Computational Intelligence in Digital Pedagogy [1st ed.] 9789811587436, 9789811587443

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Intelligent Systems Reference Library 197

Arpan Deyasi · Soumen Mukherjee · Anirban Mukherjee · Arup Kumar Bhattacharjee · Arindam Mondal   Editors

Computational Intelligence in Digital Pedagogy

Intelligent Systems Reference Library Volume 197

Series Editors Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland Lakhmi C. Jain, Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology, Sydney, NSW, Australia; KES International, Shoreham-by-Sea, UK; Liverpool Hope University, Liverpool, UK

The aim of this series is to publish a Reference Library, including novel advances and developments in all aspects of Intelligent Systems in an easily accessible and well structured form. The series includes reference works, handbooks, compendia, textbooks, well-structured monographs, dictionaries, and encyclopedias. It contains well integrated knowledge and current information in the field of Intelligent Systems. The series covers the theory, applications, and design methods of Intelligent Systems. Virtually all disciplines such as engineering, computer science, avionics, business, e-commerce, environment, healthcare, physics and life science are included. The list of topics spans all the areas of modern intelligent systems such as: Ambient intelligence, Computational intelligence, Social intelligence, Computational neuroscience, Artificial life, Virtual society, Cognitive systems, DNA and immunity-based systems, e-Learning and teaching, Human-centred computing and Machine ethics, Intelligent control, Intelligent data analysis, Knowledge-based paradigms, Knowledge management, Intelligent agents, Intelligent decision making, Intelligent network security, Interactive entertainment, Learning paradigms, Recommender systems, Robotics and Mechatronics including human-machine teaming, Self-organizing and adaptive systems, Soft computing including Neural systems, Fuzzy systems, Evolutionary computing and the Fusion of these paradigms, Perception and Vision, Web intelligence and Multimedia. Indexed by SCOPUS, DBLP, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science.

More information about this series at http://www.springer.com/series/8578

Arpan Deyasi Soumen Mukherjee Anirban Mukherjee Arup Kumar Bhattacharjee Arindam Mondal •

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Editors

Computational Intelligence in Digital Pedagogy

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Editors Arpan Deyasi Department of Electronics and Communication Engineering RCC Institute of Information Technology Kolkata, India Anirban Mukherjee Department of Information Technology RCC Institute of Information Technology Kolkata, India

Soumen Mukherjee Department of Computer Application RCC Institute of Information Technology Kolkata, India Arup Kumar Bhattacharjee Department of Computer Science and Engineering RCC Institute of Information Technology Kolkata, India

Arindam Mondal Department of Computer Application RCC Institute of Information Technology Kolkata, India

ISSN 1868-4394 ISSN 1868-4408 (electronic) Intelligent Systems Reference Library ISBN 978-981-15-8743-6 ISBN 978-981-15-8744-3 (eBook) https://doi.org/10.1007/978-981-15-8744-3 © Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Anirban Mukherjee dedicates to his mother Sunanda, wife Attreyee and son Ritam Arpan Deyasi dedicates to The teachers who ignites the passion for teaching Soumen Mukherjee dedicates to his father, mother, wife Koyal and eight-year-old son Aarush Arup Kumar Bhattacharjee dedicates to his parents, Sandhya and Mrityunjoy Bhattacharjee, for always being there with him Arindam Mondal dedicates to his mother, wife and son for always loving and supporting him

Foreword

“If the mountain will not come to Muhammad, then Muhammad must go to the mountain,” Francis Bacon writes in his essays in the year 1625. Quoting this, Swami Vivekananda introduces Maharaja of Mysore to a new idea in a letter written to him in the year 1894 that “If the poor boy cannot come to education, education must go to him.” Not leaving at that Vivekananda also suggests a technique though rudimentary yet befitting its time. In the wake of Digital Revolution, taking the education to the doorstep of one and all with ease is a dream coming true for every educationalist. Education with adroit promptness has at all times incorporated the thenemerging technologies in appropriate measure for its promotion and propagation. We are now in the era of data science, during which time it has become both trend and trade for a mass of apparently insignificant data to be subjected to the lens of algorithmic scrutiny in the hope of revealing meaningful insights into the past, present and/or future. The seemingly unobjective targets that have suffered so long with subjective bias are now, with improved precision, being rendered as estimable and thereby actionable metric. Such a reality is presented before us by a plethora of tools and techniques that have been developed over a period of time under the umbrella of soft computing which in its ever-evolving state is being broadly addressed as computational intelligence. Computational intelligence has found application in every line of business that can promise to quantify facts and figures. Digital pedagogy being one such has been chosen as the theme of this compilation in which a lot of practitioners and researchers in the field of education have contributed their thought-provoking articles, their findings that can change the way we are practicing pedagogy, and their suggestions for the improvement of practices in vogue keeping intact the principle and philosophy of pedagogy. The time is opportune for such a publication to reach the eager educator to benefit.

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I am sure that this much-awaited compilation will be widely received by educators at large and that it ushers in its trail a demand and compulsion to produce another comprehensive sequel under this theme. Swami Dhyanagamyananda Head Department of Computer Science Ramakrishna Mission Vivekananda Educational and Research Institute Belur, Howrah, India

Preface

In modern science and engineering, didactics is primarily shaped and conceptualized by introduction of technology, where information-based teaching is integrated with experimental, computational and self-learning methodologies for producing better learning outcome. Among the different pedagogic methods, active learning, flipped learning, blended learning and adaptive learning are now the choices of researchers and practitioners with encouraging flexibility and scope offered by the digital media and technology. With the continuous development of new computing technologies like machine learning, deep learning, big data, data science along with the growing computing capacities of intelligent machines, new scopes and challenges are opening up for teaching–learning in higher education segment, precisely in engineering or technical education. With national and international regulatory guideline of measurable program outcome, course outcome and program educational objectives in an ecosystem of outcome-based technical education (OBTE), it is now a challenge for the higher education institutes, administrators, educators and teaching staffs to continuously measure, monitor, analyze and redefine the outcomes and its parameters. Here comes the importance of digital pedagogy aided by computational intelligence. Intelligent capturing, analysis and interpretation of large amount of primary and secondary data lead to predictive outcome and also suggest necessary modifications in the rubrics and target outcomes. Evaluation being a major part of pedagogy, intelligent assessment of subjective and objective responses can be developed in online/offline mode that will necessitate imminent change in pedagogic strategies from the traditional ones to digital-based strategies; the later should include interactive teaching dashboard, online interactive course content (MOOC) with embedded assessment and polling mechanism, response-sensitive intelligent tutoring, etc. Good use of AI across digital pedagogic platforms can make teaching–learning more independent of human factor (teacher/student quality), time and place and at the same time more impactful and enjoyable for the learners. Providing access to the digital media and learning tools (even to the extent of mobile apps) to the students would allow them to keep pace with innovations in learning technologies, ix

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learn according to their own pace and understanding level and have instantaneous feedback and evaluation. This book is a collection of fourteen chapters that depict some of the very recent unpublished research works, survey and case studies in the field of digital pedagogy and some very intriguing discussions, thoughts and analysis of perceived changes in the pedagogic strategies in technical education system. Examples of pedagogical possibilities that are both new and currently practiced across a range of teaching contexts are featured in the book. The chapters have been carefully selected out of several submissions following a rigorous review, revision and editing process. It is indeed encouraging for the editors to bring out this collection under Springer Nature edited series Intelligent Systems Reference Library (ISRL), and the book is expected to evoke interest of researchers of different backgrounds owing to its cross-platform characteristics. An overview of the chapters of the book is given as follows: The chapter titled Authentic Pedagogy: A Project-oriented Teaching–Learning Method based on Critical Thinking discusses at length a new pedagogical concept known as authentic learning which is an instructional learning strategy based on development of tangible prototypes through project-oriented activities. This strategy helps the learners develop solutions of real-world problems following agile methodology. The authors have shown the efficacy of the method by experimenting it with a group of learners and also compared the results with flipped learning method in technical pedagogy domain. In the chapter titled A Set of Empirical Models to Evaluate E-learning Websites and their Comparison, design of an e-learning software evaluator has been proposed that will not only evaluate but also rank the different e-learning educational websites that are frequently referred by students and researchers. Students very often face problems in selecting an appropriate e-learning platform as they might not be well informed about the quality of the online courses and the e-learning software. The authors have proposed analytical hierarchical process (AHP) and Principal Component Analysis (PCA). The chapter titled Multimedia-based Learning Tools and Its Scope, Applications for Virtual Learning Environment depicts the impact of multimedia-based interactive teaching material in the understanding of the content. The authors have shown with statistical analysis how multimedia and image processing tools are inevitable in web-based learning systems for online interactive self-learning which may well turn out to be the basic mode of learning for the future generation. In modern teaching–learning as well as in academic administration, social media is gradually gaining in importance for its versatility of information dissemination and opinion exchange. In the chapter titled Social Network Analysis in Education: A Study, the authors have reported their unique research on how to detect useful data from massive databases of educational data in social media by applying some data mining algorithm. Such data is extracted to understand and measure the performance of student and is also helpful to study students’ thinking pattern, weakness, focus, etc. For the academic administrators, such media-data mining technique helps them take important decisions by detecting a trend of opinion.

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In the chapter titled Personalization in Education using Recommendation System: An Overview, the authors have presented the concept and benefit of e-learning recommender system, the primary aim of which is to assist users searching for e-learning content of their choice to cope with data overload of large pool of available materials. The authors have discussed five categories of basic recommender systems presently in practice and also compared pros and cons of some of the existing systems. Finally, while outlining few challenges of recommendation engine like gray-sheep problem, cold start problem, etc., a new recommendation framework has been proposed in this chapter which is expected to cope with some of these challenges. The authors of the chapter titled Automation of Attainment Calculation in Outcome-Based Technical Education (OBTE) targeted an intelligent system to determine the attainment of outcome of a course automatically based on the classified student data and course rubrics. In view of national and international education policy, OBTE is fast becoming a mandatory standard. In this automated processing, huge academic data for calculation of attainment can help reduce manual involvement and ensure quality of information generated. A machine learning approach has been adopted to design the prototype system. The chapter titled Quality Issues in Teaching–Learning Process deals with the quality issues mainly in the field of technical education system which is gradually transforming from traditional system to digital system but still facing the challenges of VUCA, i.e., volatility, uncertainty, complexity and ambiguity of LT process. In the context of digital pedagogy, the author discusses different ICT-based LT methodology including flipped teaching, collaborative learning, active learning and presented through a case study how digital pedagogy can promote independent learning among students. There has been a paradigm shift in the scope and pedagogy of English language teaching in technical education domain in last ten years or so with introduction of language laboratory in the technical institutions in India. The learners are facilitated to master the fundamental English communication skills through digital pedagogy techniques aided by multimedia-infused visual, aural, audio-visual and verbal communication devices. The author presents the findings of an interesting research on the justification, advantage and future scope of trending pedagogy specific to English language laboratory in the chapter titled Digital English Language Laboratory: Roles, Challenges and Scopes for the Future Development in India. The chapter titled Overview and Future Scope of SWAYAM in the World of MOOCS: A Comparative Study with Reference to Major International MOOCS presents a comparative study of SWAYAM, an online digital resource platform sponsored and developed by MHRD, Govt. of India, with respect to other international MOOCS to understand the future viability, sustainability and further scope of the same as SWAYAM is a benchmark standard of digital pedagogy in India. The study analyzes the effectiveness of SWAYAM courses on certain parameters and also proposes how intelligent tutoring features like augmented reality, simulated environment, virtual assistant and predictive guidance can be embedded in the

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SWAYAM courses to make it more user-friendly and effective for personalized learning experience. Blending of Traditional System and Digital Pedagogy: An Indian Perspective is another chapter that presents a study on changing scenario of teaching–learning in India and explores blending of digitized facilities in traditional system. At first, it gives a brief survey on blended learning. Then it presents two teaching–learning models incorporating digital technology—one is the Intelligent School Network for Research (ISNR) and the other is the Intelligent Feedback System for Classrooms (IFSC). It shows how formative assessment of learners can be done using intelligent data analysis. In the chapter titled Application of Internet of Things in Digital Pedagogy, a novel application of IoT in digital pedagogy is presented by the authors. It describes how learners can access resources in a smart learning environment using IoT applications in their mobiles. A system is proposed that enables students to connect and interact with the augmented objects in their learning space to collect information which in turn improves their collaborative learning outcome. The system is validated by a experiment using control group and experimental group of students of engineering. The authors of the chapter titled An Innovative Step for Enhancement in Students Result and Teaching–Learning Process Using Educational Technology have clearly explained the formative and summative assessment techniques of traditional pedagogy in higher education domain and in this context highlighted the possible application of AI and machine learning techniques in student’s result analysis. Finally, the authors have presented statistical results of a case study that compared the learning outcomes of a traditional classroom and online digital classroom of computer science and engineering. Digital Pedagogical Paradigm in Language Lab-based English Teaching for Higher Technical Education is another chapter of the book that deals with smart teaching of communicative English. It presents a digital pedagogical model that specifies how to handle digital language laboratory to develop communicative competence of UG engineering students. The authors elaborate how instead of using digital language laboratory as an alternative of smart classroom, specific strategies, dynamic lesson plans, interactive assessment techniques and software/hardware tools and facilities should be adopted or availed by English language teachers so as to fulfill the learning outcomes as per global accreditation norms. In the chapter titled A Novel Outcome Evaluation Model Blended with Computational Intelligence and Digital Pedagogy for UG Engineering Education, a novel model is proposed which exhibits the importance of computational intelligence applied over input academic data of a higher education institution so as to achieve the benchmark criteria in program outcome. Novelty of the proposed model also lies in the fact that it considers any change of pedagogical techniques for benefit of the students, if necessary. The authors have indicated use of different soft computing, machine learning techniques including SVM, ANN, text mining, fuzzy logic, clustering and classification in different phase of data analysis to get the desired output.

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The book does not claim to cover all possible applications of computational intelligence in educational technology but serves as an valuable pointer to the vast opportunity that exists in leveraging technological advancements in disseminating knowledge and skill to the digital age students and most seamlessly integrate digital pedagogy strategies in the day-to-day teaching–learning process so as to make it more effortless and enjoyable. The research works or ideas presented in the book are scalable in future and might become more relevant and valuable in near future when the AI wave is going to hit the higher education sector in an overwhelming manner. It is needless to mention that the effort of the editors to bring out this volume would not have been successful without the valuable contribution and cooperation rendered by the authors. The editors take this opportunity to express their thanks to Springer Nature, to provide the scope to edit such a concise and quality volume on a theme on which not many titles are available. The editors would also like to express their heartfelt thanks to Mr. Maniarasan Gandhi, the Project Coordinator, and Mr. Aninda Bose, Senior Editor, Springer, for their encouragement and support right from the proposal phase. We are overwhelmed to receive the blessings of Swami Dhyanagamyananda, Head, Department of Computer Science, Ramakrishna Mission Vivekananda Educational and Research Institute, Belur, through his illuminating Foreword that speaks about the relevance of this compilation. Last but not least, the editors gratefully acknowledge the contribution of the reviewers who have shared their valuable expertise and time in meticulously reviewing the chapters included or excluded in this volume. We sincerely hope that the proposed book would come to the benefit of researchers and administrators in education sectors. Since the book contains several methodologies of teaching and assessment using digital platform, it is likely to help the faculty members in colleges and also the teachers of schools to implement it in their day-to-day academics. Towards outcome-based course planning and assessment, this book can also be useful as it provides important pointers to application of soft computing techniques in data analysis and interpretation. We invite any suggestion and criticism of this treatise from the readers with an open mind. This will help us to better shape the future volumes of this title. We also invite innovative research ideas or proposals or case study report or pointers to new avenues in pedagogy domain that can be explored. You can mail your ideas to the School of Digital Pedagogy, RCCIIT, Kolkata (at [email protected])—an initiative of the editors to foster research and practice in intelligent educational technology. Kolkata, India

Arpan Deyasi Soumen Mukherjee Anirban Mukherjee Arup Kumar Bhattacharjee Arindam Mondal

Contents

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Authentic Pedagogy: A Project-Oriented Teaching–Learning Method Based on Critical Thinking . . . . . . . . . . . . . . . . . . . . Arpan Deyasi, Swapan Bhattacharyya, Pampa Debnath, and Angsuman Sarkar 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Comparative Studies Between Pedagogic Principles . . . . . 1.3 Procedure of Authentic Learning . . . . . . . . . . . . . . . . . . . 1.4 Incorporation of Statistical Analysis . . . . . . . . . . . . . . . . . 1.5 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.2 Quality Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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A Set of Empirical Models to Evaluate E-learning Web Sites and Their Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soumili Dey, Suchandra Datta, Anal Acharya, and Debabrata Datta 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Proposed Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Parameters Considered for Evaluation . . . . . . . . . . . 2.3.2 Proposed Model Using PCA . . . . . . . . . . . . . . . . . . 2.3.3 Proposed Model Using AHP . . . . . . . . . . . . . . . . . . 2.4 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Results Using PCA . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Results Using AHP . . . . . . . . . . . . . . . . . . . . . . . . 2.4.3 Comparison Between the Proposed Methodologies . . 2.5 Conclusion and Future Scope . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Multimedia-Based Learning Tools and Its Scope, Applications for Virtual Learning Environment . . . . . . . . . . . . . . . . . . . . . . . S. N. Kumar, A. Lenin Fred, Parasuraman Padmanabhan, Balazs Gulyas, Charles Dyson, R. Melba Kani, and H. Ajay Kumar 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Features and Challenges in Multimedia-Based Learning Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Social Network Analysis in Education: A Study . . . . . . . . . . . . Poulomi Samanta, Dhrubasish Sarkar, Dipak K. Kole, and Premananda Jana 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Basic Terms and Concepts Associated with Social Network Analysis in Education Field . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Data Mining in Educational Data and Application . 4.4 Application of SNA in Education: Related Work . . . . . . . . 4.5 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Conclusion and Future Scope . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Personalization in Education Using Recommendation System: An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Subhra Samir Kundu, Dhrubasish Sarkar, Premananda Jana, and Dipak K. Kole 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Basic Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 E-learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Integrated Classroom Teaching . . . . . . . . . . . . . . 5.2.3 Recommendation System . . . . . . . . . . . . . . . . . . 5.2.4 Collaborative Filtering . . . . . . . . . . . . . . . . . . . . 5.2.5 Content Based Recommendation System . . . . . . . 5.2.6 Hybrid Recommendation System . . . . . . . . . . . . . 5.3 Overview of Recommendation System in E-learning Sphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Proposed Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Conclusion and Future Direction . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Automation of Attainment Calculation in Outcome-Based Technical Education (OBTE) . . . . . . . . . . . . . . . . . . . . . . . . . . . Nikita Gupta and Arijit Ghosal 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Previous Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Proposed Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Description of the Software . . . . . . . . . . . . . . . . . . . 6.4 Case Study of CO, PO, and PSO Attainment Using Rubrics for a Set of Students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1 Generation of CO–PO, CO–PSO, Course–PO, and Course–PSO Mapping . . . . . . . . . . . . . . . . . . . 6.4.2 Generation of Course–PO and Course–PSO Mapping at Program Level . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.3 Measuring Course Outcomes Attained Through University Examination (External Assessment) . . . . . 6.4.4 Measuring Course Outcomes Attained Through Internal Examinations, Assignments, etc. (Internal Assessment) . . . . . . . . . . . . . . . . . . . . . . . 6.4.5 Course Outcome Direct Attainment . . . . . . . . . . . . . 6.4.6 Course Outcome Indirect Attainment . . . . . . . . . . . . 6.4.7 Total PO and PSO Attainment in Program Level . . . 6.5 Comparative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quality Issues in Teaching–Learning Process Habiba Hussain 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . 7.2 Rationale . . . . . . . . . . . . . . . . . . . . . . . 7.3 VUCA and Quality in the LT Process . . 7.4 Characteristics of Quality Teaching . . . . 7.5 Teaching Methodology . . . . . . . . . . . . . 7.6 Case in Point . . . . . . . . . . . . . . . . . . . . 7.7 Quality Indicators . . . . . . . . . . . . . . . . . 7.8 Quality Initiatives . . . . . . . . . . . . . . . . . 7.9 Professional Development . . . . . . . . . . . 7.10 Conclusion . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Digital English Language Laboratory: Roles, Challenges and Scopes for the Future Development in India . . . . . . . . . . . . Anwesha Basu 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.2 Novelty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Learning Styles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.1 Personality Types and Learning Styles . . . . . . . . . . . 8.4.2 Impact of Gender and Cultural Differences on Learning Styles . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.3 Learning English as a Second Language: The Role of the Digital Language Laboratory . . . . . . . . . . . . . 8.4.4 Difference Between a Traditional Language Laboratory and a Digital Language Laboratory . . . . 8.4.5 Roles of a Digital Language Laboratory . . . . . . . . . 8.5 A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5.1 Language Laboratory at RCC Institute of Information Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5.2 The Survey Questionnaire . . . . . . . . . . . . . . . . . . . . 8.5.3 Data Collection and Analysis . . . . . . . . . . . . . . . . . 8.5.4 Challenges of the Digital Language Laboratory in India and Probable Recommendations . . . . . . . . . 8.6 Scope for the Future Development of Digital English Language Laboratory: Role of Artificial Intelligence . . . . . . . 8.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview and Future Scope of SWAYAM in the World of MOOCS: A Comparative Study with Reference to Major International MOOCS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Madhu Agarwal Agnihotri and Arkajyoti Pandit 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Research Gap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Need for Comparison of Swayam with Major MOOCS . . . . . 9.5 An Overview of Swayam in 2019 . . . . . . . . . . . . . . . . . . . . 9.6 Research Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.7 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.8 Rationale for Choosing Each Parameter and Its Contributing Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.9 Analysis and Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.9.1 Numerical Analysis: . . . . . . . . . . . . . . . . . . . . . . . .

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Theoretical Explanation . . . . . . . . . . . . . . . . . . . . . Graphical Representation . . . . . . . . . . . . . . . . . . . . Summary Table and Graph for Horizontal Summation Analysis of Parameters Discussed in Table 9.4 . . . . . 9.10 Application of Computational Intelligence in MOOCS . . . . . 9.10.1 Stage I—Learner Enrolment . . . . . . . . . . . . . . . . . . 9.10.2 Stage II—Proposed Model for Learning Process in MOOCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.11 Limitations of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.12 Future Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.13 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Blending of Traditional System and Digital Pedagogy: An Indian Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ishita De Ghosh and Satrajit Ghosh 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Blending of Traditional System and Digital Pedagogy: An Indian Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.1 Digital Pedagogy Initiatives in India . . . . . . . . . . . . 10.2.2 Computational Intelligence: Applications in Pedagogy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Nurturing Innovation Using Digital Technology . . . . . . . . . . 10.3.1 Proposed Model 1: Intelligent School Network for Research (ISNR) . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.2 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.3 Benefits of ISNR Model . . . . . . . . . . . . . . . . . . . . . 10.4 Computational Intelligence for Formative Assessment . . . . . . 10.4.1 Proposed Model 2: Intelligent Feedback System for Classrooms (IFSC) . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.2 Benefits of IFSC Model . . . . . . . . . . . . . . . . . . . . . 10.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5.1 Contribution of the Work . . . . . . . . . . . . . . . . . . . . 10.5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Application of Internet of Things in Digital Pedagogy Monu Bhagat, Dilip Kumar, and Sushma M. Balgi 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Motivation and Contribution . . . . . . . . . . . . . . . . 11.3 Literature Survey . . . . . . . . . . . . . . . . . . . . . . . . 11.4 Advantages of IoT in Education [6, 7] . . . . . . . . . 11.4.1 Data Collection . . . . . . . . . . . . . . . . . . . 11.4.2 Personalized Learning . . . . . . . . . . . . . . .

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11.4.3 Security . . . . . . . . . . 11.4.4 Interactive Learning . 11.4.5 Increasing Efficiency . 11.5 Implementation . . . . . . . . . . . 11.6 Results . . . . . . . . . . . . . . . . . 11.7 Conclusion . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . .

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12 An Innovative Step for Enhancement in Student Results and Teaching–Learning Process Using Educational Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sudhanshu S. Gonge, Ratnashil N. Khobragade, Vilas M. Thakare, Vivek S. Deshpande, and Manikrao L. Dhore 12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1.1 Formative Assessments . . . . . . . . . . . . . . . . . . . . . . 12.1.2 Summative Assessments . . . . . . . . . . . . . . . . . . . . . 12.2 Literature Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3 Artificial Intelligence in Education Field . . . . . . . . . . . . . . . 12.4 Role of Computational Intelligence in Result Analysis . . . . . 12.4.1 Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . 12.4.2 Unsupervised Learning . . . . . . . . . . . . . . . . . . . . . . 12.4.3 Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . 12.4.4 Ensemble Learning Method . . . . . . . . . . . . . . . . . . 12.5 Fundamentals of Teachers’ Teaching . . . . . . . . . . . . . . . . . . 12.5.1 Teaching Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.5.2 Teaching Skills . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.5.3 Teaching Relationship with Students . . . . . . . . . . . . 12.5.4 Reflection with Students . . . . . . . . . . . . . . . . . . . . . 12.6 Statistical T-Test Analysis of CSE Students for Outcome-Based Teaching–Learning Process . . . . . . . . . . 12.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Digital Pedagogical Paradigm in Language Lab-Based English Teaching for Higher Technical Education . . . . . . . . . . . . . . . . . Sadhan Kumar Dey and Alice Dey 13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2 Language Lab-Based English Teaching Across Time and Clime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3 Digital Lesson Plan for Lab-Based English Teaching . . . . . 13.4 Pedagogical Progress of English Teaching Across the Globe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.5 Teaching English for Technical Communication . . . . . . . . .

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13.6 Digital Pedagogy and Teaching Strategies in Operation . 13.7 Adapting Digital Pedagogy in Technical Education . . . . 13.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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14 A Novel Outcome Evaluation Model Blended with Computational Intelligence and Digital Pedagogy for UG Engineering Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arpan Deyasi, Arup Kumar Bhattacharjee, and Soumen Mukherjee 14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.1.1 Digital Pedagogy: Significance in Present Education Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.1.2 Outcome-Based Education . . . . . . . . . . . . . . . . . . . 14.1.3 Role of Computational Intelligence in Output Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.1.4 Why Outcome Measurement Is Important in Today’s Perspective? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.1 Related Works on Digital Pedagogy . . . . . . . . . . . . 14.2.2 Related Works on Computational Intelligence Applied to Digital Pedagogy . . . . . . . . . . . . . . . . . . 14.3 Measurement of Outcome . . . . . . . . . . . . . . . . . . . . . . . . . . 14.3.1 Relation Between PO, PSO, CO, PEO for Outcome Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.3.2 Relevant Parameters Required for Estimation . . . . . . 14.3.3 Role of C.I. for Outcome Evaluation . . . . . . . . . . . . 14.4 Application of Model for Institute-Level Accreditation . . . . . 14.5 Setting Guideline for Future TLP . . . . . . . . . . . . . . . . . . . . . 14.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.7 Future Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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About the Editors

Arpan Deyasi is presently working as an Assistant Professor in the Department of Electronics and Communication Engineering in RCC Institute of Information Technology, Kolkata, India. He has 13 years of professional experience in academics and industry. He received B.Sc. (Hons.), B.Tech., M.Tech. degree from the University of Calcutta. He is working in the area of semiconductor nanostructure and semiconductor photonics, and also on pedagogic principles. He has published more than 150 peer-reviewed research papers including book chapters, and a few edited volumes under the banner of CRC Press, IGI Global, etc. His major teaching subjects are solid state device, electromagnetics, photonics and pedagogical studies in P.G. courses. He is reviewer of a few journals of repute and some prestigious conferences in India and abroad. He has delivered a few talks and conducted hands-on sessions on nanolelectronics, photonics and electromagnetics in various FDP’s, workshops and seminars. He is the Editor of various conference proceedings and edited volumes. He is a member of IEEE Electron Device Society, IE(I), OSI, IETE, ISTE, ACM, etc. Soumen Mukherjee did his B.Sc. (Physics Honours) from Calcutta University, MCA from Kalyani University and ME in Information Technology from West Bengal University of Technology. He is the silver medallist in the ME course of the University. He has done his Postgraduate Diploma in Business Management from the Institute of Management Technology, Center of Distance Learning, Ghaziabad. He is now working as an Assistant Professor in RCC Institute of Information Technology, Kolkata. He has more than 17 years of teaching experience in the field of Computer Science and Application. He has over 40 research papers published in different national and international journals, conferences and 9 book chapters in different books by international publishers. He has contributed to over 20 internationally acclaimed books in the field of Computer Science and Engineering and edited 3 book volumes. He got best paper awards in international conferences. His research fields are image processing, machine learning and pedagogy. He is a life member of several institutions like IETE, CSI, ISTE and FOSET.

