the impact of digitalisation on the role and practice of doctoral education. This meeting highlighted many differing perspectives of doctoral education in a digitalised world, with several case studies showing how graduate schools are beginning to adapt their practices in response to digitalisation of higher education (European University Association-Council for Doctoral Education, 2017). It is clear that digitalisation will become a key priority for European graduate schools, which, faced with increasing PhD numbers, must now meet the needs of a growing diversity of doctoral candidates. Since the publication of the Salzburg Principles in 2005 (Christensen, 2005), doctoral programmes are now more structured and doctoral schools are tasked with developing graduates who may pursue careers beyond academia. The past decade has seen a growing focus on mobility, diversification, increasing numbers, and broader skills development (Christensen, 2005; European Commission, 2011; Interdepartmental Committee on Science, Technology and Innovation, 2015), with digital supports becoming increasingly important in this regard. This chapter details the approach being adopted by one Irish university, Dublin City University, in introducing digital technologies into its doctoral education provision.

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BACKGROUND Digitalisation as an issue is permeating higher education policy across Europe. It is noted by the European Commission that “… rapid technological development is transforming the way in which higher education is delivered and students participate”, so much so that “[d]igital technologies and online resources now permeate all areas of teaching and learning” (European Commission, 2016, p. 1). These advances have not surpassed doctoral level education and regardless of whether doctoral graduate find themselves pursuing academic or other careers, the “digital transformation of research and the skills they acquire” will be of major important to current and future cohorts (EUA-CDE, 2017). The growing focus on digitalisation in research and doctoral education was called out in 2016 in the European University Association Publication Doctoral Education – Taking Salzburg Forward. This key publication highlights three key challenges for doctoral education in the coming years. One of these, the digital challenge, emphasises that “… universities must develop coherent policies and infrastructures for online sharing and learning in doctoral education that can be used in a coherent and responsible manner across the institution” (EUA, 2016). Digitalisation in this area brings with it many new possibilities including open access, big data research, possibilities to grow the diversity of doctoral candidates, greater collaboration, and more flexible approaches to doctoral education more broadly. In Ireland in 2015 the National Forum for the Enhancement of Teaching and Learning in Higher Education provided four recommendations in relationship to developing the digital capacity of the Irish Higher Education sector. One of these stated that higher educators must “[d]evelop a consistent, seamless and coherent digital experience for students in Irish higher education and actively engage with students and teachers to develop their digital skills and knowledge” (National Forum for the Enhancement of Teaching and Learning in Higher Education, 2015, p. ix). Dublin City University (DCU) has responded in recognising the impact of digitalisation in its Strategic Plan 2017-2022 committing to “… further digital learning enhancements …” (DCU, 2018, p.20), and “… leverage the affordances of digital technology to enhance the ‘student journey’” (DCU, 2018, p. 31). These commitments are underpinned by the University’s goal to “provide a transformative student experience …” which will enable all students of the University to excel and flourish in the outside world (DCU, 2018, p. 19). This underpinning principle directs the graduate school skills development programme, and as such supports offered to the graduate research community must not only support the students in their research, but must also prepare them for future careers.

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The introduction of digital supports at doctoral level is, however, not without its challenges. As highlighted by Radda and Mandernach (2012), the traditional view of doctoral education as an embedding of oneself into an academic community, while working closely with a mentor may have worked well in the past but now “… change in our modern society, driven by rapid advances in educational and communicative technology, are challenging the classic vision of doctoral education” (p. 1). This coupled with the introduction of a more structured approach to doctoral education means that doctoral schools must meet the challenge of addressing the needs of a more diverse population who “… have grown up in a world where information is shared and interaction happens with ease over the internet” (European University Association, 2016). There are other challenges facing many doctoral schools including few or no direct funds to support their services. Time is also frequently an issue as much of the administrative process falls on these offices to manage; overall enrolment, fee payments, attendance records, leave of absences, managing on-going reports on progress, and maintaining regulations and quality assurance. In this context, the cost of providing a set of online tools to aid students’ progress may be seen as a “nice to have” rather than a core need. Four elements should be considered, however, in costing the introduction of any additional online support: 1) freeing up supervisors’ time to focus on the subject matter/scientific relevance, 2) providing tools to aid the role of a Dean of Graduate Studies, 3) provision of supports for a growing number of distance and/or part-time doctoral candidates, and 4) filling gaps in services for the student at-risk of disengagement from their studies. These may prove to outweigh the costs of digital tools in the longer term.

THE DCU: DOCTORALNET PROJECT In 2015 Dublin City University began to look at how online supports could be incorporated into existing doctoral education provision. This commenced in the context of a European focus on digitalising higher education, and with a mind to the fact that “[n]ew generations of doctoral candidates will increasingly be familiar with modes of blended learning with online content” (EUA, 2016). The primary drive, however, was the growing number of candidates looking to pursue research awards on a part-time basis. This led to questions as to how this cohort could avail supports that were traditionally offered face-to-face, on campus, and during the working week. The Graduate Studies Office under the oversight of the Dean of Graduate Studies oversaw this project. Particular emphasis was placed on how to best support doctoral candidates who study remotely and/or part-time, as well as supporting students

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who may be at risk of disengagement. Initial issues addressed included: 1) time constraints of existing staff within the unit, 2) the cost associated with developing in-house, online supports, 3) the types of supports and courses suitable for online/ blended delivery, and 4) the technical/digital skills of current staff within the unit. After consideration of all issues, it was agreed that the Graduate Studies Office would investigate existing offerings available on the market which might meet the needs of current doctoral candidates.

DoctoralNet DoctoralNet was chosen as the only viable option on the market after consideration of the following requirements: • • • • • •

Clear guidelines and supports for system integration with DCU systems. Personalisation of the platform to meet the specific needs of DCU. Multiple platforms of delivery, include mobile phone application Personalisation of supports to meet needs of individual candidates/cohorts. Support for Dean of Graduate Studies, School Heads, Research convenors etc. Student self-assessment tools.

DoctoralNet is a web-portal/app/webinar service personalised to each university environment. This allows a university to give their postgraduate students a full range of academic writing, research, and thesis support and professional development services throughout their studies. The portal contains all of the pedagogical content, self-assessment tools, and links to other resources, at the same time linking the student back to DCU through personalised graduation videos, messages from the Dean and so on. Some tools are “opt-in” options and include: webinars, 30-day challenges, motivational emails, boxed sets (videos, articles, slides, and checklists), and work groups. Webinars and the international groups in which those conversations are embedded, are preferable to many students, giving them not only new or refresher knowledge, but also a membership in a wider, international, network. These are offered as an open educational resource (OER). Both the digital aspect of the support and the focus on transferable skills for industry leadership ensure that the university is providing the access they need to succeed (DoctoralNet, 2018). The launch of DoctoralNet in DCU commenced as a pilot in 2015/2016 and attracted 120 users. There has been a 45% increase in the number of users in 2017/2018 demonstrating its importance to our doctoral community during their studies. DCU initially rolled out a three-year pilot of DoctoralNet with the Schools

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who represented the largest part-time postgraduate research cohorts, although the supports were quickly opened up to full-time candidates as well. In the current year (2018/2019), the University has commenced rolling-out DoctoralNet to all postgraduate research students registered in DCU and following the success of the pilot, this will also include full-time research candidates.

METHODOLOGY The following study was conducted towards the end of the three-year pilot of Doctoralnet in DCU. This was an online, anonymous survey (see Appendix), the aim of which was to determine the level of engagement with, and experiences of using, DoctoralNet as a supplemental online support to other Graduate School offerings. The survey sought opinions from users as well as non-users of the digital professional development platform, with the intent of improving the postgraduate researcher experience though research and technology (DoctoralNet, 2018; James, 2017, 2018, in press). Ethical approval was sought and given by DCU’s Research Ethics Committee and a total of 32 responses were returned (a 24% response rate).

RESEARCH RESULTS Demographics The survey was sent to all participant of the pilot, representing approximately 20% of DCU research community. Of the respondents 56% were part-time with 44% studying full-time. Therefore, this study was almost balanced in terms of full and part-time responses. This was significant, as the results would emphasise if digital resources were seen as having higher importance to part-time respondents over fulltime. Notably, 21% of the survey respondents had never heard of DoctoralNet or had never used it despite being part of the three-year pilot. Those surveyed were almost evenly split over all years, with the exception of 31% been registered in Year 3. 21.8% of the respondents either never attend campus, other than registration/orientation, whereas 37.5% were daily attenders (the balance of respondents attended weekly or monthly), therefore the use of such digital technologies was surveyed on both remote and on-campus users. As expected, the majority of the full-time respondents came onto campus on a daily basis, while many of part-time respondents only attended the University either monthly or less frequently. Both part-time and full-time students adopted the digital resources.

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Access to Training Resources One important question was to determine how accessible our current on campus support and training activities are. While 9.4% indicated it was not possible, 56.3% indicated sometimes, which represented the view of 72% of part-time respondents. Therefore, the provision of supports to part-time doctoral students is a challenge compared to the accessibility being provided to full-time students. 68.8% of all respondents also indicated that they felt the supports offered by their supervisors and/ or supervisory panels was not sufficient to meet their training needs. A further 18.6% were unsure (the balance indicated that the supervisory supports were sufficient). Figure 1(a) shows the results of how many respondents access supports/training offered through the Graduate Studies Office in DCU and Figure 1(b) describes how important is was to have access to online supports/resources for graduate training/ skills development/research planning. From the results presented in Figure 1(a), 78% of regular attenders at training/ workshops were full-time, with 71% of part-time students having access either from time to time or infrequently. From the results presented in Figure 1(b), 71% of respondents valued access to online supports as either very or quite important, with 72% of part-time indicating the same. Therefore, one can draw from this that the importance of online supports was evenly expressed by both full-time and parttime respondents. In fact, 93.3% of respondents expressed a wish for more online supports/sessions to be delivered and only 15.6% of respondents were concerned if these resources were provided by outside/third-party providers. This is a considerable support to the needs of digitalisation within our Doctoral education provision and is supported by reports that “PhD students are turning to social media to meet and support each other as they take the long and demanding journey of completing their PhD” (Bendemra, 2013). Figure 2 seeks to determine such needs; while face to face delivery is important, the requirement of live and recordable online resources was equally desired so as to facilitate interaction during the delivery of a particular session and the availability to post-review the session at a later stage during their research.

Student Challenges in Postgraduate Education Gardner (2007) looked at the socialisation of Doctoral students, particularly from a professional and cognitive development point of view. The research examined the need for doctoral students to balance graduate school responsibilities along with external relationships and demands. Five major themes emerged in terms of the socialisation processes which either facilitated or impeded degree success. It appears from this study that socialisation in doctoral education exists within at least 139

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Figure 1. (a) Access to supports/training offered through the Graduate Studies Office in DCU; (b) Importance of access to online supports/resources for graduate training/skills development/research planning

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Figure 2. Preference of support “type” research resources

five distinct, but synergistic challenges and frustrations that add to any students’ likelihood to disengage. Those challenges are: 1) ambiguity, 2) work-life balance, 3) independence (too much or too little), 4) skill development, and 5) support. With this in mind, the respondents were asked to rate their motivation in relation to working on their thesis. 88% responded to this question as not all respondents were working on their thesis at that time. While 69.5% were strongly motivated, working on their thesis three or more times a week, 21% were neutral or completely de-motivated, finding it difficult to spend a couple of days a month or any time to work on their thesis preparation. Figure 3 describes the five challenges identified by researchers which negatively impacted their success. “Work/Study-Life Balance” was found to be the biggest obstacle expressed by students, and much literature has described the difficulties and stresses that doing research has on lives and study (Martinez, Ordu, Della Sala, & McFarlane, 2013; Walker, 2015). Kearns (2018) highlighted the issues for HDRs and mental wellbeing, while Nyman (2018) in assessing the work-life balance of doctoral students in Health and Life Sciences, suggests pressures of high workload was seen to lead to bad health, psychological distress, fatigue, high blood pressure, and often depression.

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Figure 3. Factors influencing a doctoral researcher’s success, adapted from Gardner, S.K., 2007

Student Outcomes With DoctoralNet The respondents were asked to indicate their awareness of the DoctoralNet online tool. 78% of the respondents had used the tool a few times to more regularly. 18.8% were aware of the tool but had not used the system. 84% of the respondents who used the DoctoralNet tool, found its support to be of some help to very helpful. 72% of the respondents indicated the various supports they used within the DoctoralNet online tool, as shown in Figure 4. Both research design and thesis progress supports were highly valued and academic writing support were also popular. Overall engagement in all of the supports were valued and it appears from the data that each student has a different support need from their peers. One student describes her tailored engagement with the site in feedback stating: ... just want to say a big thank you. I am doing the 30 day writing challenge and loving it. The short video clips get me started but more importantly they keep me focused on the need to write everyday ... Engagement with digital tools is only part of the equation. Whether those who really need the supports availed them, and whether these tools support retention and 142

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Figure 4. Supports used within the DoctoralNet online tool

completion of postgraduate studies in the long-term remains to be explored. There is no question, however, that the greatest service of online tools is to offer options to students who are feeling lost, confused, and at risk of disengagement. This is evidenced in a quote from one biology PhD student: I contacted … the DoctoralNet team during my third year of my PhD project. I was reaching the end of my laboratory experiments and needed to start writing it all up! I hit a wall and needed to regain some self-confidence. It was suggested that the student should join the PhD/ABD accelerator project: ... the project was a godsend! Every week, I, along with a group of other PhD students from around the world, connected with … DoctoralNet ... and discussed a specific element of the thesis structure, such as writing a good problematic! Each 143

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webinar/meeting comprised of a presentation, a Q&A and review of our homework! Reviewing everybody’s snippets of their thesis was so beneficial! As the weeks came and passed, I realised two things. 1: I can do this! 2: I am not alone! Though my research was very different to my peers, we had very similar questions and difficulties! Doctoralnet has a very sensitive approach and acknowledge that each university, institute and supervisor have different standards and expectations. They support the student making their thesis their own whilst meeting the set requirements. I have now completed my thesis, and graduated with only some minor corrections! I am still in academia and recommend doctoral.net to every PhD student I encounter, and often find myself referring back to the different covered materials ...

DISCUSSION AND CONCLUSION The purpose of this small-scale study was to research the efficacy of digital professional development to enhance Doctoral Education. The DCU-DoctoralNet partnership is still in its infancy, and this initial survey was the first step in gauging the desirability of digital support services for students both on and off campus as part of a transformative, global university environment. Research students who used the DoctoralNet tools found they support their studies, and aided motivation and work-life balance difficulties they experience during their studies. This is a learning experience for the students involved, the university, and for DoctoralNet and highlights the importance placed on having access to online supports and resources by graduate researchers. A key learning from the process was the importance of a collaborative partnership between the university and the technology company as demonstrated by independent research conducted by DoctoralNet (James, 2017). DCU’s experience has mirrored these findings and highlighted the importance of personalisation of the message to students. For example, when the Graduate Studies Office at DCU commenced promoting DoctortalNet webinars across campus, an additional 100 students started to make use of the tools. The supports afforded to students at risk of disengagement also appears to be a key benefit for the university with one student reporting that s/he began engaging with DoctoralNet when s/he “needed direction during times when I wasn’t able to ask others. The online chats were extremely helpful for quick question & answer sessions. Especially when I was beginning to bog myself down in too much detail!” Another respondent spoke of the value of the tool in giving her/him access to a research community:

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I enjoy the interaction and connecting with the wider community of researchers. I hate missing a session but am so relieved that I can catch up later. I also listen back to previous videos to keep myself focused and motivated. Wonderful stuff!!!” The provision of online supports through any graduate school will go towards the development of a university in their pursuit to be viewed as a global university. Wildavsky (2010) (as cited in Alberts 2010) states that one can define a global university as one engaged in “…receiving students from overseas to sending students overseas, engaging in international research and …cross-border scientific collaboration” (para. 3). A strategic focus within our university is to embrace digitalisation and we are working to embed the commitments to “further digital learning enhancements” (DCU, 2018, p.20), and “… leverage the affordances of digital technology to enhance the ‘student journey’” (DCU, 2018, p. 31) into our doctoral education offerings. We can be Global leaders and enhance global change by offering doctoral education to any student full-time/part-time, on or off campus or even remotely. Our Doctoral students already interact with online tools, therefore, it is our duty to bring these to the forefront of their studies, aiding both student and supervisor in the pursuit of this prestigious award. The story does not end here and as this paper features in the “Research-in-progress” sessions, further findings will be presented in the future.

ACKNOWLEDGMENT The authors wish to thank all of the Dublin City University graduate researchers who participated in this research study; to thank the DCU Ethics Committee for their support; to thank the other members of the DCU Graduate Studies Office, the National Institute for Digital Learning in DCU and DoctoralNet for the use of their online tool to assess the feedback of our Doctoral researchers.

REFERENCES Alberts, H. R. (2010, July 28). The globalisation of higher education. Forbes. Retrieved from https://www.forbes.com/2010/07/28/global-international-universities-collegesleadership-education-ben-wildavsky.html#6ed86ad57d95 Bendemra, H. (2013, October 28). Doing a PhD can be a lonely business but it doesn’t have to be. The Conversation. Retrieved from www.theconversation.com

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Christensen, K. K. (2005). Bologna Seminar: Doctoral programmes for the European knowledge society. Salzburg: EAU Council for Doctoral Education. DoctoralNet. (2018). DoctoralNet. Retrieved from www.doctoralnet.com Dublin City University. (2018). DCU Strategic Plan 2017-2022. Retrieved from www.dcu.ie/external-strategic-affairs/strategic-plan.shtml European Commission. (2011). Principles for innovative doctoral training. Retrieved from https://euraxess.ec.europa.eu/sites/default/files/policy_library/principles_for_ innovative_doctoral_training.pdf European Commission. (2016). Transforming higher education: How we teach in the digital age. Retrieved from https://ec.europa.eu/education/sites/education/ files/2016-pla-digital-higher-education_en.pdf European University Association. (2016). Doctoral Education: Taking Salzburg forward, implementation and new challenges. Retrieved from https://eua.eu/ component/attachments/attachments.html?id=398 European University Association – Council for Doctoral Education. (2017). Digitalisation: A game changer for doctoral education? Paper presented at the10th EUA-CDE Annual Meeting, Tallinn, Estonia. Gardner, S. K. (2007). “I heard it through the grapevine”: Doctoral Students Socialisation in Chemistry and History. Higher Education, 54(5), 723–740. doi:10.100710734-006-9020-x Interdepartmental Committee on Science, Technology, and Innovation. (2015). Innovation 2020, Excellence Talent Impact, Ireland’s Strategy for Research and Development, Science and Technology. Retrieved from https://dbei.gov.ie/en/ Publications/Publication-files/Innovation-2020.pdf James, E. A. (2017). Retrieving graduate revenue from the edge: Solving inequitable socialisation for Masters & PhD students with graduate technology support platforms. Organisational Development, 35(1), 67–83. James, E. A. (2018in press). Doctoral Platforms and Apps for Professional Development and Student Support. In K. N. Sim (Ed.), The Roles of ICT in Doctoral Education. IGI Global Publishing. Kearns, H. (2018, April). Enabling mental health for research degree students. Paper presented at the Quality in Postgraduate Research Conference, Adelaide, South Australia.

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Martinez, E., Ordu, C., Della Sala, M. R., & McFarlane, A. (2013). Striving to obtain a school-work-life balance: The full-time doctoral student. International Journal of Doctoral Studies, 8, 39–59. doi:10.28945/1765 National Forum for the Enhancement of Teaching and Learning in Higher Education. (2015). Teaching and learning in Irish higher education: A roadmap for enhancement in a digital world 2015-2017. Retrieved from http://www.teachingandlearning.ie/ wp-content/uploads/2015/03/Digital-Roadmap-web.pdf Nyman, C. (2018, April). Work-life balance among doctoral students in health and life sciences. Paper presented at the Quality in Postgraduate Research Conference, Adelaide, South Australia. Radda, H., & Mandernach, J. (2012). Doctoral education online: Challenging the paradigm. I-manger’s Journal of Educational Technology, 9(3), 1–8. Sim, K. N. (2018, April). ICT use in the doctoral research process: Whose call? Paper presented at the Quality in Postgraduate Research Conference, Adelaide, South Australia. Walker, J. (2015, November 12). There’s an awful cost to getting a PhD that no one talks about. Quartz. Retrieved from https://qz.com/547641/theres-an-awful-costto-getting-a-phd-that-no-one-talks-about/

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APPENDIX: DOCTORALNET/ONLINE SUPPORTS SURVEY DoctoralNet / Online Supports Survey Dear Research Student The following survey is being conducted by DCU’s Graduate Studies Office (GSO) to determine your level of engagement with, and experiences of, using current online supports offered through GSO. Participants are invited to answer the following short survey which will take no more than five minutes to complete. No question is compulsory and you can exit the survey at any point if you no longer wish to participate. No personal details are asked, and all responses are submitted confidentially. No student will be identifiable from the survey. It is hoped that this survey will help us to further develop online supports for research students in DCU. The findings may also be used to form part of future conference papers and/or research articles. Participation in this study is entirely voluntary and you may exit the survey at any point before submitting. If you have any queries about this study, please contact Ms. Rachel Keegan (Graduate Studies Manager) at [email protected]. If participants have concerns about this study and wish to contact an independent person, please contact: The Secretary, Dublin City University Research Ethics Committee, c/o Research and Innovation Support, Dublin City University, Dublin 9. Tel 01-7008000 1. Are you happy to proceed to the survey? * a. Yes b. No Stop filling out this form. Engagement 2. What is your mode of engagement? a. Full-time b. Part-time 3. What is your year of study? a. Year 1 b. Year 2 c. Year 3 d. Year 4 e. Year 5 plus 148

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4. How often do you come onto campus? a. Daily b. Weekly c. Monthly d. A couple of times a year e. Once a year f. Never 5. Is it feasible/practical for you to attend campus to access graduate training activities and/or other campus based supports? a. Yes b. Sometimes c. No Supports 6. Do you feel the supports offered by your supervisor/supervisory panel alone, are sufficient for your training needs? a. Yes b. No c. Unsure 7. How often do you access supports/training offered though the Graduate Studies Office? a. Regularly b. From time to time c. Infrequently d. Never 8. How important is it for you to have access to online supports/resources for graduate training/skills development/research planning? a. Very important b. Quite important c. Slightly important d. Not at all important e. I’ve never given it any consideration 9. Does the fact that current online supports are delivered by a third party (DoctoralNet) impact on your engagement with these resources? a. No, not at all. As long as they are available I don’t mind who delivers them. b. Somewhat. I may be more inclined to pay attention to DCU delivered supports.

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c. Yes, very much so. I’m reluctant to engage with third party supports/ services. 10. Would you like to see some of the Graduate Studies Office sessions/workshops made available online? a. Yes b. No 11. If yes, what would you like to see prioritised? a. Live webinars covering the research process b. Pre-recorded short videos covering the research process c. Live webinars on research methods and other skills training d. Current face-to-face workshops and seminars being recorded and made e. available online at a later time DoctoralNet Awareness 12. To what extent are you familiar with the DoctoralNet online tool provided to you by DCU? a. I haven’t heard of it Skip to question 16. b. I’m aware it exists but I’ve never used it Skip to question 16. c. I have participated briefly at tested out at least one of the tools. Skip to question 13. d. I have used a couple of the tools a few times. Skip to question 13. e. I regularly use one or more of the tools. Skip to question 13. Engagement with DoctoralNet 13. Please indicate which of these supports you have used (tick all that apply) a. Online platform of content regarding the thesis process b. Supports to self-assess your research design c. Opt-in supports for motivation d. Opt-in supports for improving your academic writing e. Opt-in supports for improving your work/life balance f. Supports for advancing towards publication g. Supports for transferring doctoral skills to non-academic employment h. Supports to help speed progress or re-engage i. Supports for organisation or time management aids j. Supports to boost critical thinking and analysis

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14. Which specific tools have you found useful (tick all that apply) a. Milestones b. Maps c. Automated self-assessment tools d. Boxed sets e. Webinars f. 365 email notifications g. 30 day writing challenges h. Writing support group i. Other: 15. Overall, how would you rate the DoctoralNet site in terms of supporting you in your research? a. Little to no help b. Of some help c. Partially helpful d. Very helpful Skip to question 17. Non-engagement with DoctoralNet 16. If you have never used the site, why not? a. b. c. d. e. f.