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Dr. Anirban Mukherjee did his Bachelors in Civil Engineering in 1994 from Jadavpur University, Kolkata and a professional Diploma in Operations Management (PGDOM) in 1998 from IGNOU. He completed his Ph.D. from Indian Institute of Engineering, Science and Technology (IIEST), Shibpur, in 2014. He is currently Professor in the Department of Information Technology at RCC Institute of Information Technology, Kolkata, India. He has more than 20 years of teaching experience and 6 years of industry experience. His research interest includes computer graphics, computational intelligence, optimization, pattern recognition and engineering pedagogy. He has co-authored two UG engineering textbooks and more than 18 textbooks on Computer Graphics/Multimedia for distance learning courses BBA/MBA/BCA/MCA of different universities across India. Besides authoring several papers and chapters published in journals/ conferences and books, he is the Co-Editor of six edited books. He is a member of the editorial review board of the International Journal of Ambient Computing and Intelligence (IJACI) and Fellow of IEI and life member of CSI. Arup Kumar Bhattacharjee did his graduation from University of Calcutta, Master of Computer Application (MCA) from University of Kalyani and M.Tech from West Bengal University of Technology. He is now working as an Assistant Professor in the Department of Computer Science & Engineering, RCC Institute of Information Technology, Kolkata, India. He is teaching core and electives courses in undergraduate and postgraduate programs in the field of Computer Science for last 18 years. He has published many research papers in different national and international journal and conferences. He has edited 2 books and contributed to over 20 internationally acclaimed books in the field of computer science and engineering. He has 4 book chapters in book volumes of international publishers. His studies continue in the areas of soft and evolutionary computing, object oriented technology. Dr. Arindam Mondal is an Assistant Professor in the Department of Computer Application, RCC Institute of Information Technology, Kolkata, India. He completed his doctorate degree in science from Jadavpur University in the year 2017. He has published over 20 peer-reviewed papers in reputed national & international journals and conferences and also edited proceedings of international IEEE conferences. His current research interests include heavy ion physics, image processing, pattern recognition and pedagogy. He is presently working on an authored textbook in Computer Graphics and Multimedia.

Chapter 1

Authentic Pedagogy: A Project-Oriented Teaching–Learning Method Based on Critical Thinking Arpan Deyasi, Swapan Bhattacharyya, Pampa Debnath, and Angsuman Sarkar Abstract Authentic learning is a typical organized and systematic learning strategy which helps the learners to develop solutions in real-world problems guided by proper instructional approaches. Development of tangible prototypes is the primary target that can be achieved through instructional learning begins form classroom and laboratory sessions, which ultimately blossoms through project-oriented activities, following agile methodology. Results obtained after implementing the proposed technique over more than hundred learners depict that proper metacognition of learned concepts along with implementation of thinking skills through project-oriented activities can improve the potentiality of students in future industry/academia sectors. Results are also partially dependent with availability of infrastructural resources and socio-humanitarian factors, but a far better compared to the data obtained when flipped learning method is invoked. Learning outcome speaks clearly in favor of implementing the technique in a wider domain and student community, precisely in engineering teaching–learning method. Keywords Traditional approach · Transfer of learning · Authentic learning · Pedagogy · Critical thinking · Project-oriented analysis

A. Deyasi (B) · P. Debnath Department of Electronics and Communication Engineering, RCC Institute of Information Technology, Kolkata 700015, India e-mail: [email protected] S. Bhattacharyya Department of Electronics and Communication Engineering, Siliguri Institute of Technology, Darjeeling 734009, India e-mail: [email protected] A. Sarkar Department of Electronics and Communication Engineering, Kalyani Govt. Engineering College, Kalyani 741235, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 A. Deyasi et al. (eds.), Computational Intelligence in Digital Pedagogy, Intelligent Systems Reference Library 197, https://doi.org/10.1007/978-981-15-8744-3_1

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1.1 Introduction In Indian academic structure, college holds the transition phase between student and professional. The transition phase is significant in terms of both academic and industry perspective. It gives the flavor of real-life applications in prototype format where mode of learning becomes changes compared to school life. In general, traditional learning systems are till days followed in most of the undergraduate level institutions which follows the input–output-based system, i.e., the traditional pedagogical approach. Students are considered as passive learner which does not give the flavor of profound mastery through assimilation of content, but a superficial idea thanks to the total marks-oriented evaluation process. This type of education is alternatively called as content-driven education which basically follows a linear learning model and is not at all suitable to meet the demand of the present century, as per the reports published by Washington accord. Though curriculum has revised by different expert bodies in different levels of education of different subjects, but owing to the adaptation of same process, outcome remains almost indifferent. However, changes are slowly incorporating in this so-called process-driven education system by adopting outcome-based method, precisely in the technological education sector where application of the knowledge learned in the lecture theater speaks about quality of the learners [1–3]. Henceforth, in this chapter, our discussion will be mostly limited in the engineering education, and the impact of outcome-based method [4] compared to the conservative method. Several methods are already discussed and published by different educationists in the last few years for incorporating the outcome-based system, and works are also extended in medical domain also where real-time work deals with life of human and animals [5]. Among the methods, flipped leaning [6], active learning [7], authentic learning [8] and blended learning [9] are the choice of teachers because of their novelties. Among them, a little bit focus is nowadays shifted into authentic learning where all the students are engaged in problem-solving irrespective of dimensions with the help of critical thinking. The problems are selected in such a way that only textbooks and conventional working formulae are not at all sufficient to reach the conclusion. It has also given some comfort zone to the teachers as it is completely structured in terms of providing instructions, and all the end results are properly collected in an organized fashion into portfolios. It is a mapping of classroom with the real world through the problem-oriented assignments, but completely controlled by instructions. Therefore, this type of learning can be considered as a series of well thought-out activities. Concept of authentic learning is not very old; actually, it evolves with changing approaches of instructions inside the classroom [10]. Simultaneously, different cognitive processes are also discussed [11] for the sake of various learning methods. Basically, authentic pedagogy helps the learners to solve equivalent real-life problems in the four walls of classroom under instructional mode which is a shift of paradigm [12] from the well-known marks-oriented approach. The prototypes developed can be made a demonstrable product which helps a similarity analysis with the actual

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product available or required in the demand situation. Role of teacher is converted from dictator to facilitator, also to become a project manager. Learning can be made from environment, but not in a whimsical fashion, but under the structured guidance. In authentic learning (AL), not only concepts are utilized, but the knowledge is blended with the practical experience to ultimately produce a feasible outcome. The design or the prototypes may not always be experimental, but simulation works are equally accepted provided it is made under the environmental constraints. In this context, student’s perspective becomes critically important [13] for successful implementation of the method. Different ways of support are also provided to the learner to achieve the goal, which can be shared with local as well as maybe with global community. Careful approach is made from teacher’s point of view to implement optimal learning which is effectively utilized to produce meaningful tasks through regular practice and that involves multi-sensory activities. Not only outcome is mattered, but quality of the method to achieve it becomes critically important and meaningful. The process should be ethical as per the norms and that is checked. Through several unit level tests and assignments, a productive skill set is generated by reiterative manner [14] within a pre-defined timeline. In this context, it is the demand of teacher from learner side that he/she should introduce the self-aggravated inquest methods to make the product, useful for at last a specific community. A learner-side approach is different from the traditional approach in the form of role reversal. In the earlier age, authoritative figure is considered as teacher and students are forced to play the passive role. However, in all modern pedagogical methods, prominence is given to the learner as well in AL also [15]. Here, the concept of authority is lost, and a shared mode of responsibility is invoked for both the parties. Learning is given predominance over teaching. This happens as instructor is converted to facilitator, so learners are forced to take additional burden coming out from the passive shell. Role of the facilitator is more complex as all the learners do not possess the equivalent background, equal interests of all the subjects and equal foundation at the school level. Therefore, form the faculty’s point of view, a deeper understanding is required. In active learning, teacher cannot impose restrictions [16], but in authentic learning, restrictions are transported in a submissive way through instructions of doing work. It is a sort of negotiation type of work. Each single module of a particular course in any academic currculum that can be described in the classroom should have blended with authentic experience [17], the way the students can be familiar and quickly adopted. This is the objective of meaningful learning, where knowledge can be transferred from ceremonial education to practice. Help can only be provided from the facilitator side when it is desired. The learning environment should be facilitative for AL, i.e., it should be supportive for all category of students as far as practicable where reflective questions are given weight. In order to do that, tasks should be organized in a careful manner where critical hypothetical situations are incorporated. This will permit the learners to think and to respond. The first is important in terms of cognitive skill, whereas the second one supports psychomotor action. A transfer between these two skills required organized debate and dialogue. Task should not be conventional, rather

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challenging, and performance should be assessed both in formative and summative manner. Henceforth, in such situation, learner will take responsibility of learning [18] with the facilitation. Here comes the importance of modern teaching pedagogies, where authentic learning becomes vital due to its structured format. Critical thinking helps to take on the role of professionals with the proper transferability of knowledge. The tools and study materials are not exactly in the textbook format, but web-based contents are given more acceptance than the traditional resources of knowledge. In this context, one point can be emphasized that the role of textbook remains the same and may never be replaced, but accessing multiple data through browsing becomes a rather adopted method simultaneously which helps to solve real-life equivalent problems. Role of collaborative work in this context [19] becomes important to develop the process of critical thinking in the multidisciplinary environment. In this present chapter, sample survey is carried out on few students where authentic pedagogy is implemented. Results are shown as is available on a few subjects and also compared with the data obtained using flipped learning technology. Though the set of students on which the modified curriculum are imposed are different than the students who have undergone the conventional teaching methodology, they belong to the same curriculum, and the same set of courses are considered. At the end, project-based work is invoked through instructional guidance, and results clearly support this pedagogic technique. Statistical analysis has been performed to test the null hypothesis, and result in certain cases speaks in favor of the choice of pedagogic method. The work has similarity with the management information system (MIS) using agile methodology and therefore established the need of this dyadic in present-day technical curriculum.

1.2 Comparative Studies Between Pedagogic Principles Till date, a group of academicians proposed in favor of implementation of flipped learning replacing traditional input–output-based system owing to its uniqueness of giving learners more space, as well as it is a learner-centric approach [20, 21]. Flipped learning involves creating a classroom at home where a learner can proceed in h(is/er) own pace at own time [6], and the assignments are solved in the next day class. This is a reverse classroom strategy and is obviously gaining attention and popularity among students [22] in various countries, as well as parts of Indian educational institutions also. However, it is heavily dependent on the availability of digital resources and also of continuous communication along with infrastructural demand. Web-based learning environment is the need of present generation for students [23], and therefore, flipped learning becomes the choicest pedagogical technique nowadays. However, recent study shows that the technique generates poor results for a few subjects compared to that obtained from traditional teaching–learning approach [24], whereas for other subjects, it generates comparatively better result. A deeper inspection reveals that for physics or mathematics-oriented subjects in engineering education, flipped learning is miserably failure [44].

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Implementation of active learning, as an alternative of flipped learning, is more feasible due to the requirement of less number of infrastructural resources. It does not require continuous uninterrupted Internet connections in the student’s home and is a more systematic and pragmatic approach [25]. In this methodology, students practice complex group problems through Think-Pair-Share mode [26], and outcome is finally justified by project-based learning [27]. Project work gets the maximum importance where all the learning of theoretical and laboratory classes are tested, and its impact is measured by human performance and behavior [28]. Noticeable differences in terms of outcome at every major and minor aspect are observed [24]. From present Indian context, implementation of active learning is much financially justified than flipped learning methodology. A combination of the abovementioned learning technologies is called blended learning, where focus is made on removal of mental barrier between learners and facilitators [24]. Lecture classes are converted into assignment-based, and emphasis is given on discussion forum [29, 30]. Findings after application of this methodology are also available in different literatures for graduate and undergraduate level of students [22, 25, 31, 32]. Results on Indian students are also reported very recently [24]. Authentic learning is very close to blended learning with very little disparity. It is primarily dependent on instructional guideline, and systematic progress is made. Proper instructional approach is applied on different set of learners, and measurable differences are recorded for its further use. Active learning is basically a subclass of authentic learning, which is project-oriented, but that has to be performed within the given instructions. This constraint is far practicable, but it is found that it produces comparatively better results than other pedagogical procedures returned. In this technique, the instructor has the opportunity to tune the performance and therefore gets some indication of student understanding of the material presented during the lecture itself. In the next section, a comparative study is presented between two different sets of students where flipped learning pedagogy and authentic learning technique are independently applied. Both the sets of students are from undergraduate technical level, and different types of courses are taken for the experiment purpose. Results and corresponding methodology become critically important, whenever applied to a large database in real world.

1.3 Procedure of Authentic Learning Education of students either in school level or in degree courses is a really complex and difficult task which has to be performed relentlessly by teachers within a predefined time frame. Highly skilled and qualified teachers provide explicit instructions to train the students, as per the demand of the educational institutions and also of society. Professionals are bit more mature, and therefore, they can adopt various ways of learning [33] to develop skill, attitude and knowledge (information). The age of science and technology serves the data to all classes of people more easily,

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and therefore, representing new information is not essential, but representing in the new mode becomes vital. The importance of right pedagogical approaches comes here and plays a vital role in shaping and nurturing young minds. Learners can get their required meta-cognitive and procedural knowledge about the outside world form the teaching–learning process and be measured as outcome of the course [33]. Professional courses are nowadays designed in such a way that it can develop the logical thinking process based on the accumulated concepts, and these concepts, whenever utilized for the benefit of mankind, may be termed as practical education. At that point, need of the education is succeeded. School level courses deal with natural science and elementary science, but these learning are mostly devoid of practical aspects, though curriculum is design for learner-centric perspective. Teaching– learning for these elementary science levels consists of classroom lecture, discussions, tutorials, laboratory classes, projects, seminars and field works. The pedagogic principle adopted at this juncture becomes the key player for outcome measurement. The most popular teaching method is the lecture method where instructor can simulate and create interest among learners, and the people on the other side of the table can express their opinions or can create it. This is basically utilized to promote the learning. Through proper instruction, it can impart meaningful information and thereby develop critical thinking [34]. This method is popular in all the places across world, and it will remain popular as expected [35]. Through this method, a large number of audiences can be set into a particular tune, thereby saving time and helps to save financial crunch. Existing academic limitations can be overcome by lecture. However, different learners need different methods of inputs and time consumption also, and therefore, tutorials can play a vital role. This tutorial method deals with small no. of group size, so this is a suitable addition with the lectures [36]. A proper combination of lecture and tutorial can clear the concepts and prepare the learner for the next level, i.e., for implementation. Lecturing is the choicest method in a large size class, and scarcity of human resources makes the demand. However, once the phase is over, hands-on experience plays the vital role, which is termed as laboratory class. Also in semester system, this method is the most suitable to cover a large size audience [34]. In the laboratory sessions, where implementation and design have the sole objectives, learners can get the space and infrastructural opportunities so that the learned concepts can take shape. Now this procedure looks almost similar to active learning [37]. However, a small difference exists between these two. Activity learning consists of different activities performed immediately after theoretical learning [29]. However, role of instructional guideline is not a major factor in the activity guideline is not a major factor is the activity guideline [38]. But in case of authentic learning, laboratory classes are based on instructional guideline initially. The implementation phase consists of design-oriented problem, i.e., the project-based activities have the flexibility, where outcome is the only fundamental criterion. The varieties of activities are not primary in authentic learning, but care is taken in such a way that optimal number of activities is enough to learn and assimilate the concepts learned in theoretical classes. Group

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discussions and seminar presentations are the two popular activities for literaturetype courses, where proper instructional guideline makes the learner to understand the real-world problems better. Project-based activity is the implementation phase where learner can be accommodated with real-world problems and will try to solve that [39]. Activities that must be followed are designed specifically for students to use it at home and in the society for promoting knowledge and can be used in many places and in solving different environmental complications. However, choice of problem is partially controlled at the initial phase through instructional guideline. So the term ‘authentic’ is justified, and more precisely, the development life cycle of the project almost follows the agile methodology [40]. This completes the total procedure of authentic learning, quite contrary to flipped learning, and almost similar to activity learning. In the next section, vis-a-vis comparative study is performed between different pedagogic procedures.

1.4 Incorporation of Statistical Analysis For authentication of the findings obtained through different pedagogical techniques, we have introduced a few statistical analyses. A few related works are already published in various literatures where multiple linear regression methods are invoked [41, 42]. It is considered as one of the major statistical techniques for predicting student’s performance and therefore can safely be chosen for understanding the impact of pedagogy on learning outcome. Also the quality parameters at the end play a pivotal role in selecting the right mode of pedagogy where significance value becomes critically important. Under this situation, t-test can be considered as a tool for performance measurement. A few works are reported earlier involving ttest [43]. Therefore, we have also incorporated his method. Results of both the methods are summarized properly in the next section along with the detailed tables obtained. Important findings are properly highlighted, and significance of the results is discussed. Key features from the analysis are critically mentioned in the conclusion section with an overall comparative study between two pedagogic principles, and pros and cons of both of them are mentioned.

1.5 Results and Discussions Two different sets of students are considered for performance analysis in consecutive two years. One group is subjected to flipped learning, whereas other group is undergone authentic learning technique. All the learning subjects are kept same under the same curriculum so that comparison can be justified. Results are also compared with the data obtained from active learning technique, wherever possible. Also the

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feedback data are compared as the effectiveness of teaching, which can be utilized to monitor the progress and the modification of applied technique in the next cycle.

1.5.1 Performance The first analysis tarts with the performance of students in the first year of UG course, and data are taken consecutively for two years. Comparative study is performed with the students’ undergone flipped learning techniques. Table 1.1 deals with the attendance for flipped learning, whereas Table 1.2 consists of the data for authentic learning. Significant positive changes are given in blue color, whereas negative changes are indicated by red color (above 70%). A closer inspection between two tables shows that flipped learning technique becomes effective in humanities discipline or where mathematical/physical application is less. It is also successful for programming-oriented papers. However, for physics and electronics engg-related papers, flipped learning failed miserably. This statement can more be justified once we will move toward the result analysis. Henceforth, flipped learning methodology can be applied from case-to-case basis. Once the performance is obtained, we have carried regression analysis on the available dataset to analyze the effect of both the pedagogic principles on their grades in classes for specific subjects. The summarized results obtained after regression analysis are organized in Table 1.3 and Table 1.4, respectively. /result is computed Table 1.1 Performance for the papers when flipped learning is invoked Chemistry

Physics

Mathemati cs

English language for communicat ion

Programmi ng language

Basic electronics engg

Mechanics

201 6

201 7

201 6

201 7

201 6

201 7

2016

2017

201 6

201 7

201 6

201 7

201 6

201 7

Abo ve 90% 80 – 89%

9.47

13.2 8

57.4 9

32.1 8

57.1 1

13.5 9

7.28

23.1

8.04

30.1 2

55.4 9

30.2 8

55.3 2

29.1 2

19.4 7

24.5 2

25.0 9

21.4 6

26.4 1

33.3 3

7.9

35.3 4

17.0 9

29.5 8

27.2 3

20.4 2

26.3 8

18.0 8

70 – 79%

29.4 7

22.1 9

13.9 4

18.3 9

11.2

9.56

29.7 3

35.2 7

25.1 3

20.1 2

14.5 2

17.9 8

12.3 4

15.8 5

60 – 69%

9.47

18.2 8

3.14

10.3 4

0.5

20.7

45.5 3

4.32

35.1 8

8.25

2.02

11.2 8

3.57

16.3 1

belo w 60%

25.7 9

21.3 2

0.33

17.1 6

0.1

21.2

3.33

0.35

14.3 7

4.93

0.45

18.9 7

0.65

18.7 9

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Table 1.2 Performance for the papers when authentic learning is invoked Chemistry

Physics

Mathemati cs

English language for communicat ion 2016 2017

Programmi ng language

Basic electronics engg

Mechanics

201 6

201 7

201 6

201 7

201 6

201 7

201 6

201 7

201 6

201 7

201 6

201 7

Abo ve 90% 80 – 89%

46.3 2

58.2 5

35.7 8

47.2 4

45.1 2

23.5 9

15.2 3

13.5 4

9.04

4.12

33.5 7

42.5 7

45.2 1

48.1 2

23.1

26.2

20.7 1

25.3 4

32.1 2

33.3 3

9.52

11.2 3

21.6 5

22.3 4

28.7

35.1 6

25.3 6

28.8 7

70 – 79%

19.9 8

7.14

15.7 4

9.65

8.58

19.5 6

26.3 8

24.3 4

27.1 8

26.1 7

20.1 4

8.9

14.3 2

9.25

60 – 69%

4.2

3.25

14.5 1

7.58

3.21

10.7

34.8 7

28.5

30.2

38.2 8

4.89

7.41

2.34

6.3

belo w 60%

2.1

2.65

5.47

3.7

2.3

11.2

5.8

9.78

4.89

3.73

7.54

2.43

7.12

1.34

Table 1.3 Summary of the data obtained after regression analysis for flipped learning Chemistry

Physics

Mathemat ics

English language for communicat ion

P-value

0.17133

0.037712

0.040468

R-Value

0.516544

0.808944

0.01331

Performance parameters

Program ming language

Basic electronic s engg

Mechanics

0.841592

0.391982

0.038087

0.051898

0.015559

0.24914

0.807736

0.766049

Table 1.4 Summary of the data obtained after regression analysis for authentic learning Chemistry

Physics

Mathemat ics

English language for communicat ion

Program ming language

Basic electronic s engg

Mechanics

P-value

0.014467

0.007663

0.120221

0.00133

0.005146

0.029309

0.00546

R-Value

0.897102

0.932131

0.607378

0.978673

0.947784

0.837428

0.945704

Performance parameters

for all the seven subjects as given in the tables, and three most significant factors are evaluated. Those are [i] Significance-F, R-value and P-value. From the above analysis, it is found that if we incorporate flipped learning, grade is severely deteriorated in physics, mathematics, basic electronics engg and mechanics, which is established from the P-value. This is one significant determination form

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regression analysis, satisfied the dataset shown in Table 1.1. For physics and electronics, this is predicted and previously also reported for another different dataset [24]; here, the same is observed from the R-value. Henceforth, flipped learning model is not applicable to the analytical subjects, but can surely be applicable for programming language, English and chemistry. Consequently, if we adopt authentic learning method, it is found that except the mathematics, it provides significant improvement for all other subjects and negative impact on programming language (combined with Table 1.2). Henceforth, for computer language teaching, flipped learning is the most suitable technique, whereas for core science and engineering papers, authentic learning pedagogy proves its supremacy. A few tables obtained from regression analysis are given below for both the pedagogical techniques (Tables 1.5, 1.6, 1.7 and 1.8). After performance evaluation of the first year of students, we have computed the attendance variation w.r.t the year 2015. Corresponding variations of result and placements are also tabulated. Data are shown in tabular format in Table 1.9. From the analysis, it is found that flipped learning is not the best alternative considering different socioeconomic scenario of learners, but in some cases, it works fine. But authentic learning, when proper instructional guidance is provided, works better for all the sections of people. Corresponding outcome is reflected in results and placement scenario.

1.5.2 Quality Analysis Next we have carried out comparative analysis of feedback for both the cases and compared with existing data. Results are shown in Table 1.10. Graphical representation of the data is given in Fig. 1.1, separately for three classes. It may be presumed that different reference levels are considered for making the classes. Figure 1.1a represents the ‘delighted’ students, Fig. 1.1b speaks for ‘confused’ students, and Fig. 1.1c indicates data for ‘boring’ students. Now the authenticity of the result can be analyzed by t-test. So we have performed t-test for all three datasets, and results are summarized in Table 1.11. From the results, both t-stat and P-value are obtained. Henceforth, it is justified from the t-test that authentic learning is enjoyable for students across all subjects compared to flipped learning (dataset is taken considering the overall experience). This pedagogy can significantly improve the quality of students by engaging more students and can be justified from the P-value for delighted students as well as from the P-value of boring students. Since P-value indicates significant changes, henceforth it is significant or both increment and reduction. A few tables from the t-test are given below (Tables 1.12, 1.13 and 1.14):

5

Observations

439.94868

Standard error

4

Coefficients

Total

9.6000771

0.4496267

28.15521775

−0.440495578

Intercept

X variable 1

333.31162

3

Residual

SS

106.63706

1

Regression

Df

10.54058215

Standard error

ANOVA

0.242385234

−0.010153022

Adjusted R-square

0.492326349

R-square

Multiple R

Regression statistics

Summary output

−0.9796918

2.9328116

t-stat

111.10387

106.63706

MS

0.3994851

0.0608623

P-value

0.9597961

F

−1.871408304

0.9904171

Upper 95% 58.70 6948

Lower 95% −2.39651215

0.399485126

Significance-F

Table 1.5 Regression analysis for performance in programming language under flipped learning

Lower 95.0% −1.8714083

−2.3965122

Upper 95.0% 0.990417149

58.70694765

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5

Observations

17.355486

−38.927389

2.9753052

Intercept

X variable 1

0.838 0803

2047.3095

Standard error

Total

393.6246

Coefficients

3

Residual

SS

1653.6849

4

1

Regression

Df

11.454615

Standard error

ANOVA

0.8077357

0.7436476

Adjusted R-square

0.8987412

R-square

Multiple R

Regression statistics

Summary output

3.550 1433

−2.2429444

t-stat

131.2082

1653.6849

MS

0.038 0868

0.110659

P-value

12.603518

F

0.3081596

−94.160291

Lower 95%

0.0380868

Significance-F

Table 1.6 Regression analysis for performance in basic electronics engineering under flipped learning

Upper 95% 5.6424508

16.305512

Lower 95.0% 0.3081596

−94.160291

Upper 95.0% 5.6424508

16.305512

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5

Observations

7.233332

0.59933

X variable 1

Coefficients

Intercept

496.7843

Standard error

4

Total

0.093369

2.301503

33.71623

3

Residual

SS

463.068

1

Regression

Df

3.352424

Standard error

ANOVA

0.932131

0.909508

Adjusted R-square

0.965469

R-square

Multiple R

Regression statistics

Summary output

6.418943

3.142874

t-stat

11.23874

463.068

MS

0.007663

0.051549

P-value

41.20283

F

Table 1.7 Regression analysis for performance in physics under authentic learning

Significance-F

0.302188

0.896472

Upper 95% 14.55774

Lower 95% −0.09108

0.007663

Lower 95.0% 0.302188

−0.09108

0.896472

14.55774

Upper 95.0%

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5

Observations

5.263826

0.711672

X variable 1

Coefficients

Intercept

1268.705

Standard error

4

Total

0.139156

4.008107

130.5471

3

Residual

SS

1138.158

1

Regression

Df

6.596643

Standard error

ANOVA

0.897102

0.862803

Adjusted R-square

0.947155

R-square

Multiple R

Regression statistics

Summary output

5.114206

1.313295

t-stat

43.5157

1138.158

MS

0.014467

0.280498

P-value

26.15511

F

Table 1.8 Regression analysis for performance in chemistry under authentic learning

Significance-F

0.268816

1.154528

Upper 95% 18.01941

Lower 95% −7.49176

0.014467

Lower 95.0% 0.268816

−7.49176

1.154528

18.01941

Upper 95.0%

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Table 1.9 Comparative study of attendance, result and placement attributes Attribute

2015

2016 (For authentic/active learning [24])

2017 (For authentic/active learning [24])

2016 (For flipped learning)

2017 (For flipped learning)

Student’s attendance (Grade = 5)

30

44

52

52

49

Student’s result (Grade = 10)

43

60

75

58

53

Student’s placement (Grade = 10)

75

88

89

80

78

Table 1.10 Comparative analysis of student’s feedback under flipped mode Parameters

Flipped learning [24] Delighted

Confused

Authentic learning Boring

Delighted

Confused

Boring

Quality of content

76

9

11

85

10

4

Delivery

74

14

10

88

6

5

Clarification of 69 concepts

15

12

76

13

9

Teaching the subject matter

73

13

12

79

13

7

Overall experience

71

14

12

76

15

6

1.6 Conclusion Data suggest that both authentic learning and flipped learning are excellent pedagogical techniques for improvement of performance. However, different socioeconomic and environmental conditions are responsible for its effective implementation. From the statistical analysis, it is found out that significance performance improvement is done when authentic learning is invoked for science as well as for core engineering papers, whereas humanities and computer engineering learning can be boosted if flipped learning is considered. Therefore, authentic learning gets the preference and that enhances the active participation of students, justified from the t-test results. Apart from statistical data, it may be considered that infrastructural requirement for flipped learning technique is a source of major concern and that is why authentic learning or active learning becomes popular irrespective of sections and categories. Different aspects are considered for learner’s feedback where appreciation is better received for authentic learning. Though these data on which preliminary investigation is carried out, can’t provide conclusive results, and therefore a large dataset is required for a prolonged period for further conclusion, a reflection can be obtained from the

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Fig. 1.1 a Bar diagram for ‘delighted’ students. b Bar diagram for ‘confused’ students. c Bar diagram for ‘boring’ students

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Fig. 1.1 (continued) Table 1.11 Results obtained from t-test for the data given in Table 1.10 t-stat

P-value

Delighted

-2.62448

0.039349

Confused

1.1163113

0.306989

Boring

4.800266

0.003

Table 1.12 t-test result for ‘delighted’ students

76

85

71.75

79.75

Variance

4.9166667

32.25

Observations

4

4

Pooled variance

18.583333

Hypothesized mean difference

0

Mean

Df

6

t-stat.

−2.624479

P(T ≤ t) one-tail

0.0196747

t Critical one-tail

1.9431803

P(T ≤ t) two-tail

0.0393494

t Critical two-tail

2.4469118

18 Table 1.13 t-test result for ‘confused’ students

Table 1.14 t-test result for ‘boring’ students

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10

Mean

14

11.75

Variance

0.6666667

15.5833333

Observations

4

4

Pooled variance

8.125

Hypothesized mean difference

0

Df

6

t-stat.

1.1163126

P(T ≤ t) one-tail

0.1534943

t Critical one-tail

1.9431803

P(T ≤ t) two-tail

0.3069886

t Critical two-tail

2.4469118

11

4

Mean

11.5

6.75

Variance

1

2.91666667

Observations

4

4

Pooled variance

1.9583333

Hypothesized mean difference

0

Df

6

t-stat

4.800266

P(T ≤ t) one-tail

0.0015

t Critical one-tail

1.9431803

P(T ≤ t) two-tail

0.0029999

t Critical two-tail

2.4469118

findings as represented in this chapter. Only first year of engineering students is considered for evaluation purpose, and implementation in higher-order classes is required for further verification and validation.

References 1. Crespo, R.M., Leony, D., Kloos, C.D., Gutiérrez, I., Najjar, J., Totschnig, M., Simon, B., Derntl, M., Neumann, S., Oberhuemer, P.: Aligning Assessment with Learning Outcomes in Outcome-Based Education. IEEE Education Engineering (2010) 2. Au, O., Kwan, R.: Experience on Outcome-Based Teaching and Learning Hybrid Learning and Education: Lecture Notes in Computer Science, vol. 5685, pp. 133–139 (2009) 3. Nakkeeran, R., Babu, R., Manimaran, R., Gnanasivam, P.: Importance of Outcome based education (OBE) to advance educational quality and enhance global mobility. Int. J. Pure Appl. Math.

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Chapter 2

A Set of Empirical Models to Evaluate E-learning Web Sites and Their Comparison Soumili Dey, Suchandra Datta, Anal Acharya, and Debabrata Datta

Abstract With the advancement of network technologies, Internet users including students and researchers have switched to online learning options or simply e-learning modules due to wide range of advantages. Many educational Web sites have come up with various online courses or e-learning software to facilitate e-learning. However, without proper guidance, students might face problems in selecting an appropriate e-learning platform as they might not be well informed about the quality of the elearning software. Such a well-designed evaluator of e-learning software would not only help to find the best-fit e-learning software, but also to resolve information overloading problem. Hence, evaluating and recommending appropriate e-learning software becomes a vital concern. In this research work, an e-learning software evaluator has been designed not only for evaluating but also ranking the e-learning educational Web sites. This article uses analytical hierarchical process (AHP) and principal component analysis (PCA) to evaluate e-learning software. The results of these were then compared parametrically. Keywords MCDM · AHP · PCA · E-learning evaluator

2.1 Introduction The exponential growth of computer networks resulting in the birth of the ubiquitous World Wide Web has led to the growth and expansion of e-learning. E-learning refers to acquiring knowledge or skill pertaining to a particular topic from materials accessible through some communication networks, notably the Internet and viewed on some electronic devices. E-learning radically changes traditional learning scenarios in the sense that everyone has access to information irrespective of previous formal education, independent of location, cost-effective solution to learning and flexibility of accessing the material. Using this method of learning, it is possible for students S. Dey · S. Datta · A. Acharya · D. Datta (B) Department of Computer Science, St. Xavier’s College (Autonomous), Kolkata, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 A. Deyasi et al. (eds.), Computational Intelligence in Digital Pedagogy, Intelligent Systems Reference Library 197, https://doi.org/10.1007/978-981-15-8744-3_2

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to obtain more exposure, to be taught by industry experts who can share their experiences in addition to the course topics, to learn from professors in some of the most premiere institutions in the world and in general to be able to keep oneself up to date with the latest breakthroughs in varied fields. The lack of infrastructure for many students, particularly those in the information technology sector, is solved by online courses offering virtual laboratories or access to expensive hardware needed for computationally expensive tasks. By utilizing the concept of resource sharing via the cloud, it is possible to provide a personalized learning experience for each and every student. There is no limit to the number of online sites which promise to offer useful courses. It is of practical interest to students and teachers alike if it is possible to establish a quality framework from beforehand against which existing and upcoming sites will be compared in order to understand at a glance which sites offer the best courses and services. Further the huge amount of data available online pertaining to students online course activities suggests the use of educational data mining (EDM) in this context. Educational data mining is a rather new application of data mining and knowledge discovery in databases (KDD) field which focuses in mining useful patterns and discovering useful knowledge from the educational information systems, such as student admissions and registration systems, course management systems (moodle, blackboard, etc.), and any other systems dealing with students at different levels of education, from schools to colleges and universities [1]. Thus, it deals with automatic extraction of potentially useful information and interesting patterns from large amounts of data. KDD is not only used to learn the model for learning process or student modeling but also to evaluate and improve e-learning systems by discovering useful learning information from learning portfolios [2]. Researchers in this field focus on discovering useful knowledge either to help the educational institutes manage their students better or to help students manage their education and deliverables better and enhance their performance. Analyzing students’ data and information to classify students, or to create decision trees or association rules for making better decisions or enhancing student’s performance is an interesting field of research, which mainly focuses on analyzing and understanding students’ educational data that indicates their educational performance and generates specific rules, classifications, and predictions to help students in their future educational performance. Knowledge Discovery in Database or data mining for short thus refers to extracting or “mining” knowledge from exponential amounts of data. Data mining techniques are used to operate on large volumes of data to discover hidden meaningful patterns and relationships helpful in decision making. While data mining and knowledge discovery in database are frequently treated as synonyms, data mining is actually part of the knowledge discovery process [3]. Educational data mining is facilitated with Web-based educational systems which provide a huge amount of information that is easily accessible and can be obtained in numerous methods including using automated scripts. Web-based educational systems which are adaptive and intelligent can provide individually richer learning environments. These systems try to offer learners personalized education by building a model of the individual’s goals,

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preferences, and knowledge [2]. These intelligent systems which provide personalized evaluation of several educational Web sites use the concept of multi-criteria decision making (MCDM). One such MCDM method is AHP or analytical hierarchical process. The evaluating software takes multiple criteria into consideration and provides precise decisions to the Internet users to select desired Web sites. To select a proper educational Web site from a list of several educational Web site, the evaluating software has to inherently involve more than one criterion. MCDM refers to methods for decision making in realistic and common scenarios in which the presence of multiple and conflicting criteria (that is multiple attributes or objectives) must be taken into consideration. MCDM may be used to select or generate a ‘best alternative,’ from a finite set of existing alternatives [4]. However, it can never find a unique solution to a problem as it considers the decision maker’s discretion in making preferences. Hence, several solutions exist for a single problem which makes decision maker’s preferences unique to differentiate between solutions. In the evaluation process, the evaluating software will require a manual interface with the user. The user would quantify weights of the criteria to help the evaluator to compare and select the most suitable set of criteria for each evaluated Web site. This manual interface which is almost precise form of quantified the weights of the criteria is the pairwise matrix of analytical hierarchical process (AHP). The application of multi-dimensional model such as MCDM has found its application in almost every field due to its capability to solve complex and difficult problems in real life. Earlier the traditional model assumed that the criteria are independently and hierarchically structured; however, in reality, problems are often characterized by interdependent criteria and dimensions and may even exhibit feedback-like effects [5]. Hence, to take a more informed and better decision, MCDM helps structuring complex problems well by considering multiple and conflicting criteria. The rest of the paper is divided into sections which are organized as follow: Section 2.2 outlines the various prior works which has been done in this field by numerous researchers. Section 2.3 is a brief discussion on the two proposed methodologies, the working formula, and how it has been applied for our purposes. Sections 2.4 and 2.5, respectively, deal with the implementation details and the obtained results. Finally, Sect. 2.5 concludes with the required discussion.

2.2 Related Work Previous works performed in this field are extensively studied and presented as follows. We note the methodologies used, the parameters considered in most cases, and base our work accordingly. First, we consider important works related to MCDM. MCDM has found its application in many fields which are mainly problems related to the measurement, design, evaluation, ranking, selection, and improvement of organizational initiatives. However, MCDM or the multiple criteria decision-making methods which generally include solving decision making problem by considering

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multiple criterion can also solve real-life problem which are characterized by interdependent criteria exhibiting feedback-like effects. A new hybrid dynamic multiplecriteria decision making (HDMCDM) was proposed to solve interdependent and feedback situations in field of economics and business. It improves interrelationships among criteria to achieve aspiration levels. Some techniques are offered to integrate performance in super-additive/non-additive value function situation. A comprehensive study on how gaining knowledge has changed in this digital age shows that information and communication technology has provided with a new improved framework for teaching and learning purpose [6]. It highlights the use of digital media like journals, Web sites, blogs, educational videos that are of use to teachers and students alike, facilitated with infrastructure to support online learning. It focuses on the numerous initiatives taken to introduce good quality online educational programs at higher education levels, thus bringing in a need to rank the numerous sites to help new learners to choose sites that would help them to learn effectively. Adequate digital access and how it affects the learning capability of the students should also be quantified [7]. The paper investigated the performance gains of students from digital pedagogy. The findings are to be focused on, so that teachers can facilitate the process while avoiding any pitfalls. The study found that the creation of material as understood by students and made available on digital platform greatly enhanced their learning procedure. It formulated four important principles, namely knowledge comes from research and practice, achievement issues addressed by digital pedagogy, evaluating the effectiveness of innovations improves practice and attention to systemic change. AHP has been studied extensively and is used in almost every application related with MCDM. It has been seen that instead of using AHP alone, it has been used with combined mathematical tools such as mathematical programming, quality function deployment (QFD), meta-heuristics, SWOT analysis, and data envelopment analysis (DEA) and was proved to be better. The analysis of application of integrated AHP has answered three questions: which integrated AHP was more widely used, which area the integrated AHP were prevalently applied to, and was there any inadequacy of the approaches. If such inadequacy existed, then what were the possible improvements and future works. AHP had also found its application in enhancing strengths, weaknesses, opportunities, and threats (SWOT) analysis. The SWOT analysis is a generally used tool which examines strengths and weaknesses of organization or industry together with opportunities and threats of the marketplace environment. AHP approach achieves pairwise comparisons among factors or criteria in order to prioritize them at each level of the hierarchy using the eigenvalue calculation. AHP is mainly used for prioritizing and comparing the SWOT factors [8]. AHP has also been applicable in economic, social, political, and technological areas. AHP is not only useful in decision making but also in planning, conflict resolution, and forecasting. The main mathematical models on which economics has based its quantitative thinking up to now are utility theory which uses interval scales and linear programming. The axiomatic foundation of utility theory uses gambles or lotteries to elicit judgments about utilities from decision makers. However, AHP offers economists a substantially different approach to deal with economic problems

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through ratio scales. AHP offers psychologists, sociologists, and political scientists the methodology that they have sought for some time to quantify and derive measurements for intangibles. AHP also helped providing people in the physical and engineering sciences with a quantitative method to link hard measurement to human values. In such a process, one needs to interpret what the measurements mean. The applicability of PCA as a ranking tool has been investigated in numerous research papers over the years. PCA has been used to rank World Universities, and the results are compared to QS world ranking results [9]. Performance indicators are selected depending on the QS World University rankings, covering aspects of research, teaching, employability, and internationalization. The variables are academic reputation as measured through a global survey, employer reputation, student-to-faculty ratio, citations-per-faculty, international faculty ratio, and international student ratio. QS World University rankings are the publication of the rankings of world universities by Quaquarelli Symonds. It is the only international ranking that has received International Ranking Expert Group approval. In contrast to QSWUR which assigns weights to the selected variables on an individual basis, PCA strives to assign weights as a whole depending on their collective contribution. It essentially investigates the correlation between the variables. These values were standardized by converting the numerical value of the ranks to corresponding percentages. The principal components are calculated, and sum of each principal component multiplied with its variability gives the final value of the rank. The higher the value of the rank, the better is the rank. There is some difference between the ranks given by PCA and those given by QS. The discrepancy arises due to the fact that PCA extracts the principal components at first, thereby establishing a relative scale of weights which is not predetermined. PCA has also been used for ranking sports related data, namely player ranking for cricket players [10]. The batting variables used are runs, batting average, batting strike rate, fours, sixes, and a new variable that was constructed combining the number of centuries with the number of fifties in an innings. The histograms for the variables were investigated, and a matrix plot of the variables revealed some correlations between the variables. High values of the variables mark the better performance of a player with respect to only that variable, but their joint contribution to performance is to be calculated. A similar treatment is made of the batting variables. Players are ranked then using the usual method for PCA, and a discrepancy with the ranking obtained from Ramakrishnan method is noted. It maybe attributed to the fact that PCA takes into consideration more variables than the other method. In [11], further applications of PCA are investigated but in a different context. An attempt is made to propose an information quality framework for ranking elearning sites, but PCA is used to rank or find the relative importance between the factors under consideration. Using linear regression, the relative importance of each dimension inside the quality factors is calculated. To measure the reliability of the research results, Cronbach’s coefficient alpha is considered where the minimum accepted value can be extended to 0.6 [12, 13]. To further improve the reliability, data items which showed very little corrected correlation were removed. The results show that there are three information quality factors in e-learning systems since the

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point of inflexion of the scree plot is at three. Contextual and representation quality factors are measuring the same aspects from user’s point of view. The final framework has 14 quality dimensions grouped under three factors: intrinsic with relative importance score of 41.157% of the overall quality; contextual representation with a score of 33.851%, and accessibility with a score of 24.992%. Under the intrinsic factor heading, objectivity is the most important dimension. Contextual representation factor saw reputation as the highest relative importance, and accessibility and response time saw almost the same relative importance within accessibility factor with scores 29.693% and 29.888%, respectively. The resulting framework will be used for comprehensive indication of information quality in e-learning systems. PCA based Web page ranking can be combined with linear regression [14]. Separate linear regression models might be used with further extension to probabilistic clustering based on EM algorithm. Clustering means grouping similar items together without well-defined labels on the items to distinguish between the data or welldefined markers that would highlight which data are similar to each other. This is done in search engines to group Web pages together based on similar content. Dependencies between similar Web pages are exploited using PCA and linear regression. PageRank makes predictions based on URL features which do not always perform well since the extracted features may be having high correlation with the subject of the site but do not in any way give us any assurance of the soundness of the material or the authority of that site. A different approach is identifying a small portion of the Web graph in the event of a few link changes, changing only that small vicinity, perform PageRank on the reduced graph and then transfer changes to the original graph. This approach involves the overhead of continuous monitoring of the Web graph to keep tabs on any link modification.

2.3 Proposed Model To evaluate the quality of the e-learning experience offered by Web sites, certain parameters are considered which take into consideration various fields of assessment such as the duration of the course, the price of contents, the rate of learning, and so on. These parameters are selected after careful examination of Wang and Strong’s data quality framework, one of the most comprehensive, popular, remarkable, and cited data quality framework, established by Richard Wang and Diana Strong in 1996 [15]. Their framework was designed empirically by asking users to give their viewpoint about the relevance of the information quality dimensions to capture the most important aspects of data quality to the data consumer [11]. Domain experts propose different sets of criteria for evaluation which may not be identical [4].

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2.3.1 Parameters Considered for Evaluation Based on the decomposition of quality dimensions into groups, the parameters chosen for consideration are as follows: 1. Intrinsic data quality: This refers to the quality dimensions like correctness and accuracy the course contents originating from the data itself, independent of the user’s point of view, preference or understanding. (a) Correctness and accuracy of the course contents: The content should be updated frequently to minimize chances of outdated information prevailing. It should include recent breakthroughs in corresponding areas of study. For example, for technical courses like programming languages or Web frameworks, the latest features introduced should be mentioned, properly highlighting deprecated ones with reference to the documentation. 2. Contextual data quality: Focuses on information quality with respect to the context of the task at hand. In this group, the quality dimensions are subjective preferences of the user. Unlike the first group, data quality dimensions cannot be assessed without considering the users viewpoint about their use of provided information. The dimensions considered are: (a) Teaching expertise of the instructor: The manner of teaching should be such that students without prior knowledge of the subject at hand would be able to grasp the basic concepts in a reasonable amount of time while enjoying the learning process. It is necessary to provide intuitive understanding instead of just outlining the theoretical concepts. For example, the intuition behind gradient descent algorithm should be provided rather than simply listing the equation. Usage of different media, video or otherwise should be properly employed in order to provide a wholesome learning experience. (b) Average time duration of the courses: Courses with different time durations would appeal differently to different users subject to their current situation in life. Students with regular college or school course work would prefer courses of shorter time duration, which would allow them to complete school and college work parallel to the online course. The duration of courses should justify the content, courses with little content, but extended duration due to redundancy would be unlikely to attract new students. (c) Number of assignments provided per course: Sufficient number of wellthought-out assignments tests the soundness of the knowledge acquired by the students and their underlying ability to apply it in practical, realworld applications. The assignments must be appropriately checked online. Student progress maybe temporarily inhibited till the assignments are completed satisfactorily. (d) Price versus the quality: The course expense should not be prohibitively huge to attract a larger number of people to enroll for the course. It should be affordable. The quality of the course should be proportional to the price.

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3. Representational information quality: This refers to the representation of information within the systems. How the information is represented affects the user’s experience of e-learning. (a) Usability of the software: The software should be such that users with no prior experience or formal introduction of using such sites would be able to do so intuitively. The software should be such that it can be learnt, understood, and operated without hassle. (b) Maintainability of the software: Usage of proper object-oriented paradigms to facilitate addition of new functionalities without disturbing the existing setup. Scalability and adaptability with new technologies are key features to be considered. (c) Lucidity of explanation of tests: The answers to the assignments should be explained in depth keeping in mind the difficulty level of the learner. Tests also should be carefully constructed with emphasis on the application of the learned items in real-world situations. 4. Accessibility information quality: Refers to the quality aspects concerned into accessing distributed information. (a) Response time to the users: This time should be sufficiently less to allow for a smooth learning experience without the learners becoming impatient of long waits. (b) Web page design encouraging easy access: A Web page design with proper UI/UX features to allow students unfamiliar with such digital learning to intuitive navigate around the site, links to forum, course material, assignments submissions, and the like. It should be properly responsive to scale well on a variety of devices allowing access to course materials from different platforms. (c) Provision for offline usage by downloading the course contents 5. Other factors: Factors which we introduce for a comprehensive evaluation of e-learning sites which focuses on user satisfaction, user experience, and how personalized an experience the Web site is capable of offering (a) Existence of active discussion forum: Discussion forums help students who are all learning the same course to interact and work together to clarify doubts and discuss assignments. When questions are encouraged and answered in a reasonable duration of time, students feel encouraged to further their progress with the course. Discussing the subjects taught with other students around the world offers richer experience learning from others with varied experience and research backgrounds. (b) Provision for an active student teacher interaction: This is imperative for doubt clearing, keeping up-to-date with latest trends, learning about future scope and expansion, and attending seminars and useful workshops. (c) Existence of courses with different difficulty levels for beginners (d) Enjoyment and satisfaction in finishing the course

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(e) Certification: The existence of certificates given at end of the course would encourage certain students. (f) Provision for controlling the learning rate: Courses where students learn at their own pace are preferred over ones where strict classroom times are enforced. College graduate students were consulted to fill out a survey form. It had participants who enrolled for and had completed online courses from at least one e-learning platform if not more. Of all the participants, 50% are men and 50% women. Two questionnaires were created; one consisted of a set of scoring 16 parameters for each Web site with 10 being the best score and 1 being the least desirable score. The other set consisted of asking users for pairwise comparison and preference of one parameter over the other, example comparing the Web site’s pricing of a course to the quality of content of the same. The first set is used for implementing PCA, and the second set is used for AHP.

2.3.2 Proposed Model Using PCA PCA or principal component analysis is a method that is commonly used for dimensionality reduction of a dataset, that is, when given a large number of variables which exhibit some form of correlation with each other, PCA will reduce those variables to a new set of variables called principal components which are not correlated [16]. Dimensionality reduction is done either by feature extraction or by means of feature elimination. When employing feature elimination, we simply do not consider those variables whose exclusion will not affect the model’s ability to rank, but when feature extraction is employed, it essentially creates new independent variables ordered by how well they predict the dependent variable and these new variables are our principal components. The intuition is that this method helps to identify a set of uncorrelated variables which forms the basis in a new subspace. Each vector of the original dataset can now be expressed as a vector in the new subspace by some linear combination of the basis and vector’s existing values. Typically, the new subspace is of dimension k, whereas the original vectors of the dataset are n dimensional and k < n. Projections should maximize the variability of data which in turn captures the uniqueness of the data, and projections are made in those directions where the data is most spread out. Variance is the most common measure of the spread of a set of points given as Var(x) = sx2 = (1/(m − 1))

m  

 − 2

xi − x

(1)

i=1

where x i is the ith member of the matrix, x¯ is the mean of the terms, m is the total number of terms in the matrix, and sx2 is the variance.

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Covariance is calculated as follows 



Cov xi x j =



 m 1 (xki − x¯i )(xk j − x¯ j ) m − 1 i=1

(2)

where x¯ j is the mean of terms for xj , and x ki is the kth term of vector x i . Essentially, PCA involves calculating how the variables relate to each other and quantifying it via a matrix. Two important properties are isolated the directions of the data and the magnitude in those directions. PCA only focuses on those directions which are most important. Next the existing data is tried to be expressed in these directions, by projecting onto a smaller space the dimensionality of the data is reduced. For the matrix of independent variables for each column, subtract the mean of that column from each entry to get a mean of zero. If the importance of features is independent of the variance of the features, each column entry is divided by that column’s standard deviation. The resultant matrix is multiplied with the transpose of the resultant matrix to get the covariance matrix. The covariance matrix is used when the data are all calculated on the same scale, and the correlation matrix is used when the scales are different. Standardizing each of the variables to zero mean and unit standard deviation is same as using the correlation matrix. For our purposes, all the variables are on the same scale, each taking values from one to ten, so explicit standardization is not needed. Given a random vector X = (X 1 , X 2 , … X p )t consisting of p random variables, having covariance matrix  and eigenvalue–eigenvector pairs (λ1 , e1 ), (λ2 , e2 ), … (λp , ep ) where λ1 ≥ λ2 ≥ … ≥ λp ≥ 0. The ith principal component L i is as follows L i = ei1 X 1 + ei2 X 2 + … + eip X p for i = 1,2,…, p where ei1 is the eigenvector for ith Eigen value. This presents principal components as linear combinations of the original random variables. Further, it can be shown that [17]: 1. Yi = ai1 X 1 + ai2 X 2 + · · · + ai p X p

(3)

is any other linear combination of these original variables, then for the first principal component, Var(L 1 ) = λ1 ≥ Var(Yi )

(4)

From these, it is observed that the principal components L i can be used to capture the important signals aggregately contained in the original variables X 1 , X 2 ,…, X p . 2. Cov(L i , L i ) = 0

(5)

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for i not equal to j. Observed that this may be done without redundancy. 3. p



Var(X i ) = p

i=1



Var(L i )

(6)

i=1

hence providing a means of identifying the contribution of each principal component since the total variance is   Var(X 1 ) + Var(X 2 ) + · · · + Var X p = Var(L 1 )   + Var(L 2 ) + · · · + Var L p = λ1 + λ2 + · · · + λ p

(7)

Prior to application of PCA, certain tests need to conducted namely Bartlett’s test and KMO test to find out whether the data collected is suitable for factor analysis. KMO and Bartlett’s test of sphericity is a measure of sampling adequacy that is recommended to check the variable ratio for the analysis being conducted. In most academic and business studies, KMO and Bartlett’s test play an important role for accepting the sample adequacy. It is a statistic that indicates the proportion of variance in the variables that might be caused by underlying factors. While the KMO or KaiserMayer-Olkintestranges from 0 to 1, the world-over accepted index is over 0.6. A value closer to one is desirable, and values below 0.5 are inadequate for PCA. KMO j =

 i= j

⎛ ⎞   ri2j ⎝ ri2j + ai2∗j ⎠ i= j

(8)

i= j

where r ij is the correlation between i and j and aij 2* is the anti-correlation image. KMO values between 0.5 and 0.7 are considered average, between 0.7 and 0.8 are considered good, whereas values between 0.8 and 0.9 is very good, and values above 0.9 are excellent [17]. If the measure of sample adequacy obtained is less than 0.5, then the variables whose measure of sample adequacy is less will be discarded, and the KMO test performed again till the desired value is obtained after one or more successive discarding of variables. The diagonal element of the anti-image correlation matrix gives the KMO values for individual variables. These values should be greater than 0.50 for all variables. Bartlett’s test of sphericity relates to the significance of the study and thereby shows the validity and suitability of the responses collected to the problem being addressed through the study. For factor analysis to be recommended suitable, the Bartlett’s test of sphericity must be less than 0.05. Bartlett test evaluates the presence of correlation between the variables. It checks whether the observed variables correlate at all using the observed correlation matrix against an identity matrix. If the test is statistically insignificant, then factor analysis cannot be employed. It tests the hypothesis that the correlation matrix is an identity matrix in which case the

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variables are uncorrelated and hence unsuited for factor analysis. So if the p-value is zero, then the test is statistically significant. Comrey and Lee [18] provided the following scale of sample size adequacy: 50—very poor, 100—poor, 200—fair, 300—good, 500—very good, and 1000 or more—excellent. Let each row of the original dataset corresponding to a set of values input by one user be represented as X = (x 1 , x 2 , x 3 , …, x 16 )t since we have 16 parameters. The correlation matrix for the input dataset is calculated. From the correlation matrix, 16 eigenvalues are obtained. The variance of each eigenvalue specifies how much of the total variance is captured in each principal component. The first principal component will be the one with the highest value of the variance. Variance means here summative or total variance. For dimensionality reduction, it is imperative to include those principal components which have the highest values of the variance. The variability of the eigenvalues is calculated as λi /λi . To determine how many of the principal components should be considered as a minimum, the scree plot is constructed with the eigenvalue as the dependent variable and the principal component number as the independent variable. The elbow in the plot is noted, and the abscissa of the point beyond which there is no appreciable change in the variability is noted. For our case, we can consider as many as principal components. Using the eigenvalues, the corresponding eigenvectors are constructed. The principal components are calculated as follows: The first principal component is L 1 = e11 ∗ x1 + e12 ∗ x2 + e13 ∗ x3 + · · · + e116 ∗ x16

(9)

where X is a column vector and each eigenvector is a row vector. The second principal component is calculated as follows: L 2 = e21 ∗ x1 + e22 ∗ x2 + e23 ∗ x3 + · · · + e216 ∗ x16

(10)

The nth principal component is calculated as follows: L n= en1 ∗ x1 + en2 ∗ x2 + en3 ∗ x3 + · · · + en16 ∗ x16

(11)

The 16 principal components thus obtained can be summed up as the total rank using Rank = L ∗1 percentage of variation of L 1 + L ∗2 percentage of variation of L 2 + · · · + L ∗n percentage of variation of L n (12)

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2.3.3 Proposed Model Using AHP AHP or analytical hierarchical process is one of the MCDM or multi-criteria decisionmaking methods that were developed by Thomas L. Satty. A complicated problem is decomposed into a multilevel hierarchical structure with respect to the objective, criteria, and alternatives and hence expressing the general decision operation. Pairwise comparisons are performed to derive relative importance of the variable in each level of the hierarchy and/or appraise the alternatives in the lowest level of the hierarchy in order to make the best decision among alternatives. When subjectivity exists and where a problem can solved by organizing the decision criteria and the subcriterion in a hierarchical way, AHP becomes an effective decision-making method [19]. The three main operations in AHP, includes hierarchy construction, priority analysis, and consistency verification. The hierarchy construction step involves breaking down complex multiple criteria decision problems into its component parts of which every possible attributes are arranged into multiple hierarchical levels [20]. The priority analysis step involves a construction of several pairwise comparison matrixes. The pairwise comparison matrix is a manual interface with users or the decision makers which contains almost precise form of quantified weights of the comparisons of the criteria in a pairwise fashion. Since the comparisons are carried out through personal or subjective judgments, some degree of inconsistency may arise while constructing the comparison matrixes. To guarantee the judgments are consistent, the final operation called consistency verification is regarded as one of the most advantages of the AHP. It is incorporated in order to measure the degree of consistency among the pairwise comparisons by computing the consistency ratio. If the consistency ratio exceeds a certain limit, then decision makers should review and revise the pairwise comparisons. Once all pairwise comparisons are carried out at every level, and are proved to be consistent, the judgments can then be synthesized to find out the priority ranking of each criterion and its attributes. The ranking procedure would enable the users to choose the most appropriate e-learning software. AHP is used to determine relative priorities on absolute scales from both discrete and continuous paired comparisons in multilevel hierarchic structures. The prioritization mechanism is accomplished by assigning a number from a comparison scale (see Table 2.1) developed by Saaty [21] to represent the relative importance of the criteria. Pairwise comparisons matrices of these factors provide the means for calculation of importance [19] (Table 2.2). In the proposed model, we break down the complex multi-criteria decision-making problem into a hierarchy of interrelated decision elements. Here, selecting the appropriate alternatives or educational Web site is the decision to be made, and multiple criteria are the parameters considered for selecting the Web site (Fig. 2.1). In the next step, we create the pairwise comparison matrix which comprises of the comparisons of the criteria and the available alternatives. The construction of the matrix is done by determining the relative importance of the criteria. In each level, the criteria are compared pairwise according to their levels of influence and based

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Table 2.1 Parameters considered for evaluation CRITERIA Intrinsic data quality Contextual data quality

SUBCRITERIA Correctness and accuracy of the course contents Teaching expertise of the instructor Average course duration Number of assignments provided per course Price versus the quality

Representational information quality

Accessibility information quality

Other factors

Usability of the software Maintainability of the software Lucidity of explanation of tests Response time to the users Web page design encouraging easy access Availability of offline version

EXPLANATION The content should be updated frequently to minimize chances of outdated information prevailing. The students without prior knowledge of the subject should be able to enjoy learning in less time. Courses with different time durations would appeal differently to different users. Sufficient number of assignments tests the soundness of the knowledge acquired by the students their ability to apply it in practical, real-world applications. It should be affordable. The quality of the course should be proportional to the price. The software should be such that it can be learnt, understood and operated without hassle. Usage of object oriented paradigms to facilitate addition of new functionalities. The answers to the assignments should be explained in depth. This time should be less to allow for a smooth learning experience. A web page design with proper UI/UX features to allow students unfamiliar with such digital learning. There must be a provision for offline usage by downloading the course contents

Existence of active discussion forum Provision for an active student teacher interaction Different difficulty levels

Discussion forums help students who are all learning the same course to interact and work together to clarify doubts and discuss assignments. This is imperative for doubt clearing, keeping up-to-date with latest trends, learning about future scope and expansion, attending seminars and useful workshops. There must be different difficulty levels of the course for beginners

Enjoyment and satisfaction

Learners must enjoy while learning and find satisfaction in finishing the course.