I didn’t know it was available I was not familiar with the supports and tools on offer It is not of interest to me I don’t have time to invest in exploring these supports and tools I have enough support from my supervisor / school My supervisor did not think it was a good idea

General Motivation 17. How would you rate your general motivation pertaining to work on your thesis? a. Completely de-motivated. Seldom put in time on working on my thesis. b. Partially de-motivated. Find it hard to make the time to work on my thesis. c. Neutral. I work on it a couple of days a month. d. Moderately motivated. I work on it one day a week. e. Strongly motivated. I work on it 3 or more days a week

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18. Of the five challenges below, which if any negatively impact your postgraduate success? a. Ambiguity - I’m unsure what I need to do to complete my research or writing. b. Balance - I find it difficult to strike a proper work-life-study balance. c. Independence - I’m finding the transition to becoming an independent researcher difficult. d. Development - I find it difficult to have all the skills I need, when I seem to need them. e. Support - I don’t feel I have the sufficient supports to complete my research.

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Online Practices in Doctoral Study

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Chapter 8

Don’t Be a Ghost Who Drops Grades in Blackboard: Findings From a Program Evaluation of an Online Doctoral Program in the United States Ambyr Rios Texas A&M University, USA Radhika Viruru Texas A&M University, USA Burhan Ozfidan Texas A&M University, USA

ABSTRACT This chapter presents the results of a program evaluation conducted to assess the effectiveness of an online doctoral program in educational leadership at a Research One University from the perspective of its students. Feedback was sought from over 80 currently enrolled students. The study focused on three aspects of the program, namely faculty social and cognitive presence. Recent changes to the program that address these areas include the creation of a thematic group model that clusters students based on academic interests over the last 2 years of the program, extensive revisions to coursework, the adoption of a problem-based dissertation model, and the use of social media and an online community portal to promote student engagement. The results indicate that although students had encountered positive experiences in all three areas, online doctoral students continue to need focused individual mentoring in order to experience success. DOI: 10.4018/978-1-5225-7065-3.ch008 Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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INTRODUCTION AND PROGRAM CONTEXT This chapter presents the results of an evaluation of program effectiveness of an online doctoral program within a large public university located in the southwestern part of the United States, particularly focusing on the relationships between the faculty and student participants in the program. This program, a doctorate of education (Ed.D) in curriculum and instruction, includes approximately 100 students resident in eight states and three countries, with most residing in various locations throughout the state in which the program is located. The eighth cohort of students was admitted in spring 2018. The authors are the current program director, assistant director, and a postdoctoral researcher in the department. Statistics show that online learning is clearly poised to be a major part of the landscape of higher education for the foreseeable future (Allen & Seaman, 2017; Legon & Garrett, 2017). In the United States, 31.6% of university students now take at least one distance education course (Seaman, Allen & Seaman, 2018). Distance students are also fairly and evenly split between those who take both distance and nondistance courses, and those who take exclusively distance courses. Further, distance education enrollments are highly concentrated, with five percent of institutions accounting for almost half of all distance education students. Data indicates that in the United States, 52.8% of students who took at least one distance course also took a course on-campus, and 56.1% of those who took only distance courses reside in the same state as the institution at which they are enrolled (Seaman et al., 2018). But perhaps the most remarkable figure is that the number of students studying on a campus has dropped by over one million between 2012 and 2016 academic years (Legon & Garrett, 2017). Given this unprecedented growth of online programs, it is more important than ever that online education programs be built and evaluated in terms of the practices they adopt and the models they espouse. Although the advantages of offering online programs has been well documented (Burns, 2013; Xu & Jaggers, 2013), it is not always clear whether these programs effectively engage students and whether student experiences in these programs are comparable to those of their face-to-face counterparts. Lasater, Bengston, and MurphyLee (2016) point out that there are conflicting reports regarding the effectiveness of online programs, with some studies such as those by the US Department of Education (2010) suggesting that they perform quite well whereas others indicate a significant gap in quality between online and face-to-face programs (Horodyskyj et al., 2018). Additionally, students in online programs can experience issues that are unique to the structure of such programs including experiencing a sense of isolation as well as limited opportunities to interact with their instructors and to get to know their peers. Doctoral students enrolled in online programs also possess unique circumstances for their studies: many work full-time and consequently study 155

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part-time. For example, Gardner and Gopaul (2012) found that professional doctoral students often struggled to balance the demands of full-time employment and family with graduate school. Additionally, their part time status as doctoral students led to further struggles with a sense of isolation and non-belonging with the programs that they were affiliated with. In Colleges of Education, driven by the demand from scholars and practitioners for a practical terminal education degree, many institutions have developed and now offer a degree specifically designed for educational leaders. This demand can be traced back to Shulman, Golde, Bueschel, and Garabedian. (2006) who spearheaded the movement that institutions create doctoral degrees that provide “rigorous, respectable, high-level academic experience to prepare students for service as leading practitioners in the field of education” (Storey & Richard 2013, p. 9). In essence, Shulman et al. (2006) called for colleges of education to either redefine their professional degrees or run the risk of becoming altogether irrelevant. The program described in this chapter represents one such effort to serve the needs of professional learners and leaders in education. In this program all instruction is provided exclusively online in an asynchronous environment to an audience of mostly mid-career education professionals. Demographic data from our survey revealed 85% of the students were employed in the early childhood through high school education systems as educators, teacher leaders, principals, and school district leaders. 34% of these doctoral students are the first in their family to attend college. These learners are termed “first generation” students because they are the first in their family to attend and graduate from an institution of higher education. In comparison, the national average of first generation college students who earn their doctorate is 12% (National Science Foundation [NSF], 2009). The part-time nature of the program allows for students to continue serving in their professional roles thereby broadening educational impact, while also honing their skills in understanding and conducting research based endeavors. For 8 years, this program has represented a diverse, highly-professional student population. During the first 2 years of the program, these students take 6 hours of coursework per semester. In many ways easily distinguished from a traditional graduate student, the average age of an Ed.D learner is 42 years old. Students possess, at a minimum, five years of teaching and/or educational leadership experience with 73.1% of students having worked more than six years as a classroom teacher. The department, like many other programs representing different institutions all over the world, chooses to offer professional programs in either completely online or hybrid formats, to meet the needs of their students who typically are mid-career individuals with demanding full-time jobs, families, and many other professional commitments (Ali & Ahmad, 2011; Kember 2007)). The combination of an online delivery system and support for part-time enrollment enables many non-traditional 156

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and diverse students from across the globe to undertake the challenging task of pursuing a professional degree. The online doctoral program focuses specifically on the goal of developing curricular leadership in PK-12 school environments; as such the program integrates a problem of practice model, culminating in the dissertation equivalent Record of Study (ROS). The Record of Study expected of students is a factor that distinguishes this Ed.D from others in the field. This final product centers around a problem of practice in education, which begins infancy development during a student’s second year in the program. Students are asked to reflect on problems facing education today, as well as those which affect their schools most specifically. The coursework for the program consists of 64 class credit hours, including 13 hours of research spent developing the dissertation equivalent ROS. In response to the fear of isolation expressed by many online doctoral students as they complete their work on the ROS, the Ed.D has adopted a Thematic Group model to cluster Ed.D students within thematic groups. Thematic group members share a common interest, a common ROS chair and graduate committee, and work with each other to facilitate their independent work on the ROS. Groups include a faculty member chair and faculty co-chair advisor and three to four students. The foci of these groups vary from year to year dependent on the expertise and research interests of the learners in each cohort. Thematic groups offer opportunity for purposeful collaboration between both students and faculty members during the most difficult part of a student’s doctoral journey. Intentionally, faculty leading each thematic group possess garnered expertise in the same field of study. The Ed.D program participates as a member of the Carnegie Project on the Educational Doctorate (CPED) and subscribes to the guiding principles of program design to develop scholarly practitioners in various educational settings (CPED, n.d.). The Ed.D program’s curriculum, offered entirely online, promotes the same rigor as that of a traditional on-campus doctoral program within three domains: leadership, application of knowledge, and discovery. Within these domains, courses are taught in topics such as research methodology, educational leadership, and curricular expertise. Ed.D students also participate in two high-impact internship experiences under the direction of their thematic chairs. The purposes of these internships are to provide opportunities for data observation and collection while students are within the proposal writing stage of their program. The program thus offers many structured opportunities for interactions among participants. The curriculum in the online doctorate is delivered through a commonly used learning management system (LMS), adopted at institutions of higher education across the world. This platform has been in existence at the university for over a decade, and online tools within the platform have evolved and adapted slowly over time. There

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are very limited additional capabilities to customise the learning interface, and few learning tools interoperability (LTI) integrations are enabled to integrate outside applications. The management system possesses such capabilities as discussion boards, wikis, content storage, quizzes, and announcements. Content is delivered mostly asynchronously, usually in weekly module disbursements, within a semester course timeline. The expectation is not one of a “work at your own pace” model, but rather a structured weekly arrangement of scaffolded activities with flexibility in daily outcomes. Observations of a generic course would reveal content consisting of readings, assignments, and discussion boards. Some committed Ed.D faculty integrate other technologies into the courses, but most faculty utilise tools existing in the LMS system exclusively. Courses utilise synchronous discussions whenever needed, often at the beginning and at major checkpoints throughout the classes. Ed.D instructors are experts in curriculum & instruction, but few are educational technology experts. Over 90% of course instructors are full-time faculty within the department, and many have experiences previously in a PK-12 setting as a teacher, leader, or school district administrator. These instructors have widely varying levels of experience teaching in an online environment, and few have had formal online instructor training. Out of scheduling necessity, instructors rarely meet in-person more than once a year; however, the Ed.D steering committee meets biannually to review the program comprehensively. The director and assistant director’s tasks are to evaluate the current strengths and needs of the program, and to develop plans of intervention and progress to address any identified areas of improvement.

SUPPORTING SCHOLARSHIP Given the dispersed nature of online learning, one of the paramount considerations for instructors and designers of online programs is the necessity of the relational elements within online courses. The goal, therefore, becomes the development of a parallel engaged learning community to that of an in-person course; however, achieving this outcome has proven to be a tremendously difficult target for online program designers and instructors. Most notably, researchers have noted key themes related to participatory visibility, or more often invisibility, in online learning courses. Derived out of the themes of visible engagement is the concept of the ghost identity occasionally utilised as an embodied metaphor for such learners. Research also reveals that the environment within which learners interact is a key measure and participant in the analysis of contextualised online learning. Below we present three ways in which invisibility has been discussed in the context of online learning, namely learner, instructor, and technological invisibility.

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Learner Invisibility The educational analysis of learner visibility is not isolated to online learners, although it is ever-present in the online learner literature where descriptions of engagement are analysed. Whereas traditional learners can easily be positively encouraged to participate in a face-to-face setting, participation in an online environment can be difficult to monitor and promote authentically. Lander (2005) highlighted the need for literally embodiment of visibility and inclusion within an online space. Personifying literally the body within the (no)body learners often exemplified in online learning environments, Lander (2005) then illuminated the importance of the body’s presence to adult learning and teaching, as well as the “subordination of the body to the mind in electronic environments” (p. 156). To supply the necessary nourishment for learning, Lander argued we need a culture of encouraging learning communities to feed and nourish the online (no)body learner. Hai-Jew (2004) suggested that the culture of a program represents a conglomeration of design, evolution and happenstance, and thus must consider the personalities involved; similarly, Ross et al. (2013) describe the need to visibly acknowledge the distance in order to plan strategies which bridge this distance with learners. Literature reveals that creating such a community of learners is not simply a luxury for an online program, but rather, an essential element to designing a successful program. Ross, Macleaod, and Gallagher (2013) and Dahlgren, Larsson, and Walters (2006) both describd interactions and engagements in online programs as invisible by design. Without a community of engagement designed and integrated intentionally into the program, learners then engage and disengage on the path either to graduation or withdrawal (Ross et al., 2013); similarly, although the convenience of online learning is frequently touted as a benefit can often be isolating, and thus discouraging and exhausting, for some (Dahlgren et al., 2006). The need for online adult learners to be socially and academically engaged in learning, as well as the positive effect this engagement has on outcomes, is well documented by researchers. Yet, Russo and Benson (2005) highlighted that one must also seek to refine and understand how a community of learners can be intentionally created and how the product of online presence is developed (p. 60). When seeking to delve deeper into the defining of such learners in an online course, Samuels-Peretz (2014) described learners with the following constructs: as stars, isolates, and ghosts. While the nomenclature of a star student, a student recognised for their exceptionality and capability, is well understood, Samuels-Peretz (2014) analysed the less visibly engaged students in a way often lumped into one category. First, typifying the isolate as one who is negatively singled out among peers, and then describing the ghost as one who is “ignored by their peers, neither chosen nor referred to” (Samuels-Peretz, 2014, p. 55). The perception is that the result 159

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of being either the invisible isolate, or the less negatively viewed although just as disengaged, ghost in a course is a student completing the minimum requirements by not contributing the to the overall experience or engaging with other participants in the course (Beaudoin, 2002; Russo & Benson, 2005). The only conclusion one could draw would to be that learning is not occurring in a course.

Instructor Visibility The research thus suggests that the online learner’s role is to engage as a visible participant in the learning process with the goal of promoting a collaborative community of learners. The literature on the roles of professors in such an environment reflects more complexity. Ideas have ranged from recommending that the instructor encapsulate the traditional professor role as the director of lecture activities or as an orchestral conductor to recommending that the instructor instead play the facilitator, setting the stage between scenes and ensuring the props are provided where needed. Mazzolini & Maddison (2003) and Jameson (2011) both discussed the role of the instructor with relation to visibility in online programs. Mazzolini & Maddison (2003) described instructor roles as pliant – “varying from being the ‘sage on the stage’ to the ‘guide on the side’ to a ‘ghost in the wings’” (p. 238). While Jameson (2011) offered that there is often a paradox and need for instructors to play different roles of visibility in the duration of the course, Mazzolini & Maddison (2003) would include a caveat that it is never advised to solely play the role of the ‘ghost in the wings’ as students view these invisible instructors as uninterested and disinvested, and as a result, unliked by students. Research showed that although perception from students with invisible instructors was negative that there was little direct correlation between instructor contribution (engagement) and student success or participation volume, rather the divergence was in student opinions of professors overall (Mazzolini & Maddison, 2003).

Invisibility of Technology A careful analysis of identity within an online learning program must include not only the relationship between the visible or invisible people within the context, but also, the complex relationship between the learner, instructor and the technological and structural environment in which these beings interact. There is a necessity to see the interactions between the beings and the educational platform as codependent and relational in nature. Verster (2009), Bødker and Klokmose (2012), Vasiliou, Ionnou, Stylianous-Georgious, and Zaphiris (2017) and Vasiliou, Iannou, and Zaphiris (2014) described this environmental interaction with learning organisms in terms of ecology. The terminology of environment is frequently utilised when 160

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describing online spaces, but adding nomenclature of ecology infuses the necessary interactions between living beings within the online space. Biological ecology is coined specifically as the “ever-evolving relationship and interaction between organisms and their environment” (Verster, 2009, p. 89); similarly, Vasiliou et al. (2014) promoted ecology as a multitude of unique devices working together in a unified system. The focus on such interaction in an online artifact ecological setting provides increased emphasis on the need for community within such a space where technologies and people coexist—thus enabling online learning goals to be “the creation and interaction of new knowledge within a learning environment” (Bødker & Klokmose, 2012; Vasiliou, 2017). The focus on creation of initial and original knowledge should be the goal of any graduate program, most especially one within a department of learning and culture. This emphasis on idea creation amongst students, faculty, and technology helps the program administrators better analyse and design collaborative interactions and innovative activities utilising the ecological artifacts to their maximum capability (Vasiliou, et al., 2017). The consideration of learning collaborative spaces as ecologies opens the metaphysical online space to another innovative thought: what if the technology itself becomes invisible to the students? Thus far, the literature has described learners as invisible or instructor interaction as needing to be visible for efficacy; however, the literature has yet to discuss the ramifications of the technology and online platform artifacts as becoming invisible. Little literature has been provided on such an exploration, but the idea of tools becoming invisible or second-nature to a user is not new. Scholars point out that ever since the utilisation of tools in the stone age, artifacts have become second-nature and an extension of reality. Roth (2007) for example characterised a blind person and their cane as, “but an extension of the hand and arm, which are already tools the body gave to itself realising the capacity of reaching out” and “tools, when they have faded away, are an invisible visible, something visible that has become transparent to consciousness, which consciousness does not perceive; but they also, as the hand, make visible the invisible” (p. 676). To best describe such a phenomenon of technology fading from conscious novelty, or frustration, Weiser (1991) coined the phrase ubiquitous computing, long before computers were in widespread use or general accessibility by the masses. Still, this ubiquitous computing is present in so many facets of technological interaction, and since Weiser, many researchers have attempted to study the interaction among technologies, learners, and designers in this dynamically invisible relationship (Vasiliou, et al., 2017, p. 642). This ubiquitousness is well represented in the field of online learning. Just as instructors no longer need to mandate the necessity for manuscripts to be typewritten rather than handwritten, the normalcy of the online tools and platform to learners and instructors immersed in this world support the 161

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idea that online learning will very soon just be learning. Paiva, Morais, Costa, and Pinheiro (2016) reinforced the claim of technology’s shift to invisibility and examines the ramifications of progressively dropping the “e” from “e-learning”, stating the shift will “will be positive because the core of discussion and concern will shift from electronic issues to learning issues” (p. 232). No longer will the discussion of online programmatic improvement be limited to the infrastructure or technological considerations. Rather, online learning will be examined as merely learning. Paiva et al. (2016) even declared “The Information Age has reached maturity” because the now invisible technology has been adopted into the everyday lives of users. Such is true for the e-tools available to our online learners (2016, p. 226-231). As online learners have adapted to their e-learning environment, they are now fully integrated to the culture and technologies required of such an endeavor and are now focused on creating efficiency practices and strategically designing the role of future innovative technology within the world of learning online (Paiva et al., 2016; Vasiliou et al. 2017). As a result of the disappearing nature of the e in e-learning, we can now begin to address the quality of online teaching and learning. The invisibility of the technology will enable the visibility of our learners as individuals and people rather than faceless numbers behind the screen. The namelessness of the technology provides enlightenment and empowerment for our learners and instructors to develop the community both crave.

The Community of Inquiry Model As the above review of literature demonstrates, online education programs particularly at the doctoral level need to be carefully crafted or they encounter well documented pitfalls such as lack of learner engagement, learners and faculty retreating into invisibility, and in some cases, withdrawal from the program. In response to these concerns, The Community of Inquiry (COI) model (Garrison, 2016) is based on a collaborative constructivist view of teaching and learning and emphasises the fact that meanings are created within social frameworks and networks; as online learning too is essentially a process of meaning making, it too should take place in such environments. According to Garrison (2016) “the effectiveness of thinking and learning collaboratively is seen as indispensable in achieving deep and meaningful learning outcomes” (p. 43). The model focuses on three inter-related processes that constitute the core of online learning environments, namely faculty, social, and cognitive presence. Studies of online programs indicate that student interaction with both their instructors and peers, or faculty and social presence, are critical to their success (Chang & Smith, 2008; Noel-Levitz, 2011). According to Kumar and Ritzhaupt (2014), the way in which online learners and instructors portray themselves as “real 162

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people” in their online interactions in the absence of face-to-face interactions is particularly noteworthy. Kumar and Ritzhaupt (2014) suggest that faculty bear even more responsibility for communication and organisation in online classes than do their face to face counterparts. According to Ali and Ahmed (2011), “the design, facilitation, and direction of cognitive and social processes for the purpose of realising personally meaningful and educationally worthwhile learning outcomes.” (p. 5) is a key responsibility of online faculty. Such activities, Kuo, Walker, Schroder, and Belland (2013) noted, lead to greater student satisfaction which in turn is positively correlated with “program quality, student retention, and student success” (p. 36). Additionally, positive student orientations towards their programs can lead to higher graduation rates and persistence in the program (Ali & Ahmed, 2011). Cognitive presence has been defined as “the construction and application of knowledge through sustained reflection and online discourse” (Kumar & Ritzhaupt, 2014, p. 60). This attribute within a program has been shown to not only be an essential part of program quality but also to contribute to high levels of student satisfaction in online programs. The Community of Inquiry framework thus became the undergirding framework for the program evaluation study described in this chapter. A representation of the model, created by the original authors is presented below. Figure 1. Community of Inquiry Model as displayed with elements of social, cognitive and teaching presence. Reprinted from Critical inquiry in a text-based environment: Computer conferencing in higher education by D.R. Garrison, T. Anderson, & W. Archer, 2000, Retrieved from http://cde.athabascau. ca/coi_site/documents/coi_model.pdf

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METHODS As mentioned above, the program described in this study recently admitted it’s 8th cohort of students, to where there are currently almost 100 students actively enrolled in the program. Although the program regularly surveys students regarding their progress in the program, and all courses are evaluated individually, a comprehensive evaluation of the program had not been conducted for several years. Thus a program evaluation survey that examined the nature of faculty and student interactions was timely. Also as mentioned above, the predominant model for examining the quality of faculty student interactions in online programs remains the Community of Inquiry model (Garrison, 2016). This study therefore drew from this model as well as the work of Kumar and Ritzhaupt (2014) and Lasater et al. (2016). The methodological considerations behind the study have been explored elsewhere (Viruru & Rios, 2017); however for the purposes of this chapter a brief overview is provided below. The approximately 35 question survey (a copy is included in the appendix) was sent electronically to all students who were either currently or were formerly enrolled in the program. Out of the then approximately 80 students enrolled, 45 responded to the survey and five volunteered for follow up interviews. The director and assistant director of the program designed and administered the survey; however, in order to allow participants to speak freely, an additional post-doctoral researcher was recruited to conduct the interviews. The identities of the participants who volunteered to be interviewed were kept confidential. The survey itself had five major categories: demographic and general information, faculty presence, social presence, cognitive presence, and open ended reflections. In the sections on faculty, social and cognitive presence students were asked to indicate agreement or disagreement with statements about the program; each section also included open ended questions. The narrative data was gathered mainly from these open-ended responses and was analysed using a qualitative content analysis approach (Krippendorf, 2013). As part of this process, two of the researchers did a preliminary read through of all the narrative data. They subsequently met to share reflections. As the data seemed to cluster according to the emphasis areas in the Community of Inquiry model, sub-components of the community of inquiry framework was used to aid the content analysis. To ensure trustworthiness, the researchers first jointly coded some of the data; subsequent data was coded independently. The final coding scheme used to code the data is shown in Table 1. For the purposes of this chapter, we will focus on those parts of the data that focus on faculty-student interactions, and particularly on how they negotiated issues around visibility/invisibility, although we draw from other parts of the larger study to support our analyses.