Certification Provision for controlling the learning rate

The certificates given at end of the course would encourage certain students. Courses where students learn at their own pace are preferred over ones where strict classroom times are enforced.

Table 2.2 Pairwise comparison scale [19] Intensity of importance

Explanation

1

Two criterion contribute equally to the objective

3

Experience and judgment slightly favor one over another

5

Experience and judgment strongly favor one over another

7

Criterion is strongly favored and its dominance is demonstrated in practice

9

Importance of one over another affirmed on the highest possible order

2, 4, 6, 8

Used to represent compromise between the priorities listed above

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Fig. 2.1 Hierarchical model used in AHP

on the specified criteria in the higher level. In AHP, multiple pairwise comparisons are based on a standardized comparison scale of nine levels [19]. Let C = {C j | j = 1, 2, …, n} be the set of criteria, where n is the number of criteria. Let A be the pairwise comparison matrix of size n × n in which every element aij (i, j = 1, 2, 3, …, n) is the weights of the criteria. This pairwise comparison can be shown by a square and reciprocal matrix. Each entry aij of the matrix A represents the importance of the criterion i relative to criterion j. If aij > 1, then the criterion i is more important than criterion j, while if aij < 1, then the criterion i is less important than the criterion j. If two criteria have the same importance, then the entry aij is 1. The entries aij and aji satisfy the following constraint: ai j .a ji = 1

(13)

Obviously, ajj = 1 for all j. Once the matrix A is built, it is possible to derive from A the normalized pairwise comparison matrix Anorm by making equal to 1 the sum of the entries on each column, i.e., each entry ajk of the matrix Anorm is computed as a jk a jk = n l=1

alk

(14)

Finally, the criteria weight vector w or the priority vector (that is an m-dimensional column vector) is built by averaging the entries on each row of Anorm , i.e., n wj =

l=1

n

a jl

(15)

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For a matrix A, aij denotes the entry in the ith row and the jth column of A. For a vector v, vi denotes the ith element of v. Here, n 

wj = 1

(16)

j=1

The priority vector for the criteria and the subcriteria is first evaluated by computing the pairwise comparison of the criteria with respect to the goal. Each criterion has been pairwise compared to each other, and each of the subcriteria has been pairwise compared to each other to obtain the priority vectors. Hence, separate comparison table has been obtained. One table is the pairwise comparison of the major criteria, and the other tables are pairwise comparison of the subcriteria. The priorities obtained are the local priorities for each of the pairwise comparison matrix. The summation of the local priority vectors result to 1.000. Now, we are to find the global priority for each of the subcriteria, so that summation of all the global priorities through the hierarchy will add up to 1.000. If we have the criteria c1, c2, c3, and c1 has the subcriteria c11, c12, c13, and c14, then the global priorities for c11, c12, c13, c14, c2, and c3 will add up to 1. If a criterion does not have subcriteria, then the local priority becomes the global priority. Else to obtain the global priority for each of the subcriteria, the local priority is multiplied with the local priority of the parent criteria. Next the pairwise comparison is made between each of the alternatives with respect to the subcriteria and the major criteria (whose subcriteria do not exist), and their respective local and then global priorities are obtained. This becomes the third hierarchical level. Adding up all the global criteria for each of the alternative would contribute to the rank matrix. The alternative which will have the maximum value is ranked 1. It is to be noted that summation of the values of the rank matrix will be equal to 1 (Table 2.3). To check the consistency of the matrix created that is to calculate the consistency ratio (C.R), a matrix A3 is created by matrix multiplication of Anorm and W matrix. λmax is obtained by averaging the summation of A3i /W i where i = (1, n) [8]. The consistency index of a matrix of comparisons is given by C.I. = (λmax − n)/(n − 1). The consistency ratio (C.R.) is obtained by comparing the C.I. with the appropriate one of the following set of numbers (See Table 2.4) each of which is an average random consistency index derived from a sample of randomly generated reciprocal matrices using the scale 1/9, 1/8… 1… 8, 9. C.R = (C.I/R.I). If C.R is not less than Table 2.3 Platform and implementation details

Operating system

Windows 10 64-bit

Processor

Intel Core i5-8400

RAM

8.00 GB

Python version

3.6.8

Microsoft excel version

2007

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Table 2.4 Comparison of the two methods Parameter under consideration

AHP

PCA

Manner of input

Explicitly list user preferences by specifying how much weight one can place on each parameter at each level of the hierarchy which is done by comparing the criterion pairwise. The comparisons are expressed in a form of matrix which is almost a precise form of quantifying the weights of the criteria

Explicitly list user preferences subject to all parameters without any hierarchy so which particular parameter is more important is not known at time of input. If he gives a large number for Web design, all we know is that that Web site has satisfying Web design, but we do not know whether he believes that particular parameter to be more important than others

Interpretation of weights assigned to parameters

Users specify how much they prefer a parameter over others at different levels of the hierarchy independent of Web sites to be evaluated. Hence, first user’s preference of parameters is obtained. If parameters have subcriteria, then the user lists preferences for those. The weights for parameters which are given via a comparison matrix are partially filled in by the user. In the matrix, if the user gives aij > 1, then we explicitly know that criterion i is preferred more than criterion j

The user specifies how much he prefers a particular aspect of a site without an explicit comparison with another site. It is implied that if he highly prefers say, content for one site, and gives a low score for another site, then he prefers the former site over the latter with respect to the content. The weights for parameters are given by the eigenvectors and variance explained by each of the eigenvalues

Interpretation of Web site preferences from user

When alternatives are listed, user explicitly specifies how much a Web site is preferred over others with respect to each and every criteria; hence, this is more rigorous

User lists preferences in general without parameter wise ranking

Interpretation of output

The final values obtained lists users preference of one site over another. The Web site with the maximum value in the rank matrix is ranked 1. Since the summation of the values in the rank matrix equals to 1, the value multiplied by 100 gives the preference percentage

The final values obtained after multiplying the dataset with eigenvectors lists the relative ranking of sites with the most negative score being the best Web site among the ones selected

(continued)

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Table 2.4 (continued) Parameter under consideration

AHP

PCA

Number of users verses number of Web site

If we have n number of Web sites, then with this method we can rank the Web sites according to a particular user according to the preference of different types of criteria. Thus, the ranking of Web sites is user specific or personalized for a particular user

For n numbers of Web sites, we have n number of users, and it is assumed that the users have already used the Web sites. Each user will weight all the criteria for the particular Web site that the user had used. After processing the weight matrix, the Web sites will be ranked according to the unbiased feedback provided the users. Thus, the ranking of the Web sites is general for any user. These users can be those who do not have any specifications about the criteria or do not have much knowledge how to choose or which criteria must be given the maximum preference

Number of question asked A large number of question for construction of paired comparisons are asked from the decision makers

Relatively less number of questions are asked

Consistency check

Does not involve consistency check, it assumes all parameters are correlated and decomposes it into a set of uncorrelated components

Since consistency is checked after construction of every pairwise comparison matrix, AHP enhances the decision makers learning and decision at every step

0.10, the problem is studied again and the judgments are revised. The AHP includes a consistency index for an entire hierarchy. An inconsistency of 10 percent or less implies that the adjustment is small compared to the actual values of the eigenvector entries.

2.4 Implementation For the purpose of data collection, a questionnaire was created to be filled up by the unbiased students who took part in the survey. Some of the questions are as follows: Q1. How would you score the price of the courses on the Web site, 10 being very satisfied and 1 being the least?

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Q2. How would you score the content of the courses on the Web site, 10 being very satisfied and 1 being the least? Q3. How would you score the average time duration of the courses on the Web site, 10 being very satisfied and 1 being the least? Such type questions provide sufficient information to perform the calculations using PCA. For AHP some of the questions formulated are as follows: Q1. How would you rank the intrinsic data factor with respect to the other factors with numerical value of the rank having the meanings as listed in Table 2.4? Q2. How would you rank teaching with duration of the course, assignments, price vs quality? Q3. How would you rank Web site A with Web site B subject to usability, maintainability, and lucidity?

2.4.1 Results Using PCA The calculation of principal components and eigenvalues is implemented in Python. It is an elegant, powerfully equipped language in terms of its rich libraries and astonishing simplistic syntax. Major operations for PCA like calculating the percentage of variance explained by each eigenvalue or calculating the eigenvectors is already implemented as part of the sklearn library. It uses singular value decomposition or SVD to project the input to a lowerdimensional space. For each feature, the data is centered which means a value is subtracted from data item so as to redefine the zero point for the feature under consideration (Fig. 2.2). However, it does not standardize the data. It uses the LAPACK implementation of the full SVD or a randomized truncated SVD by the method of Halko et al. [22], depending on the shape of the input data and the number of components to extract. It is observed that the transformed dataset obtained from PCA using scikit learn is not the one obtained by manual multiplication of input dataset and eigenvectors. The reason is that scikit learn library implementation of PCA centers the dataset prior to application of any mathematical operations. So the dataset is centered to zero mean before applying PCA. Missing values also need to be checked. The greater the magnitude of the rank, the better the rank of the Web site compared to others in that domain as ranked by unbiased students considered in the survey (Fig. 2.3). In Fig. 2.3, TE stands for teaching expertise, NA stands for number of assignments, STE stands for student–teacher interaction, WPD stands for Web page design, MS stands for maintainability of the software, REUSR stands for response to user, ENJOYM stands for learner enjoyment, and LRCNTRL stands for learner controls pace of study. The most negative rating gives the best rank. The first Web site has the highest ratings and numerically least rank of −4.75. If the scoring from the evaluators had

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Fig. 2.2 Scree plot for eigenvalues

Fig. 2.3 Table containing the dataset

been in reverse like 1 denotes best and 10 denotes worst, then the ranking would have been in reverse order (Fig. 2.4).

2.4.2 Results Using AHP The first table constructed is the pairwise comparison matrix of the major criterion (refer to Sect. 3.3), from which the global priority or the local priority (the w matrix) of the first level of the hierarchy is obtained. All the pairwise comparison matrices were consistent as the consistency ratio (C.R.) is less than 0.1. Next three tables are pairwise comparison matrix of the subcriteria of major criteria c2, c3, and c4.

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Fig. 2.4 Ranking of the Web sites

The weight matrixes obtained are local priorities to c2, c3, and c4. The next 17 tables are pairwise comparisons between alternative with respect to subcriteria or a major criterion whose subcriteria do not exist. This forms the third hierarchical level. The final table contains the rank of the alternatives. The rank matrix contains value greater than zero but less than one, and summation of the values is equal to one. The alternative with the maximum value is ranked 1. Here, the AHP model tries to rank three Web sites: Web site A, Web site B, and Web site C according to a user. The user gives personal preference and scales each criterion according to what he or she thinks is best. The user can be an expert who can give general views for students seeking advice to choose a Web site (Figs. 2.5, 2.6, 2.7, and 2.8).

2.4.3 Comparison Between the Proposed Methodologies Our proposed methodologies differ in many respects from the mode of implementation to the manner of assigning weights to the parameters. Summarized in the table below are the main differences between the rankings obtained using AHP and PCA.

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a

b

Fig. 2.5 a Pairwise comparison for the first hierarchical level that is among the major criterion. b Normalized matrix obtained from the pairwise comparison matrix for finding the consistency index, C.1. = 0.06278 and consistency ratio, C.R. = 0.04213

a

b

Fig. 2.6 a Pairwise comparison matrix for the subcriteria of the major criteria c2 of the second hierarchical level. b Normalized matrix obtained from the pairwise comparison matrix for finding the consistency index, C.I. = 0.07 and consistency ratio, CR = 0.08

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a

b

Fig. 2.7 a Pairwise comparison matrix comparing the alternatives for the global criteria c1. b Normalized matrix obtained from the pairwise comparison matrix for finding the consistency index, C.I. = 0.037447 and consistency ratio, C.R. = 0.072014

Fig. 2.8 Snapshot of rank matrix calculation. Rank of Web site A is 1 with 75% preference, Web site B is 2 with 18% preference, and Web site C is 3 with 7% preference

2.5 Conclusion and Future Scope The ranking methodology of PCA varies drastically from that used in AHP. It offers a more generalized approach in which a set of Web sites are compared subject to user’s score of various parameters like time and cost of courses. These scores indirectly imply how much one user will prefer a Web site compared to another, however, the implication is not directly obtained as input from the user. In AHP, users rank the Web sites subject to parameters and explicitly specify how much they prefer one parameter over another for a set of Web sites. So in PCA, if a user scores duration of a course from Web site A as a 9 (highly satisfied) in the pairwise comparison matrix, Web site A will be more preferred compared to the other sites. Considering the fact that AHP involves taking a larger number of inputs than PCA, we construct some of the comparison matrix values from those obtained from PCA as described in the previous example. Since the magnitude of the consistency ratio is acceptable, our hypothesis stands justified. Using PCA, we first rank Web sites using the correlation between

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the parameters. Next, we rank the Web sites subject to how much weight each user assigns to each parameter and how much he prefers one Web site’s attribute over another Web site’s. Future work maybe done by establishing a system to incorporate the ranking of both methods (AHP and PCA) into a single value, as PCA ranks one Web site taking the weights from multiple users and AHP will rank Web sites with respect to one user. The latter is more user-specific. Further to find out if the ranking of the Web sites is in accordance with the experience of other users, a group must be assigned who has not yet used a site and asked for feedback after taking a course from that site with the different ranks calculated by AHP and PCA. The feedback for each site is noted and compared with the predicted value of user satisfaction.

References 1. Saa, A.A.: Information Technology Department, Ajman University of Science and Technology Ajman, United Arab Emirates, educational data mining & students’ performance prediction. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 7(5) (2016) 2. Romero*, C., Ventura, S.: Educational data mining: a survey from 1995 to 2005. Expert Syst. Appl. 33, 135–146 (2007) 3. Baradwaj, B.K., Pal, S. (2011) Mining educational data to analyze students’ performance. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 2(6) (2011) 4. Hwang, G.J., Huang, T.C., Tseng, J.C.: A group-decision approach for evaluating educational web sites. Comput. Educ. 42(1), 65–86 (2004) 5. Liou1, J.J.H., Tzeng2, G.-H.: Comments on “Multiple criteria decision making (MCDM) methods in economics: an overview”.Technol. Econ. Dev. Econ. 18(4), 672–695 (2012). https:// doi.org/10.3846/20294913.2012.753489.ISSN 2029-4913, Print/ISSN 2029–4921, Online 2012.n 6. Kumar, V.: The state of digital pedagogy in higher education. In: Proceedings of International Conference on Digital Pedagogies (2019) 7. Jesson, R., McNaughton, S., Wilson, A., Zhu, T., Cockle, V.: Improving achievement using digital pedagogy: impact of a research practice partnership in New Zealand. J. Res. Technol. Educ. (2018). https://doi.org/10.1080/15391523.2018.1436012 8. Saaty, T.L., Vargas, L.G.: Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. Springer, New York, Heidelberg, Dordrecht, London (2012). https:// doi.org/10.1007/978-1-4614-3597-6.ISSN 0884–8289 ISBN 978–1–4614–3596–9, e-ISBN 978–1–4614–3597–6 9. Muzamhindo, S., Kong, Y., Famba, T.: Principal component analysis as a ranking tool—A case of world universities. Int. J. Adv. Res. (2013). Jiangsu University School of Finance and Economics No. 301 Xuefu Road, Zhenjiang 21, P.R. China 10. Manage, A., Scariano, S.: An introductory application of principal components to cricket data. J. Stat. Educ. 21, 1 (2013). https://doi.org/10.1080/10691898.2013.11889689 11. Alkhattabi, M., Neagu, D., Cullen, A.: Information quality framework for e-learning systems. Knowl. Manage. E-Learn. 2(4), 340 (2010) 12. Pallant, J.: SPSS Survival Guide: A Step by Step Guide to Data Analysis Using SPSS for Windows, 3rd edn, Open University Press, New York (2005) 13. Peter, J.P.: Reliability: a review of psychometric basics and recent marketing practices. J. Mark. Res. 6, 351–354 (1979) 14. Zacharouli, P., Titsias, M., Vazirgiannis, M.: Web page rank prediction with PCA and EM clustering. In: Avrachenkov, K., Donato D., Litvak N. (eds.) Algorithms and Models for the Web-Graph. WAW 2009. Lecture Notes in Computer Science, vol. 5427. Springer, Berlin, Heidelberg (2009)

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15. Wang R.Y., Strong D.M.: Beyond accuracy: what data quality means to data consumers, total data quality management programme (1996) 16. Pearson, K.: On lines and planes of closest fit to systems of points in space. Philos. Mag. Ser 6, 2(11), 559–572 (1901) 17. Hutcheson, G., Sofroniou, N.: The Multivariate Social Scientist: Introductory Statistics Using Generalized Linear Models. Sage Publication, Thousand Oaks, CA (1999). https://doi.org/10. 4135/9780857028075. Accessed 04 Oct 2019, 08:47PM 18. Comrey, A.L., Lee, H.B.: A first course in factor analysis, 2nd edn, Lawrence Erlbaum Associates, Inc (1992) 19. Görener, A.: Comparing AHP and ANP: an application of strategic decisions making in a manufacturing company. Int. J. Bus. Soc. Sci. 3(11) (2012) 20. Ho, W. (2008). Integrated analytic hierarchy process and its applications—A literature review. Eur. J. Oper. Res. 186(1), 211–228 (2008) 21. Saaty, T.L.: The Analytic Hierarchy Process, New York: McGraw Hill. International, Translated to Russian, Portuguese, and Chinese, Revised editions, Paperback (1996, 2000). Pittsburgh, RWS Publications (1980) 22. Halko, et al.: Stochastic algorithms for constructing approximate matrix decompositions. https://arxiv.org/pdf/0909.4061.pdf(arXiv:909) (2009) 23. https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html Accessed 07 Oct 10 2019, 10:51PM 24. https://www.dii.unisi.it/~mocenni/Note_AHP.pdf

Chapter 3

Multimedia-Based Learning Tools and Its Scope, Applications for Virtual Learning Environment S. N. Kumar, A. Lenin Fred, Parasuraman Padmanabhan, Balazs Gulyas, Charles Dyson, R. Melba Kani, and H. Ajay Kumar Abstract The advancement in technology plays vital role in teaching, learning and faculty development. Computer-aided learning media is the usage of computer for presenting the learning materials for better understanding and visualization. The multimedia-based teaching material comprises of text, audio, video, animation and graphics. The multimedia-based interactive teaching material provides ease in understanding and efficient understanding of a course. The multimedia and image processing-based learning techniques gain importance in web-based learning systems for online learning. A set of questions are prepared, and response is collected from faculty and students, and the importance of multimedia-aided tools are analyzed. Based on the questionnaire generated, response is collected and statistical analysis is also done. The statistical analysis by t-test reveals the efficiency of multimedia tools in the teaching and learning process. The web-based learning resources are presently an optional and supplementary for the users; in future, they will be the S. N. Kumar (B) Amal Jyothi College of Engineering, Kanjirappally, Kerala, India e-mail: [email protected] A. Lenin Fred · C. Dyson · R. Melba Kani · H. Ajay Kumar Mar Ephraem College of Engineering and Technology, Elavuvilai, Tamil Nadu, India e-mail: [email protected] C. Dyson e-mail: [email protected] R. Melba Kani e-mail: [email protected] H. Ajay Kumar e-mail: [email protected] P. Padmanabhan · B. Gulyas Cognitive Neuroimaging Centre, Lee Kong Chian School of Medicine, Nanyang Technological University, 50 Nanyang Avenue, Singapore e-mail: [email protected] B. Gulyas e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 A. Deyasi et al. (eds.), Computational Intelligence in Digital Pedagogy, Intelligent Systems Reference Library 197, https://doi.org/10.1007/978-981-15-8744-3_3

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main course materials. The role of multimedia-based resource is inevitable in interactive teaching and learning. The multimedia-based learning materials help in the efficient achievement of course objective and accomplishment of course outcome. The multimedia-based online learning will be the future generation teaching and learning technique. Keywords Multimedia · Image processing · Online learning · Technology · Web-based learning

3.1 Introduction Multimedia fosters creativity, self-expression and a sense of ownership by allowing students to control their materials. Multimedia technology has made it possible to access information nonlinearly, creating an active learning experience for thought, reading and interaction [1]. Object-oriented tools support the development and execution of coordinated multimedia presentations. The components created are the userfriendly visual synchronization editor and the synchronizer which covers the requirements of media object delivery in a heterogeneous environment. Synchronization editor is used to build multimedia display synchronization and design specifications. The Global Synchronization Manager monitors the development and transfers the presentation units from the basic information units to the distributed system [2]. To a large extent, learning technology initiates what happens in classroom and since school is so heavily dependent on books. Multimedia learning technology encourages active participation and monitors the pupil [3]. The worldwide use of multimedia illustrates the concept of electronic audio and video. The homework tasks are designed to help students make the transition from the overview or demo mode to the laboratory mode of implementation. The introduction of sophomore course to discrete-time signal processing illustrates how to teach multimedia through the interest. The sophomore-level course on discrete-time signal and structures is inspired to create a more inspiring introductory electrical or computer engineering course than conventional linear circuits [4]. A multimedia courseware approach is used to conduct lecture in classroom for engineering. Courseware approach means that multimedia software will generate the lectures. The courseware product could make lectures in the classroom more informative and at the same time more enjoyable by student engagement in classroom discussions. The reaction of students about the multimedia courseware is positive and very effective [5]. The multimedia-based learning tools are depicted in Fig. 3.1. The vital application of web-based learning system is in medical field, where the 3D visualization of anatomical organs and its functionality can be studied. The webbased learning helps in accessing the contents from anywhere, and even online exams are also there for certification. The web-based resources can be accessed, reused and studied even after a course end. The multimedia-based learning gains importance in school, college education and research. The web-based resources can be accessed

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Fig. 3.1 Multimedia-based learning tools

in mobiles, and hence, the users can access at any time. The electronic textbooks replace the conventional textbook in the learning process. The mobile applications also can be created on specific topics. The usage of e-learning for language study is common nowadays. The interactive multimedia-based mobile applications are used in learning languages: Interactive multimedia-based mobile applications for learning Iban language (I-MMAPPS). The Iban language is an indigenous language spoken by people in Malaysia. With the digital technologies, creative teaching methods can be easily implemented using computers to gain students interest in the context of image processing. Computer tutors are meant to capture a great deal, if not all of human tutor feature including the ability to inductively display mathematics derivations, direct the student through reasoning process and perform detailed natural-language dialog with the student. Specific teaching goals can help student to learn effectively and can make the students study much more efficient [6]. The multimedia projects aim at involving students in their own learning to improve the teamwork, decision-making and dynamic problem-solving skills [7]. Compared to traditional teaching methods, the creation of learning tools is particularly challenging, as expertise from different areas must be implemented and incorporated. The promise of computer-aided learning (CAL) lies in its ability to present knowledge in various ways, depending on the learning material characteristics and the needs of the learners. Human–computer interaction (HCI) is a dynamic field in which information flows subtly across different modes of the processing system of human data. CAL’s advantages and potential are linked to its ability to deliver knowledge reliably, quickly and cheaply [8]. Internet is a knowledge resource and delivers learning materials more difficult to implement in an easy manner [9].

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A randomized controlled trial measures the efficiency of a CD-ROM training program compared to a lecture that was commonly used in teaching. Objective measures indicate equivalence in the acquisition of knowledge and significantly increase computer-based teaching skills [10]. GIS is a technology based on complex spatial principles that is well adapted for interactive representations and learning. The training modules are created using Macromedia Manager and are interactively distributed through the web. A multimedia self-learning approach for enriching GIS education explores the pedagogical learning issues that surround technological advancement and module design practical issues. Multimedia-based training design to sustain a GIS program has the potential to enhance both content and learning facility [11]. In [12], the merits of computer-assisted learning (CAL) through Internet-based learning and web-based learning were described in a detailed manner. The new information technologies make it possible to meet the needs of modern society, so many higher education institutions around the world are developing and offering the students new teaching methods and so-called digital teaching. Operation of the modules of research and evaluation is carried out with the aid of a Petri network. Petri network helps the tutor to create the way to study so that the student can go through all the places that are essential to absolve the subject the straight line and also offer the possibility of more detailed information—the curve lines [13]. A virtual learning environment (VLE) is a collection of interactive learning tools designed to improve the learning of a student. The main components of a virtual learning environment kit include curriculum mapping, student monitoring, instructor and student online support, electronic communication and Internet connections to tools outside the programs [14]. Multimedia learning system can be built with information from the online databases to turn learning into an active process, where learners can view interactions over time, engage with complex content and evaluate their knowledge immediately. Designing high-quality interactive learning multimedia requires developers to integrate best practices in curriculum, teaching software and communication with human computers to create a useful and effective online learning experience for learners, an effort to define and record strategies for producing new and creative content in educational media [15]. An interactive storytelling Web site has been developed to investigate how web-based technology can help to overcome obstacles. It promotes the relevance and educational value of the EFL teaching and learning platform for digital storytelling. Users can write a new story or edit an existing story through a web browser. Instructional tool has great potential in solving problem that teachers usually face while trying to introduce storytelling in a language classroom [16]. It describes the element of the b-learning model, the implementation issues and the results of the first module piloting. The online software is an immersive multimedia environment which houses in one central web platform all the resources and ICT tools that learners need. The findings reflect the progress of the applied b-learning system and are in line with the team efforts to create an online immersive language learning platform for multimedia. The new hope given to the teachers and students will be more effective in teaching and learning [17].

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There is an increasing trend to use software to facilitate and improve student communication and collaboration. Schoolbook has been developed and used for neuroradiology, web-based, interactive teaching and learning software. In a LAMP environment, schoolbook is technically based as a content management system. With the support of the established framework, the content is created and stored in a database [18]. A PHP framework specifies the template, and web pages are created from the program. E-learning offers a variety of teaching and learning resources in educational, research and economic contexts. Schoolbook provides the economic advantages of promoting coordination and handling human resources efficiently [19]. Self-regulated Internet or hypermedia learning includes not only strategies of cognitive learning but also broad and general meta-cognitive strategies. Learners can process the material at any time and from any location; based on their individual preferences and strategies provided, an Internet connection is available. Working interactive learning tasks will help learners to recognize knowledge gaps, correct errors and independently control the further learning process [20]. The rapid development in information technology provides i-maestro, an improved technique for music training [21]. Educational technology has been developed to provide expert functional knowledge to surgical trainees outside the operating theater. The reliability of the developed method was evaluated by modifying an existing system that was tested using surgery, usability and education experts. The performance of the developed method has been evaluated by surgical trainees who used it to assess a digital CD-ROM designed to teach basic surgical skills. The growing use of multimedia in medical education involves the creation of standardized resources to assess teaching and learning performance [22]. Multimedia anatomical learning resources enable a seamless transition between traditional learning tools such as textbooks, atlases, models and the cadaver. The full potential of multimedia-based learning technologies is not yet being used, a user-centered approach to create more effective learning applications with respect to the concept of human–computer interaction and educational theory. The classical textbook could provide the organized details and description of the subject, while a multimedia tool could provide computer-dependent elements such as 3D models and deeper material interlinking [23]. Microblogging methods of teaching with cirip.eu focused on multimedia. It investigates closely how microblogging could lead to the development of the educational process through teaching methods that are similar to the student way of thinking. The use of microblogging technology in education dramatically shifted the focus from knowledge acquisition to achieving the expected academic target [24]. The developers with a brief digital creation guide focused on literature review and Delphi methodology for professional educators, designers and programmers. Multimedia learning allows a degree of interactivity that can improve both digital text and conventional lecture-based classroom environments and develop at all educational levels. Multimedia learning projects are not a new phenomenon in the classroom, but advanced computer technologies allow multimedia projects to be created with increased interactivity, greater ease and lower cost than ever [25]. Project-based learning experiences of instructor applicants were implemented easy with the production of digital educational material. The purpose is to assess the

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study of teacher candidates with corresponding interactive instructional material in project-based learning according to their opinion on the skills it offers. The teacher candidates assessed the questionnaire designed in conjunction with the digital instructional materials created in problem-based learning [26]. For medical education, the multimedia learning tools (MMLTs) are used as alternative learning material. A more relevant question about multimedia learning tools in medical education provides an enhanced and more efficient method of learning. Multimedia learning tools can reduce learning time substantially without compromising the retention of information in ophthalmology. The MMLTs used in the analysis consist of video outlining a method for treating an acute visual impairment patient and video exploring how cataracts are diagnosed and treated [27]. The problem-based learning tool in teaching is helpful for the teachers for the motivation of the students, and it encourages the students to solve problems independently and to find solutions. It introduces the new skills to the students for better understanding of a concept and is helpful in developing the problem-solving skills and provides a new way of teaching [28]. The e-learning program was found to be successful for teaching the digital image processing at the university level. The course when taught using multimedia aids was found to be much efficient for better understanding of the students [29, 30]. The efficiency of interactive learning tool was analyzed; Torrance Test of Creative Thinking (TTCT) was employed for analyzing the thinking ability of students after the use of multimedia tool. Mobile application in interactive multimedia plays a vital role for learning Iban language. Mobile application in learning allows the learner to learn at any time and at any place. Mobile application learning tool is beneficial in designing and developing the spelling, vocabulary and also useful in pronouncing a new word for language learners. The benefit of Iban language is to develop a positive interest and helpful in non-native speakers to understand and speak [31]. Multimedia-based online education is an efficient way of transferring information. Multimedia-based e-learning provides students with the ability to use video lecture at any time when delivered and registered. Interactive digital e-learning aims at developing cost-effectiveness models and addressing excellent research questions from interactive e-learning to instructional design apps [32]. The interactive multimedia learning in elementary education was very effective and innovative. The questionnaire collected from the students about the multimedia learning tool indicates that ease to use, readability, proper material content and better media appeal [33]. The interactive communication environment was highlighted in [34], and metaliteracy learning was described in detail with its merits and challenges. The article [35] discusses the issues and challenges of the multimedia teaching aids in the learning environment. In [36], a detailed analysis has been carried out on the impact of visual aids in the enhancement of learning technique. The response of teachers for the usage of multimedia aids in the learning process was analyzed by SPSS software. The virtual reality and 3D prototyping improve the learning capability of students in the context of project-based learning [37]. The emerging technologies for learning such as virtual labs, robotics and computer simulations are described in [38]. The instruction design strategies for e-learning is discussed in [39], and selection of appropriate learning strategy based on the requirements is also highlighted in this

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work. The types of teaching aids and instruction materials for teachers and students are discussed in [40]. The impact of video pedagogy for education is highlighted in [41], video recording, video clubs and video study group’s merits are analyzed, and interactive video tool such as hyper video is also discussed. Section 3.2 describes the challenges in multimedia-based learning tools, Sect. 3.3 highlights the results and discussion that comprises of statistical analysis for the analysis of response collected from the course instructors and students based on the questionnaire framed, and finally, conclusion is drawn in Sect. 3.4.