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Table 1. Themes and categories Theme

Category

Theme

Personal/affective interactions Open Communication Social presence

Group cohesion

Category Design and organisation of the program

Faculty presence

Relationships

Facilitating discourse Instructional practices Faculty and program expectations Assistance and support

Cognitive presence

Memorable events

Identity

Exploration of ideas

Negative Experiences

Integration of ideas

Growth

Opportunities for application of ideas

Learners

Complexity of content Program Quality Assistance and support

Demographic and Background Information of Participants Seventy-eight percent of the participants who responded to the survey identified as female and the others identified as male. The same percentage of our participants also self- identified as White, with 11% identifying as Black, 2% as Asian and approximately 9% as Other (this category included students of Hispanic origin). Eighty-four percent of the participants were employed in the US public school system in various capacities with others working in private school and private and public sector jobs. Participants from all seven cohorts then enrolled in the program participated in the study. Cohort 1 enrolled in the program in 2011, with a new cohort joining the program almost every year since. Approximately 15 faculty members work closely with the students in the program. The faculty members belong to both the tenured and academic professional tracks and to varying ranks within those tracks. In order to chair the committees of students in the online doctoral program, faculty have to be appointed to full graduate faculty status at the university; this status is only conferred upon those faculty members who have met a certain threshold in terms of publications. It is also worth repeating that it was rare for these students and faculty to ever meet in person, given the wholly online nature of the program. In most cases, they had never met one another. The

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Table 2. Distribution of participants according to cohort #

Answer

%

Count

1

Cohort I

4.44

2

2

Cohort II

2.22

1

3

Cohort III

13.33

6

4

Cohort IV

13.33

6

5

Cohort V

26.67

12

6

Cohort VI

20.00

9

7

Cohort VII

20.00

9

Total

100

45

most common occasion upon which students and faculty did meet one another was often at the students’ final defense, that is, upon the conclusion of their formal relationship.

Results The most striking and consistent finding of this study in the area of faculty student relationships was in fact the theme of relationships: students ardently desired a close relationship with their faculty advisers and committee members and were disappointed when this did not happen. Invisibility thus was not appreciated. All other findings were subordinate to this overarching theme. Although peer relationships were important, and often cited as relationships that provided support, they were characterised as less important than a strong relationship with faculty. Further, students clearly indicated that content in the program came alive only when faculty were actively engaged in co-constructing knowledge with them. Students thus did not view the online environment as a neutral vacuum from which they could download information; they appreciated and in many cases craved interactions through which content knowledge could be enlivened and made personally relevant. In a somewhat striking finding, it appeared as though students in the program did not perceive these online relationships as mediated through technology; rather they simply perceived them as relationships. For the students, in many cases, it appeared as though e-learning was in fact learning; they did not overly distinguish between the two. The word “technology” was only mentioned in their responses in students’ open responses in two instances. The invisibility of technology was thus clearly evident in this study: as described earlier, fingers and keyboards seemed to have become seamless extensions of one another.

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The students in the program also demonstrated a keen awareness of the supports that were essential for their learning. Once again, despite the fact that these supports had to be implemented in an online medium, this reality was for the most part ignored. The word “online” was specifically mentioned only fifty-nine times throughout the over 15,000 words of student responses given. Even less present was the specific online platform used for curricular delivery—which was mentioned only five times in student survey responses. The online medium was so rarely mentioned because students assume the platform as “normal”, and thereby invisible, as related to their general learning process. However, rather conversely, students expressed a strong preference for the platform to be dynamic rather than static. Areas in which students indicated a need for faculty support included timely feedback, the need for coherence and organisation at both an organisational and individual level and the importance of deep interaction with the content.

The Role of Faculty My advice to professors, don’t be a ghost who drops grades in BlackBoard every now and then. Engage with the students. We want to know you. If you don’t want to do videos or chats with the cohort, then be really active in our online discussion boards. We know that this is a doctoral program and that we need to be able to motivate and assess ourselves, but when we see a professor’s name on our schedule and syllabus, we assume that we will interact with you during the semester. The quote above summarises many of the perspectives expressed by students who responded to the survey. Students much preferred the idea of a professor as either a “sage on the stage” or a “guide on the side” to that of a ghost in the wings (Mazzolini & Madison, 2003). As one student elaborated, above all they wanted to work with “faculty members who believe in the students’ potential and who challenge them with high, yet attainable expectations.” This desire to deeply engage with professors was reflected in multi-faceted ways in the data. Strikingly, when commenting on relationships with faculty students overtly reflected on the use of technology and how it facilitated the ability to “know” one another without meeting to face to face. Student comments included: The faculty of our program have all been very supportive of our research interests, goals, and ambitions. They have consistently been there for us via email, phone calls, Google Hangouts, and other university-based media modes of communication. Additionally many faculty have been exceptionally receptive to and instrumental in assisting with organising, scheduling, and conducting the Summer Retreat. They

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unselfishly and graciously made themselves available to us for advice, assistance, instruction, encouragement. What has helped is ‘seeing’ our professors and interacting with them in a meaningful way. The multifaceted ways this is done (voicethreads, videos, collaboration requirements, conference calls) has helped make the cohort more personal. I feel like the VoiceThreads and live chat sessions with the professors and other classmates has really helped me to feel like I was actually a part of a community of actual human beings. Students thus were deeply appreciative of professors who shed the invisible cloaks of their online presences, to take on robust and real identities. Students also commented on how relationships with faculty varied but that they enjoyed this variation as it seemed to reinforce the fact that they were truly dealing with real human beings rather than a disembodied program. Disparate relationships thus made the program seem more human. They were also protective of the unique nature of their relationships with one another and with their professors, as part of a defined cohort. Furthermore, as with any relationship, students acknowledged their own roles in maintaining relationships. As students commented: I know sometimes I find myself questioning some of the activities but I find it very refreshing that each instructor makes an effort to demonstrate and engages us in different ways. I also appreciate how the feedback is used. Although I have enjoyed each class and the interaction with the professors, I would like to acknowledge that some treat us more like adult learners than others. I want to accept more personal responsibility for my learning. Make sure all professors genuinely want to teach the Ed.D students and help them through to understanding. Don’t blend in other random TAs or students that are not part of the cohort. This builds a sense of distrust. Although students did not explicitly evaluate the technology used in the program, despite being asked to reflect on those experiences, technology seemed to mediate the expectations that students had for the relationships with their professors. At some level, students seemed to come into the program with lower expectations in terms of forming relationships with their professors and peers. Reflecting the ideas presented in the literature students said:

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I know that this program is not instructor centered, but it does help to know that the professor is a part of the learning community and not just rolling out the curriculum. Not all faculty members knew how to separate the brick and mortar college experience from that of an online experience. Take, for example, deadlines. For me, the motivation for an online degree was the ability to complete coursework at any time (realistically) and in any place (with wi-fi!) At times, it seemed like faculty did not understand that we could not complete coursework during the day. Having realistic deadlines and more than 24 hours advance notice for change best supports the online learner. One student in particular discussed how the fact that the program was online impacted relationships in the program: The weaknesses of this program are those of any online program - you will find students that contribute the minimal possible, and instructors that do the same. Although these problems exist in any program - whether online or face-to-face, graduate or undergraduate - it seems as if it is more common online, when the students don’t have to stare the professor in the face and admit to slacking off and the professors don’t have to see the students they are accountable to. Disconnection from each other in this environment is inevitable, and it is the reality of online environments. The same is true for social media. Young people may do more of their socialising through social media than any other generation before them, but we are also beginning to see the fractures that type of socialisation creates. Online socialising lacks the personal connection that humans are made to look for. Without the personal connections, accountability to each other recedes, and we become more disconnected, even while we communicate more. Students were also forthcoming about instances in which the relationships they had with faculty did not meet expectations. The shortcomings identified often centered around lack of engagement and effort to create genuine relationships. As students commented: Be more supportive of students whose work or home situation makes degree requirements difficult to achieve. Investigate issues brought forward by students in a transparent way so they feel that at least one person understands the problem and is seeking an equitable solution. I have been unpleasantly disappointed in some of the professors who have been part of the program. Some have seemed “scattered” or unconnected with us - unresponsive 169

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to emails, late with assignments and feedback, etc. Others have given assignments that did not seem particularly helpful or well thought-out. Others have seemed inept with the technology they were trying to use with us. Some have been very boring and long-winded and unfocused in their presentations to us. I know this seems very critical, but my expectations for a (very expensive) doctoral program were high. I know good teaching, and not-so-good teaching. I expected better.

The Need for Support for Learning As mentioned above, students also identified a number of ways in which they expected to receive support from faculty in terms of furthering their learning. These centered around issues of receiving feedback from faculty, program organisation, and engaging with content. It is important to note that a variety of perspectives were expressed on these issues and that overall, student satisfaction with the program was very high. 77.5% of respondents agreed that they would recommend the program to a friend. Further, as the table below illustrates, students generally agreed that the faculty played a positive role in their learning. Figure 2. Faculty presence in the program

x-axis: percentage of students represented by each category

*For a more accurate representation see the electronic version. 170

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Feedback The importance of feedback to student learning has been well documented in the literature (Price & O’Donovan 2006; Carless, Salter, Yang, & Lam, 2011). The students in this program were no exception. However, in the responses to this survey, it was noticeable that the students did not mention the format of the feedback (such as whether it was provided through email or the learning management system) but focused on it in a generalised sense, appearing to indicate once again that the technology was more or less invisible to them. Students commented on the timeliness of the feedback as well as the quality but not the format through which it was provided. As students said: The majority of the faculty gave me timely feedback. However, I did have two professors who did not give feedback in a timely fashion. This made it difficult to gauge my progress in this course. Some faculty were very good at providing timely, helpful and encouraging assistance. Others were very haughty and unhelpful in the learning experience.

Organisation Students consistently commented on how they found organisation to be a barrier to forming better relationships with their professors and how much they appreciated order and sequence in particular. As in all doctoral programs, these students too are required not just to take a sequence of courses but also to complete typical requirements such as attempting preliminary examinations, writing and defending a proposal and their Records of Study. Statistics show that although attrition is not uncommon in doctoral programs, it is most likely to occur during the candidacy phase of a student’s journey (NSF, 2009). It was therefore not surprising that students expressed a strong desire for organisation and coordination among the faculty. As the students said: The professors were the best but it seems to me that the students would have benefited from the professors getting together and arriving at an understanding of the format. The template changed several times for the ROS (Record of Study). I have struggled with balancing a full time job and adhering to the lead professors schedule.

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Faculty interactions during the coursework were great and much better than I had expected. I have been much less satisfied with the support I’ve received during the ROS process, however. All professors need to be on the same page about the expectation of the program!!!!!! Other students made it a point to mention that they found the structure of the program, including faculty participation, to be well organised and helpful. I think the strengths are in the professors, the logical flow of content, and in the flexibility with time. The program feels very well thought out and purposeful. I appreciate that. I believe the structure of the program is an asset. Each semester builds on the other. We are encouraged to move beyond what we are learning and find new ways of application. I think the course expectations are rigorous but achievable and each semester I feel accomplished having completed the work at a high level.

Relationships to the Content Given that all of the students in the program were working professionals who had undertaken this degree to further professional careers, there was a definitive preference for learning content that was either immediately or foreseeably useful. Furthermore, almost all of the students were either current or former educators themselves and thus were quick to discern quality in content. It was in this area that the students seemed to ignore the technology altogether. Students did not particularly focus on the relationship between technology and the content they were learning: in this domain, it was truly close to being invisible. For example, 39% of the students surveyed generally agreed that faculty had helped them use online tools to further learning but almost 30% neither agreed or disagreed with that idea. When asked to comment on the quality of the content they learned in the program as well as the instructional practices they had seen, students focused on how the content related to their professional lives; they also specifically commended or disparaged specific instructors. However, they rarely mentioned the technology as being either a help or a hindrance or even as being part of the process. For example, many of the students commented that the most valuable learning experiences they had had within the program had to do with conducting or understanding research but never speculated as to how this experience would have been different if they had learned it in a faceto-face setting. Comments included:

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The evaluation of research and questioning what was found has been very helpful. Too often in education people jump on something because of success somewhere else. This program has given me the insight and knowledge to ask questions instead of just going along because someone told me it was a best practice. I believe having a deeper understanding of research (including approaches, paradigms, and strategies) has been one of the most valuable learning experiences. This deeper understanding has been through learning concepts, reading examples, and being asked to apply concepts. Similarly, although students commented quite liberally on the teaching styles and preferences of instructors and how those preferences impacted their learning, they only occasionally mentioned technology. One of the rare comments that did so focused on a common frustration in online classes, namely the use of discussion boards: Throughout the program I have found many of the professors are out of touch with the realities of education as it exists within the PreK-12 sector. Also, simply requiring an arbitrary number of discussion board posts, typically required to be in APA format, does nothing to encourage the development of a sense of community among the cohort, nor does it help me learn or take advantage of the online environment. The comments about specific instructors were often quite specific but did not deal with technology. For example, students made statements like: Dr. A’s general discussion questions - you *could* answer them inauthentically, based in research but not in your experience in relation to research, like anything in life, I suppose. If you really think about the questions, and answer them in a real way, the questions posed can make you think about even the most minute details of the decisions you make in your day. Anything from Dr. B. I always felt that Dr. B. brought whatever we were working on back to the K-12 perspective. So, I always ended the class having a clear understanding of how it pertained to my practice. I also greatly appreciated the time with Dr. C. All of the research classes were very good. Dr. C taught me to value a different type of research and to trust my observations. Hearing Dr. C’s talks/in person. I think we can get a little short-sighted in our practice in a district. She reminds me there is more out there. I appreciate it:)

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Dr. A had us research issues in education last summer that really allowed me to see some issues in the field and make changes. Dr. D’s grant writing class really had me examine school and district leadership positions.

DISCUSSION We would like to acknowledge at the outset that our study was not without limitations. It drew from only one online program in the United States and thus cannot be considered to have generated generalisable knowledge. Nor was this our intention. Further, the study was explicitly framed using the Community of Inquiry model described above. There may have been elements that pertain to online learning that might have emerged if a different approach had been used, that do not fall within the purview of this framework. However, a review of the literature and our own constructivist-oriented program philosophy led us to this framework as the most comprehensive one available that matched the values and intentions of the program. Despite the limitations, we do believe that our findings may have value to our colleagues in higher education. Our chapter discussed the results of a program evaluation designed in part to address conceptual and pragmatic implementations of technology and we conclude with what we consider a hopeful message. The impact of online education has already reimagined the face of education. Rather than learners turning in papers during prescribed class meetings, even at undergraduate brick and mortar institutions, they are now submitting assignments via an LMS with Saturday deadlines. Not only does the lack of structured hours of operation change the communicative and relational nature of education, it also impacts the pace at which our learners interface with faculty, each other, and the content. Still, great fears surround the changing face of education about what some might consider the robotisation, or sterilisation, of learning. Science fiction writers have predicted the mechanical takeover of society for decades: the question is therefore raised whether online learning is the beginning of the obsolete human instructor in learning. Does advancement of technology equate to the dissipation of instructor necessity? Further, what is the broader impact on education as a whole? We know from the literature that invisibility as a construct in online learning is hardly a new topic of discussion; however, invisibility has previously been researched primarily in the context of engagement of both learners and faculty in an online learning setting (Dahlgren et al., 2006; Jameson, 2011; Lander, 2005; Ross et al, 2013; Samuel-Peretz, 2014). Further, the connotation of

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this invisibility is often, if not always, negative. We would suggest something that is perhaps the opposite: in our study it appeared as though both online students and instructors are moving towards becoming unaware of the nontraditional nature of their lived experiences in an online second-life. To them, the keyboard interaction of their fingers is simply an extension of thoughts—like the eyes and mouths of their on-campus counterparts. The discussion board by which they communicate has become somewhat synonymous with the small group share-out they’d participate in on Monday evenings in a classroom. While some would be alarmed that such technology could seemingly disappear from our psyche, others would proclaim this as an accomplishment towards the advancement of online learning because, in fact, the most profound technologies are those that vanish from our consciousness and “weave themselves into the fabric of everyday life” until there is no recognisable difference between new and old (Weiser, 1991, p.94). Converse to the fears highlighted above, the data revealed a desire for an online program with a relational foundation. When asked about the online program, the absence of student responses with any markers of being online learners at all was remarkable. The outsider if reading the unmarked survey data would likely have no indicators this was an online program. In fact, terms relating to technology, online learning or specific programs were sparsely included in comments at most. The online element from our online program seemed to be missing. At face value, this was concerning and caused serious reflection as to whether this meant we were not requiring enough technology integration in the program. Further reflection and research however developed a fresh perspective. Rather than our students being invisible learners, or our faculty being ghosts behind a discussion board, the technology was now the missing or invisible participant. It appeared rather that because the characteristics of online learning were so within the lived experience of our learners, they did not consider it worth noting. Instead, our students focused on the relational needs they had to connect with peers and faculty online. To the Ed.D student online learning was simply learning (Paiva et al., 2016). Ultimately it appeared that the data derived from students revealed an encouraging message: humanity is essential to a successful online program. This study suggests that as online learning comes of age it has become ubiquitous enough to often disappear from explicit consciousness for many students. However, when it does not perform up to expectations, it is very much noticed and commented upon. Our study thus raises the question as to how does the invisibility of technology mindset hinder/ help integration of future technologies in an online learning environment and perhaps most importantly asks what then, is the next invisible player in the online learning continuum?

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REFERENCES Ali, A., & Ahmad, I. (2011). Key factors for determining students’ satisfaction in distance learning courses: A study of Allama Iqbal Open University. Contemporary Educational Technology, 2(2), 118–134. Allen, I. E., & Seaman, J. (2017). Digital Learning Compass: Distance Education Enrollment Report 2017. Babson, MA: Babson Survey Research Group. Beaudoin, M. F. (2002). Learning or lurking? Tracking the “invisible” online student. The Internet and Higher Education, 5(2), 147–155. doi:10.1016/S10967516(02)00086-6 Bødker, S., & Klokmose, C. N. (2012, October). Dynamics in artifact ecologies. In T. Pederson, L. Malmborg, G. Jaccuci, & K. Hornbaek (Eds.), Proceedings of the 7th Nordic Conference on Human-Computer Interaction: Making Sense through Design (pp. 448-457). New York: NY: ACM. Burns, B. A. (2013). Students’ perceptions of online courses in a graduate adolescence education program. Journal of Online Learning and Teaching / MERLOT, 9(1), 13–25. Carless, D., Salter, D., Yang, M., & Lam, J. (2011). Developing sustainable feedback practices. Studies in Higher Education, 36(4), 395–407. doi:10.1080/03075071003642449 Carnegie Project on the Educational Doctorate. (n.d.). About CPED. Retrieved from http://www.cpedinitiative.org/page/AboutUs Chang, S. H., & Smith, R. A. (2008). Effectiveness of personal interaction in a learner-centered paradigm distance education class based on student satisfaction. Journal of Research on Technology in Education, 40(4), 407–426. doi:10.1080/15 391523.2008.10782514 Dahlgren, M. A., Larsson, S., & Walters, S. (2006). Making the invisible visible. On participation and communication in a global, web-based master’s programme. Higher Education, 52(1), 69–93. doi:10.100710734-004-5784-z Gardner, S. K., & Gopaul, B. (2012). The part-time doctoral student experience. International Journal of Doctoral Studies, 7, 63–78. doi:10.28945/1561 Garrison, D. R. (2016). E-learning in the 21st century: A community of inquiry framework for research and practice. New York: Taylor & Francis. doi:10.4324/9781315667263

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Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2-3), 87–105. doi:10.1016/S1096-7516(00)00016-6 Hai-Jew, S. (2004). WashingtonOnline virtual campus: Infusing culture in dispersed web-based higher education. International Review of Research in Open and Distance Learning, 5(2), 1–16. doi:10.19173/irrodl.v5i2.187 Horodyskyj, L. B., Mead, C., Belinson, Z., Buxner, S., Semken, S., & Anbar, A. D. (2018). Habitable worlds: Delivering on the promises of online education. Astrobiology, 18(1), 86–99. doi:10.1089/ast.2016.1550 PMID:29345987 Jameson, J. (2011). Distributed leadership and the visbility/invisiblity paradox in on-line communities. Human Technology, 7(1), 49–71. doi:10.17011/ht/ urn.201152310899 Kember, D. (2007). Reconsidering open and distance learning in the developing world: Meeting students’ learning needs. Abingdon, UK: Routledge. doi:10.4324/9780203966549 Krippendorf, K. (2013). Content analysis: An introduction to its methodology. Thousand Oaks, CA: Sage. Kumar, S., & Ritzhaupt, A. (2014). Adapting the Community of Inquiry Survey for an online graduate Ppogram: Implications for online programs. E-Learning and Digital Media, 11(1), 59–71. doi:10.2304/elea.2014.11.1.59 Kuo, Y.-C., Walker, A. E., Schroder, K. E. E., & Belland, B. R. (2013). Interaction, internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. Internet and Higher Education, 20, 35–50. doi:10.1016/j. iheduc.2013.10.001 Lander, D. A. (2005). The consuming (no)body of online learners: Re-membering e-communities of practice. Studies in Continuing Education, 27(2), 155–174. doi:10.1080/01580370500202331 Lasater, K., Bengtson, E., & Murphy-Lee, M. (2016). An online CPED educational leadership program: Student perspectives on its value and influence on professional practice. Journal on Transforming Professional Practice, 1(1). Legon, R., & Garrett, R. (2017). The changing landscape of online education (CHLOE). Annapolis, MD: Quality Matters and Eduventures.

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Mazzolini, M., & Maddison, S. (2003). Sage, guide or ghost? The effect of instructor intervention on student participation in online discussion forums. Computers & Education, 40(3), 237–253. doi:10.1016/S0360-1315(02)00129-X National Science Foundation (NSF). (2009). Doctorate recipients from U.S. universities: Summary report 2007-08. Chicago: National Opinion Research Center. Noel-Levitz. (2011). National online learners priorities report. Retrieved from https:// www.noellevitz.com/upload/Papers_and_Research/2011/PSOL_report%202011.pdf Paiva, J., Morais, C. C., Costa, L., & Pinheiro, A. (2016). The shift from ‘e-learning’ to ‘learning’: Invisible technology and the dropping of the ‘e’. British Journal of Educational Technology, 47(2), 226–238. doi:10.1111/bjet.12242 Price, M., & O’Donovan, B. (2006). Improving performance through enhancing student understanding of criteria and feedback. Innovative assessment in higher education, 100-109. Ross, J., Macleod, H., & Gallagher, M. S. (2013). Making distance visible: Assembling nearness in an online distance learning programme. International Review of Research in Open and Distance Learning, 14(4), 51–67. doi:10.19173/irrodl.v14i4.1545 Roth, W.-M. (2007). On mediation: Toward a cultural historical understanding of the concept. Theory & Psychology, 15(5), 655–680. doi:10.1177/0959354307081622 Russo, T., & Benson, S. (2005). Learning with invisible others: Perceptions of online presence and their relationship to cognitive and affective learning. Journal of Educational Technology & Society, 8(1), 54–62. Samuels-Pertez, D. (2014). Ghosts, stars, and learning online: Analysis of interaction patterns in student online discussions. International Review of Research in Open and Distance Learning, 15(3), 50–71. Seaman, J., Allen, I. E., & Seaman, J. (2018). Grade increase: Tracking distance education in the United States. Babson, MA: Babson Survey Research Group. Shulman, L., Golde, C., Bueschel, A., & Garabedian, K. (2006). Reclaiming education’s doctorates: A critique and a proposal. Educational Researcher, 35(3), 25–32. doi:10.3102/0013189X035003025 Storey, V. A., & Richard, B. M. (2013). Critical friends groups: Moving beyond mentoring. In V. Storey (Ed.), Redesigning professional education doctorates. New York: Palgrave Macmillan. doi:10.1057/9781137358295_2

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U.S. Department of Education. (2010). Evaluation of evidence-based practices in online learning: A meta- analysis and review of online learning studies. Office of Planning, Evaluation, and Policy Development, Policy and Program Studies Services. Washington, DC: US Department of Education. Vasiliou, C., Ioannou, A., Stylianous-Georgious, A., & Zaphiris, P. (2017). A glance into social and evolutionary aspects of an artifact ecology for collaborative learning through the lens of distributed cognition. International Journal of Human-Computer Interaction, 33(8), 642–654. doi:10.1080/10447318.2016.1277638 Vasilou, C., Ioannou, A., & Zaphiris, P. (2014). Understanding collaborative learning actvities in an information ecology: A distrubted cognition account. Computers in Human Behavior, 4, 544–553. doi:10.1016/j.chb.2014.09.057 Verster, M. (2009). Creating an online learning ecology in support of mathematical literacy teachers. International Journal of Education and Development Using Information and Communication Technology, 5(5), 85–100. Viruru, R., & Rios, A. (2017). Student perspectives on experiences in an online doctoral program. In J. Dron & S. Mishra (Eds.), Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2017 (pp. 681-687). Chesapeake, VA: Association for the Advancement of Computing in Education (AACE). Weiser, M. (1991, September). The computer for the 21st century. Scientific American, 265(3), 94–104. doi:10.1038cientificamerican0991-94 Xu, D., & Jaggars, S. S. (2013). The impact of online learning on students’ course outcomes: Evidence from a large community and technical college system. Economics of Education Review, 37, 46–57. doi:10.1016/j.econedurev.2013.08.001

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APPENDIX Survey Questions Please note: demographic questions were not included in this copy of the survey. The survey was divided into three major sections: faculty, social and cognitive presence. Those questions are included below. A section titled overall reflections was also included in the survey: those questions are also presented in this appendix. Please indicate your level of agreement with the following statements in Table 3. Q: Are there any other comments you would like to make about your interactions with faculty? Please indicate your level of agreement with the following statements in Table 4. Q: Would you like to see an Ed.D. Facebook/ Slack group created to help build community in the cohort? Please feel free to explain your answer. Q: Would you like to see more group interactions in the program? Please feel free to explain your answer. Q: What can the program do to further a sense of belonging? Please indicate your level of agreement with the following statements in table 5. Table 3. Faculty presence Strongly Agree (1)

Somewhat Agree (2)

Neither Agree nor Disagree (3)

Somewhat Disagree (4)

Strongly Disagree (5)

The faculty helped keep the cohort on task in a way that helped me learn. (1)

o

o

o

o

o

The faculty encouraged the cohort to explore new concepts. (2)

o

o

o

o

o

Faculty actions reinforced the development of a sense of community among the cohort. (3)

o

o

o

o

o

The faculty provided feedback in a timely fashion. (4)

o

o

o

o

o

The faculty helped me take advantage of the online environment in a way that assisted my learning. (5)

o

o

o

o

o

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Table 4. Social presence Strongly Agree (1)

Somewhat Agree (2)

Neither Agree nor Disagree (3)

Somewhat Disagree (4)

Strongly Disagree (5)

Getting to know others in the cohort gave me a sense of belonging in the program. (1)

o

o

o

o

o

I felt comfortable conversing through the online medium. (2)

o

o

o

o

o

I felt comfortable interacting with peers in the cohort. (3)

o

o

o

o

o

I felt that my point of view was acknowledged by peers in the cohort. (4)

o

o

o

o

o

I learned a lot from my peers in the Ed.D. cohort. (5)

o

o

o

o

o

I learned a lot from the oncampus orientation (January) session. (6)

o

o

o

o

o

I learned a lot from the eCampus community page. (7)

o

o

o

o

o

Table 5. Cognitive presence Strongly Agree (1)

Somewhat Agree (2)

Neither Agree nor Disagree (3)

Somewhat Disagree (4)

Strongly Disagree (5)

Learning activities in the program helped me construct explanations/ solutions. (1)

o

o

o

o

o

Reflection on content and discussions helped me understand fundamental concepts. (2)

o

o

o

o

o

Program activities have improved my understanding of research. (3)

o

o

o

o

o

I have applied knowledge or skills gained from the program with my peers or colleagues outside of the program. (4)

o

o

o

o

o

Following my participation in the program, I have a better understanding of my role as an educational practitioner. (5)

o

o

o

o

o

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Q: What has been your most valuable learning experience in the program so far? Q: Give us an example of an assignment that has caused you to reflect on your professional practice. Q: What opportunities have you had to engage with complex content in the program? Q: How has the process of writing a problem-based dissertation helped you to engage with and solve situated problems? (if applicable) Overall Reflections: Q: Thinking back to when you were first accepted in the program, think of what additional supports you think you needed that we could provide in the future. Q: How has the online Ed.D. contributed to your personal and professional growth? Q: Overall, what do you believe are the strengths of the online Ed.D. program? Q: Overall, what do you believe are the weaknesses of the online Ed.D.? Q: I would recommend the online Ed.D. to a friend. Q: Please feel free to share additional comments/ thoughts about your experience in the online Ed.D. in the space below.

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A Framework for E-Mentoring in Doctoral Education Swapna Kumar University of Florida, USA Melissa L. Johnson University of Florida, USA Nihan Dogan University of Florida, USA Catherine Coe University of Florida, USA

ABSTRACT The upward trend in online graduate degrees, the mobility of graduate students, and the increase in the number of dissertations completed at a distance from universities poses several challenges for faculty who supervise research virtually, students being mentored virtually, and institutions invested in the quality of doctoral education. At the same time, emerging communication technologies present new opportunities for mentoring approaches that build upon those used in traditional on-campus environments. Based on qualitative research with 29 graduates who completed their dissertations at a distance, this chapter presents a framework for the e-mentoring of research and dissertations that encompasses strategies and support at the institutional, mentor, small group, and mentee levels.

DOI: 10.4018/978-1-5225-7065-3.ch009 Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

A Framework for E-Mentoring in Doctoral Education

INTRODUCTION The upward trend in online education at institutions of higher education in the United States (Seaman, Allen, & Seaman, 2018) has also been reflected in the increasing number of blended and online doctoral degrees being offered (Kung & Logan, 2014). Such doctoral programs can vary widely by discipline and institution, where some consist of online coursework, seminars, and individual research, and others focus only on individual research and on-campus or virtual meetings. Dissertation formats across new programs also vary and include traditional five-chapter dissertations, a collection of articles, or collaborative dissertations. Regardless of program structure and dissertation formats, faculty mentoring of research continues to play an important role in students’ completion of dissertations in blended and online doctoral programs (Erichsen, Bolliger, & Halupa 2014; Kumar, Johnson, & Hardemon, 2013). When doctoral programs are offered partly or completely online, and mentors and mentees are geographically dispersed, the development of research designs, implementation of dissertation research, and generally, the mentoring of dissertations becomes more complex and challenging. At the same time, the online environment, emerging and current communication technologies, and social media offer new possibilities for mentoring in additional ways to those in traditional on-campus environments. In this chapter, we present a framework for the e-mentoring or virtual supervision of dissertations based on data collected during three iterations of an online doctoral program. Given the scarcity of research on virtual mentoring of dissertations for online students, or students conducting their research at a distance from universities in which they are enrolled, it is important to identify the ways in which institutions and supervisors can mentor research and support students at a distance, be it in oncampus programs or online programs. Literature on traditional apprenticeship models of doctoral supervision abounds, but little is known about the kinds of interactions and strategies that lead to successful doctoral degree completion when dissertations are completed at a distance.

BACKGROUND Doctoral education scholars have used terms such as “supervision”, “advising”, and “mentoring” (Lyons, Scroggins & Bonham-Rule, 1990) to refer to interactions between a supervisor and doctoral student. Advising has alluded to more of a managerial and support role, and supervising has been the preferred term in the literature about educating, supporting, and managing the process of dissertation research (Kadushin, 1976; Winston & Polkosnik, 1984). In our research, we adopted the term mentoring that includes advising, supervising, mentor as well as mentee growth, and is learner184

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centered (Zachary, 2002). Educational development, professional development, and psychosocial development are the focus of doctoral mentoring processes, and much of the research emphasizes the importance of the mentoring relationship to the completion of doctoral dissertations (Boud & Lee, 2009; Burnett, 1999; Crisp & Cruz, 2009; Golde, 2007; Hayes & Koro-Ljungberg, 2011; Ives & Rowley, 2005). Strategies used by dissertation mentors in the areas of feedback, pedagogy, research experiences, psychosocial support, and career development have been studied in doctoral education (Heath, 2002; Rose, 2003; Manathunga, 2007; Wisker, 2015), as have models of peer support such as cohorts and research teams (Boud & Lee, 2005; Burnett, 1999; Carr, Galvin, & Todres, 2010; Robertson, 2017). In addition to the research on dissertation supervision in on-campus programs, much has been written about online education and online learning in higher education. (Anderson, 2008; Moore, 2013). Much of the literature in higher education has focused on online courses and programs at undergraduate and Master’s, but not the doctoral level. Theoretical frameworks in online education have recommended a social-constructivist approach to help online students overcome physical distance and build community – examples of this are the theory of transactional distance (Moore, 1993), and the community of inquiry framework (Garrison, Anderson, & Archer, 2000). Often, research in online education has compared face-to-face offerings to those offered online or virtually (Zawacki-Richter & Anderson, 2014). The term “face-to-face” refers to environments where participants are physically present in the same place and time. In the online environment, interactions that take place at the same time in a virtual space are termed “synchronous” (audio or video conversations online). The terms “hybrid” or “blended learning” have been used to describe the integration of online components in face-to-face or on-campus offerings or programs. The research on doctoral education that takes place completely online, experiences of online doctoral students, and the outcomes of doctoral programs that are offered only at a distance is scarce compared to research on the use of Information Communication Technology (ICT) or online components that are integrated into face-to-face programs. Recently, Dowling and Wilson (2017) studied the use of different technologies, including online tools, by doctoral students for their research and in their doctoral experiences. Hutchings (2017) and Maor, Enser, and Fraser (2016) also studied the use of technology for the supervision of individual dissertations as well as mentoring groups of students. Both these studies were not conducted in completely online programs. In the last few years, the rapid development of online doctoral programs in the United States has been accompanied by research on the design, implementation, and evaluation of these programs (Dawson, Cavanaugh, Sessums, Black, & Kumar, 2011; Exter, Korkmaz, & Boling, 2014; Jones et al., 2014; Kumar, Dawson, Black, Cavanaugh, & Sessums, 2011; Kumar & Dawson, 2012, 2014, 2018). However, 185

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research on virtual supervision or the online mentoring of dissertations is scarce in both the literature on online education and doctoral education. E-mentoring or virtual mentoring occurs when the electronic medium serves as the main means of communication between mentors and mentees (Hamilton & Scandura, 2003). The absence of face-to-face communication poses several challenges for both mentors and mentees. These include misunderstandings due to the non-verbal nature of online communication, difficulties developing trust and a relationship online, problems with the technology used, and inadequate online communication skills on the part of the mentor or mentee (Ensher, Heun, & Blanchard, 2003; Kumar & Johnson, 2017; Kung, 2017). The virtual medium can also be isolating because communication only takes place when the mentor or mentee initiates it. To overcome these challenges, researchers have proposed that mentors use multiple technologies and develop strategies to build student trust, community, and communication practices that can facilitate learner progress (Ensher & Murphy, 2007; Headlam-Wells & Gosland, 2005; Kumar & Johnson, 2017; Kumar & Dawson, 2018; Schichtel, 2010; Thornett & Davey, 2006). Seven competencies have been identified for e-mentors: Online developmental competence; Social competence; Cognitive competence; Teaching competence; Communication competence; Managerial competence; and Online technical competence (Schichtel, 2010). E-mentors should communicate their online availability and time taken for feedback (Erichsen et al., 2014; Kumar & Coe, 2017; Kumar & Johnson, 2017). Furthermore, they should provide clear, thorough, and timely feedback on the mentee’s work (Erichsen et al., 2014; Kumar & Coe, 2017; Schichtel, 2010). Finally, competent e-mentors should also be able to adapt their strategies to the mentee’s needs (Horvath, Wasko, & Bradley, 2008; Kumar & Johnson, 2017). In addition to the above practices, in online doctoral education, mentor provision of structure and resources focused on research quality (exemplars, templates) can contribute to both degree completion and quality dissertations (Kumar & Johnson, 2017). Based on the literature reviewed, it is apparent that mentors supervising dissertations in the online environment and mentees completing dissertations at a distance from the university need to possess additional skills to those needed in traditional on-campus programs, and that they might need additional forms of support. The increased number of online doctoral programs and mentoring of research at a distance in traditional programs make it important to identify the strategies and technologies used by research e-mentors and by online students that contribute to successful doctoral degree completion. Additionally, there might be other support structures that online doctoral students need in addition to e-mentoring interactions with faculty members or their research mentor.

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MAIN FOCUS OF THE CHAPTER This chapter presents a framework for mentoring dissertations at a distance that includes structure and support at the institutional, program, mentor, and mentee levels, defining the role played by each of these areas in the application of educational technology for research mentoring. The framework was developed based on qualitative research with three cohorts of students in an 8-year old online professional doctoral program. This section focuses on the research and includes a) an overview of the context in which the research was conducted and the framework developed, b) the research design, and c) an overview of the results of the research with three groups of graduates.

Context The online doctoral program, first offered in 2008, is offered largely online with an on-campus meeting in the summer. Similar to many doctoral programs in the US, the degree involves required coursework followed by a dissertation. The first meeting is a 2-day orientation, followed by online seminars and a one-week meeting on campus at the end of the first and second years, respectively. Students then focus on their dissertations with their dissertation chair, termed the faculty mentor in this research. All students are professionals in educational environments located in the US and abroad. They complete the program at a distance, except for their required attendance at the three on-campus meetings and their dissertation defense. The online doctoral program is offered at a research university; therefore, program leaders are cognizant of rigor and quality in the dissertations. Students choose research problems that are often related to the improvement of their professional contexts. The dissertation is expected to fuse theory, research, and practice; should correspond to the dissertation expectations of the program; and should advance contextualized knowledge of the field in some manner. Exploratory, semi-structured interviews were conducted with 29 students who graduated from the first three cohorts about their experiences working on a dissertation at a distance from the university and being mentoring in the online environment. The first 12 students who graduated from each of the first three cohorts (within 3-4 years of beginning the program) were contacted for interviews (Table 1). Semistructured interviews were mainly conducted in Skype or on the phone, with only two being conducted face-to-face. The interviews ranged from 14 to 40 minutes, were audio-recorded with participant consent, and transcribed verbatim. Interview questions focused on the dissertation experience and process. For example, the following questions were asked: How did you work with your faculty mentor? What were some of the challenges you faced? What were some strategies 187

A Framework for E-Mentoring in Doctoral Education

Table 1. Participation in interviews # of Graduates

Group 1

Group 2

Group 3

Contacted

12

12

12

Participated

9

10

10

used by you or your mentor that worked well? What role did the online environment play in your dissertation process? What can be done to support students in future online offerings through this process? For each group, two transcripts were first open coded independently by two researchers who focused on making meaning of what was being communicated by the participants about their experiences. The researchers met to discuss their codes, then the remaining seven transcripts in Group 1 and eight transcripts in Groups 2 & 3 were coded by one of the two researchers. The second researcher then reviewed all the codes for the group, and areas of disagreement were discussed and resolved. The two researchers then engaged in axial coding, looking for connections between the codes and collapsing overlapping codes. Finally, a third researcher reviewed the categories and themes for each group once more.

Research Results In this section we provide a brief summary of the interview results from the three groups in the four areas that emerged. Student participants discussed mentor strategies, mentee challenges, mentee strategies, and other forms of support as they described their experiences with completing their dissertations in the online environment.

Mentor Strategies All the graduates who were interviewed emphasized that it was important for faculty to use different types of media during the dissertation process when mentoring at a distance. Their faculty mentors used e-mail, the phone, synchronous meeting software (e.g., Adobe Connect, Skype), etc. to provide feedback and discuss student work. This was reflected in a student’s comment about her mentor: I don’t even know where she was most of the time when I was dealing with her.... But I know that I could communicate with her at any time and we could utilize any resource that we had available to us in order to, you know, ease our communication and do so quickly and efficiently so that we could get things done.

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The use of asynchronous communication for initial feedback and synchronous for feedback discussion was highlighted as a good practice. Frequent communication and meetings, especially those initiated by the mentor, the setting of deadlines, and timely feedback was appreciated. One mentor in Group 1 structured small group meetings and online interactions that were valued highly by students. This data was shared with all e-mentors in Group 2 as a good practice, leading to some others adopting this strategy. Several Group 2 and Group 3 students thus shared that they had very positive experiences in the mentor-structured small group interactions. Students in Group 1 felt well supported in their academic endeavors by their mentors, but felt a need to meet with them face-to-face when they ran into problems or experienced challenges in their research. With advancements in communication technologies, later groups in the online program used video conferencing and screen sharing software more frequently. Students in later groups, therefore, did not perceive physical distance from the mentor to be a challenge, and found that their mentor was available and supportive when needed. For example, a student stated: The online format was conducive to community building among the students and among the faculty so we all could create a community of practice of sorts where we’d work together for the good of all the members. So that was very helpful. I didn’t find it hindering in any way, except that it would have been nice to be able to see each other more face-to-face, but there was no hindrance. Table 2 lists the different codes mentioned by students as mentor strategies across the three groups.

Student Challenges During Dissertations in the Online Environment Students in the online program studied were full-time professionals who also had personal commitments (e.g., families, parents, community service) that placed demands on their time. Their main challenge was, therefore, finding the time and motivation to write when dealing with all their other commitments. Furthermore, several students in the Groups 1 and 2 described challenges in handling the feedback they received on their writing, and moving forward with revisions to their work. These are challenges faced by all students in doctoral programs, but the distance from the university and not being part of a physical university community compounded these challenges for online doctoral students. Life events such as job changes, pregnancy, and family illnesses or changes in their professional environment hindered some students’ implementation of research. One student reflected:

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Table 2. Mentor strategies valued by students across groups Mentor Strategies

Group 1 (n=9)

Group 3 (n=10)

Group 2 (n=10)

Used multiple technologies

√

√

√

Was available

√

√

√

Timely communication

√

√

√

Set deadlines

√ (1 mentor)

√

√

Provided structure

√ (1 mentor)

√

√

Provided timely feedback

√

√

√

Scheduled regular meetings

√

√

√

Structured small group meetings

√ (1 mentor)

√

√

Structured peer interactions

√ (1 mentor)

√

√

√

√

√

√

Provided examples of dissertations Provided psychosocial support

√

There was some point in time where I was collecting data in my project and I was getting ready to have a baby. So, time became quite a challenge for me to be able to accomplish my project and defend my dissertation in the time that I desired. In the first group, students were not confident to reach out to the faculty mentor in the online environment when such events occurred. In Groups 2 and 3, students were facile with video technologies and far more comfortable communicating with their online mentors when such problems arose. Likewise, the first group that was interviewed experienced a lack of peer support, leading them to feel isolated and frustrated when trying to work on their research in the absence of a support group. Based on the data collected, program activities to strengthen cohort networking were implemented. The second group of students proactively connected with each other, leading to guidelines for cohort support and a strong network in Group 3. Table 3 lists the different codes that emerged as students across the three groups described their challenges with completing dissertations in the online environment.

Successful Strategies Used by Mentees The strategies described by the graduates of the online doctoral program as essential to their staying motivated and completing their dissertations fell largely in three areas: 1. Strategies related to their individual work, such as time management, organization, managing their writing by working consistently or establishing 190

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Table 3. Mentee challenges with completing dissertations Group 1 (n=9)

Mentee Challenges

Group 2 (n=10)

Group 3 (n=10)

Time management

√

√

√

Writing the dissertation

√

√

√

Setbacks while Implementing research

√

√

Irregular Communication with mentor

√

Isolation

√

Lack of Peer Support

√

Inadequate institutional resources for online doctoral students

√

√

Problems handling feedback

√

√

a rhythm, establishing a timeline and deadlines for completing smaller chunks of work. These strategies were reported by all three groups. 2. Strategies related to their online interactions with their faculty mentor, such as taking the initiative in working with the mentor, maintaining consistent communication, using multiple technologies to communicate, turning around mentor feedback, asking questions, and reaching out in times of stress. These strategies were also described by all three groups. 3. Strategies related to their online interactions with their peers that could prevent isolation and connect them with resources as well as feedback. Graduates in the first group described their use of critical friends – peers with whom they felt comfortable sharing their work, receiving feedback, and sharing their problems. Group 2 created a Facebook group and found this invaluable to their progress during their dissertations. They asked questions, requested advice on resources, and also expressed their frustrations, as described by a student, who said, “I knew - when I could call, on certain people in the cohort, who to call, who to email, who would get back to my via a Facebook page - and just knowing that I have these other people to go to. Those were the people that I counted on.” Based on the data from the previous groups, the program was purposefully designed to enable Group 3 to build a network of peers who could read and provide feedback on their research and writing. Most of the strategies described here might appear to correspond to those needed by doctoral students in any program. However, the strategies pertaining to interactions with the mentor and peers were used exclusively in an online environment, where students and their peers or mentors were situated in different states in the US and 191

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sometimes, different continents. These strategies were therefore perceived to be crucial by the graduates interviewed, to help them work consistently on their dissertation and stay connected with their doctoral community. The data from Groups 1 and 2 was shared with Group 3, enabling them to learn from the experiences of previous graduates and adopt what worked for them. Table 4 lists the different strategies mentioned by graduates across the three groups.

Other Support During the Dissertation Phase for Online Students Other forms of support for online students that were experienced by the three groups in this study are listed in Table 5. Given that the online doctoral program was a new offering, the first group of students who were interviewed highlighted the absence of online resources for doctoral students at the university. For example, they did not feel adequately skilled in the use of the library and did not have access to the

Table 4. Mentee strategies described by students across groups Mentee Strategy

Group 2 (n=10)

Group 1 (n=9)

Group 3 (n=10)

Consistent communication with mentor

√

√

√

Use of multiple technologies

√

√

√

Time management

√

√

√

Setting deadlines

√

√

√

Writing consistently

√

√

√

Taking the initiative

√

√

√

Organizational strategies

√

√

√

Working with critical friends

√

√

√

√

√

Communicating and supporting peers (cohort)

Table 5. Other forms of support experienced by online students during dissertations Group 1 (n=9)

Other Forms of Support Library instruction and online resources

√

√

Instruction in IRB guidelines and processes Online support for formatting dissertations

Group 3 (n=10)

Group 2 (n=10) √ √ √

√

√

√

Online graduate student resources and workshops Advice from previous graduates

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workshops that were offered for on-campus graduate students. Likewise, they were unaware of Institutional Review Board (IRB) procedures and dissertation formatting guidelines for which they had to reach out to on-campus services. Based on the data, students in Group 2 were connected with these on-campus resources, and online library instruction was designed for the second group. Students found the online library resources valuable, praising it with comments such as “I really like the way the library was able to develop their support as far as responding to the distance learner,” and “It was very good support I think. The library was very, very helpful.” However, when interviewed after they graduated, they highlighted the absence of other resources (e.g., IRB procedures). Students in Group 3 were thus provided with on-campus sessions in these areas when they visited campus, or webinars that they found very valuable. At the same time, they emphasized the need for online resources such as tutorials or a help desk for online students for the successful completion of dissertations. All three groups expressed their frustration at the number of resources and workshops available to on-campus doctoral students as opposed to online doctoral students. Since then, university resources for online students are being developed and hopefully will be useful to future students.

SOLUTIONS AND RECOMMENDATIONS: AN E-MENTORING FRAMEWORK Based on the literature reviewed in on-campus doctoral education that highlights the importance of the mentor-mentee relationship to dissertation completion (Boud & Lee, 2009; Lee, 2008), the initial focus of the research reported in this chapter was to identify strategies that e-mentors could use with online doctoral students to help them conduct independent research and complete their dissertations while at a distance from the university. The results of the research with Group 1, however, made it apparent that while online mentees have to practice much of what is considered essential in on-campus doctoral education (e.g., time management, writing discipline), they have to adopt additional strategies unique to the online medium to succeed. These results were communicated to Group 2 before they began their dissertations. Group 2 described additional peer interactions as essential to their online dissertation experience when they graduated. Both Groups 2 and 3 reported that further support was needed at the program and university level to help online doctoral students succeed during their dissertations. Based on the strategies and challenges discussed by the 29 program graduates interviewed, and additional research with faculty mentors who supervised online dissertations (Kumar & Johnson, 2017), a framework for the e-mentoring of online doctoral students was developed. This framework (Figure 1) encompasses the institution (or in some cases, the college 193

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Figure 1. E-mentoring framework for dissertations completed at a distance

or program), the mentor, the small research group (and the cohort, if the doctoral program includes a cohort model), and the mentee. Each of these elements of the framework plays an important role in the successful completion of dissertations in the online environment, as described in detail below.