3.2 Features and Challenges in Multimedia-Based Learning Tools The multimedia-based learning tools have its own features and challenges. The features and challenges rely on the perspective of students in learning and on the perspective of teachers in the teaching process. Some of the features and challenges are as follows. 1. Training for the development of multimedia tools The classical teaching aids like blackboard and chart preparation does not require any specific training. The multimedia-based teaching aids require little bit training, when it involves the incorporation of audio and animated pictures. A lot of softwares are there for the development of multimedia-based teaching aids. The students are easily adaptable to technology; however, an investigation has to be made regarding the requirement of training in the development of multimedia tools for course instructors. 2. Selection of teaching aid methodology The selection of teaching methodology relies on the nature of course and the intention of course instructor. The courses in general can be classified into three types: (i) theory, (ii) semi-analytical and (iii) analytical. The traditional methods of teaching are lecturing, usage of blackboard and power point presentation. Course instructors have to decide, which teaching aid methodology will be beneficial for better understanding of the course. For analytical courses like mathematics, blackboard teaching is must, and for understanding of some concepts, multimedia aids are used. 3. Requirement of audio in multimedia teaching aid The audio requirement in a multimedia teaching is an optional one, and it relies on the course instructor. The impact of audio in a multimedia presentation is good, since it creates a virtual teaching environment. The advantage of using audio in multimedia presentation is that course instructor does not have to take much effort, and since the recorded audio will be providing the necessary information, more over the voice modulation will be continuous and an efficient delivery of

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5.

6.

7.

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course content will be there. Rather than simply using a power point, if audio is incorporated, it will increase the attention of students. The voice of course instructor itself can be used in the presentation so that it will improve the clarity of concept. Requirement of animation in multimedia presentation The animation is also an optional requirement in the multimedia presentation; however for some of the courses, it will increase the understanding of students. The animation in a presentation will generate a real-time exposure of content. For example, the working of DC motor when shown in animated picture will give more insight on the working of various components and its properties rather than a 2D image. The 3D effect in animation will further increase the understanding level of the students. The analytical course contents when explained with animation will be really beneficial for the students. The practical exposure increases for the students, when animation is used by the course instructor. For example, electrical machines are a course in undergraduate electrical engineering; when the concept of motor or generator working principle is explained with an animation, it will improve the understanding level of students. Also in medical field, while explaining the anatomy of human body, animation will be an added advantage for better understanding. Requirement of interactive sessions The interactive session in multimedia will increase the attention of students. At the end of a multimedia presentation, an online quiz will be a testing aid for the evaluation of session efficiency. The interactive session will create enthusiasm for students. The issue is the time consumption, when interactive sessions are used. Challenges in the multimedia presentation Though multimedia presentation has enough merits, the preparation of a multimedia presentation requires some skills. Some of the issues in the usage of multimedia presentation are time consumption in content delivery, when interactive sessions are used. The location of classroom should be properly designed for good visibility and stability in power, and proper network connections also should be there. The challenges have to be overcome by a course instructor for an efficient content delivery. Analyzing students and course instructor interest

First one is selection of teaching aid (lecturing, hands-on demo and multimedia aid). Second one is submission mode of assignment (classical method of writing in paper, e-assignment submission by email and multimedia-aided submission). Third one is the writing scheme of examination (classical mode of writing in paper and web-based online examination). Based on these criteria, questionnaire is framed and responses are collected. The statistical analysis is performed for the questionnaire to judge the requirement of multimedia-based learning tools. The multimedia aid is a tool; however, course instructors can add motion or emotion in it to make it more beneficial for the students. In the current scenario,

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most of the countries in the world are affected by coronavirus (COVID-19) disease, and the schools and colleges are closed. The government encourages the development of online content, online teaching and evaluation. Most of the colleges and universities in the world are now engaged in the design and development of multimedia aids for the beneficiary of the students. The UNESCO prefers multimedia-based distance learning solution in the current COVID-19 scenario [42]. The multimedia presentation with the audio of course instructor embedded in it improves the efficiency of teaching. Online quiz is conducted as a part of multimedia aids to evaluate the proficiency of students. The Zoom is one of the interactive video conferencing tools based on artificial intelligence for taking online class. The attendance of students can be monitored by the course instructor, doubts can be asked and group discussion is possible, and the class can be recorded for future reference. The image processing algorithms are used in the creation of 3D animation and also gain importance in the virtual reality and augmented reality. The hyper video is an interactive video tool, and the design of multimedia aids relies on image processing techniques. The virtual reality is blended with the project-based learning for product development using 3D software [37]. The virtual laboratory enriches the students’ knowledge in a course and provides a real-time exposure. The virtual laboratory improves the performance of students [41].

3.3 Results and Discussion The goal of this chapter is to analyze the need and the status of acceptance level for multimedia aids in teaching and learning. A detailed survey based on the response from engineering college students and faculty of different branches has been conducted for the need of multimedia tools in the teaching and learning process. A questionnaire was prepared, and the response was collected from students and course instructors. The questionnaire comprises of nominal and ordinal questions. For nominal questions, Likert scale was used. The statistical analysis was performed from the response of questionnaire. The questionnaire for course instructor comprises of 11 questions, and the questionnaire for students also comprises of 11 questions. The questionnaire for course instructor is depicted in Table 3.1. The questionnaire for students is depicted in Table 3.2. The hypothesis formulated for the questions is depicted in Table 3.3. For the statistical analysis, hypothesis formulation is a needy one, and for setting that, relevant questions from course instructor and students side are taken into account. Out of 11 questions in each case, 8 questions are nominal and 3 are ordinal. For nominal questions, ranking or weights are assigned and the Likert scale is used. For ordinal questions also, weights are assigned for analysis. Figure 3.2 depicts the analysis of questionnaire. For each pair of question, null hypothesis and alternate hypothesis are formulated. For statistical analysis, t-test was used. The response was collected from 106 respondents from students and course instructors of engineering college. The students

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Table 3.1 Questionnaire for the course instructor Sl. No.

Questionnaire

Response

1

As a staff do you need training for the preparation of multimedia aids for teaching?

(a) Strongly disagree, (b) disagree, (c) neutral, (d) agree, (e) strong agree

2

Is audio required in multimedia aids-based teaching methodology?

(a) Strongly not needed, (b) not needed, (c) neutral, (d) needed, (e) strong needed

3

What is your opinion about multimedia-based library for learning?

(a) Extremely bad, (b) bad, (c) neutral, (d) good, (e) very good

4

How do you rate multimedia-based aid for an analytical course?

(a) Extremely bad, (b) bad, (c) neutral, (d) good, (e) very good

5

Do the interactive multimedia session are required in class?

(a) Strongly not needed, (b) not needed, (c) neutral, (d) needed, (e) strong needed

6

What is your opinion regarding mobile app for learning?

(a) Extremely bad, (b) bad, (c) neutral, (d) good, (e) very good

7

Do you think that learning capability is improved, if the course instructor uses multimedia aid for teaching?

(a) Strongly disagree, (b) disagree, (c) neutral, (d) agree, (e) strong agree

8

Is animation required in multimedia teaching?

(a) Strongly not needed, (b) not needed, (c) neutral, (d) needed, (e) strong needed

9

Which one of the teaching methodology (a) Lecturing, (b) blackboard and chalk, (c) creates attention from the students? multimedia tools, (d) role play

10

Which mode of assignment is good for a (a) Conventional writing and submission in course? paper, (b) e-assignment, (c) multimedia-aided submission

11

For conducting examination, which one (a) Classical mode (writing in paper), (b) is preferred well? web-based online examination

and course instructors are from various disciplines like Electrical and Electronics Engineering, Electronics and Communication Engineering, Computer Science and Engineering, Civil Engineering and Mechanical Engineering. For the Questionnaire Q9, Q10 and Q11, percentage analysis was done, since the questions are nominal. The t-test results are depicted in Table 3.4. The t-test was carried out on the nominal questions Q1–Q8. The responses from the 106 respondents were analyzed based on the hypothesis framed in Table 3.4. The t-test was found to be successful favoring the alternate hypothesis (Ho). The t-value and p-value are obtained from the calculation, and for carrying out t-test, free statistics calculator (https://www.socscistatistics.com) is used. The significance level of 0.05 was chosen in the t-test, and a smaller p-value represents the strong evidence against the null hypothesis. The higher value of t indicates that the ‘net’ difference between the scores for each participant is relatively large and could be evidence that the intervention variable or the treatment was effective. Here from the t-test results, it is evident that p-value is high, and hence, null hypothesis is accepted.

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Table 3.2 Questionnaire for the students Sl. No.

Questionnaire

Response

1

As a student do you need training for the preparation of multimedia aids?

(a) Strongly disagree, (b) disagree, (c) neutral, (d) agree, (e) strong agree

2

Is audio required in multimedia aids-based teaching methodology?

(a) Strongly not needed, (b) not needed, (c) neutral, (d) needed, (e) strong needed

3

What is your opinion about multimedia-based library for learning?

(a) Extremely bad, (b) bad, (c) neutral, (d) good, (e) very good

4

How do you rate multimedia-based aid for an analytical course?

(a) Extremely bad, (b) bad, (c) neutral, (d) good, (e) very good

5

Do the interactive multimedia session are required in class?

(a) Strongly not needed, (b) not needed, (c) neutral, (d) needed, (e) strong needed

6

What is your opinion regarding mobile app for learning?

(a) Extremely bad, (b) bad, (c) neutral, (d) good, (e) very good

7

Do you think that learning capability is improved, if the course instructor uses multimedia aid for teaching?

(a) Strongly disagree, (b) disagree, (c) neutral, (d) agree, (e) strong agree

8

Is animation required in multimedia teaching?

(a) Strongly not needed, (b) not needed, (c) neutral, (d) needed, (e) strong needed

9

Which one of the teaching methodology (a) Lecturing, (b) blackboard and chalk, (c) creates attention from the students? multimedia tools, (d) role play

10

Which mode of assignment is good for a (a) Conventional writing and submission in course? paper, (b) e-assignment, (c) multimedia-aided submission

The t-test result shows that there is a strong agreement in the usage of multimedia aids for the teaching and learning process. The questions Q9, Q10 and Q11 are ordinal questions and hence for that percentage wise analysis was performed, depicted in Figs. 3.3, 3.4 and 3.5. Fig. 3.3a depicts the percentage analysis response of Q9 corresponding to course instructor and (b) depicts the percentage analysis response of Q9 corresponding to students. The response results show that both course instructor and students support the multimedia aids for teaching. In Fig. 3.4a depicts the percentage analysis response of Q10 corresponding to course instructor and (b) depicts the percentage analysis response of Q10 corresponding to students. The response results show that course instructor supports for assignment submission in convention mode and students supports the multimediaaided submission. Fig. 3.5a depicts the percentage analysis response of Q11 corresponding to course instructor and (b) depicts the percentage analysis response of Q11 corresponding to students. The response results shows that both course instructor and students support for web-based online examination. The multimedia aids have very good impact in the teaching learning process, and it has a fruitful outcome [43–45]. Some of the course instructors and students in the

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Table 3.3 Hypothesis formulation for the questionnaire Sl. No. Questionnaire Hypothesis formulation 1

Q1

Ho: There is need for training in the preparation of multimedia aids among students and course instructor H1: There is no need for training in the preparation of multimedia aids among students and course instructor

2

Q2

Ho: There is need for audio in the multimedia aids among students and course instructor H1: There is no need for audio in the multimedia aids among students and course instructor

3

Q3

Ho: The course instructor and students favors the multimedia library system H1: The course instructor and students does not favor the multimedia library system

4

Q4

Ho: The course instructor and students support the multimedia aids for analytical course H1: The course instructor and students does not support the multimedia aids for analytical course

5

Q5

.Ho: The course instructor and students favor the interactive multimedia session in class H1: The course instructor and students does not favor the interactive multimedia session in class

6

Q6

Ho: The course instructor and students favor the mobile app for learning H1: The course instructor and students does not favor the mobile app for learning

7

Q7

Ho: The course instructor and students favor that learning capability is improved, when multimedia aids are used H1: The course instructor and students does not favor that learning capability is improved, when multimedia aids are used

8

Q8

Ho: The course instructor and students favor the usage of animation in multimedia aids H1: The course instructor and students does not favor the usage of animation in multimedia aids

rural areas are not still aware of the advantages of multimedia aids. The appropriate training has to be provided for the teachers and course instructors in the rural areas, and multimedia-based teaching and learning require good hardware setup comprising of personal computers or laptops and projectors. The Internet availability in some of the rural areas in developing countries is also a challenge in the usage of multimedia aids. The virtual laboratory based on multimedia was found to be efficient in the teaching of DSP for different branches comprising of graphical user interfaces [46]. The virtual laboratory was found to be efficient in the teaching of power engineering concepts [47]. The digital videos were found to enhance the performance of the students in laboratory practical [48]. The virtual learning environment was found to

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Fig. 3.2 Analysis of questionnaire Table 3.4 Statistical t-test analysis results Question number

t-value

p-value

1

0.94823

0.34519

0.06

2

0.46966

0.63957

0.04

3

0.51460

0.60791

0.04

4

0.56890

0.58921

0.03

5

0.66907

0.50492

−0.06

6

0.73695

0.46279

0.06

7

0.41046

0.68231

−0.03

8

0.45663

0.64888

0.05

Fig. 3.3 Percentage analysis of question Q9

Mean

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Fig. 3.4 Percentage analysis of question Q10

Fig. 3.5 Percentage analysis of question Q11

be proficient in the learning of laboratory experiments [49]. The image and video processing algorithms play significant role in multimedia-based aids [50]. The future work will be the selection of appropriate multimedia aid based on the nature of course, free supporting tools for the development of multimedia aids will be analyzed, and the merits of virtual reality and augmented reality can also be analyzed in a detailed manner. In the mere future, the usage of multimedia aids will be increased and especially in the today’s scenario, multimedia-based teaching aids are being very beneficial for learner’s community due to the outbreak of n-Cov 19 disease, most of the schools and colleges now rely on multimedia aids like animation, video and virtual labs for enhancing the students’ knowledge through online mode. The online faculty development program and webinars are found to be beneficent for the course instructors.

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3.4 Conclusion This chapter discusses the types of multimedia-based learning resources and applications in different domain. The usage of multimedia-based resources in the present scenario is highlighted. The response is collected from course instructors and students based on the questionnaire framed, and statistical analysis was performed. The t-test was used for analysis, and the test results reveal that multimedia aids are effective in the today’s scenario for better understanding of a course from the perspective of students and improve the understanding ability of students from the perspective of course instructors. The analysis of nominal questions reveals that training is required for the development of multimedia aids, and audio and animation are required in multimedia aids. The course instructor and students support multimedia-based aids for analytical course and also favor the interactive multimedia session. The survey also reveals that mobile app was beneficial, and overall, the learning capability was improved. The outcome of this work reveals that multimedia-based learning tools are beneficial for students and course instructors, thereby enhancing the technical knowledge.

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13. Hadzi-Kostova, B., Styczynski, Z.A.: Teaching renewable energy using multimedia. In: IEEE PES Power Systems Conference and Exposition, 2004. 2004 Oct 10 (pp. 843–847). IEEE (2004) 14. Dong, A., Li, H.: Multimedia access platform for virtual learning environment. In: 2005 IEEE International Conference on Electro Information Technology 2005 May 22, p. 6. IEEE (2005) 15. Huang, C.: Designing high-quality interactive multimedia learning modules. Comput. Med. Imaging Graph. 29(2–3), 223–233 (2005) 16. Tsou, W., Wang, W., Tzeng, Y.: Applying a multimedia storytelling website in foreign language learning. Comput. Educ. 47(1), 17–28 (2006) 17. Bañados, E.: A blended-learning pedagogical model for teaching and learning EFL successfully through an online interactive multimedia environment. CALICO J. 23(3), 533–550 (2013) 18. Moreno, R.: Learning in high-tech and multimedia environments. Curr. Dir. Psychol. Sci. 15(2), 63–67 (2006) 19. Zajaczek, J.E., Götz, F., Kupka, T., Behrends, M., Haubitz, B., Donnerstag, F., Rodt, T., Walter, G.F., Matthies, H.K., Becker, H.: eLearning in education and advanced training in neuroradiology: introduction of a web-based teaching and learning application. Neuroradiology 48(9), 640–646 (2006) 20. Narciss, S., Proske, A., Koerndle, H.: Promoting self-regulated learning in web-based learning environments. Comput. Hum. Behav. 23(3), 1126–1144 (2007) 21. Ng, K., Nesi, P.: I-maestro framework and interactive multimedia tools for technology-enhanced learning and teaching for music. In: 2008 International Conference on Automated Solutions for Cross Media Content and Multi-Channel Distribution 2008 Nov 17, pp. 266–269. IEEE (2008) 22. Coughlan, J., Morar, S.S.: Development of a tool for evaluating multimedia for surgical education. J. Surg. Res. 149(1), 94–100 (2008) 23. Adamczyk, C., Holzer, M., Putz, R., Fischer, M.R.: Student learning preferences and the impact of a multimedia learning tool in the dissection course at the University of Munich. Ann. Anatomy-Anatomischer Anzeiger 191(4), 339–348 (2009) 24. Grosseck, G., Holotescu, C.: Microblogging multimedia-based teaching methods best practices with Cirip. Eu. Procedia-Soc. Behav. Sci. 2(2), 2151–2155 (2010) 25. Frey, B.A., Sutton, J.M.: A model for developing multimedia learning projects. Merlot J. Online Learn. Teach. 6(2), 491–507 (2010) 26. Özdamli, F.: The experiences of teacher candidates in developing instructional multimedia materials in project based learning. Procedia-Soc. Behav. Sci. 1(15), 3810–3820 (2011) 27. Steedman, M., Abouammoh, M., Sharma, S.: Multimedia learning tools for teaching undergraduate ophthalmology: results of a randomized clinical study. Can. J. Ophthalmol. 47(1), 66–71 (2012) 28. Liu, M., Wivagg, J., Geurtz, R., Lee, S.T., Chang, H.M.: Examining how middle school science teachers implement a multimedia-enriched problem-based learning environment. Interdisc. J. Probl.-Based Learn. 6(2), 3 (2012) 29. Al-Ghaib, H., Adhami, R.: An E-learning interactive course for teaching digital image processing at the undergraduate level in engineering. In: 2012 15th International Conference on Interactive Collaborative Learning (ICL) 2012 Sep 26, pp. 1–5. IEEE (2012) 30. Kassim, H.: The relationship between learning styles, creative thinking performance and multimedia learning materials. Procedia-Soc. Behav. Sci. 6(97), 229–237 (2013) 31. Chachil, K., Engkamat, A., Sarkawi, A., Shuib, A.R.: Interactive multimedia-based mobile application for learning Iban language (I-MMAPS for learning Iban language). Procedia-Soc. Behav. Sci. 8(167), 267–273 (2015) 32. Farhan, M., Aslam, M., Jabbar, S., Khalid, S.: Multimedia based qualitative assessment methodology in eLearning: student teacher engagement analysis. Multimedia Tools Appl. 77(4), 4909–4923 (2018) 33. Rachmadtullah, R., Ms, Z., Sumantri, M.S.: Development of computer-based interactive multimedia: study on learning in elementary education. Int. J. Eng. Technol. 7(4), 2035–2038 (2018)

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34. Ma, J., Li, C., Liang, H.N.: Enhancing students’ blended learning experience through embedding metaliteracy. In: Education Research International (2019) 35. Sarowardy, M.H., Halder, D.P.: The issues and challenges of using multimedia at a district level, specialized girls’ college in Bangladesh. Creative Educ. 10(07), 1507 (2019) 36. Shabiralyani, G., Hasan, K.S., Hamad, N., Iqbal, N.: Impact of visual aids in enhancing the learning process case research: district Dera Ghazi Khan. J. Educ. Pract. 6(19), 226–233 (2015) 37. Halabi, O.: Immersive virtual reality to enforce teaching in engineering education. Multimedia Tools Appl. 79(3), 2987–3004 (2020) 38. Oliveira, A., Feyzi Behnagh, R., Ni, L., Mohsinah, A.A., Burgess, K.J., Guo, L.: Emerging technologies as pedagogical tools for teaching and learning science: a literature review. Hum. Behav. Emerg. Technol. 1(2), 149–160 (2019) 39. Karthik, B.S., Chandrasekhar, B.B., David, R., Kumar, A.K.: Identification of instructional design strategies for an effective E-learning experience. Qual. Rep. 24(7), 1537–1555 (2019) 40. Olayinka, A.R.: Effects of instructional materials on secondary schools students’ academic achievement in social studies in Ekiti State. Nigeria. World J. Educ. 6(1), 32–39 (2016) 41. Radhamani, R., Sasidharakurup, H., Sujatha, G., Nair, B., Achuthan, K., Diwakar, S.: Virtual labs improve student’s performance in a classroom. In: International Conference on E-Learning, E-Education, and Online Training 2014 Sep 18, pp. 138–146. Springer, Cham (2014) 42. https://en.unesco.org/covid19/educationresponse/solutions 43. Joshi, A.: Multimedia: a technique in teaching process in the classrooms. Curr. World Environ. 7(1), 33 (2012) 44. Shah, I., Khan, M.: Impact of multimedia-aided teaching on students’ academic achievement and attitude at elementary level. US-China Educ. Rev. 5(5), 349–360 (2015) 45. Oshinaike, A.B., Adekunmisi, S.R.: Use of multimedia for teaching in Nigerian university system: a case study of university of Ibadan. Library Philosophy and Practice (e-journal), 682 (2012) 46. Alexiadis, D.S., Mitianoudis, N.: Masters: a virtual lab on multimedia systems for telecommunications, medical, and remote sensing applications. IEEE Trans. Educ. 56(2), 227–234 (2012) 47. Butz, B.P., Duarte, M., Miller, S.M.: An intelligent tutoring system for circuit analysis. IEEE Trans. Educ. 49(2), 216–223 (2006) 48. Croker, K., Andersson, H., Lush, D., Prince, R., Gomez, S.: Enhancing the student experience of laboratory practicals through digital video guides. Biosci. Educ. 16(1), 1–3 (2010) 49. Berruti, L., Davoli, F., Zappatore, S., Massei, G., Scarpiello, A.: Remote laboratory experiments in a virtual immersive learning environment. Advances in Multimedia (2008) 50. Furht, B., Smoliar, S.W., Zhang, H.: Video and Image Processing in Multimedia Systems. Springer Science & Business Media (2012)

Chapter 4

Social Network Analysis in Education: A Study Poulomi Samanta, Dhrubasish Sarkar, Dipak K. Kole, and Premananda Jana

Abstract These days social media sites have been gaining huge attention. Millions of people are accessing social media like Facebook, Instagram, Twitter, etc. Because of very affordable Internet, people are spending hours on it. People are taking interest on social network for information, user’s opinion on diverse subject matters. A wide range of techniques of data mining allowed us to detect useful data from massive database by some of the algorithms where we can find the patterns of users’ thinking and trend on social network. It also reduces difficulties by the time of discovering contents. Not just that, a huge data also comes from the educational system. This data is used to realize the knowledge in decision-making. Educational data mining methods are designed to understand and measure the performance of students and also helpful to study students’ behaviour. Data mining technique is also important to survey the history and application in traditional educational system, intelligent teaching system, e-learning, and web-based educational system. Over a time period, education in the rural areas has improved and available. Still there are lot of countries cannot stand uniquely. So the system of education has to bear a major alteration by redesigning its framework. To solve that problem, different sectors have evolved in educational environment changes for the further development in urban areas. In the educational environment, different attributes are associated between each other like the location and type of the college, groups, courses, etc. By the mining technique, the data will find the unknown rules, and it will analyse which is suitable and can be built for academic planning in higher learning. It is helpful for a proper understanding P. Samanta · D. Sarkar (B) Amity Institute of Information Technology, Amity University Kolkata, Kolkata, India e-mail: [email protected] P. Samanta e-mail: [email protected] D. K. Kole Department of CSE, Jalpaiguri Government Engineering College, Jalpaiguri, West Bengal, India e-mail: [email protected] P. Jana Netaji Subhas Open University, Kalyani, West Bengal, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 A. Deyasi et al. (eds.), Computational Intelligence in Digital Pedagogy, Intelligent Systems Reference Library 197, https://doi.org/10.1007/978-981-15-8744-3_4

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of the educational environment different sides to the course construction and other improvement for student’s performance theoretical areas. In this chapter, there are used data mining technique and strong rules in education environment which identify and understand the pattern of students’ success in different areas. Analysing and prediction from data are covered in this chapter. Keywords Online social networks · Collaborative learning · E-learning · Social network analysis · Education

4.1 Introduction Social networks are a platform which creates a image in various fields. Social networking analysis is helping us to understand the modern society which is gained a useful and significant following on development of education, communication, information science, economy, politics, etc., and now it is smoothly available as the consumer weapon [1]. Social network analysis (SNA) in education is the process which investigating education in social structures using theory of graph and network relationship. This characterizes the structure of network as the edges (links or relations) and the nodes (individual person, or things in the network structure), where the main domain is the classrooms and relation or links working between students. From this larger network and the relationship, we come to know some important effects in students’ behaviour also analysis and understand of a classroom network. Analysing of network in the education system can help us to understand the network formation of students and also the teachers in the classroom, and all the types of consequence of those connections have among them. The methodological and theoretical approaches help us to solve different problems about study, pedantry, educational policy, equity, organization, etc. Nowadays, it seems educational industries are adapting these into the systems for farther development and trusting on different mechanisms and resources to improve the path of the student life. Also, social media in education is providing us useful information. It connects different educational systems and the learning groups which make education handy and easier. It gives multiple opportunities to the educational institutions which enhance learning methods [2]. The goal of this chapter is to understand the basic terms and concept, which will outline the structure and graphical representations and network partners. We will briefly discuss the method of application and organize data, challenges, social influence in SNA education and the future scope, and the outcomes. We will also discuss the importance of educational policy studies and network evolution.

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4.2 Basic Terms and Concepts Associated with Social Network Analysis in Education Field A software architecture has also one or group of components of software. The software components have interrelations and visibility. The external visible factors are service, features, error processing, function, etc. The information flow is in the structure of social network, where the structure is individual nodes of collection. SNA studies how people, groups or organizations interact with each other within the network field. As an analysis tool, graph theory was used in this paper [3]. As you can see in the graph (Fig. 4.1), every single person is not connected with every single person, the introduction happen the connection happened in the graph (Fig. 4.1). The concern is the social network analysis (SNA) on relationships; SNA in education includes the nodes or actors or student where the individuals within an educational environment network and implementing a communication or edges with each. Nodes which represent teachers, students and other actors and edges are representing the ties’ interactions among them. By the SNA, we can analyse the student; it means position of the students, consulting with each other, sharing for work-related information or exchange ideas and constructed knowledge, etc. SNA researches a student’s positions, and the learning effects, academic achievements and understand the considerable volume and explain it. Student who has the better connection has better resources and has educational support which motivates the person. On the other hand, this type of analysis may give us some pattern of collaborative groups. It is calculated by clustering coefficient and consistency of interactions. Consistency is calculated by group diversity also by contributions. The architecture

Fig. 4.1 Strong ties and weak ties [3]

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to very lightweight quantification of conversation and consistency is a same type of measurement which rises according to the more participants. Interdependence and cohesion are main concepts of collaborative learning. In SNA education, in the 1990s, importance was given only on effects of using analysing on social network, but it is not about on the prospective that they gained.

4.3 Architecture At first: Build a relation to understand the CSCL [4, 5], then find the areas that required for inquiry in three different courses, in this first term [6]. Second: Build an mechanism which analyse data and take from the first step. Last stage: Compare all the quarries (pre and post) and do the whole experiment for first term [6]. Subjects: Subjects have some courses that exist, course A, course B, course C. Those are a full-time duration courses as shown in [7] the paragraph. The mechanism of teaching in all the courses is to mix learning and showing better result. Table 4.1 shows in each course, the number of posts, students and topics. The first examination, subjects were invigilated by real-time analysis, and the data was gathered at the end of the term and tested. And based on first mid-term and the end of the next term, both the data were analysed and compared. Gathering interaction data was cancelled with the help of two methods: Structured Query Language (SQL) queries and graphics web service [6]. Graphics web service was took for the understanding the CSCL in this type of system. It extracts the data and makes SNA image, from Moodle group, about course intro. It is also useful for getting a view of courses interactions and the final results required extraction of metadata, and depth analysis users and their quality, the types of the messages, which drived SQL usage. It was used to understand all the data (parent forum, subject, reply, content, author, ID, creation time, ID of groups, modification time) and data which holds by the user that are ID of the user, email, name of user, course that the user chosen. In course A, the students who were marked as G1-17, in course B—S1-33, and in the course type C-it is L1-32. The superiors of the groups were marked or given code as T-1, Q-1 and D-1 for each groups. Finally, those ID and other data were placed into a layout of Gephi.

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[6] Subject characteristics of all the courses Course are

Topics are

Course A

20

634

17

Course B

21

1396

33

Course C

3

390

32

44

2420

82

Total

Posts are

Participants

Analyse the data: We used two types of indicators, SNA and IA, which are groups and the individual levels. In group level: It is added some topics, position and the contribution in average form, for each and every nodes. Individual level: Replies, performances and communications which are received were added there. Level of activity: The level of actions was measured by the centrality movement which we called in, out degree and degree itself. The degree in the position of out, centrality was indicated the participants’ quantity, interactions were calculated, in-degree centrality also had an importance under the measurement influence. A participant usually communicates when they share knowledge. The centrality is a sign of impact and characteristics of achievements as polling and giving votes by peers, in the section in-degree, of exchange area, good in-degree takes as a sign of better knowledgeable nodes, leadership and level of popularity. The third or last measurement of node weight was the degree level of central attraction which is known as centrality, that’s measured with the total number of out-degree (contributed) followed by in-degree (received) communications (in + out degree = centrality). Exchange of position of information between nodes: The give and take of information was look after by the three measures. At first was (i) betweenness under centrality; it means, a node played role among the group and it is coordinating and interacting with others, the number of times. If it is not, then unconnect collaborators. It is an involvement indication in argumentations in a forum. Higher value of betweenness centrality means a higher brokerage capital. Lower betweenness centrality means difficulty in or reach to other members in the group without any mediator [6]. And the other important thing, which was (ii) closeness in the centrality attribute. It means that the closeness of the particular user, with the other users or nodes in the field or simply to reach out and interaction with other one. The higher grade of the closeness indicates the place in information passing with others and less values is imagined as less or poor communications between the nodes and social detachments. The third using of social network analysis is to monitor and guide improvement in online collaborative learning.

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Natures into the groups field: For the role or the nature identification, analysing technique is used to visually, and centrality shows the different roles and the labels of each participants’. Marcos-Garcı’a et al. create this method to describe of each participants’ nature or role. They says particularly about three roles for the high-level node into the group, that is teacher: facilitator, guide and the node how observes. Facilitator teacher is all about the discussions of topic, answers all the queries that come to them. The criteria of SNA to identify and understand a teacher on the area of their functionalities which are reachability or may the closeness centrality, medium level of involvement which known as out-degree, less level of communication or influence, known as in-degree and average to high of involvement or closeness, known as betweenness centrality. The SNA helps to show an image of the relations and makes dynamic view of that nature. On other hand, regarding the student nature also the range of the student nature ware ranked by level of communication which depends on the nodes who arranges the group and maintain it (like leader, animator, peripheral, operative, missing and clam). Since all the scenarios have the flexible feature, we expect students to be took part and also expect the crisis by some students. On the other hand, the roles like who makes groups and coordinate the group and leaders who encourage others, highly active, and helps to mediate of discussions. The criteria of SNA to know the nature of the nodes are as follows (Table 4.2): Table 4.2 Different terms and descriptions Term

Description

• Leader

• It is a high level of activity; it plays an positive role and in the communication and holds a good positions (medium to high level of all the attributes)

• Coordinator

• It is a medium level of activity (medium level of degree centralities, in the out and in centralities), good position of coordination • (medium—high position holes of closeness and also the betweenness centrality)

• Active

• Participatory itself is a active role (medium activity); it communicates with the other nodes of student, on the level of medium–low platform in information exchange

• Peripheral

• A low activity (low in-degree) had less number of involvement in give and take of information The parameters of analysing technique level is added group communicational parameters which are average of three types of degree. With the divisional activity, the total degree of all participants with the size of group we got the average degree

• Group links or cohesion

• It was understood by clustering coefficient and density of nodes’ communications

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Fig. 4.2 Node communication within students and the teachers [6]

Results The first mid-term • As we can see (Fig. 4.2) by the figure we understand degree of communication is in a good level with the teacher (in the pattern instructor-centric), where thick arrows (frequent of interactions) are pointing towards teacher nodes, followed by B and also C courses. In this educational world, we naturally look forward in non-dominating role and a facilitator teacher (a moderator). • Student-to-student interactions were very low. Student nodes in course A have very much low interconnections, gradually B course student has more interconnections, than A and in C it has few lines. This type of student communication tells us the another sign of poor network collaboration. • The degree of teachers has larger than any student when it compared with the bases of to the centrality, Teacher-student communication or connection is very low, for C course. It makes us to understand the medium interactions, which we could see by arrows. • Teachers relation betweenness is very high, means the centrality (nodes that showed as dark green), compared to the students (light nodes, low betweenness centrality), which means students played less role in connecting others in the conversations and relaying information. In this diagram (Fig. 4.3), it shows that in the networks, teachers are (D1, T 1, Q1) in the centre or the nodes who are most important in information transaction. The centre closer nodes carries more valuable degree in information transaction. From the research, we got the following values of Course A, Course B, Course C down below in (Table 4.3). From Table 4.4, we able to understand the different degree which are out, in and average degree from the activity. Section and from the position in information exchange section, we understand the betweenness, closeness and information of different coerces. From Table 4.4, we understand roles of the subjects in the courses. From where we were calculated the totals of the AP (active participatory), ANP (active nonparticipatory), P (participatory) and also the totals of the courses.

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Fig. 4.3 Different nodes with different degrees [6]

Table 4.3 It shows level of activity of the courses, the courses itself and the position or degree in information exchange [6] Construct

Course A

Course B

Course C

17.29

20.12

3.03

0.47

2.73

2.91

17.76

22.85

5.94

0.00

0.00

0.01

Level of activity Out-degree In-degree Average degree Position in information exchange Betweenness Closeness

0.99

0.66

0.44

Information

0.06

0.24

0.71

Table 4.4 Roles of the subjects in the courses Course

Role AP

ANP

P

Total

Course A

0

16

1

17

Course B

10

22

1

33

Course C

5

0

27

32

15

38

19

82

Total

ANP is active non-participatory. P is peripheral, and AP is active participatory [6]

The Second Mid-Term By the graph (Fig. 4.4), as you can see each node connecting to participants. The degree centrality is proportional to the node size. Arrows donate interactions, improved between in the nodes representation, and also the thickness of all arrows, say frequency is improved.

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Fig. 4.4 Communication between students and teachers at second tern [6]

By the result of the second mid-term into the network of analysing in education shown us with the good improvement, simply means it has some positive difference compared to the first mid-term, in the three constructs of SNA from where we understand the role on information sharing and information sharing, interactions with students and role in groups. As shown in second mid-term picture, it gives a view of all interactions in all the three courses with lot of improvement an increase in student–student interactions and thick connections in the relation in the student actors in between. Also creates a good view in teacher-student connections which is marked mostly from the three course, in courses B and C, and little bit lesser extent in the course A. Another significant development was shown in coordination students, and betweenness is also showing some increment, centrality also which shone with the dark green actor or nodes, and also, we can see involved on connecting and relaying other. A closer node is near the canter (Fig. 4.5) compared to the pre-intervention. More students are close to the canter here means more important role that it carries in information transfer network. Table 4.5 shows below in each course, the number of students with their improved positions. Also shows students who did not change. It does the comparison of students’ ranks across different measures in pre-intervention and post-intervention.

Fig. 4.5 Different nodes with the degrees [6]

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Table 4.5 Subject courses and their centrality and rank [6] Course

Centrality

Rank

P

Course

Positive

Negative

No change

Course A

Degree

12°

4

1

Course A

In-degree

14°

0

3

Course A

Out-degree

6

10

1

Course A

Betweenness

12°

0

5

Course A

Closeness

15°

2

3

Course A

Information

17°

0

0

Course B

Degree

Course B

In-degree

Course B

Out-degree

Course B Course B

1

32°

0

22°

4

7

0

33°

0

Betweenness

31°

1

1

Closeness

27°

4

2

Course B

Information

33°

0

0

Course C

Degree

19°

5

8

Course C

In-degree

13

12

7

Course C

Out-degree

21°

5

6

Course C

Betweenness

7

16

16

Course C

Closeness

31°

1

0

Course C

Information

31°

1

0

(We got the value from the research)

Where the good rank (positive) implies an growth in the centrality score, as well as a very low, score means decline (negative). From the research, we get positive, negative and no change value from the second-term result. From Table 4.6, we understand the course totals and their leaders, coordinators. Actives, active non-participatory and peripherals of the second mid-term. From the research, we come to know, all students result showed positive improvement in the statistically significant in measures across all the three constructs of the Table 4.6 Active non-participatory = ANP, L = leader, AP = active- paticipatory, C = coordinator, participatory, P = peripheral Course

Role L

C

AP

ANP

Course A

2

Course B

2

Course C Total

P

Total

1

8

6

0

17

3

20

7

1

33

2

0

12

6

12

32

6

4

40

19

13

82

4 Social Network Analysis in Education: A Study

75

centrality. In the information exchange indicator, the improvement was marked. More than 98% of the nodes or actors are with reachability growth. The huge change we can see in information centrality also that the other centrality measures are indicated of a shift change towards efficient or more positive information exchange into the field of education. The information about centrality calculated (Table 4.7) is shown in Fig. 4.6. Before and after network image and property comparison and communication indicators intervention. Table 4.8 finds the communication parameters (in in-degree, degree and out-degree) of the average values and across the two points of measurement, group indicators of each and every course which ensure that the increment of density and the clustering more than before. In each course, it shows the positive changes in cohesion indicators. The research and analysis are used in analysis of education. At first, we understand each participant’s holding degree or frequency of activities. At the second, we find the position and role, in the frequency of communication to exchange data. At third, by each participant, the role was played in the collaboration. It includes group cohesion indicators and interactivity on the group level. Table 4.7 Here, OD = out-degree, ID = in-degree, D = degree, Bet = betweenness, Cls = closeness, info = information Rank

OD

ID

D

Bet

Cls

Info

Positive rank

60

47

64

50

73

81

No change

14

18

10

22

5

0

8

17

8

10

4

1

Negative rank

Chart of Outdegree, Indegree, Degree, Betweeness, Closeness, ... 90

Rank Negative Rank No Change Positive Rank

80 70

Data

60 50 40 30 20 10 0

Outdegree

Indegree

Degree

Betweeness

Closeness

Information

Fig. 4.6 Bar chart 1 chat of out-degree, in-degree, betweenness, closeness, information

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Table 4.8 AV = average, Co = coefficient Course

AV degree

AV in-degree

AV out-degree

Before

After

Before

After

Before

After

Course A

17.76

26.24

0.47

8.59

17.29

17.65

Course B

22.76

14.36

2.73

6.67

20.12

7.70

Course C

5.94

8.78

2.91

4.84

3.03

3.94

The before after chat [6]

4.3.1 Data Mining in Educational Data and Application Data: By the different technology, this field has producing huge data in this educational network. That shows us some information that types of information especially point out student. Different systems generate LMS system. With the help of mining method in the education system, data can be filtered from the education groups, which show us specific knowledge which are either simple or very complicated like in applying relational mining. Objective: In the mining methods, any single or group of data is derived by objectives. Most importantly, it is used to improve learning as well as the processes of teaching. This is the research to understand the environment and make a very deep knowledge of the learning and teaching fields. That influences the aim occasionally. Sometimes, it is difficult to apply traditional method of researches to achieve the goals [8]. Techniques: The application in data mining, any types of difficulty is moved by the research objectives, and the type of data. So that, for applying successfully, data mining to data about education needs specific acceptance. The acceptance may for the data mining methods [8]. Some methods on the topic can be applied in the previous works in the educational area, and there are simply no need of any kind of modifications and any change. Not just that some techniques of mining are used in only specific problems in the domain. However, selecting some of the techniques of DM depends on researchers of the future problem and research [8]. As an example, data mining in education mechanism can improve, and the teaching processes into the classroom give you the information about different-risk students, developed teaching block, and gives us some advice to students and the teachers. In the current research, only students and teachers are involved. However, in research, other groups can be involved which have other different objectives like some course development, as follows at Table 4.9.

4.4 Application of SNA in Education: Related Work Table 4.10 shows the different researches on SNA in education.

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Table 4.9 Different objective and key application Category

Objectives

Key applications

Future

In the model development field, it is based on other variables; we need to predict some variables. The predictor variables may be extracted from a specific set of data or constant

• Recognize at-risk of students • Understand the student outcomes in education

Clustering

The amount data which is • It finds the differences and group specific to different similarities between schools clusters is based on the data or students behaviour. The cluster number It categorized behaviour of the new students can be different that is based on the clustering objective process and the mode

Mining the relations

Between the variables into the group of data, it filters the relationship

• Finds the relations between drop-out students from the school and education level of parent • In sequences of course, discovers the curricular associations; in which strategies of pedagogy lead to much more workable and very efficient

Find some new ideas with the The aim is to make a model help of models about the phenomenon with the help of clustering, knowledge engineering or prediction

• Find out the relationships on the students’ characteristics and student movement and the variables of the field • With the help of research, different questions are analysed, from the wide unique and optional variety into the contexts

For human judgement data distillation

• The patterns of human identification in actors’ behaviour, gaining knowledge, or making collaboration • Labelling of data to use in other or development in the future, to prediction of modelling

This model easily finds new ways to active researchers to classify or to identify features in the data set

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4.5 Challenges By the analysis, we came to know that social media a educational field that offers opportunities for every student, teachers including parents. It is a very effective way to build a community and share information.

Table 4.10 Researches on SNA in education Researches relater SNA in education How Data Mining helping us in the education [8]

Abstract

• Data mining techniques are used to analyse and filter useful data from raw data element. Extracted knowledge is very important which makes us prepared to take decisions. With this method of educational data mining, we can extract information which has potential to give a big impact in organization. The increasing popularity in the technology is also used in educational field which drives us to the storage of huge number of data of students and gives its impertinence to use these types of mechanisms to learning processes and improve educational slandered. In the different areas, EDM is very useful. In identifying of at-risk students, understanding priority of learning which varies on different student groups, passing rates increasing, effectively on institutional performance, spreading resources, EDM is useful, and also it optimizes to take about subject decisions to renewal. This paper examines the studies on mining area and also the methodologies and data involved in those studies Networks for learning and development across • This paper is guiding us about policy school education guiding principles for policy development principles of recent research in development on the use of networks in school a context and in the area developments of education systems [2] school education. Those are illustrated with examples. It is taken from countries, which are discussed and shared by the members of the Working Group on this project. We will understand how various types of networks have been used into practice, with different results. The data comes from series of different meetings which held in Brussels, research exercise, and peer learning activity. Here we can see examples of 12 case studies, which are presented by organizations and countries in the peer learning activity (PLA). The report was covered by Hannah Grainger Clemson and Laurie Day in October 2017, with the help of Jonathan Allen and Janet Looney, and validation and review by all the members (continued)

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Table 4.10 (continued) Researches relater SNA in education Networking for educational innovation: a comparative analysis [7]

Abstract

• This chapter works on the relevance and rise of networking in a educational field on national, regional and also on some cross-national levels. This describes this day’s popularity towards in education, networking which is a platform in social interaction to growing interest. This analyses the bigger educational and social groups in the structure of educational networking field. We also come to know the role and aims in the networks in an educational field. This describes types of networks, initiators, stakeholders, leadership, membership and some organizational factors. It also examined preconditions and incentives which make successful networking in education. The chapter also discusses the role policy implications in education In the influences of education, SNA reforms • In this chapter, we get an idea about a social on teachers’ practices and communications [9] network analysis, in the recent few years graph about teachers’ field. It is hard to implement in educational reforms, and the network of teachers’ is also important. It describes the three approaches on social network. First, graphical representation of data to understand and build the structure of network by which the knowledge and information about re-model might gives big impact. Second, use of social influence models we understand the teachers’ and their behaviours and attitude. Those are changing by other person with whom the teachers interact; third or last, selection models. To explain the selection of teachers with whom they have linked or engaged in communication about the reforms. Here also discussed implementing the practice of educational policy-makers, educational policy, school administrators, etc. (continued)

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Table 4.10 (continued) Researches relater SNA in education

Abstract

Understanding the classroom activities and need in social network analysis [10]

• Social links between students are on top and which is not much given attention specially in undergraduate education. It helps you to understand the relation formation in the undergraduate classrooms, and also, it tells you the impacts. Those relationships’ outcome helps you to inform about actors in educational field and its methodology, and it improves the educational reform. SNA provides some useful key for searching various and innovative questions about relational data. This will make you introduce here with the collection of data, processing of data, and also the methods of analysis data, with the use of some study into the classroom with the help of undergraduate field of people. Generative processes which are used here help us to understand study networks between the students relation in the network, and also, it tests associations’ network position success in the examinations. It also covers some important issues, such as for network studies, reviews of the different aspects of the human subjects. The aim is to convince all the readers that the usage of SNA in the classroom environments is to allow us to rich. Informative analyses also takes place and used to provide initial tools, in some processes which inspire in future studies in education on relational data • The purpose is to analyse and study the relationships between leadership team of district-level education and two primary schools. This study uses SNA as the methodological and the analytic approach to understand the structural degree of the advice giving and receiving field of networks in the schools. To know more in the network relationships, knowledge impact and collaboration, it is also used. The study researches, advice giving and receiving affect the knowledge of the staff members also the relationship shown on the network, albeit indirectly, school performance. Moderate findings were present within the network properties and divergence of the education knowledge that the type of lesson being given and received and also the effect lesson or advice being giving or receiving has on knowledge based of the staff members. A relationship which is indirect also found between school performance and network properties. The study also discussed the changes of the professional practice and the further work and research on the distributed leadership (continued)

Using SNA to investigate the diffusion of special education [11]

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Table 4.10 (continued) Researches relater SNA in education

Abstract

How SNA helps us to understand online learning and guide us [6]

• It says about online collaborative learning and stimulates learning which meets the goal. The mechanisms are needed to understand the efficiency of collaboration (online). The study finds that the way of SNA may be used to understand the pattern of collaborative learning online and finds aspects of more improvement, it guides and inform intervention also and assess. With the using of SNA-based quantitative and visual analysis, it is understood that three constructs of SNA for each node: in that the role, level of interaction, and position of the information is exchanged and the actors playing role by the each participant into the collaboration. We also can see interactive and group link indicators in the group level. Here intervention is build into the five actions. The concept of awareness, increasing, collaboration, preparing teachers, promoting, improving of the content and finally practising with feedback are also explained here. The activities of communication are efficient and important for successful matter about content. Also it helps us to understand the goals with the collaboration. Here, it suggests us that this type of analysed approach or direction gives us a good impact on learning and teaching in various educational domains. It supports learning and teaching in education.

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Online learning has been on the increased in the last some years, and the answer of why it is not hard to see [1]. On the other hand, those types of courses have become very popular by the simple piety of being more convenient than traditional courses like face to face. Social network analysis is very useful tool which has actual effects within the change of complexities, into organization learning and the school-wide, into how relationships influence in education practices and in the new initiatives. Teachers change won’t help by SNA, but serves or can be used as a tool for education leaders which helps teachers change the leaders by helping and understand the flow of the information. How to boost the relationships responsible for change is also helped to identify and understand the critical resources [12]. The SNA is not a solution but a tool to consider, evaluate to identify. Now the application number which is increasing in the workplaces so we must think something different and innovative type of methods for the communication building within educational nodes. Other hand, this type of development in social media simply defines further and good impact in education. This is a great way to get involved in educational big field and to help each other and interact. It makes learning and sharing information so interesting and involved in children mind, with the help of different educational environment and group. By this paper, we come to know the importance in implementing social media in the school and other educational organization also communication and collaboration in educational network.

4.6 Conclusion and Future Scope In the educational field, the increase of using of technology is getting higher day by day that generating a huge amount of data is making interest to target for different researchers in education all over the world. In the educational field, the growth of data mining is very first, and it has some advantages of containing various new algorithms and techniques to develop in various data mining areas as well as machine learning. The data which comes from educational circle helps us to make developed methods which are useful to get data further. This type of data is interpretable, and the information is high in demand. That leads to understand further, to the students closely, learning evolved very first towards the future to a practical approach in education. It will continue and upgrade towards an extremely and useful classroom with updated teaching tool and the platform of self-study. With the upcoming technology and solutions of augments, experimental with the subjects, skilled learning and training that come heavily onto the e-learning solution. The providers of a different education technology are also giving importance towards mobile learning which is also known as m-learning, as the advanced stage of education technology at the future [13]. FaceTime- and iPhone-based tutorial has become so popular, and it is termed or called as i-learning. Students can use learning sites for educational help where many services are available as online like tutoring, project help, programming help, different editing services, thesis, essay writing and course related to some guidance.

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References 1. Brown, M.: Why Social Network Analysis. https://bookdown.org/chen/snaEd/why-social-net work-analysis.html, Accessed on 14/12/2019 2. General Education, Youth, Sport and Culture Schools and multilingualism (2018). https://www. schooleducationgateway.eu/downloads/Governance/2018-wgs5-networks-learning_en.pdf 3. Morrison, D.: Can Social Network Analysis Help Teachers Change? (Book review: Social Networking Theory and Educational Change. Harvard Education Press, 2010), (2016). https://onlinelearninginsights.wordpress.com/2016/01/26/can-social-network-analysis-helpteachers-change/. Accessed on 08/12/2019 4. Hrastinski, S.: What is online learner participation? A literature review. Comput. Educ. 51(4), 1755–1765 (2008). https://www.sciencedirect.com/science/article/abs/pii/S03601315 08000791 5. Jesús Rodríguez-Triana, M., Juan I., Asensio-Pérez, Dimitriadis, Y.: Scripting and monitoring meet each other: aligning learning analytics and learning design to support teachers in orchestrating CSCL situation. Br. J. Educ. Technol. (2014). https://doi.org/10.1111/bjet.121 98 6. Mohammed, S., Uno, F., Matti, T., Jalal, N.: How social network analysis can be used to monitor online collaborative learning and guide an informed intervention. PLOS ONE, 13(3) (2018). https://doi.org/10.1371/journal.pone.0194777 7. Anne, S.: Networking for educational innovation: a comparative analysis. Networks of innovation towards new models for managing schools and systems (ISBN 92-64-100342), OECD/CERI, 50—63 (2003). https://www.oecd.org/site/schoolingfortomorrowknowled gebase/themes/innovation/41283632.pdf 8. Algarni, A.: DataMining in education. Int. J. Adv. Comput. Sci. Appl. 7(6), 456–461 (2016). https://thesai.org/Downloads/Volume7No6/Paper_59-Data_Mining_in_Education.pdf 9. Kenneth, A., Yun-Jia, F.L., Min, S.: Social network analysis of the influences of educational reforms on teachers’ practices and interactions. Z. Erziehungswiss (Suppl) 17, 117–134 (2014). https://education.uw.edu/sites/default/files/u1406/Frank%2C%20Lo%2C% 20Sun%2C%20ZfE.pdf 10. Daniel, Z., Grunspan, Wiggins, B.L., Goodreau, S.M.: Understanding classrooms through social network analysis. CBE Life Sci. Educ. 13(2), 167–178 (2014). https://www.ncbi.nlm. nih.gov/pmc/articles/PMC4041496/ 11. von Mering, M.H.: Using Social Network Analysis to Investigate the Diffusion of Special Education Knowledge within a School District. Doctoral Dissertation submitted at University of Massachusetts Amherst (2017). https://scholarworks.umass.edu/cgi/viewcontent.cgi?article= 1964&context=dissertations_2 12. Dexway Team: 5 Reasons Why Online Learning is More Effective. https://www.dexway.com/ 5-reasons-why-online-learning-is-more-effective/ . Accessed on 08/12/2019 13. Importance of e-Learning and its FutureScope.https://www.assignmenthelp.net/blog/e-lea rning-education-and-its-future-scope/. Accessed on 18/12/2019

Chapter 5

Personalization in Education Using Recommendation System: An Overview Subhra Samir Kundu, Dhrubasish Sarkar, Premananda Jana, and Dipak K. Kole

Abstract In the modern era, when everyone has access to all the information around the world, Internet plays the most crucial role in making the process much smoother. It has already been able to flow its legacy in nearly all paths and age groups, education as a whole is also not excluded from this association factor. Internet has thus made its footprint in the world of education known as e-learning. E-learning takes into account different techniques of Internet such as Integrated Classroom Teaching to provide education to common masses. This high-tech education is a significant component of modern world today, delivering, aiding, and maximizing teaching quality. E-learning encompasses the involvement of educators and learners and mentors to enhance their work using this technology. In today’s era, everyone searches any information using the help of sites like Google, Yahoo, YouTube, etc., and it is readily available to them within the blink of an eye. Nowadays, with the advent of voice assistants, more and more people are shifting towards e-learning. Like everything in the world, elearning has certain merits and demerits. Nevertheless, if it is utilized in a proper way can become a blessing for the human life as a whole. The unstable development of the World Wide Web (WWW) has made data innovation a prevalent stage for giving e-administration, e-learning administration. The upsides of e-learning might be decreased by and large cost, diminished learning times, steady conveyance, master information, confirmation of fruition, and so forth if the perspective of a preparation

S. S. Kundu · D. Sarkar (B) Amity Institute of Information Technology, Amity University Kolkata, Kolkata, India e-mail: [email protected] S. S. Kundu e-mail: [email protected] P. Jana Netaji Subhas Open University, Kalyani, West Bengal, India e-mail: [email protected] D. K. Kole Department of CSE, Jalpaiguri Government Engineering College, Jalpaiguri, West Bengal, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 A. Deyasi et al. (eds.), Computational Intelligence in Digital Pedagogy, Intelligent Systems Reference Library 197, https://doi.org/10.1007/978-981-15-8744-3_5

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supplier or an association is taken. While if learner’s perspective is taken, the advantages may be on-demand availability, increased knowledge retention and engagement, higher variety of materials, increased confidence, etc. On the contrary, there are some disadvantages of this system overall, they being technology dependent, lack of motivation, lack of help/training support, lack of human touch, inflexible, requires a capital cost for start-up, etc. A recommender system’s primary aim is to assist users to cope with data overload by pre-selecting data that might assist to achieve the required objective. The applications have been extended to fields such as movies and music suggestion, bookstores, and education. On the basis of data being acquired, recommendation system can be divided into five approaches primarily viz. non-personalized and stereotyped, product association, content-based recommendation system, collaborative filtering, and hybrid recommendation system. There are few gaps created by the disadvantages of e-learning with normal students which can be somewhat bridged by the use of recommender systems and this will be an attempt to do so. It will present a notable notion of how and why the student and the user can benefit by using the e-learning recommender system. Conclusion and future scope the aforementioned topic will be a proper amalgamation of the recommendation system technology with Digital Learning Pedagogy, marring a few loopholes which are to be mended later. Keywords E-learning · Recommendation system · Collaborative filtering · Content-based recommendation system · Hybrid recommendation system

5.1 Introduction In this age, with the advent of the Internet, there is an explosion of information throughout the world. Each and every topic is made available online to everyone and takes a very less amount of time to get this information from those available sources. While some have exploded exponentially, others are taking some time to do the same. Education or the process of gaining knowledge which was previously passed down from one generation to another either using verbal modes or written modes following certain pedagogy, i.e. from basics to advanced is the topic which has not yet been able to expand its roots using the technologies until the very recent years. It has been termed as e-learning, i.e. learning over the Internet using certain tools of learning and electronic media, information, and communication technology (ICT) in the field of education. This is a relatively new field since the advent of the Internet, it does not have any personalized systems to cater for specific personal needs rather has a system which caters the need of individuals on a general basis. In this era, where there is a huge advent in the voice assistants and other intelligent devices, people are moving more towards the education system which is provided and shared with the help of the internet. Like facets of the same coin, e-learning has its merits and demerits. Nevertheless, it is used for the betterment of human lives thus proves to be a merit to all. It has more advantages than flaws and thus is a merit

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to us. The certain flaws which it has can be mended using other technologies such as recommendation systems which would be the main topic of our discussion. The primary advantages of this system include reduced overall cost, i.e. since the learning material is shared over the Internet, there is no use of printing or giving books rather this can be shared a multiple number of times when produced once. Reduced learning times meaning a provider can provide any material anytime depending on their availability. Consistent delivery which is the material is produced only once and is shared and thus all the people get the same copy of the material. Expert knowledge, i.e. experts prepare the documents and those experts can be contacted when there is a need, proof of completion, or the provider can easily keep track of the performance of the learner via various methods, etc., if the point of view of a training provider or an organization is taken. While if learner’s perspective is taken, the advantages may be on-demand availability that is to say a learner can get whatever they demand whenever they demand; Increased knowledge retention and engagement since most of the courses are designed as an audio visual presentation or utilizes audio visual aids for the delivery; it makes the user more readily interacting and since it is audio visual; it helps a lot in memory retention, higher variety of materials meaning that the learner gets a variety of options to choose from about the topic they are learning, increased confidence many a times this material which is available can help to boost the confidence of the learner by providing them with materials at their fingertips, etc. On the contrary, there are some disadvantages of this system overall, they being technology dependent that is to say that technology is ever changing and they are not that adaptable with the technology, lack of motivation since the learners have to take the course themselves. They sometimes procrastinate things due to less motivation, lack of help/training support often times people find lacking support as it is designed for all in general and not for special needs. Lack of human touches there is an almost no human interaction, inflexible as it is not adaptable to all the requirements, requires a capital cost for start-up, etc. As a rule, a recommender framework is a PC program that enables a user to find items or substance by anticipating everything by the user’s appraising and afterward indicating them the items that they are probably going to rate high. For instance, with Web-based shopping, buyers have practically limitless options. Nobody has sufficient opportunity to attempt all items accessible. Suggestion framework assumes a significant job in helping users find significant substance that they care about without investing their energy burrowing items that they do not need. Proposal framework is a subclass of data separating framework that is utilized in regions, for example, motion pictures, music, news, books, look into articles, search questions, social labels, and items when all is said in done. The recommendation system also has different filtering grounds other than user ratings. Google’s software crawls billions of emails on gmail and then uses software to suggest/recommend phrases from that database that may be used as a response or confirmation message, i.e. a “Chabot” designed to recommend words or phrases. A recommender system’s primary aim is to assist users to cope with data overload by pre-selecting data that might assist to achieve the required objective. The applications have been extended to fields such as movies and music suggestion, bookstores,

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and education. On the basis of data being acquired, recommendation system can be divided into five approaches primarily, viz. non-personalized and stereotyped, product association, content-based recommendation system, collaborative filtering, and hybrid recommendation system. These approaches form the basis of how recommendation works and how they are designed. They would provide the personal touch which digital learning pedagogy yearns for. A few recommenders as of now utilize a cross breed model that fuses collaborative filtering, content-based filtering, and different methodologies. There is no motivation behind why it was impractical to hybridize a wide range of systems of a similar kind. Half breed techniques can be applied in a few different ways: by freely making and afterward coordinating substance based and synergistic predictions; by including content-based abilities (and the other way around) to a cooperative methodology; or by joining approaches into one model. Numerous trials that exactly differentiate the nature of the model with customary communitarian and substancebased methodologies have indicated that half breed strategies can give more solid counsel than single approaches. These procedures could likewise be utilized to determine a portion of the major issues in recommender frameworks, for example, chilly start and scarcity issue, and maybe even the bottleneck of mastery data innovation in information-based methodologies. Netflix is an ideal case of half-breed recommender frameworks being utilized. The site makes suggestions by differentiating needing to watch and looking through propensities for comparable to users (e.g. collective separating) and by attempting to offer movies that offer highlights with films profoundly appraised by some user (content-based filtering). The motivation of this study is to provide a recommendation to the students based upon the choices of the students. It will act as a system which helps in recommending the user the best course or path that they may follow to reach their goal in an efficient manner. This will also act as a guideline for the user following which they can achieve success without much of a hassle. This will also incorporate the old and the new users by having encompassed the attributes of matching behaviour and using the same for recommendation. The developers of [1] recommend a new, deep learning methodology that copies an effective smart recommendation from previously knowing the users and stuff. In the underlying point, it is found independently comparing low-dimensional vectors of customers and items, which incorporates the semantic data mirroring the relationship between the customer and the object. A feed-forward neural network is used during the expectation arrangement to re-enact the relationship between user and item, where the contrasting circa-trained accurate vectors are taken as contributions of the neural systems. A few analyses relying on two benchmark datasets (MovieLens 1 M and MovieLens 10 M) are conducted to test the practicalities of the proposed model, and the outcome exposes that their model dominates past approaches that potentially improve circa-owned feed-forward neural systems and performs equally on both datasets with pioneering techniques.