The Institution Students in on-campus doctoral programs have access to on-campus services and resources that have been developed over several decades. Institutions developing new online doctoral programs, blended models of doctoral programs that include on-campus and online components or programs where students complete their dissertations at a distance, often focus on curriculum design, but do not always take into consideration the institutional resources needed by online doctoral students (McCallin & Nayar, 2012). Institutions with long-standing online Master’s programs might also need to create new resources or adapt existing resources to help online doctoral students with their research, writing, and professional needs in order to ensure quality programs and student success. Institutional support for e-mentors and students in doctoral programs plays a key role in successful e-mentoring, the completion of dissertations at a distance, and maintaining as well as improving the quality of doctoral education.

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Technology Resources and Support Most campuses provide technical support for their students, and all higher education institutions with online programs provide some form of technical support for their online students. In addition to software used to host online courses and communicate with faculty and peers, online doctoral students need to be able to access on-campus resources such as the library (often provided with some form of VPN), research software, any discipline-specific software, and bibliographic software in order to be successful in their dissertation endeavors. They also need to be aware of such resources, and to have access to a) tutorials and documentation that help them learn how to use such resources, and b) technical support when they use such resources. Likewise, dissertation e-mentors should be aware of and able to use such resources in order to guide their students appropriately.

Administrative Support and Institutional Resources Online registration procedures and student information systems are common to many college campuses. Additionally, administrative processes and paperwork, for instance, have to be available virtually and communicated to online students so that they can stay on track. According to the participants of the research that informed this framework, online resources such as tutorials and online support services for institutional research processes (e.g., Institutional or Ethical review boards), academic writing, and dissertation guidelines would be helpful to online doctoral students. Graduate student professional development, career resources, and opportunities to connect with alumni should also be provided virtually to online doctoral students.

Information Literacy Resources and Support Information literacy resources are often available online to all students enrolled in institutions of higher education. Online doctoral students embarking on dissertations need off-campus access to research resources as well as instruction in how to expertly find, evaluate, and manage resources. According to participants in this study, instruction in these areas should be provided both in the form of webinars and tutorials that ensure just-in-time access. The initial use of embedded librarians in online seminars or research groups can also be valuable for virtual research and those completing dissertations at a distance.

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E-Mentor Development and Resources The earning of a doctoral degree and scholarship in the discipline has traditionally served as the experiences needed to supervise doctoral students in higher education. In the case of virtual supervision or e-mentoring, faculty who act as e-mentors have rarely previously experienced e-mentoring, online teaching, or online learning. Many faculties who mentor dissertations adapt successfully to the online environment, but the provision of faculty development to be e-mentors and resources (e.g., to learn online technologies) can contribute to the quality of online dissertation mentoring and doctoral programs in higher education institutions.

The Mentor The deciding role played by the supervisor in the dissertation process has been widely acknowledged in the literature, as have the strategies used by supervisors during the dissertation process (Bastalich, 2017; Boud & Lee, 2009; Burnett, 1999; Crisp & Cruz, 2009; Ives & Rowley, 2005; Rose, 2003; Wisker, 2015). The mentor plays a crucial role in the e-mentoring framework presented in this chapter, because in the online environment, s/he is the main (and sometimes the only) point of contact at the university for the dissertating student who is at a different geographical location. The online mentor’s “management of the interface between people, their learning and developmental process, and the supporting technology” (Schichtel, 2010, p. 251) cannot be overstated in this context. Dissertation mentors in on-campus programs often guide students to independent research in research apprentice, laboratory, and even small group settings, although students’ individual work in these contexts is still essential. They are also able to provide feedback in person in addition to any feedback in writing. In an online environment, where the student works on a dissertation in a context not located at the university, mentors have to adapt to communicating, mentoring, and guiding research online.

Use of Various Technologies During the Dissertation Process Similar to online students, e-mentors have to be familiar with the different technologies that can be used during the dissertation process, be able to choose technologies based on the purpose, and most importantly, possess online communication skills to guide students and provide feedback online. Prior research has found that e-mentors prefer to discuss their written feedback to students using video conferences or phone calls, to ensure the fidelity of what they want to communicate, and to be able to motivate students to continue working (Kumar & Johnson, 2017). Familiarity with discipline-

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specific software and research software is often assumed for all dissertation mentors, but e-mentors also need to be facile with video conferencing software, and with screen sharing in virtual classrooms to be able to provide real-time feedback on student work or data analysis. Key components of mentoring during the dissertation phase are also professional development and psychosocial support (Hayes & KoroLjungberg, 2011), therefore online developmental competence (Schichtel, 2010) is essential for e-mentors when using technology to communicate with students.

Research Guidance and Structure Dissertation and research guidance by mentors is presumed in this framework for doctoral programs. Mentors guide the development of research designs, research implementation, dissertation writing, and students’ development to become a scholar and an active member of the discipline. Mentors might connect students with experts in the field, point them to resources, or provide exemplars of scholarship. In the online medium, mentor-provided structure, different types of online feedback, and additional resources are needed during each phase of the dissertation process. If students are not working with the mentor on research projects or have not had previous experience conducting independent research, research education is integral to e-mentoring to help students successfully complete their dissertations. E-mentors often have to source or create online resources or demonstrate data analysis procedures online to fulfill the needs of specific students and achieve rigor in dissertations. The ability to guide research virtually, using some of the technologies described previously in this section, is essential for e-mentors in this framework.

Availability and Timeliness Mentors’ availability, responsiveness, and timeliness in providing feedback were praised highly by all participants in this research, and were reported to influence dissertation completion. While this is true of all mentors, in the online environment, e-mentors have to convey their presence in order for students to feel supported. Even if they are unable to provide feedback immediately, the establishment of timelines for student submission of work and the communication of expectations for feedback provision can help students greatly. It is also important for e-mentors to communicate their availability and investment in students’ progress because online students sometimes experience life events or challenges with their research implementation and might hesitate to reach out if they are unsure about how to communicate with the mentor.

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Group Mentoring The mentoring of online students in groups was a highly valued practice in the research that formed the basis of this framework. Prior research has also reported several benefits of team supervision in on-campus and online environments (Hutchings, 2017; Robertson, 2017). E-mentors have to adapt their strategies to work with individual students, but if they are engaged in mentoring several students, the organization of regular (e.g., monthly) group meetings can greatly help students stay motivated and make progress. In addition to functioning as research groups with common disciplinary research interests, such groups can share resources online, discuss common issues, and collaborate virtually. Mentors can structure peer interaction if they wish, or allow students to drive the interactions. Group mentoring also prepares mentees for future work environments where research projects often involve virtual collaboration and scholarship.

Small Group/Cohort The following section highlights mentee-initiated support networks, where online doctoral students can find critical peers to support and critique their work, engage with colleagues or friends who support them, or use social media for learning resources and professional support. Additionally, online doctoral programs and curriculum can be designed to foster peer collaboration and community building. Students in on-campus doctoral programs collaborate with peers or become part of a community by mutual participation in academic, scholarly, and research experiences. However, such opportunities often need to be intentionally created and structured by programs or faculty in online doctoral programs. Cohort models where students complete coursework together have been found to be effective in on-campus and online programs. They help students succeed and stay connected, and to reduce attrition (Burnett, 1999; Carr et al., 2010; Conrad, 2005; Nimer, 2009; West, Gokalp, Edlyn, Fischer, & Gupton, 2011). This model has also been successful in online doctoral programs that include coursework or initial doctoral experiences that are sequenced for all students (Kumar et al., 2011). All the participants in the research that informed the e-mentoring framework described in this chapter were enrolled in a cohort-based program. Twenty-three of the 29 students highlighted the cohort model as helping them stay motivated and supported during their dissertation process. Participation in a closed social media group (e.g., Facebook) can help online students stay connected, share their experiences, share research and career resources and advice, and create a virtual community when completing dissertations at a distance. While not always feasible and possible in doctoral programs, the cohort model is included in this e-mentoring framework as an element that can contribute to online 198

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student success. E-mentors can also collaboratively supervise students in a cohort (Maor et al., 2006; Robertson, 2017) and smaller groups of students within a cohort can also be e-mentored by faculty. Small group mentoring is common in on-campus doctoral education where students working with the same faculty member meet regularly, collaborate on research projects, and even co-author or attend conferences together. In a virtual environment, students with similar research interests or those working with the same mentor can be encouraged to work together and meet regularly online. In our research study, e-mentors structured group work and interactions, and fostered the sharing of resources and feedback amongst small groups. Eventually, online students in the small groups self-organized to support each other through dissertation completion. This was termed a best practice by all the research participants who experienced such strategies. Virtual small group interactions, support, and feedback were described as instrumental in the completion of dissertations at a distance. While small groups might need initial organization and the provision of structure by the e-mentor, they can function as virtual research groups that share their research, read and critique drafts, share resources, and collaborate on scholarship. Ideally, institutions should provide access to platforms and technologies that facilitate such interactions between e-mentors, mentees, and their peers, but free technologies (e.g., Skype, Google Drive) that enable communication and collaboration are also available to small groups working virtually. While small groups might evolve on their own during online doctoral programs, the purposeful creation of such groups by e-mentors and online programs, and the provision of virtual spaces and technologies to foster small group collaboration and support can greatly help online doctoral students being mentored on their dissertations at a distance.

The Mentee Students’ experiences during the dissertation process have been well-documented in doctoral education research over several decades (Gardner, 2009; Odena & Burgess, 2017; Wisker et al., 2010). The challenges experienced during the dissertation process, as well as the ways in which doctoral students overcome these challenges, have also been studied. Likewise, the challenges experienced by online learners, whether undergraduates or graduates, and the strategies that they have to adopt to succeed, have been studied in efforts to improve online education (Anderson, 2008; Beaudoin, Kurtz, & Eden, 2009; Dabbagh, 2007). The findings in the research presented in this chapter reinforce the importance of self-regulation in virtual environments (Zimmermann, 2001) and correspond to prior research on learning presence in online environments (Shea & Bidjerano, 2010), where learners manage and self-direct their learning. The most essential part of the e-mentoring framework, therefore, is the 199

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mentee and the role s/he plays in driving and managing the dissertation process, both as a doctoral candidate and as an online learner. The mentee in an online environment should be cognizant of his/her role in the dissertation process. On-campus doctoral students are often socialized into what they must do to succeed, as they observe others around them and work with faculty members. In the online environment, doctoral students often hesitate to contact their supervisor, and are often unaware of administrative procedures and support at the university. Moreover, prior educational experiences (e.g., Master’s degrees with tight deadlines and clear requirements) do not prepare them for the individual nature of work in a doctoral degree, which involves setting their own deadlines and finding a pace at which to work in the online environment. Finally, they have to make a conscious effort to create a group of peers, either online or in their immediate environment, who can support them through the process. In addition to scholarly approaches and research processes that are integral to success in their discipline, the following strategies can help doctoral mentees succeed in the online environment:

Use of Various Technologies During the Dissertation Process It is presumed that students who choose to study online are comfortable in the online environment, but this is not always the case, and building relationships with mentors and peers necessitates frequent and open communication. The use of video and audio communication can be useful to online mentees, especially if they are not comfortable asking multiple questions, discussing faculty feedback on their research or writing, and sharing problems in e-mail communications. Mentees across groups in this research study provided examples of how virtual meetings can ameliorate issues with tone or written feedback. In addition to using technologies that can help all doctoral students (such as software for data analysis or bibliographic software), online doctoral students have to become familiar with the use of cloud services to collaboratively share documents and data with mentors and peers. Most importantly, they have to learn to screen-share during virtual meetings to present or ask questions about their data analysis. Virtual meeting software such as Adobe Connect, Zoom, or Skype can greatly help reduce distance between online doctoral students and their mentors or peers, if they learn how to use it.

Time Management and Self-regulation Pacing their work, establishing deadlines for writing and feedback, and scheduling regular meetings with mentors and peers can greatly help online doctoral students stay on track, especially because they do not have to be present on a campus and are immersed in professional and personal commitments at a distance from the university. 200

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Structuring their work and communications, adopting good organizational practices for resources, organizing meetings with their mentor and identifying a timeline for feedback, setting small goals and creating a timeline, and also scheduling breaks from the dissertations are strategies that can help them succeed.

Motivation and Virtual Support In order to be successful and make progress on their dissertations, online doctoral students have to find strategies to keep motivated and work consistently. In the research that formed the basis of this framework, virtual communication and virtual interactions with peers were cited as a huge motivating factor in the completion of dissertations. Students reflected on the value of peer support and peer feedback that came about within the structure of the online program. At the same time, online doctoral students can also seek out colleagues or friends working on doctoral degrees or who have completed doctoral degrees, and also those with common research interests in their own online program who can support them through the process. Social media platforms, (currently in the form of Twitter and Facebook), can help online doctoral students build a network of peers and experts who can provide resources and advice as they work on their dissertation.

FUTURE RESEARCH DIRECTIONS The E-mentoring framework presented in this study is based on qualitative data collected from 29 students in an online doctoral program in the United States that included three on-campus experiences. The students in the program were professionals who worked in various contexts who were adept at using technology due to the nature of the discipline studied. The online doctoral program was offered at a research-intensive institution where e-mentors were full-time faculty with established research agendas. Although the research was conducted in a very specific context, it was focused on strategies and support needed for the e-mentoring of dissertations and led to the development of the e-mentoring framework in this chapter. Future research will involve studying the four elements of this framework in two other online doctoral programs to evaluate its applicability in other online professional doctorates. An area of the framework that has not been studied as extensively in research on doctoral education both on-campus and online is the mentoring of small groups, or the e-mentoring of small groups working on dissertations at a distance. Given the benefits of peer networks, online peer interactions, and community building in small groups, this is an area that merits further study. While data collected from mentors and mentees served as the basis for this framework, future research should also 201

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include institutional stakeholders and program leaders who can provide insight into all areas, but particularly the institutional area of this framework. Furthermore, the framework can also be studied in the context of doctoral programs across disciplines where students might be enrolled on campus, but travel to collect data and complete their dissertations at a distance.

CONCLUSION The doctoral journey is a challenging endeavor for all those who aim to complete a doctorate, whether they work with their dissertation supervisors on-campus in an apprenticeship model, meet with their supervisors at regular intervals, or choose to complete their doctoral work at a distance from the university. Being located at a distance and being disconnected from the university, peers, or their e-mentor can pose additional challenges to online doctoral students, and to those who mentor them. At the same time, ICTs present several opportunities to resolve these challenges, if doctoral mentors, students, program leaders, and institutions adapt and adopt new approaches. The framework presented in this chapter encompasses several elements of effective mentoring of dissertations in all environments, but focuses specifically on virtual environments and e-mentoring as requiring additional elements to be successful. It can be used by leaders of online doctoral programs, faculty mentoring student research online, and online graduate programs that use a cohort model or those that involve the e-mentoring of projects. It also includes strategies that can be useful to both mentors and mentees enrolled in on-campus doctoral programs but who are engaged in e-mentoring. As more institutions of higher education, departments, and faculty embrace technology for research mentoring and new models of doctoral education, it is important to explore effective approaches and develop frameworks that can improve teaching and learning in virtual and technology-enhanced environments.

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Hutchings, M. (2017). Improving doctoral support through group supervision: Analysing face-to-face and technology-mediated strategies for nurturing and sustaining scholarship. Studies in Higher Education, 42(3), 533–550. doi:10.1080 /03075079.2015.1058352 Ives, G., & Rowley, G. (2005). Supervisor selection or allocation and continuity of supervision: Ph.D. students’ progress and outcomes. Studies in Higher Education, 30(5), 535–555. doi:10.1080/03075070500249161 Jones, G., Warren, S. J., Ennis-Cole, D., Knezek, G., Lin, L., & Norris, C. (2014). Transforming the doctorate from residential to online: A distributed PhD learning technologies. TechTrends, 58(4), 19–26. doi:10.100711528-014-0765-2 Kadushin, A. (1976). Supervision in social work. New York, NY: Columbia University Press. Kumar, S., & Coe, C. (2017). Online mentoring and student support in online doctoral programs. American Journal of Distance Education, 31(2), 128–142. doi :10.1080/08923647.2017.1300464 Kumar, S., & Dawson, K. (2012). Theory to practice: Implementation and initial impact of an online doctoral program. Online Journal of Distance Learning Administration, 15(1). Retrieved from http://www.westga.edu/~distance/ojdla/ spring151/kumar_dawson.html Kumar, S., & Dawson, K. (2014). The impact factor: Measuring student professional growth in an online doctoral program. TechTrends, 58(4), 89–97. doi:10.100711528014-0773-2 Kumar, S., & Dawson, K. (2018). An online professional doctorate for researching professionals. Athabasca University Press. Retrieved from http://www.aupress.ca/ index.php/books/120272 Kumar, S., Dawson, K., Black, E. W., Cavanaugh, C., & Sessums, C. D. (2011). Applying the community of inquiry framework to an online professional practice doctoral program. The International Review of Research in Open and Distributed Learning, 12(6), 126. doi:10.19173/irrodl.v12i6.978 Kumar, S., & Johnson, M. (2017). Online mentoring of dissertations: The role of structure and support. Studies in Higher Education, 1–13. doi:10.1080/03075079 .2017.1337736

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Kumar, S., Johnson, M. L., & Hardemon, T. (2013). Dissertations at a distance: Students’ perceptions of online mentoring in a doctoral program. Journal of Distance Education, 27(1). Retrieved from http://www.ijede.ca/index.php/jde/article/view/835 Kung, F. W. (2017). Perceptions and career prospects of the distance doctor of education degree: Voices from the mid-career ELT tertiary practitioners. Innovations in Education and Teaching International, 54(1), 42–52. doi:10.1080/14703297.2 015.1018919 Kung, M., & Logan, T. J. (2014). An overview of online and hybrid doctoral degree programs in educational technology. TechTrends, 58(4), 16–18. doi:10.100711528014-0764-3 Lee, A. (2008). How are doctoral students supervised? Concepts of doctoral research supervision. Studies in Higher Education, 33(3), 267–281. doi:10.1080/03075070802049202 Lyons, W., Scroggins, D., & Bonham-Rule, P. (1990). The mentor in graduate education. Studies in Higher Education, 15(3), 277–285. doi:10.1080/030750790 12331377400 Manathunga, C. (2007). Supervision as mentoring: The role of power and boundary crossing. Studies in Continuing Education, 29(2), 207–221. doi:10.1080/01580370701424650 Maor, D., Ensor, J. D., & Fraser, B. J. (2016). Doctoral supervision in virtual spaces: A review of research of web-based tools to develop collaborative supervision. Higher Education Research & Development, 35(1), 172–188. doi:10.1080/07294 360.2015.1121206 McCallin, A., & Nayar, S. (2012). Postgraduate research supervision: A critical review of current practice. Teaching in Higher Education, 17(1), 63–74. doi:10.10 80/13562517.2011.590979 Moore, M. G. (1993). Theory of transactional distance. In D. Keegan (Ed.), Theoretical principles of distance education (pp. 22–38). New York: Routledge. Moore, M. G. (Ed.). (2013). Handbook of distance education (3rd ed.). New York: Routledge. doi:10.4324/9780203803738 Nimer, M. (2009). The doctoral cohort model: Increasing opportunities for success. College Student Journal, 43(4), 1373–1379.

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Odena, O., & Burgess, H. (2017). How doctoral students and graduates describe facilitating experiences and strategies for their thesis writing learning process: A qualitative approach. Studies in Higher Education, 42(3), 572-590. Robertson, M. J. (2017). Trust: The power that binds in team supervision of doctoral students. Higher Education Research & Development, 36(7), 1463–1475. doi:10.1080/072 94360.2017.1325853 Rose, G. L. (2003). Enhancement of mentor selection using the ideal mentor scale. Research in Higher Education, 44(4), 473–494. doi:10.1023/A:1024289000849 Schichtel, M. (2010). Core-competence skills in e-mentoring for medical educators: A conceptual exploration. Medical Teacher, 32(7), e248–e262. doi:10.3109/01421 59X.2010.489126 PMID:20653366 Seaman, J. E., Allen, I. E., & Seaman, J. (2018). Grade increase: Tracking distance education in the United States. Babson Park, MA: Babson Survey Research Group. Retrieved from http://onlinelearningsurvey.com/reports/gradeincrease.pdf Shea, P., & Bidjerano, T. (2010). Learning presence: Towards a theory of selfefficacy, self-regulation, and the development of a communities of inquiry in online and blended learning environments. Computers & Education, 55(4), 1721–1731. doi:10.1016/j.compedu.2010.07.017 Thornett, A., & Davey, R. (2006). The educational foundations of e-learning for healthcare professionals. In J. Sandars (Ed.), E-learning for GP educators (pp. 27–37). Oxford, UK: Radcliffe Publishing. West, I. J., Gokalp, G., Edlyn, V., Fischer, L., & Gupton, J. (2011). Exploring effective support practices for doctoral students’ degree completion. College Student Journal, 45(2), 310–323. Winston, R. B., & Polkosnik, M. C. (1984). Advising graduate and professional school students. In R. Winston, T, Miller, S, Ender, & T. Grites (Eds.), Developmental academic advising: Addressing students educational career and personal needs (pp. 287-314). San Francisco, CA: Jossey-Bass. Wisker, G. (2015). Developing doctoral authors: Engaging with theoretical perspectives through the literature review. Innovations in Education and Teaching International, 52(1), 64–74. doi:10.1080/14703297.2014.981841

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Wisker, G., Morris, C., Cheng, M., Masika, R., Warnes, M., Trafford, V., . . . Lilly, J. (2010). Doctoral learning journeys: Final report. University of Brighton and Anglia Ruskin University. Higher Education Academy. Retrieved from http://www.academia. edu/download/45591755/doctoral_learning_journeys_final_report__HEA_version. pdf Zachary, L. J. (2002). The role of teacher as mentor. New Directions for Adult and Continuing Education, 2002(93), 27–38. doi:10.1002/ace.47 Zawacki-Richter, O., & Anderson, T. (Eds.). (2014). Online distance education: Towards a research agenda. Edmonton, Canada: Athabasca University Press. doi:10.15215/aupress/9781927356623.01 Zimmerman, B. J. (2001). Theories of self-regulated learning and academic achievement: An overview and analysis. In B. J. Zimmerman & D. H. Schunk (Eds.), Self-regulated learning and academic achievement. Theoretical perspectives. Mahwah, NJ: Lawrence Erlbaum.

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The Use of ICT in Researcher Development Sam Hopkins University of Surrey, UK Erin A. Henslee Wake Forest University, USA Dawn C. Duke University of Surrey, UK

ABSTRACT This chapter provides a case study highlighting the importance of ICT use in researcher development, exploring both training pedagogy and ICT skill development, utilizing the authors’ experience of managing and delivering ICT-based researcher development across a wide range of disciplines for researchers, including parttime and distance researchers who conduct their research away from campus. Participant feedback and examples of best practice will be highlighted alongside potential challenges to encourage readers to confidently utilize a wide variety of ICT in order to create innovative researcher development material to best support the next generation of researchers.