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5.2 Basic Terms 5.2.1 E-learning E-learning to be very specific is to get the education available to anyone and everyone by the means of Internet which is completely opposite to the general course of action by which education is received and given. Here, the user can get personalized recommendations based upon the fact that they have already studied a subject matter or has already acquired the degree. By this, one can get a degree or course online easily without the hassle of spending a lot of time and fortune [2]. A learning structure subject to formalized instructing in any case with the assistance of electronic assets is known as e-learning. Training can at present be arranged in or out of assessment but instead the usage of PCs and Web is liable for mapping the monstrous bit of e-learning. E-learning can in like way be named as a system which draws in by trading of cut-off focuses and information, and headway of the course is made accessible to unlimited beneficiaries at corresponding or various occasions [3]. In the ongoing past, it was not seen wholeheartedly as it was standard that this framework came up short on the human segment required in learning. In any case, with the more brilliant strategies being made gained ground in learning structures, it is over the long haul grasped by a lot bigger part. The presentation of PCs was the explanation of this human activity against the customary and regular strategies and with the improvement of time, as we get caught to cell phones, tablets, and so on; these contraptions at present have a basic spot in the homerooms for learning. Books are a squeeze at a time getting expelled by electronic lighting up materials like optical plates or pen drives. Information can besides be shared by procedures for the Internet, which is open by anybody and everybody, any place, at whatever point.

5.2.2 Integrated Classroom Teaching Integrated Classroom Teaching is the concept by which different advancements of information technology are used to educate the one and all through the process of audio, video and pictures. There is a very famous Chinese proverb which says that teaching through various methods such as audio, video, and images makes the child to remember much longer than usual. Thus, this concept of teaching is incorporated in the same. Integrated classrooms, generally called mainstreaming or thought, is the demonstration of placing understudies with failures into general guidance homerooms with non-remarkable needs understudies. This stems from an administration law that communicates that understudies with specials needs should be told “at all restrictive condition” possible and given a comparative standard of preparing as adolescents who are not hindered.

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This sort of learning has different points of interest for the two sorts of understudies, yet furthermore goes with its drawbacks. As a parent of a child with substance awkwardness, it is needy upon you to know your youth’s character and pick what kind of acknowledging condition would best suit them.

5.2.3 Recommendation System As a rule, a recommender framework is a PC program that enables a user to find items or substance by anticipating everything by the user’s appraising and afterward indicating them the items that they are probably going to rate high. For instance, with Internet shopping, purchasers have practically unending options. Nobody has sufficient opportunity to attempt all items accessible. Suggestion framework assumes a significant job in helping users find significant substance that they care about without investing their energy burrowing items that they do not need. Framework for suggestion is a subclass of data separating framework that is utilized in regions, for example, video, audio, news, books, examine articles, search questions, social labels, and items as a rule. The recommendation system also has different filtering grounds other than user ratings. Recommender system frameworks work with two sorts of data: (i) Trademark data—This is data on items (watchwords, classes, and so forth) and users (inclinations, profiles, and so forth), (ii) User Item Interactions—This is data, for example, appraisals, buy numbers, likes, and so forth. On this premise, recommenders are ordered into two: content-based, which utilizes trademark data, and shared separating, which user-item connections are based. In content-based filtering, they expect that later on they will again be keen on it if a user was keen on an item before. Comparative items are generally gathered by their qualities. Collaborative filtering frameworks depend on the presumption that if a user prefers an item and another user loves a similar item and another item, item B, the subsequent item may likewise intrigue the main user. Consequently, they plan to anticipate new authentic communications.

5.2.4 Collaborative Filtering Collaborative filtering is a method for making modified figures (filtering) about the interests of a user by get-together propensities or test data from different users (teaming up). The hid uncertainty of the shared separating approach is that if an individual A has a similar assessment as an individual B on an issue, A will without a doubt have B’s supposition on an unanticipated issue interestingly with that of an abstractly picked individual. For instance, a supportive confining suggestion framework for book tastes could make evaluates about which books a user should like given a fractional once-over of that user’s tendencies (preferences or hatred). Note

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that these wants are unequivocal to the user, yet use data collected from different users. This distinction from the clearer strategy of giving a customary (darken) score for everything of vitality, for instance, subject to its number of votes. In the more extensive sense, collective filtering is the way towards secluding for data or models utilizing frameworks including joint effort among various executives, perspectives, information sources, etc. Uses of collaborative filtering normally incorporate tremendous educational lists. Collaborative filtering procedures have been applied to a wide scope of sorts of data including: recognizing and checking data, for instance, in mineral examination, common identifying over enormous domains or various sensors; fiscal data, for instance, budgetary assistance foundations that fuse various cash related sources; or in electronic exchange and Web applications where the consideration is on customer data, etc. The remainder of this discussion revolves around network situated filtering for customer data, yet a segment of the methodologies and approaches may apply to the following noteworthy applications as well.

5.2.5 Content Based Recommendation System Content-Based Recommender Systems are conceived from utilizing the substance of every item for suggesting purposes, and attempting to tackle the issues, for example, Cold-start for new users or shortage of past information to work upon; New-thing issue is another element and how it is being put away and retrieved; Sparsity or the relationship of the considerable number of items may not generally be valid; and transparency or the information or data is not constantly straightforward. The three head parts of Content-Based Recommender System are: A Content Analyzer that gives us an order of items, utilizing a type of portrayal. A Profile Learner that makes a profile that speaks to every user’s preferences. A Filtering Component that takes every one of the sources of info and produces the rundown of suggestions for every user. The substance of an item is an extremely conceptual thing and gives us a ton of alternatives. We could utilize various factors. For instance, for a book, we could think about the writer, the class, the content of the book itself … the rundown goes on. At the point, when we realize which content we will consider. We have to change this information into a Vector Space Model, a logarithmic portrayal of content reports, discussed in [4] describes for the most part, and we do this with a Bag of Words model that speaks to records overlooking the request for the words. In this model, each record resembles a pack containing a few words. Consequently, this technique permits word demonstrating dependent on lexicons, where each pack contains a couple of words from the lexicon. A particular execution of a Bag of Words is the TF-IDF portrayal, where TF is for Term Frequency and IDF is Inverse Document Frequency. This model consolidates how significant is the word in the archive (nearby significance), with how significant is the word in the corpus (worldwide significance).

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This is also seen in [5] which is unparalleled and gives a vivid description of the same for their work.

5.2.6 Hybrid Recommendation System This is a kind of recommender system wherein two remarkable techniques of the same comparable techniques are solidified to shape a singular one. These kinds of proposal structures are regularly being utilized nowadays. Most recommender structures eventually utilize a hybridized approach, combining cooperative filtering, content-based filtering, and different procedures. There is no motivation driving why several novel procedures for a relative kind could not be hybridized. Crossover (hybrid) approaches can be executed in two or three different ways: by making content-based and supportive-based gauges uninhibitedly in a brief timeframe and later joining them; by adding content-based capacities to a collaborative framework (and the alternate way); i.e. joining the methodologies into one model. Two or three ponders that observationally separate the presentation of the cream and the unadulterated system and substance-based techniques and demonstrated that the mix frameworks can give more correct recommendations than unadulterated strategies. These procedures can in like way be utilized to beat a piece of the fundamental issues in recommender structures, for example, cold-start and the sparcity issue, comparatively as the information building bottleneck in information-based methodologies. Some hybridization structures include: Weighted: Combining the score of various suggestion sections numerically. Exchanging: Choosing among suggestion parts and applying the supernaturally chosen person. Blended: Recommendations from various recommenders are demonstrated together to give the suggestion. Highlight Combination: Features got from various information sources are consolidated and given to solitary suggestion estimation. Highlight Augmentation: Computing a segment or set of highlights, which is then piece of the pledge to the going with system. Course: Recommenders are given extraordinary need, with the lower need ones breaking ties in the scoring of the higher ones. Meta-level: One proposition method is applied and makes a kind of model, which is then the data utilized by the going with framework. Netflix is a genuine occurrence of the utilization of hybridized recommender frameworks. The site makes recommendations by separating the watching and looking through tendencies for comparative users (i.e. organize masterminded segregating) likewise as by offering motion pictures that offer characteristics with films that a user has surveyed essentially (content-based clustering).

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5.3 Overview of Recommendation System in E-learning Sphere During the last couple of decades, with the climb of YouTube, Amazon, Netflix, and various other such Web organizations, recommender systems have expected progressively more position in our lives. From electronic business (propose to buyers’ articles that could interest them) to online business (prescribe to customers the right substance, planning their tendencies), recommender structures are today unavoidable in our step by step online undertakings. In a general manner, recommender systems are computations made arrangements for proposing critical items to customers (items being films to watch, substance to examine, items to buy or whatever else depending upon adventures). Recommender systems are amazingly fundamental in specific endeavours as they can create a huge proportion of pay when they are powerful or in like manner be a way to deal with stand separated from contenders. As a proof of the importance of recommender systems, we can make reference to that, two or three years earlier, Netflix made a troubles (the “Netflix prize”) where the goal was to convey a recommender structure that performs better than anything its very own estimation with a prize of 1 million dollars to win. When the previously mentioned locales advanced, there was a development of destinations which made e-learning a bit of cake to all. In this setting likewise recommender frameworks assume an essential job. That is by giving explicit and customized proposals to the user over the destinations to buy the courses which are generally important to them. This is another path by which the user doesn’t get a handle on the left of the circle when discussing various progressions in the field of IT, for example, recommender structures utilized in the e-Learning area moreover. Here one item is only that is with every single creation and progress there are its downsides which gives to be prevention to all. The primary deterrent is that the information is regularly expanding and considerably more productive calculations are required by the equivalent to give an exact outcome in this constantly evolving world. They might be new progressions of IT which can be applied to illuminate the inconsistencies. Here, we can discover barely any specialists who have talked about the equivalent with different applications in their work. In [6], the author examines the adequacy of utilizing e-learning in instructing in tertiary foundations, [7] recognizes and investigates rising patterns and models in e-learning for educator instruction and expert advancement from the creating research base, both global patterns and momentum improvements, while [8] says with the exponential advancement of mechanical improvement comes a solid sense that occasions are moving too rapidly for our schools and that instructors might be losing control of them all the while. But again [9] says in the present dynamic and global condition, all divisions of the economy and, specifically, tertiary segment (administration area) need to keep track with the data and correspondence innovations and to line up with this quick advancing innovation so as to fulfil every one of

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partners’ needs and desires and to create, improve, and upgrade the nature of the gave administrations. Every one of these papers is clarified distinctively in the related works segment.

5.4 Related Work The author in [10] researches the complexities between general recommender structures and informational recommender systems and gives a general diagram about the points of interest, troubles, and imperatives of recommender structures in enlightening settings. The objective of [11] is to plan the present data that is available as regards recommender structures that have been used inside the preparation space to help instructive practices. By playing out an efficient mapping appraisal, a total of 44 research papers had been picked, evaluated, and explored from a fundamental outline of 1181 papers. Their results gave a couple of disclosures concerning how recommender structures can be used to help standard zones in arranging, what approaches strategies or figuring recommender systems use and how they address different issues in the savvy world. Also, this work had in like way been useful to see some examination openings and key areas where further assessment ought to be performed, like the introduction of data mining and man-made hypothesis in recommender structure figuring to improve personalization of clever choices. The investigation [12] presents a recommender e-learning approach which utilizes proposal structures for enlightening data burrowing unequivocally for seeing eLearners’ learning affinities. Their proposed technique relies on three modules, a space module which contains all of the data for a particular zone, an understudy module which uses to see understudies’ learning inclinations and practices and a proposal module which pre-structures data to make fitting suggestion overview and envisioning appears. Upheld resources are gotten by using level of data on understudies in different advances and the level of proposal philosophies reliant on content-based isolating and obliging methodologies. A few frameworks, for instance, game-plan, gathering, and intrigue rules are used to improve personalization with isolating strategies to give a recommendation and help understudies to improve their presentation. While the study [13] says that the recommender framework mulls over encounters recently put away and positioned by previous understudies. So as to offer fruitful learning guidance, utilizing the information the recommender framework breaks down the understudy’s present skill levels against comparable previous understudies’ exhibitions. As per [14] virtual contact between the educator and the understudy is much of the time undeniably increasingly advantageous for them two. Preparing and elearning courses are prevalent. They are completed in school or scholastic condition as well as in the business one. Separation instruction is utilized as an apparatus to help

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learning procedure, and its utilization may permit simple administration of materials, permitting to make adaptable instructive strategies. Another study [15] takes the administration of instructive assets and the huge information which structure the stage as the foundation, and designs a customized suggestion calculation of instructive assets as indicated by the users’ very own characteristics. Personalized suggestion calculation of EDX stage and customized paper proposal of CiteULike are utilized to check the judiciousness and adequacy of the proposed calculation. On the other hand, in [5], the creators use vector space models and inverse term frequency to archive recurrence strategies. The ideas of group conduct and aggregate conduct and has clarified all in a similar way with striking quality. Similarly, the authors of [4] enquire about and dismantle the association between the articles (like photos, posts, and so on) and its watchers (companions, accomplices, and so forth) for a given user and to discover advancement relationship among them by utilizing the TF-IDF plan of Vector Space Model. After vectorization the vector information has been displayed through a weighted diagram with different properties. On the contrary the authors of [16] propose a novel way of thinking which utilizes recommender structure frameworks for instructive information mining, particularly for imagining understudy execution. To support this technique, they have separated recommender structure frameworks and standard lose the faith strategies, for example, decided/straight fall away from the faith by utilizing instructive information for shrewd educating frameworks. Exploratory results show that the proposed system had improved guess results. The study in [17] on the Indian preparing circumstance, e-learning content course of action and presentation mechanical assemblies, usage of eLearning to spread guidance to the remote districts, points of interest and hindrances of e-learning and inevitable destiny of e-learning in India. Two or three recommendations had been made to use e-learning for easy-going and expert getting ready, which is outstandingly fruitful for a country like India where a prevailing piece of people is living in nation/remote districts and has gotten for all intents and purposes unimportant traditional guidance. Again, the authors of [6] examine the reasonableness of utilizing e-learning in educating in tertiary establishments. In relationship of bleeding edge preparing, the issue of using current data and correspondence advances for educating and learning is basic. This assessment surveys making and gives an academic foundation to the appraisal by evaluating several obligations made by different specialists and establishments on the plausibility of e-changing, especially its usage in preparing and learning in higher enlightening affiliations. It reveals a few of view that individuals and foundations have shared all around on the portion and coordination of e-learning movements in getting ready through reviews and different perceptions. They in like way take a gander at the tremendousness or ramifications of e-learning as given by various scientists and the action that e-learning plays in higher enlightening establishments in relationship with educating and learning structures, and the central focuses and weights of its social event and execution.

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While [18] proposes the utilization of Web mining frameworks to assemble such a position, that could prescribe Web-based learning practices or basic courses in a course site dependent on understudies’ way history to improve course material course comparatively as help the Web learning process. These systems are viewed as made Web mining rather than isolated Web mining utilized by pro users to find online access plans [19]. Tries to give the present condition respects to recommendation systems and their application on segment direction over the Internet. Their evaluation tries to show the presence of mind of utilizing Recommendation Systems applications in instructive conditions. This article moreover shows the work that is being done to give the useful condition a proposal framework. Their work remarks on the present condition of parcel learning and the issues it presents, by then supporting the utilization of a recommendation framework as a reaction to the issues that are confronted, by then they try to exhibit the general reasons for the appraisal, finally a delineation and an introduction of the outcomes got in the fundamental time of the evaluation. But [7] perceives and separates rising examples and models in e-learning for teacher preparing and master improvement from the making research base, both allinclusive examples and stream headways. Enlightening foundations and empowering staff have various points of interest in view of ascent of present-day advancement. Teachers have their own one of kind frameworks through which they interface themselves with various instructors over the globe. Establishments have Web-maintained homerooms. So likewise, it also improved the commitments of schools, schools, and universities that should have such teachers who can make such understudies, who in the wake of getting their guidance can alter themselves at any stage. Likewise [8] says with the exponential progress of imaginative improvement comes a solid sense that occasions are moving too rapidly for our schools and that educators might be losing control of at the same time. They audited the effect of e-learning and e-educating in schools, from both the understudy and instructor point of view. Specifically, it is indicated that e-instructors should concentrate not just on beyond what many would consider possible and parts of IT materials and exercises, yet should attempt to significantly more thoroughly see how their e-understudies see the learning conditions. From the e-understudy point of view, this paper shows that essentially having IT instruments open doesn’t along these lines change over into all understudies winding up being successful understudies. Continuously confirmation based evaluative research is required to permit e-learning and e-preparing to arrive at most prominent farthest point. The authors of [9] say in the present dynamic and in general condition, all zones of the economy and, expressly, tertiary part (association division) need to keep track with the data and correspondence advancements and to concur with this quick driving development in requesting to fulfil the entirety of accessories’ needs and needs and to make, improve and redesign the possibility of the gave associations. In this novel circumstance, colleges need to alter the associations they give and their substance since they cannot overlook the social models identified with the data and correspondence headways in perspective on the way in which that one of the basic limits in

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our present period is mechanical ability. The adjustment in front line preparing affiliations doesnot depend just on these advances, yet more on the HR and how they can approach and utilize all the new improvements and e-learning potential outcomes. ICT and e-inclining can update forefront preparing through inventive techniques by developing the understudies’ inspiration, intrigue and obligation, by enabling the securing of aptitudes and by redesigning educator preparing which will finally improve correspondence and trade of data. The study [20] says with the improvement of refined e-learning conditions, personalization is changing into an imperative part in e-learning structures because of the separations in foundation, targets, limits, and characters of the enormous measures of understudies. Personalization can accomplish utilizing specific kind of suggestion systems. This study shows an outline of the most basic prerequisites and issues for masterminding a recommender framework in e-learning conditions. The reason for this paper is to show the different controls of the present time of suggestion methodology and potential developments with model for naming exercises and tag-based recommender structures, which can be applied to e-learning conditions so as to give better proposal limits. In the study [21], the creators build up another personalization approach that gives to understudies the best learning materials as indicated by their inclinations, premiums, foundation information, and their memory ability to store data. They have utilized another suggestion approach dependent on community oriented and contentbased separating is displayed: NPR_eL (New multi-Personalized Recommender for e Learning). This methodology was incorporated in a learning domain so as to convey customized learning material. They exhibit the adequacy of their methodology through the plan, execution, examination, and assessment of an individual learning condition. The authors of [22] have drawn nearer by saying, the chilly start issue which exists in conventional proposal calculations are still left over in e-learning frameworks and a couple of them have genuinely influenced the learning objectives of users. Therefore, a smart e-learning framework has been created which can prescribe proficient and focused on courses as per their profession objectives. Initial, an improved community-oriented separating (CF) approach is proposed thinking about users’ vocation objectives and foundation data. At that point, the importance between vocation objectives and courses is determined to reduce the cool beginning issue and prescribe particular courses for users. At long last, a PrefixSpan calculation is joined with the above strategies to produce a customized learning way bit by bit. A few trials are completed with genuine users of various callings to test the exhibition of the half and half calculation. The authors of [23] express that, recommender structures have been a useful gadget to propose items in various online systems, including e-learning. Regardless, next to no examination has been done to measure the learning consequences of the understudies when they use e-learning with a recommender system. Or maybe, most of the investigators was focusing on the exactness of the recommender system in predicting the proposal rather than the data gain by the understudies. This investigation means to break down the learning aftereffects of the understudies when they use a couple

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of sorts of e-learning recommender systems. Considering the connection made, they propose another e-learning recommender system structure that usages content-based filtering and incredible understudies’ assessments to recommend learning materials, and in this manner can assemble the understudy’s introduction. The results show that understudies who used the proposed e-learning recommender system made an in a general sense better result in the post-test. The results moreover show that the proposed e-learning recommender structure has the most essential degree of score gain from pre-test to post-test. The investigation [24] presents a novel proposition design that can recommend intriguing post messages to the understudies in an e-learning on the Web trade discourse reliant on a semantic substance-based isolating and understudies’ negative assessments. They have surveyed the proposed e-learning recommender system against leaving e-learning recommender structures that usage equivalent filtering strategies to the extent recommendation accuracy and understudies’ show. The acquired exploratory outcomes show that the proposed e-learning recommender framework outflanks other comparative e-learning recommender frameworks that utilization non-semantic content-based filtering technique (CB), non-semantic content-based filtering technique with learners’ negative ratings (CB-NR), semantic content-based filtering technique (SCB), as for framework precision of about 57, 28, and 25%, separately. Besides, the acquired outcomes additionally show that the learning execution has been expanded by in any event 9.84% for the students whom are upheld by proposals dependent on the proposed system when contrasted with other comparable suggestion procedures. The developers of [1] recommend a new, deep learning methodology that copies a effective smart recommendation from previously knowing the users and stuff. In the underlying point, it is found independently comparing low-dimensional vectors of customers and items, which incorporates the semantic data mirroring the relationship between the customer and the object. A feed-forward neural network is used during the expectation arrangement to re-enact the relationship between user and item, where the contrasting circa-trained accurate vectors are taken as contributions of the neural systems. A few analyses relying on two benchmark datasets (MovieLens 1 M and MovieLens 10 M) are conducted to test the practicalities of the proposed model, and the outcome exposes that their model dominates past approaches that potentially improve circa-owned feed-forward neural systems and performs equally on both datasets with pioneering techniques. Table 5.1 is the comparison of all the same.

5.5 Challenges A recommendation engine can be of greater valuation while same engine can be bad dream on the off chance that it very well may be effectively tricked by the individuals on the framework can be controlled effectively.