INTRODUCTION Over the past two decades, there has been a global drive to increase numbers and widen the diversity of doctoral candidates as governments recognise the value to their respective national economies of highly educated doctoral graduates (Denicolo, Duke, & Reeves, 2016). There has also been an increased focus on international and DOI: 10.4018/978-1-5225-7065-3.ch010 Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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inter-sectorial mobility for doctoral researchers (European Ministry of Education, 1999; Lisbon Summit, 2000), and recognition that doctoral researchers should develop a wider range of transferable skills, including ICT skills, during their doctoral studies (Kemp & Kemp, 1999; Roberts, 2002). This focus has led to a rapid increase in support for transferable skills within doctoral programmes, and the birth of researcher development consisting primarily of structured training and support for a wide range of transferable and employability skills for researchers, primarily at doctoral level (Denicolo et al., 2016). Over the years, governments and universities have invested sustainably in this infrastructure to help their doctoral researchers develop a broader range of skills, which have been identified in the Researcher Development Framework (RDF) (Figure 1), commissioned by Vitae (2010) . Skills that are crucial for researcher development are those in ICT. In the RDF, ICT skills are situated within Domain A: Know and Intellectual Abilities; however, strong ICT skills are also critical for successfully developing and effectively demonstrating skills across the entire RDF. This is particularly the case with the ever-increasing pressures on researchers to communicate their research openly and broadly, to engage with the public, and to ensure their research has societal impact. Moreover, with rapid developments in ICT that enable people to promote their professional profiles and their work internationally, the new generation of researchers who are not ICT savvy may well get left behind. It is likely, therefore, that the development of complex and agile ICT skills will be one of the most critical skill areas that researchers should develop for future career success not only in academia, but in a wide range of sectors. Effective transferrable skills development for researchers requires supervisors, researcher developers, and other professionals supporting researchers to continually develop their own skills and understanding of how ICT is being used within the research context. Continual ICT skills development is important not simply to deliver appropriate training content in traditional face-to-face courses and workshops, but also to reach out to researchers who are often off campus, perhaps in different time zones, and so unable to attend this traditional mode of training delivery. Providing effective skills development for researchers, then, means radically changing the ways in which it is normally facilitated. Using ICT to deliver effective skills development for researchers is a resource-heavy activity. However, when it is done well, the reward is great as doctoral researchers thrive by developing the necessary transferable skill base and can become members of virtual researcher communities, creating distance learning cohorts and combating isolation. This chapter explores issues of ICT skills development within the researcher development context in two ways. First it identifies the ICT skills that researchers need to develop and shares best practice in training techniques. Second it considers how those involved in delivering researcher development training can best utilise 210

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Figure 1. Vitae’s Researcher Development Framework which identifies four specific domains in which key researcher skills belong. ICT skills fall within Domain A, under Knowledge base. However, ICT skills also are important to the other domains as well. Source: Vitae Researcher Development Statement (2010), retrieved from www.vitae.ac.uk/rdf

ICT teaching pedagogies to extend accessibility of this training to distance doctoral researchers. Therefore, the first section explores the ICT skills development necessary for the newer generation of researchers in light of contemporary expectations that researchers should communicate their research widely within and beyond academia, engage with the public about research, and create avenues for research collaboration and impact within society at large. The authors provide insight into supporting 211

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doctoral researchers to confidently and pragmatically utilise ICT to become more outward-facing researchers. The second section focuses on the challenge of making skills development for doctoral researchers more flexible and inclusive, so that all doctoral researchers have access to the development throughout their doctorates, wherever they are conducting their research study. In the authors’ experience, ICT skills development is particularly suited to this type of distance learning approach and can lead to the creation of learner cohorts, in which more traditional doctorate skills are developed together in virtual space. The authors wish to openly share their experiences, and hope to provide doctoral researchers and those supporting them with helpful insights. To do this, the chapter concludes with specific recommendations for best practice in utilising ICT in researcher development.

ICT SKILLS DEVELOPMENT FOR RESEARCHERS Doctoral and early career researchers utilise a wide variety of ICT. Traditionally ICT has been essential for academic writing and reference managing, as well as data collection, storage, and analysis. Therefore, it is not surprising that ICT skills are a featured component of the skills necessary to be a researcher within the Researcher Development Framework (Bray & Boon, 2011). However, the level of ICT competency at doctoral level has been found to be highly variable (Sim, 2016; Sim & Stein, 2016), which demonstrates the need for researcher development skills training and support in this area. It is now fairly common for universities to offer a wide array of ICT-focused training, particularly around the use of data analysis software, such as SPSS and NVivo (Aiken, West, & Millsap, 2008; Johnston, 2006). This training and support is immediately important for the progress of the doctorate and, therefore, is often popular with doctoral researchers. However, as indicated in the introduction, the skills needs of researchers have changed dramatically in the last two decades. As researchers progress in their careers, it is highly likely that they will not just be expected to be competent in research processes and project-linked ICT skills, but will also need ICT skills necessary for outward engagement. Researchers can now benefit from interacting with peers through virtual researcher communities and research sharing utilising inter-sectorial communities. This type of ICT use can stimulate research impact, collaboration, and public understanding and engagement with research. However, doctoral researchers are often conservative in their use of these outward-facing digital tools, particularly if their supervisors and immediate peers are not actively engaged in these activities (Dowling & Wilson, 2017). Therefore, it is the development of these newer, outward and engaging ICT skills which are the focus of this section. 212

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New Skills for Outward-Facing and Engaging Researchers It is becoming increasingly important for researchers to work across discipline, sector, and national contexts (Denicolo et al., 2016; Duke & Denicolo, 2017), requiring them to utilise ICT in order to raise their professional profile, to work collaboratively with people outside their institutions, and to promote their research beyond their academic field as well as to the broader public. This need is bolstered by a number of factors such as the pressure to encourage greater and broader academic use of research outputs (citations), to demonstrate the real-world benefit of research (impact), and to better engage the public with research (Denicolo, 2013). This is driven primarily by government mandate, enacted by research funding agencies; however, there is also an ever-growing ethical movement demanding transparency of academic practice and increased academic responsibility in communicating research findings and implications to combat the pandemic of “fake news” (Kahan, 2017; Verma, Fleischmann, & Koltai, 2017). Funding agencies are also prioritising highly collaborative research which requires researchers to find ways to work collaboratively at an international level. All of these relatively new pressures require researchers to develop increasingly complex and dynamic skills, including high-level ICT skills, to succeed as an “outward” facing researcher. Whilst these are skills all researchers should strive to improve, they are absolutely essential to early-stage researchers for their future careers, either within or outside academia (Denicolo & Reeves, 2013). One challenge here is that more established researchers, who serve as mentors and supervisors to doctoral researchers, may not possess the relatively new, advanced communication or technical ICT skills required, and thus are unable to support this demand for skills development; indeed, they may even discourage it (Dowling & Wilson, 2017; Poliakoff & Webb, 2007). Therefore, collaborative working of academics and professional staff with key expertise is required to support this new generation of researchers to develop the skills necessary to best promote themselves professionally, engage across sectors and with the public, and ensure their research has true social benefit (Duke & Denicolo, 2017). ICT provides researchers with an array of opportunities for these types of engagement activities; new tools and platforms are being developed at an astonishing rate, such as LinkedIn, Twitter, Wordpress, Facebook, Instagram, SnapChat, etc. It can be a challenge for doctoral researchers, as well as those supporting them, to keep up with the pace of these innovations. For this reason, the most important ICT skills are those that help researchers strategically plan what they want the various tools to do for their research, and enable them to readily embrace and master new ICT innovation as it develops. Therefore, throughout this section, the importance of supporting the “transferableness” of these ICT skills is stressed, as it is important that

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researchers are prepared not just to use the technology of today but to confidently adapt to utilising emerging technologies.

Developing Strategies and Choosing Platforms for Wider Engagement With such a wide range of ICT tools at researchers’ fingertips, the first obstacle to using ICT to outwardly engage various audiences is to decide what tool to use. However, before researchers can choose the appropriate tools, it is advantageous, in the authors’ experience, to thoughtfully and strategically identify specific goals the researcher wants to accomplish through the use of ICT. Whether engaging with the public, building a professional or collaborative network, or working to increase the academic impact of one’s research, it is helpful to have a desired measurable outcome defined to determine whether the engagement activity has been successful. Once the goals and success measures are determined, it is easier for researchers to critically evaluate and choose the ICT that is likely to be most effective. Different ICT platforms will work better for different tasks and for engaging different subsections of the overall population (Barrett, Notaras, & Smith, 2014; Davies, 2013). For example, communicating through LinkedIn will connect a researcher with a very different audience than communicating through Facebook. The cultures and conventions of each platform will also vary and may be more appropriate for specific types of engagement, for example, Twitter vs blogs. Therefore, the first step in choosing what ICT to utilise must be to create SMART (specific, measurable, achievable, realistic and time-bound) goals for the activity. Examples of this include: I need to create a series of podcasts for my website to provide more information to participants about the project, aimed at encouraging more participation in my study. I want to develop a family-friendly workshop for the upcoming Festival of Wonder to encourage more participation in our future Public Astronomy Evenings. Once the goals are set, researchers will need to choose an ICT platform which best fits the goal. Confidently creating personal strategies for choosing the best ICT platform is an essential transferable skill that is often either only tacitly performed or entirely overlooked. Therefore, an essential aspect of any researcher development training should include exercises that help researchers develop this skill explicitly and learn to make informed choices. Once the strategy is in place, researchers need support in developing good understanding of the different ICT options, along with their strengths and weaknesses. Researchers should understand how to make ICT choices based on various cost-benefit considerations, such as resource (time and 214

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money commitment) to outcome (desired impact) comparisons. To acquire this skill, researchers must understand enough about the ICT options to make a judgement about the resource intensity, as well as the likelihood that they will be able to achieve their desired outcomes through the use of the particular ICT. The researcher of tomorrow must be savvy enough to choose their ICT wisely as the cost of overcommitting resources to low impact activities can negatively impact their productivity. This must be balanced against the danger of non-engagement with professional and researchpromoting ICT, which could reduce career competitiveness.

Raising Professional Profile/Personal Brand For new generation researchers in today’s world, another key skill is building a personal or professional brand. This involves researchers in using ICT to market themselves as experts in their field and advertising to the world their particular niche. One challenge here is maintaining control of their personal brand in a context where whatever they do or say online becomes a part of this brand, intentionally or not. This blurring of personal and professional boundaries may impact on professional reputation and may deter people from establishing an online presence (Thompson et al., 2008). However, the benefits of creating a strong personal brand heavily outweigh the challenge of managing it all, particularly for newer researchers trying to establish themselves. Having a strong personal brand and professional profile can have a number of benefits: • • • •

Increased professional self-awareness. When creating a personal brand, researchers should conduct honest self-assessment, highlighting areas of strength and passion in their profile. More developed networks and collaboration. By showcasing interests and key areas, other researchers with similar (or counter) ideas are likely to be attracted. A greater opportunity to stand out from the crowd. Researchers all over the globe are likely to be doing similar activities, but a strong personal brand will distinguish researchers (and their institutions) from the rest of the field. Increased credibility with the public. Increased visibility of a strong personal brand is useful in showcasing areas of strength to the public.

Therefore, helping researchers to develop ICT skills as part of integrated career development training is necessary to build their virtual professional profile. Providing support and guidance for researchers as they create their online profiles can combat the relative wariness some researchers have about using online platforms. An example 215

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of how successful this one simple activity can be is evidenced by the following feedback from a doctoral researcher, who previously had never had an online profile but was supported to create one during a training course: During the past few months, I have been contacted by several recruiters, and some companies have even made me offers just by looking at my profile and asking directly for my CV. Whilst researchers cannot control everything that is available about them online, there are simple strategies they can be taught to help them keep their professional brand separate from personal content and boost the content to the top of search queries: • •

• •

Keep accounts consistent. Have a professional photo taken and use it for all professional accounts. Just as a company would use several different logos, keep accounts consistent with usernames and profile images. Use existing pages well. Researchers should ensure their institution page is up to date and link out to their other pages and content. Using email signatures to highlight web pages, blogs or other accounts is also a useful way to promote their content. Choose keywords carefully. Researchers need to appeal to as broad an audience as possible, so using heavy jargon in posts or descriptions may limit the search criteria the content falls under. Set time aside for updating and assessing the effectiveness of the content. Researchers should know how to find information on how many visits to pages are occurring, what links are being clicked, and what content is being downloaded.

Of equal importance to creating an online profile is maintaining and nurturing that profile, which also requires insight into ICT tools and platforms. It can be particularly beneficial for doctoral researchers to track what others are saying about their research and the research in their field by following other researchers or funding groups and participating in public debates through these channels. This gives feedback and insight into the thoughts, ideas, and arguments of the peer-reviewing community and can facilitate critical thinking. Learning from, and debating with, other researchers from around the world can greatly enhance one’s research reputation. In this way, the newer researcher can start to become integrated into the virtual research community in a way that is analogous to becoming a part of the physical face-to-face researcher community through conference attendance. However, as these communities can be combative at times, negatively affecting doctoral researchers (Bennett & Folley, 2014), support and training for effectively 216

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engaging and, when necessary, disengaging with lines of argument can be vital to the wellbeing of newer researchers. Therefore, it is important to provide space, time and support for doctoral researchers to explore social media platforms such as LinkedIn, Twitter, WordPress, and to discuss the various strengths and weaknesses of each with trusted support providers. Demonstrating examples of best practice in utilising different tools will help these researchers of tomorrow not only to become proficient users of this ICT now, but also to evaluate the benefits of future ICT developments as they happen.

Collaboration, Public Engagement and Impact As stated previously, one of the benefits of having a strong personal brand is the increased visibility of a researcher’s areas of strength and interests. This could encourage potential research collaboration, increase the public’s trust, and encourage public participation in research when necessary. ICT gives the researcher international visibility. Additionally, it provides tools to facilitate future partnerships and collaborations in efficient and cost-effective ways. Document sharing, video conferencing, and project management software are all examples of tools which have now become commonplace for academics involved in research collaborations. Doctoral researchers should be aware of these tools and become experienced users during their doctorate, so that they are career ready when they graduate. These skills are particularly important for newly-graduated researchers as they make it easier to establish new partnerships for first grants, or collaborative projects with established experts in their field, which can greatly enhance the chances of receiving funding. Supporting researchers to develop these skills early means they are ready to take opportunities when they arise. A new lecturer at the authors’ institution had this advice for doctoral researchers: …find the right collaborators. I ended up going for a Global Challenges call, so revised my impact statements, reached out to new collaborators and was ultimately successful. ICT skills can provide researchers with both competitive advantage and the ability to involve the public with their research and disseminate their research successfully beyond academia to sectors that are able to turn research into innovation. This leads to research impact, which is so critical to success in the new academic context. The need for researchers of tomorrow to ensure that their research will benefit the common good is unlikely to diminish with the complex global challenges facing the world. Developing a wide range of skills, including ICT skills, will enable researchers to reach outside academia to build inter-sectorial collaborations, influence policy, and 217

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engage in public discussions and debates, all of which will be critical for our next generation of researchers. As these skills are complex and rapidly evolving, experts in ICT, researcher developers, academics, and experts in public engagement need to work together to support the skills development of newer researchers. Researcher Development and institutional departments such as Information Technology, Technology Enhanced Learning, Marketing, and the Press Office can work together with academic supervisors to ensure researchers are appropriately guided on how to address their public. Together, these experts can help empower researchers to develop their skills so that the new-generation researchers are indeed ready to drive our knowledge economies, and maximise the societal benefit of research.

UTILISING ICT TO PROVIDE INCLUSIVE RESEARCHER DEVELOPMENT As governments across the world continue to pin their hopes of future researchers to drive the knowledge economy and solve global challenges, the number of doctoral researchers being recruited year-on-year is increasing. This necessarily has led to increased diversity of the doctoral researcher population and has dramatically expanded the number of doctoral researchers undertaking part-time study or those conducting some, or even most, of their doctoral study away from their university campus. Increasingly, people from professions are returning to university to complete a doctorate that will have an impact on their professional field. Often these people undertake their doctorate on a part-time basis alongside their career and family life, reducing the time they can be physically present on campus. Furthermore, the increase in international doctoral researchers, together with the increase in truly global research, means that many doctoral researchers conduct their research at a distance from their university. Since it is the responsibility of a university to provide support and skills development for all doctoral researchers, regardless of the mode of study or research location, ICT is an essential enabler. However, merely transferring face-to-face workshops or courses to a virtual platform rarely is optimal or even sufficient. Further, pedagogy surrounding online and distance learning is primarily in the context of taught programmes. These programmes tend to have more structure, can be more proactive in their approach to learning and tasks, and have compulsory components with regards to individual and group assessment. In this section the challenges and, importantly, the opportunities encountered when using ICT to provide skills training for part-time and distance researchers will be explored. A particular challenge to providing ICT-based researcher development is that a critical aspect of becoming a researcher is learnt through becoming part of a 218

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community of fellow researchers (Becher & Trowler, 1989; Tight, 2015). This is important, not only because it makes the doctoral journey less isolating and more enjoyable, but also because it is essential for developing the ability to discuss, debate, and work with other researchers (Denicolo, Reeves, & Duke, 2017). Therefore, the researcher community is essential to the development of researcher skills. It is a challenge for any university to provide an inclusive and positive research culture that inspires the creation of these types of communities. It certainly adds to the challenge when researchers are frequently absent from campus and may well be in different time zones. However, ICT can be used to develop and host a variety of different virtual researcher communities, some supportive, some focused on specific research-related activities or skills. A resource as simple as a closed Facebook site can provide researchers with space for discussion and debate, as can flexibility about scheduling one-to-one skills coaching sessions, as the doctoral researcher states here: Grateful to have a Facebook site for those of us not much on campus so you feel you have a little community of your own and for fun things like visitors who answer questions (or stimulate debate!). Also really useful to have things like one-to-one sessions which can be arranged to your own timetable.

Providing One-To-One Flexible, Individualised Support The authors’ central ethos in providing training and support for doctoral researchers is one of inclusivity. Therefore, one-to-one support is offered for all doctoral researchers. This support could be to cover workshops otherwise offered face-to-face, discussion of specific development issues, or more personalised support on a particular skill or challenge. Embedded in the authors’ university regulations is a requirement for all doctoral researchers to undertake three compulsory workshops, one at the beginning of their research, one nine months in and one three months from the hand-in of the final thesis. To require a distance or part-time researcher to attend such workshops that take place on campus and during the working day is exclusionary; therefore, all part-time and distance students are offered the opportunity to take the workshop over a video conference at a time that suits the student. One-to-one support sessions can also be focused on skills development such as time and project management, upward management, and presentation skills. This can take the form of a discussion or practice, providing critical and supportive feedback on topics such as academic writing skills or mock Viva voce exams. These sessions can be of particularly great importance to those struggling with specific challenges, such as personal and intrapersonal skill development, issues with supervision, imposter syndrome, and workload. These types of conversations generally do not work well in a group, and so offering one-to-one support for all researchers is vital. 219

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Researchers who are not able to visit campus are offered this support over platforms such as Skype, Google Hangouts, and Facebook Messenger. The choice of what ICT to use is pragmatic, often based primarily on ease of use and availability, as the researcher we are meeting with could be anywhere in the world. Often this requires multiple platforms and avenues of engagement, as well as an openness and flexibility on the part of the support provider to experiment with different platforms in order to best support researchers with specific requirements. It is recognised that virtual support may not be “equal” to face-to-face; however, the aim of this support is that it is equitable. With this in mind, this approach does allow the researchers to feel part of the community as they can access support similar to that accessed by their full time and on-campus counterparts. The researchers who access this one-to-one support are on a spectrum of “offcampusness”. They may be away from campus for a few weeks or months on fieldwork; they may live a distance from campus and come onto campus infrequently; or they may be part-time and able only to access support around their work. Because of the nature of the users of this service, it is important to provide flexibility in terms of times at which the service can be accessed as some people will need to engage outside of normal working hours, before work, in the evenings and at weekends owing to their work commitments or time-zone. At first, this thought may seem daunting with concerns of strain on a workforce that may cause it to seem impractical. However, in the authors’ experience, the uptake of these sessions is not as onerous as may be imagined. This makes the workload manageable for the team providing the support as long as a flexible mode of working is allowed. From a management perspective, embracing this dynamic approach to how the team works to meet the needs of all researchers can actually have specific benefits for staff. It can allow individual staff members to create a unique working pattern that may better suit them than strict nine-to-five working patterns. That said, even being quite open and flexible to all requests, the overall out-of-hours burden is fairly low and, in the experience of this case study, has not required any team member to work consistently out of normal business hours to provide inclusive support for a researcher population of over 1,500. Critically, the impact on individual researchers accessing this support is huge. Researchers comment that the one-to-one attention prevents them from feeling isolated and allows them to ask the questions they feel unable to ask their supervisory team. On a practical level, this enables them to attend workshops without taking days off work or travelling long distances, maximising their time so that they are more able to focus on their research. Although I’m half a world away, I don’t feel that far away.

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Looking back at my PhD, one of the biggest regrets I have was that I couldn’t live closer to University. Due to my partner’s job, I wasn’t able to relocate to Guildford from Scotland. While I was on campus a couple days a week during term-time in order to teach (thanks to the Sleeper Train), residing in Scotland the rest of the year meant I should have missed out on a lot of developmental activities. While I couldn’t make it to the weekly Researcher Cafes, the team often went out of their way to make sure I could participate in other events. From Skype sessions, to having additional options for workshop dates, to virtual writing retreats, to just being at the end of the phone to answer questions, the team made me feel like part of a research community – even when I couldn’t be on campus. Plus, the Part Time and Distance RDP Facebook page kept us all in touch, helping forge connections that would have never come about otherwise. Using the one-to-one model rather than solely relying on recorded workshops or online teaching material provides the personal touch, actively engaging researchers in a bespoke manner to address their individual needs. There has long been evidence that this one-to-one coaching style is more effective than passive instruction (Bloom, 1984). The impact of this personal approach is evident from the researchers’ feedback to this type of support. Great to know that there is always somebody there at the end of the line to help, guide, listen. They bring caring and expertise advice to the PGR experience ... and always with a smile, which is an added bonus! They make me feel that little bit closer to the hub-bub of things going on in the PGR community, despite the kilometres in-between.

Preparing Virtual Online Courses for Researchers In the authors’ experience, creating and delivering online courses is more complex, resource-heavy and time-consuming than face-to-face courses. This is counter to the intuitive (or wishful) belief of many university managers, as they propose online courses as a way to maximise reach and minimise resource. Sadly, while the former is true and of critical importance, the latter is far from reality. One key reason is that the pedagogy of effective online courses must be qualitatively different from that of face-to-face courses (Anderson & Dron, 2011; Passey, 2013). Furthermore, the classic tools of student engagement possessed by skilled classroom lecturers do not work virtually, making the process of sustaining meaningful participation in itself more 221

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time-consuming and resource-intensive. Throughout the past decade, the pedagogy of online learning has developed, and there is growing literature to support those developing and facilitating online learning (Anderson, 2008; Magruder & Kumar, 2018; Sunal, Sunal, Odell, & Sundberg, 2003). However, this is almost entirely within the context of taught programmes. In the context of researcher development-based virtual courses, these challenges are magnified due to their unstructured nature, the general lack of required engagement in courses, and the individualised nature of the degree programme. The authors have tried various approaches to overcome these challenges. Described below are two specific cases: 23 Things for Research and AcWriMo (Academic Writing Month). These cases are based on the experience of one institution. 23 Things for Research was developed in 2006 by the Public Library of Charlotte and Mecklenburg (Blowers, 2006). Adapted for researchers by the Bodleian library team in Oxford (McCarthy, Lindsay and Eyre, 2012) under a creative commons licence, it is open to all institutions. The course takes the form of a series of blog posts which introduce the participant to a different online or digital tool that will either help the progress of their research, support the dissemination of their research, or support the researcher’s career development utilising a pedagogy that is aligned well with the content of the course. There are 23 blog posts (Things), ranging from reference management software to crowdsourcing. The Things are updated each time a new technology becomes popular. The programme is delivered via a blog open to all, through which each week a few “Things” are posted that the researchers can engage with in their own time. Each Thing has a task that the researcher is expected to read and carry out. They are also expected to post a reflective blog about their experience of accomplishing the Thing. This allows researchers scattered throughout the world to read and comment on each other’s blogs, creating a virtual community. The combination of reflective blog writing, and discussion and dialogue with peers supports the development of a wide range of skills, despite each Thing representing a relatively simple task, for example, “Build a LinkedIn profile”. The scheme is extremely popular. One of the benefits of it is that the greater the number of researchers participating in it, the better the programme due to more interaction and comments. As it is a virtual programme, the normal challenges associated with the running of physical programmes, such as limited room seating capacity, do not exist. However, staff time involved in supporting such a course should not be underestimated. The course does require active facilitation, updating content and commenting on posts, particularly at the beginning to achieve the necessary participant engagement. This effort has its rewards, however, as feedback on the programme is largely positive with researchers continuing their blogs after the course has finished. 222

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It’s really making me think about my online presence and I’ve started tweeting more, blogged about giving my first lecture and have other blog ideas coming up… so it’s definitely useful:-) I would like to say that not only this blog won’t be closed but it also will be updated as often as possible. I have already written a few posts regarding one of my main engineering passions (i.e. Finite Element Analysis), trying to explain some of its features in a easy way so that people who are new can understand its basis. However, from now on, I will try to include other posts related to engineering in general…. In addition, I have also confirmed the collaboration of a few other engineers who are well disposed to write some posts from time to time. Great news, right? The success of this programme may be in part due to the Things being ICTbased, focused primarily on helping participants to utilise ICT for their research and to raise their professional profile. This means that the delivery method of the course is very closely aligned with the learning objectives of the course (Biggs, 1996). Researchers are able to learn to utilise a variety of different ICT-related tools, whilst being supported through ICT use. This creates an environment where researchers become increasingly comfortable and confident in utilising ICT skills to develop their thoughts and promote their research. In fact, this programme allows researchers to extend their reach beyond academia, creating avenues for research impact. This is an example of how ICT skills development, of the kind described in the previous section, can utilise ICT platforms to their best advantage. As their skills are developed, the participants become confident in their ability to communicate their research and build a virtual researcher community through ICT. Through participation in this course, doctoral researchers have found that becoming a more outward facing researcher has benefits they may not have foreseen. Indeed, a few participants have been invited to write for other blogs, journals, and publications and offered jobs as a result of their participation. Something I’d like to remark here is how glad I am to have finally a LinkedIn and Twitter profile. The Twitter account allows me to keep myself updated on scientific news, read interesting posts or articles and share opinions and thoughts, on the other hand LinkedIn is something I discovered to be more powerful than I thought. It has been less than two months from my first appearance on this social network and I’ve already received job offers. Even if I’m not looking for a job at the moment, I am very surprised it works so well, and it’s helping me to be known and to show who I am and what I have done to potential employers.

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Although this programme is rated highly positively, it is not without challenges, beyond the staff resource issue mentioned above. The first is that although it is easy for researchers to engage in, it is also easy for them not to; thus, the attrition rate is higher than in a face-to-face workshop. The second, admittedly less frequent, issue is that a small number of participants have dropped out early due to having difficulties setting up a blog in the first few steps of the programme. In a virtual context, it can be harder to identify those struggling and to support them in a time frame that prevents frustration. Proactive facilitation and early checks to ensure participants are not experiencing technical problems can help. Despite these challenges, based on the feedback received and the growing popularity of the programme, the authors conclude that the positive impact to our researchers is worth the effort and resource. This is particularly true as it enhances such a wide range of transferable skills, combining ICT skills with communication skills, writing skills, and reflective practice. Furthermore, the virtual communities this course has built have proved to be enduring and have translated into a virtual community culture within our institution.

Building Virtual Writing Communities As researcher communities are vital to support the development of a variety of researcher skills, particularly those involving critical thinking, argument development and collaboration (Duke & Denicolo, 2017), the authors have prioritised initiatives that enable doctoral researchers working away from campus to use their ICT skills to engage in virtual communities. These communities not only serve to develop researcher skills, but combat isolation that can lead to problems with doctoral student wellbeing. The building of virtual research communities helps to overcome the isolation of research, which can be particularly important in the “writing” stage of the doctorate. Academic Writing Month (AcWriMo) is the brainchild of Charlotte Frost (Frost, 2011) From PhD to published. Charlotte adapted the National Novel Writing Month (Faulkner, 2017) to cater for the academic writing community. Now, November is writing month for thousands of people across the world, an enormous writing community. Institutions adopt the month and put on events for their own academic population. At the author’s institution, a mixture of in-person events, online goal creating and declaring, along with daily blogs about writing from the teaching fellow in academic writing skills and senior members of staff have been facilitated to involve the research community in AcWriMo. The month ends with a virtual writing retreat, addressed in the next case study. The strength of this scheme is that the community is united in their writing goals, irrespective of whether they are on campus or based elsewhere. It brings the researchers together and gets everyone writing. 224

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AcWriMo is an international example of a massive writing community; however, this is only a once-a-year event, and researchers are writing throughout the year, often in relative isolation. To combat this, many different institutions have utilised the writing retreat model, bringing together writers to share experience and communally work on their individual writing (Murray & Newton, 2009; Swaggerty, Atkinson, Faulconer, & Griffith, 2011). Writers get together with no distractions and write for an allotted period of time. However, this requires participants to be able to physically come together, which is not always feasible. Virtual writing retreats are different in that the participants take part from their own desk, utilising ICT to create a writing community. All they need is an internet connection. The retreats are quite labour-intensive, although mainly for the time period of the retreat once materials have been created. The virtual retreat starts with participants creating goals for their two days of writing and sending these to their writing mentors via email. Writing mentors work with participants before the retreat to hone these goals into specific and measurable targets against which progress can be measured. A specific timetable is created for the two days which includes times at which participants check in with their writing mentors to discuss progress. The first day starts off with a virtual PowerPoint presentation on getting started with writing, covering some common challenges such as internet distractions as well as introducing productivity techniques, including the Pomodoro technique (Cirillo, 2016). During the first day, the writers work on their goals, and the writing mentors have Skype calls or instant messaging with the writers to check how they are progressing. At the end of the first day, a second virtual PowerPoint is sent with the whys and so whats of writing, touching on the subject of critical thinking and impact of research to a variety of audiences. Amended targets are requested for day two and check-in times are agreed upon. On the second day, participants focus on completing targets, and a virtual PowerPoint themed “when to stop writing” is sent. The feedback from the participants is consistently positive, with emphasis on the virtual one-to-one support. The retreat gave me the chance to experience the stimulating feeling of a learning environment, even though I was at home. The meetings during the day to check on the progress of the work were very useful; a deadline, no matter how important, is always a good incentive. I got good advice on how to proceed with my work, and it was nice to have small chats about my project. The face/to /face skype meeting at the start was important to me in this respect and useful. The worst aspect of working independently is the lack of shared experiences which make one feel part of a group and also being able to judge one’s progress. Talking over the problems and blocks actually helped. 225

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Overall, the participants attribute their progress in writing to the retreat and the input from the writing mentors; however, it is the ICT that enables researchers to take part in these writing communities with individualised, bespoke writing support from anywhere in the world.

ESTABLISHING AN E-MENTORING PROGRAMME The individualised support provided by writing mentors shows the power of mentorship in the context of one particular skill area. However, mentoring can support a wide variety of skill and development needs. Mentoring is proven to be effective in higher education (Crisp & Cruz, 2009) and is used in staff and researcher development. Here two forms of e-mentoring are described which can support key transitions within the doctorate context: transition into a doctorate and transition to employment. The scheme for students transitioning into a doctorate matches new PhD researchers with later stage PhD researchers. The focus of e-mentoring is not necessarily on the research area, but on making a positive start in the PhD process, settling into the university, and having questions answered that new researchers may not feel comfortable asking their supervisors. The mentors are trained in some basic mentoring and coaching techniques and given guidance on all of the services available to the students to help with the settling-in period. This scheme is valuable for new researchers who are part-time or working at a distance, as they can be matched with mentors in the same situation, utilising ICT to connect both of these researchers virtually. This allows the sharing of experience and the passing on of techniques to manage the process. It also helps new researchers to engage with technology early, learning how to use ICT to stay connected with fellow researchers and the university campus. ICT also supports mentoring to help the student transition into employment as it allows researchers to connect with mentors from different sectors anywhere in the world, matching researchers with people working outside of academia by primarily utilising the university’s doctoral alumni. The purpose of the scheme is to allow researchers to have conversations about their next steps, get advice on their current work, and find out how their research is viewed in other settings. The very nature of the scheme befits e-mentoring because the mentors are spread throughout the world, and it is often not possible for the mentoring pairs to meet in person. ICT enables the students to interact with mentors from a greater variety of careers and locations than face-to-face mentoring could provide. This is particularly important at doctoral level since the job market is truly global. Bespoke mentor-mentee pairing is key to the authors’ e-mentoring schemes. Mentees are asked what they would like to gain from the mentoring programme and this is matched specifically with the skills the mentor has identified as having. 226

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Furthermore, the pair’s preferred methods of communicating are identified and supported. Throughout the process, email is used to check in on the pair and ensure all is working according to expectations. The feedback indicates that both mentees and mentors value the experience. [Through the mentoring I] knew better what to expect within the PhD [and] felt more relaxed. As I do research remotely, I wanted to ask questions that I would have posed to other PhD candidates had I seen them regularly. I wanted to know some details about the scope of supervision that were not made clear to me….. [As a result] I had a more effective PhD monthly review that brought me closer on track with my lit review and I was able to relax and make progress. I have learnt to look at my results in a more simplistic manner and being able to see the “bigger picture” of my work. Applying for jobs even when I don’t meet 100% of the criteria and applying for jobs that I felt were to outside of my field. I’ve been a lot more confident in discussions with my supervisors, trying to get across my point of view. I’ve also actually kept up my to-do method…… I just feel more relaxed and confident in my pgr role. And I’ve had a rethink of what I plan on doing in 3 years’ time when I finish. These examples show how e-mentoring can support key transitions within the doctoral journey. ICT enables these key connections between researchers and those who can support them, regardless of where in the world their research takes them, providing a dynamic and always accessible researcher community.

Maintaining Virtual Researcher Communities As mentioned previously, conducting research can be an isolating experience and even more so if the researcher is based off campus. ICT, as demonstrated above, can enable the building of researcher communities across distances. The network built by researchers during their time at the institution is important for a number of reasons. Firstly, when a researcher is new to the institution, the community around them can help them to settle in, to become accustomed to what is expected of them, and to familiarise them with the norms and values of the department or research group they are in. Secondly, as a group, researchers can discuss ideas, questions 227

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and plans, and gain valuable peer feedback. Thirdly, the community of researchers can offer support when the research is difficult or not going as planned. Finally, and probably most importantly, the community of researchers can celebrate the achievements of each researcher in the group and help with the progression of that researcher. When the researcher moves on, this network may form valuable collaborations or support for future activities. However, building and maintaining this community can be especially challenging for part-time researchers and those who undertake some or all of their research away from campus. Staff engagement is critically important and is key to building a true researcher community. Those away from campus greatly value the input of academic and professional staff (Dowling & Wilson, 2017). Virtual events and expert sessions are highly popular with part-time and distance researchers. The key to these sessions is virtual discussion, bringing university expertise to the off-campus researchers. The discussions are always fast and furious as the researchers do not often gain access to these experts, and so they greatly value such opportunities for interaction. This is truly an example of ICT bringing the campus to the researcher.

RECOMMENDATIONS AND CONCLUSIONS The authors of this chapter have been working on embedding transferable skills within the UK doctorate for over a decade. We have been utilising ICT to increase inclusivity for the same length of time. While some initiatives have worked, others have failed. In this chapter the authors have shared what has worked in the context of their institution with a diverse, new-generation researcher population of around 1,500 representing disciplines from Dance to Engineering. This experience has led to the following recommendations, which are summarised in the sentiment that embracing ICT in Researcher Development means not being afraid of being unconventional: 1. A key area of ICT skill development must be to support doctoral candidates to become more outward-facing researchers. Researcher Development should not just focus on skills needed to succeed in graduating with a doctorate, but should prepare doctoral graduates for future careers and opportunities. This means acknowledging that most doctoral graduates find careers outside academia. Even if they are inside academia, they will need to function as an outward-facing researcher to thrive. Therefore, mastering these outward-facing ICT skills is essential to all doctoral researchers. 2. The driver for ICT-based researcher development should be inclusivity, not cost savings. High quality ICT-based researcher development is often more resource-heavy than face-to-face models. However, if inclusivity is valued, 228

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the benefit in terms of supporting a diverse range of doctoral researchers is worth the cost. This may be a hard pill for university finance departments to swallow, but enhancing student experience rarely comes with a price cut. 3. Flexible working is key. If doctoral researchers are to be supported across time zones, asynchronous ICT is an option, but sometimes they do need real-time contact. Managers should encourage staff to work flexibly in a way that works best for them. This provides both staff and student benefits. This is a personal story from the author team. When one of my team told me she was moving quite far away because of her husband’s job, but wanted to remain working in our department, as her manager, I agreed to try and find a way to make it work. My manager wasn’t sure how we could make this work, particular as this role is very student-facing. However, it was an incredible success. I am convinced that this flexible arrangement has greatly enhanced our programme. We ended up with a model where she works away half the time and comes back half the time. Whilst she is away, she leads on our ICT-based researcher development support. Her personal understanding of working remotely has strengthened our provision more than I could have anticipated. 4. ICT skills development is the easiest type of researcher development to do virtually. The learning objectives and activities work naturally in the virtual environment. It also provides researchers with the skills (and community) they need to be able to fully engage in other ICT-based skills training. If anyone is at the beginning of their ICT-based researcher development journey, the authors recommend they start with an ICT skills development course, such as 23 Things. The authors are happy to share this material and experience with those interested. 5. Pragmatism is important. Some ICT purists may think that every new initiative is equally important or that all people should be using a particular form of technology. However, it is important when working with doctoral researchers across all disciplines and with varying degrees of comfort with technology in general to realise that one size does not fit all. People need the freedom to pick what works best for them, and researcher support staff must adapt. The world of research continues to change at a rapid pace, challenging our doctoral researchers to push boundaries of discipline, country, and sector. Those responsible for supporting the development of researcher skills must be aware of the new challenges their researchers are facing, as well as the tremendous opportunities available to these doctoral graduates. Now, more than ever, it is important for those supporting researcher development to work closely with our colleagues who specialise 229

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in ICT, both technically and pedagogically, to develop innovative training that utilises ICT, and also to ensure newer researchers have the skills to engage with the whole range of ICT tools. It is particularly important not simply to focus on developing the ICT skills needed to do the research currently undertaken, but to support the development of those complex ICT skills necessary for the more outward-facing researcher to succeed in the varied career trajectories now available to them. The academic community of support for researchers must be agile, utilising a wide range of ICT that creates an environment where doctoral researchers across the world can be fully supported and made to feel part of a researcher community. In keeping with this value of community, the authors welcome correspondence and are happy to share our community and our material with our colleagues.

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Frost, C. (2011). How I went from PhD to Published: Part I. PhD2Published. Retrieved from http://www.phd2published.com/2011/02/17/how-i-went-from-phdto-published-part-i/ Johnston, L. (2006). Software and method: Reflections on teaching and using QSR NVivo in doctoral research. International Journal of Social Research Methodology, 9(5), 379–391. doi:10.1080/13645570600659433 Kahan, D. (2017, February). The psychology of fake news. Paper presented at the 2017 American Association for the Advancement of Science Annual Meeting. Kemp, D. A., & Kemp, D. (1999). Knowledge and innovation: A policy statement on research and research training. Canberra, Australia: Department of Education, Training and Youth Affairs. Magruder, O., & Kumar, S. (2018) e-Learning Instruction: Identifying and Developing the Competencies of Online Instructors. In Leading and Managing e-Learning. Educational Communications and Technology: Issues and Innovations. Springer. McCarthy, L., Lindsay, K., & Eyre, S. (2012). All about the 23 things. 23 things for research. Available at: http://blogs.bodleian.ox.ac.uk/23things/all-about-23-things/ Murray, R., & Newton, M. (2009). Writing retreat as structured intervention: Margin or mainstream? Higher Education Research & Development, 28(5), 541–553. doi:10.1080/07294360903154126 Passey, D. (2013). Inclusive technology enhanced learning: Overcoming cognitive, physical, emotional, and geographic challenges. Routledge. doi:10.4324/9780203073742 Poliakoff, E., & Webb, T. L. (2007). What factors predict scientists’ intentions to participate in public engagement of science activities? Science Communication, 29(2), 242–263. doi:10.1177/1075547007308009 Roberts, G. G. (2002). SET for success: The supply of people with science, technology, engineering and mathematics skills; The report of Sir Gareth Roberts’ review. London: HM Treasury. Sim, K. N. (2016). An investigation into the way PhD students utilise ICT to support their doctoral research process (Thesis, Doctor of Philosophy). University of Otago. Retrieved from http://hdl.handle.net/10523/6263 Sim, K. N., & Stein, S. (2016). Reaching the unreached: De-mystifying the role of ICT in the process of doctoral research. Research in Learning Technology, 24(1), 30717. doi:10.3402/rlt.v24.30717 232

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Sunal, D. W., Sunal, C. S., Odell, M. R., & Sundberg, C. A. (2003). Supported best practices for developing online learning. Journal of Interactive Online Learning, 2(1), 1–40. Swaggerty, E., Atkinson, T., Faulconer, J., & Griffith, R. (2011). Academic writing retreat: A time for rejuvenated and focused writing. Journal of Faculty Development, 25(1), 5–11. Thompson, L. A., Dawson, K., Ferdig, R., Black, E. W., Boyer, J., Coutts, J., & Black, N. P. (2008). The intersection of online social networking with medical professionalism. Journal of General Internal Medicine, 23(7), 954–957. doi:10.100711606-0080538-8 PMID:18612723 Tight, M. (2015). Theory development and application in higher education research: Tribes and territories. Higher Education Policy, 28(3), 277–293. doi:10.1057/ hep.2014.11 Verma, N., Fleischmann, K. R., & Koltai, K. S. (2017). Human values and trust in scientific journals, the mainstream media and fake news. Proceedings of the Association for Information Science and Technology, 54(1), 426–435. doi:10.1002/ pra2.2017.14505401046 Vitae. (2010). The Researcher Development Framework. Careers Research and Advisory Centre (CRAC) Limited. Retrieved from https://www.vitae.ac.uk/vitaepublications/rdf-related/researcher-development-framework-rdf-vitae.pdf

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PEOPLE EFFECTING CHANGE IN THE DOCTORATE IN, THROUGH AND ABOUT TECHNOLOGICAL ACTIVITY: AN OVERVIEW OF ICT ROLES IN DOCTORAL EDUCATION Many changes and developments in doctoral education around the world are reflected in the variety of examples of country systems, programme arrangements, supervision approaches, and strategies presented in this collection of papers. Despite variety, the common thread linking all the cases illustrated in this volume is the shared aspiration of those behind the cases to develop, support, and enhance doctoral students’ research abilities and capabilities through, with, and about the clever incorporation of technology. In common language, the word “technology” is used in a number of ways, referring to: human-made products or artefacts (e.g., Gardner, Penna, & Brass, 1990); processes, steps and methods (e.g., McGinn, 1991); areas of work or fields of study (e.g., Gardner, 1994); a form of cultural activity; and even the entire enterprise of a society (e.g., Gardner, 1994; McGinn, 1991). Across these meanings emerge attributes of need, opportunity, and improvement. Technological artefacts, systems, processes, and activities are developed and/or undertaken by people. Essentially, these outcomes and activities meet some sort of human purpose or need to improve the quality of life or the situation. Jones and Carr (1993) summarised the essence of these ideas well, when they wrote: Technology is a purposeful activity aimed at meeting needs and opportunities through the development of products, systems or environments. It takes place within specific contexts and constraints, and is influenced by value judgements. (p. 2) In the Preface, Oliver notes that, “Doctoral education has changed fundamentally, at exactly the same time that it has remained unchanged in many important aspects” (p. 7, this book); and the chapters in this book demonstrate this insightful observation well. The many important aspects of doctoral education that remain

Conclusion

unchanged include, for example, the need for opportunities to learn about core research processes, the ability to design and implement a manageable project having impact and significance, and the development and utilisation of skills to disseminate research outcomes. Not only are the formalities of getting the degree and producing a thesis important, but also dissemination and application of ideas that benefit the wider research community. Over the past 50 years or so the evolution of academic practice, including doctoral education, has been dramatically affected by changes and developments in technologies that govern, determine, and influence almost all aspects of life. The ever-present need/desire to improve doctoral student experience and increase effectiveness and efficiency of research and research processes point to aspirations to improve quality of life for researchers, those learning to be researchers, and those who benefit from research. This need/desire is inherently human, and inherently technological. Integral to all human action, including technological action, is decision-making and judgement; the consequences of which can be both positive and negative. Many wonderful, positive aspects of ICTs were discussed by the authors in this volume. Negative, darker results of technological decision-making were also highlighted, for example, as described by Endong (Chapter 2) concerning the “veritable plague” that is plagiarism. Ironically, the internet, along with sophisticated digital applications - the same technologies being used to positive advantage to support research skill development - are being used also to facilitate plagiarism. While painting a less dismal picture, Johnston and Berry (Chapter 4) discusses a similar yet different example, of the possibilities ahead for big data and doctoral research. Despite the potential for new and exciting opportunities for research and doctoral research education that access to big data can bring, Johnston also notes that there is much to be learned concerning ethical and appropriate use and management of big data. Like all areas of education, the world of the doctorate is evolving. Hopkins, Henslee and Duke (Chapter 10) point out in that while making one’s stand in the research world has always been about having an impact on thought and practice - one indicator of impact being citation levels – the use of online technologies increases the likelihood that research outputs are seen and that impact is acknowledged. Whether limited ability to use online technologies in the form of social media to “push out” research activity is really the “obstacle” to success that the authors say it is, the place of online facilities to aid dissemination and wider community engagement needs to be understood by doctoral students. Perhaps even more importantly, this chapter highlights that knowing oneself in order to make reasoned judgements about the advantages of one online application over another for disseminating research and engaging with community - whether it is a social media tool or not - is essential. While being “outward-facing” is not a new activity where promoting one’s research outputs is concerned, it is the way researchers are enacting that skill, namely 235

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through the facility provided by ICTs, that indicates the changing form of research dissemination. As the authors of Chapter 10 argue, how to engage in social media and other online community-engagement applications and platforms to promote one’s research is now an essential component of any doctoral education. Posing the question about what is going on when we teach online was the focus of Rios, Viruru and Ozfidan (Chapter 8), through their discussion of an online EdD programme. Garrison, Anderson & Archer’s (2000) online Community of Inquiry framework was used to explore student experiences of the EdD programme and its various elements. This investigation highlighted the connection among humans (learners and teachers/supervisors), task/goals to be achieved (studying, learning, teaching/supervising, researching) and technological artefacts (courses, modes, and processes of interaction/communication, tools). Each element, being integral to the (designed) technological process within the EdD programme described, served to solve the educational need to provide a related set of opportunities and experiences for doctoral research student cohorts. In the envisioned smoothlyrunning programme, the authors suggest the possibility of the technologies becoming invisible, even though they enable and contribute to the creation of an environment where essential elements, including instructors, learners, and research processes, become highly visible. Intentions with any programme may not be achieved in the way it is envisioned however, when those contributing to the programme demonstrate behaviours that are counter to expectations. The cautionary note – don’t be a ghost! – illustrates this well. Underpinning any successful research is a well-designed project. Developing the skills and knowledge to explore and articulate the rationale for research, make explicit the paradigm within which the research is taking place, and design and plan the project itself, are some of the essential elements of doctoral education. Enabling learning through information, guidance and support are the “Idea Puzzle Software” described by Morais and Brailsford (Chapter 3), and a structured range of topics and support activities to enhance the doctoral student experience, made accessible through institutional-based platforms described by Rowland (Chapter 5), James (Chapter 6), and Stokes, Keegan, Brown, and James (Chapter 7). The Idea Puzzle Software focusses sharply on research design; educating the novice researcher about research design, through research project design. Chapters 5, 6, and 7, while set in different contexts, are all about the development of institutional platforms for broader doctoral education support. Like the focus of the Idea Puzzle Software, they offer guidance for research design education, but in addition, provide resources to scaffold doctoral students’ learning and studies more broadly. These three situations illustrate the many factors that must be taken into account when solving typical problems students encounter during the course of doctoral study, with online information and opportunities to interact, communicate and engage being presented 236

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as useful and workable solutions. Chapter 6, in particular, demonstrates how the integration of new ways to use technology within the context is disrupting practice. The changes are much deeper than at the practice level though; understandings about what it means to work with staff, students, and the institution as a whole are being reconceptualised. Solving the problem of “how to best meet the needs of those who have less social and financial capital and how to do that at scale” involved much more than a simple introduction of ICTs. All four cases – Chapters 3, 5, 6, and 7 – highlight the linked impact on institution, people, tasks, and tools; explaining judgements and decision-making within action. The introduction of ICTs in the form of platforms and apps are resulting in qualitatively different levels of engagement among students and supervisors and new ways of thinking about the doctoral experience that were not possible before. Drawing on a range of ideas from documented strategies for facilitating and generating discussion forums, Hartnett and Rawlins (Chapter 1) report the development of a blended model that suited specific needs of their specific context. Student progress and academic professional development along with administrative requirements were tracked during the development of the model. This innovation aimed to reduce what the authors called the “dislocation effect” that doctoral students often experience. Likewise, with attention being paid to the role in learning played by interaction and engagement, Kumar, Johnson, Dogan, and Coe (Chapter 9) present a framework for e-mentoring of doctoral students. The framework highlights the advantages of supervising online, mapping the place of the student and supervisor, and their connections with groups and institution. In many ways, in this chapter, the supervisor’s significant place in doctoral education remains unchanged. However, foregrounding this aspect of doctoral processes by casting the supervisor as e-moderator demonstrates how supervision is changing. The familiar, yet crucial, supervisor role is being modified and altered by the facilities the internet and web-based applications provide. In summary, the chapters in this volume explore purposeful technological activities aimed to create systems that enhance the quality of learning for doctoral students. The projects reported involved the use of knowledge and skills, judgements and decisions about technology design, development, and implementation. The designerly thinking engaged in by those involved in the development of the tools, systems, products, and environments highlighted how technological activity incorporates dynamic and intertwined aspects of context and situation. Knowledge and understanding of doctoral education needs are paramount, as is knowledge and understanding of the context in which the doctoral education occurs. Key to making good use of technologies within and for doctoral education involves balancing needs, resources, expectations, and usability, to achieve aims of solving problems and/or capitalising on opportunities. Pressures on those who become users of resultant technologies must bring together their current and developing knowledge of doctoral research with their current and developing knowledge of the online world. 237

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This book thus illustrates that doctoral education is happening in, through, and about technology. Rios et al (Chapter 8) point to Roth’s (2007) words about a blind person’s cane being “an extension of the hand and arm, which are already tools the body gave itself realising the capacity of reaching out” (p. 676). So too, is technology to doctoral education. Technology is an integral part of learning about and undertaking research, and vice versa. Technology is helping doctoral education to remain the same, yet at the same time is influencing it, determining it, and changing it. Sarah Stein University of Otago, New Zealand Sarah Stein is the Director, Distance Learning at the University of Otago, New Zealand. In this strategic role within the University, Dr Stein works in collaboration with the Deputy Vice Chancellor (Academic), Pro-Vice Chancellors, Heads of Departments and others to facilitate change and development in distance education. Her past work experience includes school teaching, curriculum advising in primary and secondary schools, and academic staff development in Australian universities, including the University of New England and the University of Queensland. Dr Stein researches and publishes and in the areas of teaching, learning, curriculum and evaluation in higher education settings, and has a specific focus on distance education, student evaluations, teacher professional development, and technology and science education. Her interest and experience with teaching staff has included supporting their ability to articulate what they do as teachers and helping them to explore and develop their beliefs underlying their practices.

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About the Contributors

Kwong Nui Sim’s research is focused on ICT beliefs and practices held by students as they undertake their tertiary education. What is the role of ICT among undergraduate students in their daily study practice? How does ICT play a role in postgraduate students’ day-to-day research practice? ICT literacy could be a significant aspect in today’s tertiary learning context. Therefore, studies on students’ ICT literacy offer a new perspective in the emerging area of research on ICT utilisation and integration in tertiary education. At the same time, Kwong Nui also has a growing interest in the ICT beliefs and practices held by university staff members in their daily working life. The use of ICT is likely to be a learning process for our everyday work in today’s complex world, thus she is very interested to investigate the complicated relationship between staff members and their use of ICT in their daily working practice. *** Richard Berry is an eResearch Analyst at Intersect Australia Ltd. He focusses on helping researchers from all disciplines overcome IT related research problems, work more efficiently, and explore new research areas using everything from spreadsheets to cutting edge supercomputers. He has a keen interesting in teaching practical coding skills to researchers and research students, and raising awareness of the possibilities that digital research skills and techniques can bring to research. Dr Berry’s diverse background includes an early career as a scientist, obtaining a PhD in Neuroscience from the Australian National University in 2006 through both laboratory based investigation and computational modelling of invertebrate visual systems. After several successful follow-on research projects, Dr Berry moved to the Department of Defence. There he was responsible for developing and maintaining business intelligence and reporting systems related to aircraft maintenance, and was awarded the Australia Day Medallion in recognition of his work. Dr Berry also holds postgraduate qualifications in IT, and is an accredited Software Carpentry instructor.

About the Contributors

Ian Brailsford completed his PhD in modern American history at the University of Auckland in 1999. Subsequently he worked as a lecturer in the Department of History teaching American history and historical methods. Since 2006 he has been employed first as an academic developer and, more recently a postgraduate learning adviser assisting doctoral candidates and early-career academics with both teaching and researcher development support activities. He currently co-ordinates the University of Auckland’s Doctoral Skills and the Mapping your Masters Research programmes to help candidates develop and improve the skills needed to complete a major research project. He has published journal articles on the history of university teaching and learning and various aspects of doctoral education. Mark Brown is Ireland’s first Professor of Digital Learning and Director of the National Institute for Digital Learning (NIDL) at Dublin City University (DCU). He is a Fellow and Executive Committee member of the European Distance and E-learning Network (EDEN)and serves on the Supervisory Board of the European Association of Distance Teaching Universities (EADTU). He also chairs the Innovation in Teaching and Learning Steering Committee for the European Consortium of Innovative Universities(ECIU). Mark has played key leadership roles in the university-wide implementation of new models of teaching and learning and has published extensively in the fields of Blended, On-line and Digital (BOLD) education. In 2017, the Commonwealth of Learning (CoL) recognised Mark as a world leader in Open and Distance Learning and he is Chair of the ICDE World Conference on Online Learning which will be held in Dublin in November 2019. Catherine Coe has been involved with the Honors Program, Peer Mentoring, Intramurals, and Project Mascot, which connects college students with elementary students in disadvantaged schools, since she was a student. She was privileged to have the opportunity to mentor peers and to serve as a role model for aspiring young students. Her passion for education is grounded in research, problem solving, a desire to do things better, student success, and reaching out to those most in need. Before returning to the Heavener School of Business as the Director of the Online Business Program, she spent the last four years in instructional design and faculty professional development. And before that she was an advisor and career coach in the Online Program, and in her first advising role at University of Florida she spent four years advising on-campus business majors. Nihan Dogan is a graduate student in Educational Technology program at the University of Florida. Her research interests are technology acceptance, technologyfocused teacher professional development and technology integration.

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About the Contributors

Dawn Duke is the Head of Researcher Development within the University of Surrey’s Doctoral College. She leads the team that supports the transferable/employability skills of postgraduate researchers and early career researchers across all disciplines, as well as delivers supervisor training. Dawn received her PhD from Imperial College in the field of Neuroscience. In 2008, she moved from researching and teaching in the field of Neuroscience to concentrate fully on researcher development. Over the years, she has worked to embed and normalise skills training in order to better prepare researchers for the variety of opportunities available to them. A key aspect of this is dissemination of research results and interdisiplinary and intersectoral collaboration. Dawn believes that the world would be a better place if the amazing research that is done within our Universities had an even greater impact on policy, society and the economy and is dedicated to enabling the next generation of researchers so that they can take on this challenge. Floribert Endong (PhD) is a research consultant in the humanities and social sciences. He is a reviewer and editor with many scientific journals in the social sciences. His current research interest focuses on international communication, communication research, digital media, media laws, international relations, culture and religious communication He is author of numerous peer-reviewed articles and book chapters in the above-mentioned areas of interest. Maggie Hartnett is a senior lecturer at Massey University where she teaches in the areas of e-learning and digital technologies. She is programme coordinator for the postgraduate digital education programmes, course coordinator for the postgraduate professional inquiry and is associate editor of the New Zealand based Journal of Open, Flexible and Distance Learning. Her research interests include motivation and engagement in digital environments, teaching and learning with digital technologies, learner support, digital inclusion, and spaces for digital learning. Erin Henslee has BS degrees from Virginia Tech in Engineering Science & Mechanics and Mathematics, where she then worked as the undergraduate recruiter for the College of Engineering within the Center for Enhancing Engineering Diversity. During this time she also completed an MSc in biomedical engineering and enjoyed teaching several undergraduate courses. She worked for a short time at the UT Southwestern Medical Center as a biomedical engineering specialist before being awarded a fellowship to pursue her PhD abroad. Erin came to the UK where she completed her PhD in Biomedical Engineering at the University of Surrey in 2016. During this time she also enjoyed coordinating undergraduate labs for the department of Mechanical Engineering Sciences. Her PhD work led to a BBSRC funded postdoc in circadian electrophysiology of red blood cells. Erin left the lab 271

About the Contributors

to join the Researcher Development Programme at the University of Surrey in October 2016 and is enjoying supporting other students on their doctoral journeys as a Researcher Development Officer. Sam Hopkins joined the Researcher Development Programme at the University of Surrey in 2011. She has designed a range of training and support activities for researchers at PGR and postdoctoral level and leads the mentoring programmes. These mentoring programmes cover four main transition points in the academic career. The first is mentoring for final year undergraduates thinking about moving into academia, the second is for new PhD students, the third for early career researchers making the move toward funding or a permanent academic position and the final transition is out of academia and into other areas of work. Emily James was born in the US and is now living in Ireland. Dr E. Alana James’s early career included teaching at high school level, running projects for disadvantaged young people and working for a number of American online Universities at the beginning of the online education revolution in the 1990’s. Her early work saw her mentoring over 50 PhD students through to successful completion of their degrees. To raise PhD completion rates around the world and to tackle the ups and downs inherent in PhD research and in the writing process with many postgraduates struggling with finding the support they need and balancing their life with work and home, Dr James developed an online professional development platform that partners with global universities, bringing 24/7 365 support to both Masters and Doctoral students. A published author with Sage Publications, she authored three textbooks including her latest entitled, “Writing your doctoral dissertation or thesis faster: A proven map to success”. Melissa L. Johnson currently serves as the Associate Director of the University of Florida Honors Program, as well as an Affiliate Faculty with the Bob Graham Center for Public Service and a Field and Fork Faculty Fellow. Melissa earned her Ph.D. in educational technology from the University of Florida. She is a Fellow of the National Collegiate Honors Council, where she has served on the Board of Directors, as co-chair of the professional development committee, and as an external program reviewer. She is also a member of the NACADA Academic Advising Consultant and Speaker Service and a frequent e-Tutorial facilitator for that association. Lucy Johnston is currently the Dean of Graduate Research at the University of Newcastle. As part of this position, Professor Johnston plays a key leadership role in driving growth and improvements in research training across the University of Newcastle. Professor Johnston joined the University of Newcastle from 272

About the Contributors

the University of Canterbury, where she was Dean of Postgraduate Research and Professor of Psychology. She is a recognised experimental social psychologist, whose research is concerned with understanding, predicting and modifying the behaviour of individuals in social interactions, with two distinct foci of social perception and social information processing. Professor Johnston completed her BA (Hons) in Experimental Psychology at the University of Oxford, UK and PhD in Social Psychology at the University Bristol, UK and has more recently completed a MSc Sport and Exercise Psychology at the University of Staffordshire, UK. She lectured at the University of Cardiff before joining the University of Canterbury in 1994. Professor Johnston was a member of the inaugural management team of the New Zealand Institute of Language, Brain and Behaviour, leading the Language and Social Cognition theme and following the Christchurch earthquake in 2011, was appointed to the Psychosocial Recovery Advisory Group for the Joint Centre for Disaster Research. In 2004, she was a Distinguished Visiting Professor at the University of Connecticut, US and has published extensively in the fields of social psychology, and learning and teaching. Recognised for her engagement with postgraduate research, Professor Johnston was the Chair of the New Zealand Deans and Directors of Graduate Studies (NZ DDOGS) between 2012 and 2016 and was actively involved in the development of the Australian Best Practice Guidelines for Higher Degree Research. She was Convenor of the Universities New Zealand Scholarship Committee from 2011 to 2016 term and has been an invited participant to a number of Council of Graduate Schools Global Summits. Lucy was awarded Oxford Blues and full colours at the University of Bristol for basketball and played for the British Universities. She rowed for her Oxford College and City of Bristol and played soccer for the University of Bristol. She recently retired from 10 seasons completing in road cycling and triathlons. Rachel Keegan has been working in the area of postgraduate research administration in Dublin City University (DCU) for over 12 years and is the Manager of DCU’s Graduate Studies Office. She works closely with the Dean of Graduate Studies in developing the unit’s strategy, managing external and collaborative engagement, drafting and updating policy, regulations and process and monitoring national and international policy and research developments pertaining to doctoral education. Rachel is currently completing an Education Doctorate at DCU and her interests include the contribution of professional doctorates to professional practice, quality at doctoral level and the experiences of part-time research students. Swapna Kumar is a Clinical Associate Professor of Educational Technology at the School of Teaching and Learning, University of Florida, USA. She directs the

273

About the Contributors

online doctoral program in Educational Technology at the College of Education. Her research interests include online education (design, development, facilitation and assessment) and blended learning. Ricardo Morais is Head of the Management Department at Católica Porto Business School, coordinator of the seminar ‘How to design your PhD’ at the European Institute for Advanced Studies in Management in Brussels, founder of Idea Puzzle, and alumni of HPI School of Design Thinking in Germany. He holds a PhD in Strategic Management from the University of Jyväskylä, Finland, having graduated in Management from the Faculty of Economics of the University of Porto. His research interests are interdisciplinary, including Philosophy of Science, Strategic Thinking, and Design Thinking. He has lectured these topics in over 70 higher education institutions in nearly 20 countries. Burhan Ozfidan has completed his Ph.D. at Texas A&M University-College Station where he is currently working as a Postdoctoral Research Fellow. He has published widely in a number of well-respected peer-reviewed journals and has made numerous presentations at national and international conferences. He has culturally and linguistically teaching, mentoring, and tutoring experiences, along with his research background. He is also a scholar from an under-represented minority who has dedicated his life’s work in academic achievement. Peter Rawlins teaches and researches in the areas of assessment and mixed methods at the undergraduate and post-graduate levels. He also co-teaches the postgraduate professional inquiry course. Current research projects include a number of initiatives in primary and secondary schools including policy development and its impact. Ambyr Rios is the Assistant Director of Online Education in the Department of Teaching, Learning and Culture at Texas A&M University. Upon her entrance to the doctoral program at Texas A&M in the summer of 2018, she will hold a Master’s of Arts in Teaching from Johns Hopkins University, a Reading Specialist Certificate from George Washington University, and Principal Certificate coursework from Texas A&M- Corpus Christi. Her background in education includes six years of teaching English at the secondary level, three years of school and district administration, and a year of non-profit leadership. Her research interests include online education in K-12 and higher education settings, new literacies, and writing intervention for secondary learners.

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About the Contributors

Jennifer Rowland is an Australian biomedical scientist with 20-years of experience in the academic research sector, working across a range of research foci and disciplines internationally. She is formally trained in Science, Biomedicine, Economics and Development, Education, and Project Management. Jennifer currently oversees the Master of Research Program in the Faculty of Medicine and Health Sciences at Macquarie University, and supports and trains researchers in the faculty. Joseph Stokes, Associate Professor, is currently the Dublin City University Dean of Graduate Studies, with responsibility for strategy, policy and regulation relating to graduate research in the University, and for providing support to DCU’s doctoral and research master’s students and their supervisors. Prof Stokes leads the Graduate Studies Office, and chairs the DCU Graduate Research Studies Board. He is a member of the Irish Universities Association (IUA) Deans of Graduate Studies group, and is a regular contributor to meetings of the European Universities Association Council for Doctoral Education (EUA- CDE). He was awarded the degrees of B.A. and B.A.I. in Mechanical and Manufacturing Engineering from Trinity College Dublin in 1997 and began his PhD studies that year in DCU. He attained his PhD in Mechanical and Manufacturing Engineering from Dublin City University in 2002, graduated March 2003. He lectured part-time for two years in Dublin Institute of Technology Bolton Street from 1998 to 2000. In September 2000 he became a permanent member of the academic staff in the School of Mechanical and Manufacturing Engineering in Dublin City University. He was promoted to his current position in DCU in 2009. Radhika Viruru is a Clinical Professor in the Department of Teaching, Learning and Culture (TLAC) at Texas A&M University, having joined the faculty in 1998. Her research interests include early childhood education, postcolonial childhood studies and technology integration into education. From 2011-2013 she served as the Associate Department Head for Undergraduate Programs in the Department of TLAC. Currently she is the director of the Online Ed.D program in TLAC, one of the first programs of its kind in the state. In 2016 she was named a College of Education and Human Development Transforming Lives Faculty Fellow. Her recent professional interests center around the potential of online education. She is the author of two books on early childhood education (Early Childhood Education: Postcolonial Perspectives from India) published by Sage in 2001 and Childhood and Postcolonization: Power, Education and Contemporary Practice (co-author) published by Routledge in 2004. She is currently chairing or co-chairing the committees of 7 PhD and EdD students. Her former graduate students serve in leadership roles in both university and K-12 institutions around the world.

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Index

A advisor 8, 10-12, 14-16, 22, 94, 96, 157

B blended learning 1-2, 6-7, 9-10, 17, 22, 88, 102, 134, 136, 185 BPhil 89, 91, 93, 107

C case 4, 7, 17, 29, 35, 38, 46, 74-75, 77, 85, 124, 134, 196, 200, 209-210, 220, 224 cognitive skills 54 conceptual framework 26, 50-51, 65

D development 1-2, 8-11, 14-15, 17, 23, 36, 48-49, 53-54, 58, 61, 72, 75-76, 79, 85-87, 90, 95-99, 102-103, 108-112, 114-117, 120-127, 133-135, 137-141, 144-145, 157-158, 173, 184-185, 195197, 201, 209-215, 218-219, 222-224, 226, 228-230 digital badges 8, 14, 16, 23 digital learning 58, 109, 112, 118, 124, 135, 145 digital support 85, 102, 144 digitalisation 58, 134-135, 139, 145 distance 1-4, 7-8, 17, 109, 111, 118, 120, 136, 155, 159, 183-189, 193-195, 198-202, 209-212, 218-221, 226, 228

doctoral 1, 3-4, 17, 46-47, 51-62, 67-69, 71-76, 78-81, 86, 90-91, 95-98, 102, 108-112, 114-119, 122, 124, 126-127, 133-137, 139, 141-142, 144-145, 154157, 162, 165, 167, 170-171, 183-187, 189-202, 209-213, 216-219, 223-224, 226-227, 229-230 doctoral curriculum 60, 62 doctoral education 62, 109-111, 118, 127, 133-136, 139, 144-145, 183-186, 193194, 199, 201-202 doctoral researcher 95, 142, 216, 218-219 doctoral supervision 184 DoctoralNet 133, 136-138, 142-145, 148, 150-151

E education 1-4, 7, 17, 25, 30, 33, 35, 38-39, 46-47, 53, 62, 75, 99, 108-114, 117118, 120, 123-124, 127, 133-136, 139, 144-145, 155-157, 162-163, 173-174, 183-186, 193-197, 199, 201-202, 210, 226 e-mentoring 183-184, 186, 193-194, 196199, 201-202, 226-227 engagement 8, 10, 13, 16, 86-87, 91, 94, 96, 99-100, 102-103, 127, 138, 142, 148, 150, 154, 158-160, 162, 169, 174, 212-214, 217-218, 220-222, 228

F FMHS 85-89, 93, 96-99, 102-103, 107

Index

G Graduate Schools 52, 112, 117, 125, 133134

online presence 37, 159, 215, 223 online program 158-159, 169, 174-175, 189, 201 online supervision 4

H

P

HDR 85-91, 95-100, 102-103, 107 higher degree research 85-86, 88, 94, 107, 133

PhD 24-27, 31-37, 39-41, 47-48, 55, 58-61, 79, 86, 89-91, 95-97, 99-103, 107, 110, 112, 119, 121, 134, 139, 143-144, 221, 224, 226-227 Philosophy of Science 46-49, 51-52, 54-55, 57-60, 62, 66 plagiarism 24-41, 90, 94 plagiarism detection systems 24, 26, 28, 30, 34, 37, 40 postgraduate 1-5, 7-8, 11-12, 15-17, 23, 30-33, 36-41, 55, 57, 62, 86, 88-89, 91, 95-96, 103, 108-114, 122, 125, 133-134, 137-139, 143 postgraduate researcher 23, 138 problem solving skills 54 professional development 10, 15, 87, 95, 98, 103, 108, 110-112, 114-115, 120-123, 126-127, 137-138, 144, 185, 195, 197 professional inquiry 1-2, 7-10, 12-13, 15, 17-18, 22-23 public engagement 217-218

I iLearn 88, 91, 93, 97, 107 integrative thinking 46, 54, 56, 59, 62, 65

J jigsaw puzzle 49, 55, 61-62, 65

K Knowledge Visualization 46, 65

M Massey University 1-2, 7, 17 Master of Research 86, 89, 91-93, 102, 107 mentoring 15, 90, 99, 112, 154, 183-188, 196-199, 201-202, 226-227 MRes 86, 89, 91-97, 99-103, 107

O online 1-13, 15, 17-18, 23, 25, 31, 37-38, 40, 47, 57-58, 61-62, 68, 88, 97, 99-100, 102-103, 109-112, 116, 119-122, 124, 133-140, 142-145, 148, 154-169, 172175, 183-202, 215-216, 218, 221-224 online education 4, 112, 124, 155, 162, 174, 184-186, 199 online learning 3-4, 6-7, 23, 155, 158-162, 174-175, 185, 196, 222 Online Learning Community 23 online mentoring 186

R research 1-5, 7-18, 22-28, 30-36, 38-41, 4662, 66-81, 85-100, 102-103, 107-118, 120-122, 124-128, 133-142, 144-145, 148, 154, 156-158, 160, 167, 172-175, 183-190, 193-202, 209-214, 216-230 research community 15, 25, 85, 88-89, 92, 99, 133, 135, 138, 144, 216, 221, 224 research design 46-52, 54-55, 59-60, 62, 66-68, 111, 113, 117-118, 142, 187 research impact 212, 217, 223 research misconduct 27 research software 66, 195, 197 research training 1-2, 17, 60, 80, 89, 9597, 103

277

Index

S similarity index 25, 30, 34, 36, 38-39 STEM 86, 103, 107, 119-120 supervision 1-2, 4-5, 7-8, 11-12, 15-17, 23, 32, 58, 68, 88, 110, 114, 134, 184-186, 196, 198, 219, 227 supervisor 5, 14, 23, 34, 90, 96, 100, 109, 114, 117, 119, 122, 144-145, 184, 196, 200 survey 31, 46-47, 59-62, 68, 116-117, 120, 126, 138, 144, 148, 156, 164-165, 167, 171, 175, 180

T training 1-2, 9, 17, 23, 31, 39, 54, 59-60, 69, 71, 74, 76, 78-81, 85-90, 94-100, 102-103, 111, 114, 120, 124, 134,

278

139-140, 158, 209-212, 214-216, 218-219, 230 Turnitin software 24, 33, 36-37, 40

U usability testing 46-47, 58-59, 61-62, 66

V virtual mentoring 184, 186 visual metaphor 48, 62, 66 visual representation 48, 65-66