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Table 5.1 Comparison of the recommender systems proposed by the models above Model/concept implemented

Objectives

“Educational recommender systems: a pedagogical-focused perspective” [10]

• Researches the complexities between general recommender structures and informational recommender systems, and gives a general diagram about the points of interest, troubles, and imperatives of recommender structures in enlightening settings

“Recommendation systems in education: a systematic mapping study” [11]

• To plan the present data that is available as regards recommender structures that have been used inside the preparation space to help instructive practices. By playing out an efficient mapping appraisal, a total of 44 research papers had been picked, evaluated, and explored from a fundamental outline of 1181 papers • Their results gave a couple of disclosures concerning how recommender structures can be used to help standard zones in arranging, what approaches strategies or figuring recommender systems use and how they address different issues in the savvy world • This work had in like way been useful to see some examination openings and key areas where further assessment ought to be performed, like the introduction of data mining and man-made hypothesis in recommender structure figuring to improve personalization of clever choices

“Personalized recommender system for e-learning environment based on student’s preferences” [12]

• A recommender e-learning approach which utilizes proposal structures for enlightening data burrowing unequivocally for seeing e-learners’ learning affinities • Their proposed technique relies on three modules, a space module which contains all of the data for a particular zone, an understudy module which uses to see understudies’ learning inclinations and practices and a proposal module which pre-structures data to make fitting suggestion overview and envisioning appears. Upheld resources are gotten by using level of data on understudies in different advances and the level of proposal philosophies reliant on content-based isolating and obliging methodologies • A few frameworks, for instance, game-plan, gathering, and intrigue rules are used to improve personalization with isolating strategies to give a recommendation and help understudies to improve their presentation (continued)

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Table 5.1 (continued) Model/concept implemented

Objectives

“A recommender system for students based on social knowledge and assessment data of competences” [13]

• The recommender framework mulls over encounters recently put away and positioned by previous understudies. So as to offer fruitful learning guidance, utilizing the information the recommender framework breaks down the understudy’s present skill levels against comparable previous understudies’ exhibitions

“The role of e-learning in educational process” [14]

• Virtual contact between the educator and the understudy is much of the time undeniably increasingly advantageous for them two. Preparing and e-learning courses are prevalent • They are completed in school or scholastic condition as well as in the business one. Separation instruction is utilized as an apparatus to help learning procedure, and its utilization may permit simple administration of materials, permitting to make adaptable instructive strategies

“Research on personalized recommendation of educational resources based on big data” [15]

• The administration of instructive assets and the huge information which structure the stage as the foundation, and designs a customized suggestion calculation of instructive assets as indicated by the users’ very own characteristics. Personalized suggestion calculation of EDX stage and customized paper proposal of CiteULike are utilized to check the judiciousness and adequacy of the proposed calculation

“A statistical model to determine the behavior adoption in different timestamps on online social network” [5]

• The creators use vector space models and inverse term frequency to archive recurrence strategies. The ideas of group conduct and aggregate conduct and has clarified all in a similar way with striking quality

“Analyzing user activities using vector space model in online social networks” [4]

• Inquiries about and dismantle the association between the articles (like photos, posts, and so on) and its watchers (companions, accomplices, and so forth) for a given user and to discover advancement relationship among them by utilizing the TF-IDF plan of vector space model. After vectorization, the vector information has been displayed through a weighted diagram with different properties (continued)

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Table 5.1 (continued) Model/concept implemented

Objectives

“Recommender system for predicting student performance” [16]

• Propose a novel way of thinking which utilizes recommender structure frameworks for instructive information mining, particularly for imagining understudy execution • To support this technique, they have separated recommender structure frameworks and standard lose the faith strategies, for example, decided/straight fall away from the faith by utilizing instructive information for shrewd educating frameworks • Exploratory results show that the proposed system had improved guess results

“Role of e-learning in a developing country like India” [17]

• Indian preparing circumstance, eLearning content course of action and presentation mechanical assemblies, usage of eLearning to spread guidance to the remote districts, points of interest and hindrances of e-learning and inevitable destiny of eLearning in India • Two or three recommendations had been made to use e-learning for easy-going and expert getting ready, which is outstandingly fruitful for a country like India where a prevailing piece of people is living in nation/remote districts and has gotten for all intents and purposes unimportant traditional guidance

“The role of e-learning, the advantages and disadvantages of its adoption in higher education” [6]

• The reasonableness of utilizing e-learning in educating in tertiary establishments • In relationship of bleeding edge preparing, the issue of using current data and correspondence advances for educating and learning is basic • This assessment surveys making and gives an academic foundation to the appraisal by evaluating several obligations made by different specialists and establishments on the plausibility of e-changing, especially its usage in preparing and learning in higher enlightening affiliations. It reveals a few of view that individuals and foundations have shared all around on the portion and coordination of e-learning movements in getting ready through reviews and different perceptions • They in like way take a gander at the tremendousness or ramifications of e-learning as given by various scientists and the action that e-learning plays in higher enlightening establishments in relationship with educating and learning structures, and the central focuses and weights of its social event and execution (continued)

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Table 5.1 (continued) Model/concept implemented

Objectives

“A survey paper on e-learning recommender system” [18]

• Proposes the utilization of Web mining frameworks to assemble such a position, that could prescribe Web-based learning practices or basic courses in a course site dependent on understudies’ way history to improve course material course comparatively as help the Web learning process • These systems are viewed as made Web mining rather than isolated Web mining utilized by pro users to find online access plans

“Using recommendation system for E-learning environments at degree level” [19]

• Tries to give the present condition respects to recommendation systems and their application on segment direction over the internet. Their evaluation tries to show the presence of mind of utilizing recommendation systems applications in instructive conditions • This article moreover shows the work that is being done to give the useful condition a proposal framework. Their work remarks on the present condition of parcel learning and the issues it presents, by then supporting the utilization of a recommendation framework as a reaction to the issues that are confronted, by then they try to exhibit the general reasons for the appraisal, finally a delineation and an introduction of the outcomes got in the fundamental time of the evaluation

“The relevance of e-learning in higher education” • Perceives and separates rising examples and [7] models in e-learning for teacher preparing and master improvement from the making research base, both all-inclusive examples and stream headways. Enlightening foundations and empowering staff have various points of interest in view of ascent of present-day advancement. Teachers have their own one of kind frameworks through which they interface themselves with various instructors over the globe. Establishments have Web-maintained homerooms. So likewise, it also improved the commitments of schools, schools, and universities that should have such teachers who can make such understudies, who in the wake of getting their guidance can alter themselves at any stage (continued)

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Table 5.1 (continued) Model/concept implemented

Objectives

“The impact of e-learning and e-teaching” [8]

• The exponential progress of imaginative improvement comes a solid sense that occasions are moving too rapidly for our schools and that educators might be losing control of at the same time • They audited the effect of e-learning and e-educating in schools, from both the understudy and instructor point of view. Specifically, it is indicated that e-instructors should concentrate not just on beyond what many would consider possible and parts of IT materials and exercises, yet should attempt to significantly more thoroughly see how their e-understudies see the learning conditions • From the e-understudy point of view, this paper shows that essentially having IT instruments open does not along these lines change over into all understudies winding up being successful understudies. Continuously confirmation-based evaluative research is required to permit e-learning and e-preparing to arrive at most prominent farthest point

“ICT and e-learning—catalysts for innovation and quality in higher education” [9]

• The present dynamic and in general condition, all zones of the economy and, expressly, tertiary part (association division) need to keep track with the data and correspondence advancements and to concur with this quick driving development in requesting to fulfil the entirety of accessories’ needs and needs and to make, improve, and redesign the possibility of the gave associations. In this novel circumstance, colleges need to alter the associations they give and their substance since they cannot overlook the social models identified with the data and correspondence headways in perspective on the way in which that one of the basic limits in our present period is mechanical ability • The adjustment in front line preparing affiliations doesn’t depend just on these advances, yet more on the HR and how they can approach and utilize all the new improvements and e-learning potential outcomes. ICT and e-inclining can update forefront preparing through inventive techniques by developing the understudies’ inspiration, intrigue and obligation, by enabling the securing of aptitudes and by redesigning educator preparing which will finally improve correspondence and trade of data (continued)

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Table 5.1 (continued) Model/concept implemented

Objectives

“Recommender systems in e-learning • The improvement of refined e-learning environments: a survey of the state-of-the-art and conditions, personalization is changing into an possible extensions” [20] imperative part in e-learning structures because of the separations in foundation, targets, limits, and characters of the enormous measures of understudies • Personalization can accomplish utilizing specific kind of suggestion systems. This study shows an outline of the most basic prerequisites and issues for masterminding a recommender framework in e-learning conditions. The reason for this paper is to show the different controls of the present time of suggestion methodology and potential developments with model for naming exercises and tag-based recommender structures, which can be applied to e-learning conditions so as to give better proposal limits “Personalized recommender system for e-learning environment” [21]

• Builds up another personalization approach that gives to understudies the best learning materials as indicated by their inclinations, premiums, foundation information, and their memory ability to store data. They have utilized another suggestion approach dependent on community-oriented and content-based separating is displayed: NPR_eL (new multi-personalized recommender for e learning) • This methodology was incorporated in a learning domain so as to convey customized learning material. They exhibit the adequacy of their methodology through the plan, execution, examination, and assessment of an individual learning condition (continued)

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Table 5.1 (continued) Model/concept implemented

Objectives

“Career goal-based e-learning recommendation using enhanced collaborative filtering and prefix span” [22]

• The cold start issue which exists in conventional proposal calculations are still left over in e-learning frameworks and a couple of them have genuinely influenced the learning objectives of users • Therefore, a smart e-learning framework have been created which can prescribe proficient and focused on courses as per their profession objectives. Initial, an improved community-oriented separating (CF) approach is proposed thinking about users’ vocation objectives and foundation data • At that point, the importance between vocation objectives and courses are determined to reduce the cool beginning issue and prescribe particular courses for users. At long last, a PrefixSpan calculation is joined with the above strategies to produce a customized learning way bit by bit • A few trials are completed with genuine users of various callings to test the exhibition of the half and half calculation

“Measuring learner’s performance in e-learning recommender systems” [23]

• Recommender structures have been a useful gadget to propose items in various online systems, including e-learning • Regardless, next to no examination has been done to measure the learning consequences of the understudies when they use e-learning with a recommender system. Or maybe, most of the investigators was focusing on the exactness of the recommender system in predicting the proposal rather than the data gain by the understudies • This investigation means to break down the learning aftereffects of the understudies when they use a couple of sorts of e-learning recommender systems. Considering the connection made, they propose another e-learning recommender system structure that usages content-based filtering and incredible understudies’ assessments to recommend learning materials, and in this manner can assemble the understudy’s introduction • The results show that understudies who used the proposed e-learning recommender system made an in a general sense better result in the post-test. The results moreover show that the proposed e-learning recommender structure has the most essential degree of score gain from pre-test to post-test (continued)

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Table 5.1 (continued) Model/concept implemented

Objectives

“Utilizing learners’ negative ratings in semantic content-based recommender system for e-learning forum” [24]

• Presents a novel proposition design that can recommend intriguing post messages to the understudies in an e-learning on the Web trade discourse reliant on a semantic substance based isolating and understudies’ negative assessments • They have surveyed the proposed e-learning recommender system against leaving e-learning recommender structures that usage equivalent filtering strategies to the extent recommendation accuracy and understudies’ show • The acquired exploratory outcomes show that the proposed e-learning recommender framework outflanks other comparative e-learning recommender frameworks that utilization non-semantic content-based filtering technique (CB), non-semantic content-based filtering technique with learners’ negative ratings (CB-NR), semantic content-based filtering technique (SCB), as for framework precision of about 57, 28, and 25%, separately • Besides, the acquired outcomes additionally show that the learning execution has been expanded by in any event 9.84% for the students whom are upheld by proposals dependent on the proposed system when contrasted with other comparable suggestion procedures

“A novel deep learning-based collaborative filtering model for recommendation system” [1]

• Recommends a new, deep learning methodology that copies an effective smart recommendation from previously knowing the users and stuff. In the underlying point, it is found independently comparing low-dimensional vectors of customers and items, which incorporates the semantic data mirroring the relationship between the customer and the object • A feed-forward neural network is used during the expectation arrangement to re-enact the relationship between user and item, where the contrasting circa-trained accurate vectors are taken as contributions of the neural systems • A few analyses relying on two benchmark datasets (MovieLens 1 M and MovieLens 10 M) are conducted to test the practicalities of the proposed model, and the outcome exposes that their model dominates past approaches that potentially improve circa-owned feed-forward neural systems and performs equally on both datasets with pioneering techniques

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Content-based filtering is the system of prescribing items to users with the assistance of other comparable users. The centre thought is that individuals with comparative inclinations will like comparative kind of items. Content-based filtering is a general term and there are numerous calculations that utilize this idea to prescribe items. Latent collaborative filtering is one of the most utilized community-oriented filtering calculations that perform lattice or matrix factorization to prescribe most important item to users. There is likewise deep learning approach for collaborative filtering that beats the majority of other conventional strategies. While executing these calculations in our application, we think of not many issues that are portrayed beneath: Cold Start Problem: How would you manage new users and items that do not have any history? Arrangement: Utilize content-boosted filtering approach. It is blend of content-based filtering and collaborative filtering. One can utilize item portrayal and characteristics just as user statistic to prescribe items to users. For example, this is one of the key issues that decrease recommendation system presentation. The profile of such a new user or item would be unfulfilled as he has not appraised anything; henceforth, the system does not know his taste. Data Sparsity: User-Item rating network is meagre (numerous invalid items) in the light of the fact that stores have numerous items and every one of those items will not be appraised by numerous users. In reality, not many individuals rate items. Consider how often have we appraised items after you purchased items on the Web? This sparsity makes preparing computationally wasteful. Arrangement: Use dimensionality decrease. Expel pointless users and items from where we are not adapting a lot and lessen sparsity of user item rating grid. For example, users determine only a few of the complete number of items that are accessible in a database. Grey-Sheep Problem: Presently, here comes a weirdo individual in our application. From the name grey-sheep, we comprehend his conduct are flighty. For example, He may state Game of Thrones 1 is great and Game of Thrones 2 is most exceedingly terrible. Essentially how would we manage these abnormal individuals whose suppositions are conflicting? Arrangement: Unadulterated collaborative filtering doesn’t work. So, utilize content-boosted filtering like in cold start problem. Synonymy: By what means will we manage the items that are for all intents and purposes same however extraordinary. For instance, various versions of a book or pdf or physical duplicate of book. Since we don’t utilize item portrayal for community separating you can miss the data about synonymy. Since online stores have various codes for these items, discovering synonymy can be issue. Arrangement: Latent collaborative filtering is the kind of calculation that can distinguish concealed elements from the information. This calculation works truly well for synonymy too. So, in the event that we have part of things with synonymy this is the best approach. For example, a recommendation system that is proficient when the quantity of dataset is constrained might be not able to create acceptable number of suggestions when the volume of dataset is expanded. Along these lines, it is essential to apply proposal methods which are fit for scaling up in a fruitful way as the quantity of dataset in a database increment. Strategies utilized for tackling adaptability issue and accelerating proposal age depend on dimensionality decrease systems, for example, singular value

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decomposition (SVD) technique, which can create solid and proficient suggestions. Different techniques, for example, programmed term extension, the development of a thesaurus, and singular value decomposition (SVD), particularly latent semantic indexing are equipped for tackling the synonymy issue. The inadequacy of these strategies is that some additional terms may have various implications based on what is proposed, which now and then prompts quick debasement of suggestion execution. Shilling or Peddling Attacks: How would one manage individuals who are attempting to game the proposal framework? For instance, a framework may have an abnormal writer who gave ton of evaluations for his books and huge amounts of pessimistic appraisals for others’ book. Arrangement: Avoid potential risk and screen user conduct. The above are in general the challenges faced by any recommendation systems that have been already in use. The same can also be observed in the case of recommendation systems for education. But the major threat which this field is facing is the humongous explosion of data for which the previous frameworks fail to a gain any proficiency. For those kinds and volumes of data, the prevalent methods fail and thus we need to shit to deep learning techniques and algorithms for the faster analysis and results.

5.6 Proposed Model The proposed model of [1] proposes to build a system based upon the prior knowledge on user and items and then make predictions leveraging these obtained prior knowledges. Instinctively, the previous information will promote and support user behaviour forecasting. This earlier knowledge can start from the user’s past experience. Creating the earlier user knowledge from their previous experience, enlivened by the word deployment in NLP which can convert syntactic and semantic word details into low-dimensional vector based on the specific case. They agree the user’s qualitative details may even be taken by bringing in the contrasting implant from the user’s “special case”, where the user’s co-event may be viewed as the user’s environment in the previous history of the company. Likewise, the knowledge regarding items may also be informed via the co-event of items. They then attempt to suggest utilizing the neural network to build perceptions from the user and stuff pre-learned embedding. This system may therefore generally be broken into two significant stages: (1) comprehension and (2) forecasting. Server and object embedding will capture the application and item co-event in the primary level, independently. The prescient neural network will replicate the collaboration between item and user in the subsequent point (Fig. 5.1).

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Fig. 5.1 Proposed framework [1]

5.7 Conclusion and Future Direction In the recent past, we have seen the progress and changes made by various recommendation systems in our daily lives. Various approaches such as collaborative, contentbased, and hybrid methods were applied along with some “industrial-strength” systems. Regardless of them, a contemporary recommender framework requires some additional enhancements in the data to improve recommendation for a wide range of applications. In this regard, in e-learning, there are many data which needs to be provided to make accurate predictions. Also, in this study, we have seen the various domains dealing with the subject matter which speaks for itself. Following that, we have seen all the various studies that have been done on this subject matter and where we stand now. Also, most of the challenges which are faced by the same have also been duly discussed. In all the above discussions, it is quite eminent that there are some of the things which needs proper modifications and once those modifications are incorporated in making the model of recommender framework and thus is implemented, it will be able to solve all the prevalent issues which the system has. Also, the ever-growing nature of data poses a problem to the already prevalent old techniques, which needs a proper upgrading so that the machine can itself understand all the problems and give the solutions, i.e. incorporation of the deep learning methods in the same. Further study will focus on validating the performance and quality of recommendation of the semantic recommendation algorithms and implementing the proposed framework in recommendation systems, thus will be an attempt to solve the prevalent problems in this field by us. This also has the extension using deep learning and computer vision which is also the further steps we are in consideration about.

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References 1. Fu, M., Qu, H., Yi, Z., Lu, L., Liu, Y.: A novel deep learning-based collaborative filtering model for recommendation system. IEEE Trans. Cybern. 2168–2267 (2018). https://doi.org/10.1109/ TCYB.2018.2795041 2. Fry, K.: E-learning markets and providers: some issues and prospects. Educ. Train. 233–239 (2001) 3. Thinh, D.V.: The role of e-learning. In: Management, Enterprise and Benchmarking in the 21st Century. Budapest (2016) 4. Sarkar, D., Jana, P.: Analyzing user activities using vector space model in online social networks. In: National Conference on Recent Trends in Information Technology and Management (RTITM 2017) 5. Sarkar, D., Roy, S., Giri, C., Kole, D.K.: A statistical model to determine the behavior adoption in different timestamps on online social network. Int. J. Knowl. Syst. Sci. 10(4) (2019) 6. Arkorful, V., Abaido, N.: The role of e-learning, the advantages and disadvantages of its adoption in higher education. Int. J. Educ. Res. 2(12) (2014) 7. Wani, H.: The relevance of e-learning in higher education. J. Kaji. Pendidik. 3(2) (2013) 8. Mohammad, M.: The impact of e-learning and e-teaching. World Acad. Sci. Eng. Technol. Int. J. Educ. Pedag. Sci. 6(2) (2012) 9. Pavela, A., Fruthb, A., Neacsu, M.: ICT and e-learning—catalysts for innovation and quality in higher education. Procedia Econ. Financ. 704–711, 2212–5671 (2015). In: 2nd Global Conference on Business, Economics, Management and Tourism, Prague, Czech Republic, 30–31 Oct 2014. Elsevier 10. Garcia-Martinez, S., Hamou-Lhadj, A.: Educational recommender systems: a pedagogicalfocused perspective (2013). https://doi.org/10.1007/978-3-319-00375-7_8 11. Rivera, A.C., Tapia-Leon, M., Lujan-Mora, S.: Recommendation systems in education: a systematic mapping study. In: Proceedings of the International Conference on Information Technology & Systems (ICITS 2018). Advances in Intelligent Systems and Computing, vol. 721 12. El Fazazi, H., Qbadou, M., Salhi, I., Mansouri, K.: Personalized recommender system for elearning environment based on student’s preferences. IJCSNS Int. J. Comput. Sci. Netw. Secur. 18(10) (2018) 13. Chavarriaga, O., Florian-Gaviria, B., Solarte, O.: A recommender system for students based on social knowledge and assessment data of competences. In: EC-TEL 2014. LNCS, vol. 8719, pp. 56–69 (2014) 14. Duda, A., Korga, S., Gnapowski, S.: The role of e-learning in educational processes. Adv. Sci. Technol. Res. J. 8(24), 61–65 (2014) 15. Seng, D.W., Chen, X.L., Fang, X.J., Zhang, X.F., Chen, J.: Research on personalized recommendation of educational resources based on big data. Educ. Sci. Theory Pract. 18(5), 1948–1959 (2018) 16. Thai-Nghe, N., Drumond, L., Krohn-Grimberghe, A., Schmidt-Thieme, L.: Recommender system for predicting student performance. In: 1st Workshop on Recommender Systems for Technology Enhanced Learning. Procedia Comput. Sci. 1, 2811–2819 (2010) 17. Agarwal, D.: Role of e-learning in a developing country like India. In: Proceedings of the 3rd National Conference; INDIACom-2009 Computing For Nation Development, 26–27 Feb 2009 18. Sikka, R., Dhankhar, A., Rana, C.: A survey paper on e-learning recommender system. Int. J. Comput. Appl. 47(9) (2012). 0975-888 19. Martínez, O.S., G-Bustelo, C.P., Crespo, R.G., Franco, E.T.: Using recommendation system for e-learning environments at degree level. Int. J. Artif. Intell. Interact. Multimed. 1(2) (2009) 20. Klašnja-Mili´cevi´c, A., Ivanovi´c, M., Nanopoulos, A.: Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artif. Intell. Rev. 44, 571 (2015). https://doi.org/10.1007/s10462-015-9440-z 21. Benhamdi, S., Babouri, A., Chiky, R.: Personalized recommender system for e-learning environment. Educ. Inform. Technol. 22(4), 1455–1477 (2017)

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22. Xueying, M., Lu, Y.: Career goal-based e-learning recommendation using enhanced collaborative filtering and PrefixSpan. Int. J. Mob. Blended Learn. 10(3), 23–37 (2018) 23. Khairil Imran, G., Nor Aniza, A.: Measuring learner’s performance in e-learning recommender systems. Australas. J. Educ. Technol. 26(6), 764–774 (2010) 24. Albatayneh, N.A., Ghauth, K.I., Chua, F.-F.: Utilizing learners’ negative ratings in semantic content-based recommender system for e-learning forum. Educ. Technol. Soc. 21(1), 112–125 (2018)

Chapter 6

Automation of Attainment Calculation in Outcome-Based Technical Education (OBTE) Nikita Gupta and Arijit Ghosal

Abstract The aim of this work is to build an intelligent software system which will determine the attainment of every course automatically based on the provided data of every student. Data will be given by faculty member, along with the course outcome mapping. Course attainment will be calculated by the marks attained by each student. The design of this software helps in a presentable view of the details of the institution along with many functions which can be operated by the user. Such an automated processing of calculation of attainment can help in a massive reduction of manual labor of storing the data in MS-Excel which can allow some possible errors. With such an automation, error possibility decreases to a great minimum. Not only that, but also the records are maintained and can be obtained with ease. An analysis report can be also be generated. This software can be used by any institution to maintain a detailed record for itself. Keywords Outcome-based technical education (OBTE) · Attainment calculation · Module

6.1 Introduction Nowadays, the whole teaching–learning process has been switched to outcome-based assessment from marks-based assessment. In most of the technical institute, outcomebased technical education (OBTE) has been adopted. In this type of assessment, emphasis is paid on the learning outcomes of students in a course and collectively at program level. There exists no single prespecified style of teaching in OBTE. Rather classes, opportunities, and assessments should help students to attain the specified outcomes. OBTE is a complex process. Any department in an institute adopts certain N. Gupta · A. Ghosal (B) Department of Information Technology, St. Thomas’ College of Engineering and Technology, Kolkata, West Bengal 700023, India e-mail: [email protected] N. Gupta e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 A. Deyasi et al. (eds.), Computational Intelligence in Digital Pedagogy, Intelligent Systems Reference Library 197, https://doi.org/10.1007/978-981-15-8744-3_6

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assessment processes to measure the learning outcomes of students in the course level and gradually at the program level. It involves certain steps: Step 1: Groundwork for making efficient course outcomes (COs)—Learning outcomes are straight proclamations that express the indispensable and stable disciplinary awareness and abilities that learners should acquire, and the depth of education that is expected upon end of a program or course. They focus on transportable awareness, proficiencies, and behaviors that can be monitored and judged, and are reflective of disciplinary perspectives. Course objectives are wide-ranging ambitions that the course guesses to accomplish, delineating in moderately universal phrases and the awareness and expertises that the program faculty will assist its students to accomplish. As against this, course outcomes are operational definitions. Since course objectives are widely stated, they do not endow with enough fact to be teachable and assessable, that is, to steer teaching in the curriculum and to be consistently judged. So, course outcomes should be inscribed in a way that is demonstrable; explicitly, they should affirm what it means to show the awareness and expertisation named in the objectives. While designing COs, faculty member should take about the level of learning as per Bloom’s taxonomy. Step 2: Identify evidences for assessment of course outcomes—Once the key learning outcomes have been identified, assessment methods for collecting student data can be chosen. Generally evidences are collected through internal examinations, assignment/quiz/viva-voce/class test, and end-semester examination. Step 3: Assessment of course outcome—The evaluation of course outcome is measured based on direct and indirect methods. The direct method considers the performance in the internal examinations, assignments and/or quiz, and end-semester examination; and indirect method includes course end survey. Internal assessment includes student performances based on internal examinations and assignments. External assessment is the end-semester examinations. Then in order to achieve total attainment, 80% of weightage has been given to direct method, and 20% of weightage has been given to indirect method as per NBA criteria in India. All these assessments are generally done through laborious manual process. Manual process is very much prone to error. So there is a huge requirement of making the whole system software based.

6.2 Previous Works Some research works has already been done by so far in the domain of outcomebased education. As the importance of improvement in the quality of teaching– learning process is increasing, more work has been performed in this domain. Some researchers have already tried to develop software related to this. Kavitha et al. [1] have studied the methods of assessment as well as the achievement of course outcomes along with program outcomes. Balasubramani and Chiplunkar [2] have

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presented a case study on achievement of program outcomes. They have considered attainment level between 1 and 5. Bhatia et al. [3] have proposed a data miningbased automatic survey designing means for indirect assessment in outcome-based education. Namasivayam and Fouladi [4] have discussed over the employment of learning outcome information to improve the quality of teaching in engineering courses in Taylor’s University through a case study. Kavitha et al. [5] have also worked with course outcome and program outcome for NBA Tier II engineering endorsement. Ramesh et al. [6] have developed a software package for outcome-based education. Khwaja [7] has proposed a Web-dependent program outcome measurement means. This tool has been developed for King Faisal University. Rajak et al. [8] have discussed about automating the accomplishment of course and program outcomes in outcome-based education in engineering colleges approved by AICTE in India. Dandin et al. [9] have proposed a spreadsheet-based tool to estimate the course outcomes of computer networking course only. Rajak et al. [10] have discussed about achievement and measurement of program educational objectives only for post-graduate courses. Poornima [11] has explained the importance of outcome-based education at engineering colleges in India.

6.3 Proposed Scheme A Web-based software is proposed here for automating the process of calculating the attainment of program outcome (PO) and program-specific outcome (PSO) in program level. It is also able to calculate the attainment of every course outcome (CO) of a course automatically based on data of every student provided by faculty member.

6.3.1 Automation In present day, there is a trend to make everything automated to reduce the load of manpower. In technical education, a student of a particular program has to study a huge number of theoretical courses as well as laboratory courses. Some of these courses are scoring and some are non-scoring. It is obvious that for course outcome calculation what rubrics is used for scoring courses, same cannot be used for nonscoring courses. If rubrics for scoring courses are applied to non-scoring courses, course outcome attainment level will be vey much low resulting poor attainment level of program outcome and program-specific outcome in program level. Similarly if rubrics for non-scoring courses are applied to scoring courses, attainment level in all cases will be very high. Both of these will not reflect actual scenario. Moreover, measuring of CO, PO, and PSO attainment level is carried out as a quality measure. If two separate rubrics are used, it will reflect the actual scenario as well as the quality

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of teaching–learning process. Because of these, use of two rubrics—one for scoring courses and another for non-scoring courses—is justified. Rubrics is nothing but an assessment tool which converts percentage of students obtaining a certain percentage of marks information into attainment level. Rubrics for non-scoring courses is expected to be lower than that of scoring courses. It will be very much time consumable if manually scoring and non-scoring course division is done for all the courses of a particular program. So, an automation is becoming very much essential to divide all courses into scoring and non-scoring. Concept of machine learning has been applied in this work to provide the automation. Departmental assessment committee (DAC) for a certain department decides the rubric for the scoring courses and for the non-scoring courses for a program for a certain academic year. Once a course is classified as scoring or non-scoring course, corresponding predefined rubrics is applied by the system for that particular course. This concept of automation is the unique feature of the system. Sample rubrics for scoring and non-scoring courses for CO attainment is given in Tables 6.1 and 6.2, respectively. For scoring courses, if 60% students achieve more than 60% percentage marks, then attainment level is 3; if 50% students achieve more than 60% percentage marks, then attainment level is 2; if 40% students achieve more than 60% percentage marks, then attainment level is 1. Obtaining 60% marks is considered as base value of marks for scoring courses in this sample. For non-scoring courses, if 50% students achieve more than 50% percentage marks, then attainment level is 3; if 40% students achieve Table 6.1 Course attainment rubric for scoring courses Course outcome (CO) attainment rubrics—base value 60% of marks Attainment level

Percentage (students)

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Table 6.2 Course attainment rubric for non-scoring courses Course outcome (CO) attainment rubrics—base value 50% of marks Attainment level

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more than 50% percentage marks, then attainment level is 2; if 30% students achieve more than 50% percentage marks, then attainment level is 1. Obtaining 50% marks is considered as base value of marks for non-scoring courses in this sample.

6.3.1.1

Classification of Scoring and Non-scoring Courses

Classification of scoring and non-scoring courses is purely machine learning problem with supervised classifier. Hence, it involves two steps—feature extraction or computation and classification. Figure 6.1 explains the machine learning approach with supervised classifier to classify courses into two categories.

Feature Computation Scoring and non-scoring classification is performed based on the previous year results of the students in those courses. How many students (in percentage) have obtained ‘O’ grade, how many students (in percentage) have obtained ‘E’ grade, how many students (in percentage) have obtained ‘A’ grade, and how many students (in percentage) have passed in that courses work as feature for this classification. So, dimension of feature vector is 4 in this work.

Classification Discrimination of courses into scoring and non-scoring category is a two-class problem. Linear support vector machine (SVM) is very much suitable to solve a two-class problem. Hence, support vector machine (SVM) has been used in this scenario.

Fig. 6.1 Flowchart of classification of courses

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6.3.2 Description of the Software The objective of this system is to build an intelligent software system which will determine the attainment of every course of a program, along with the course outcome mapping, calculated by the marks attained by each student. This software also determines total program outcome (PO) and program-specific outcome (PSO) attainment for a particular batch of students in a department for a specific program. The design of this software aims a presentable view of the details of the department along with many functions which can be operated by the user very easily. It is seen in the DFD diagram (Fig. 6.2) that the software involves three main users—Students, teachers, and administer. The students can only read the information provided by the administrator, the teachers, or faculty members can either read/modify the student information/marks, whereas the administrator has the access to the entire database of the institution and can modify/remove according to the requirement. This software is fully customizable. More features and modules may be added at any time in this software to serve the additional requirements of any institution. Such an automated processing of calculation of attainment can help in a massive reduction of manual labor of storing the data in MS-Excel or in Spreadsheet which can allow some possible errors. With such an automation, error possibility decreases to a great minimum. Not only that, but also the records are well maintained and can be retrieved any time easily. An analysis report can be also be generated from this software. This software can be used by any educational institution to maintain a detailed record for itself. Since departments define a variety of educational goals and objectives for a program, comprehensive assessment strategies frequently require the use of more than one assessment instrument to determine program effectiveness. These assessment instruments are broadly divided into direct and indirect categories.

Fig. 6.2 Data flow diagram (DFD) of the software

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Module Information

The application includes the following users: 1. Students 2. Teachers 3. Administrator. Students, teachers, and administrator can log in to the account with their respective username and password, and then, they will be directed to their respective pages. 1. Students Students has access only for providing necessary information regarding course end survey after login. 2. Teachers/Faculty members Initially faculty members/teachers have to provide dept (IT, CSE, EE, ECE, etc.), semester (1st/2nd, etc.), year (1st/2nd, etc.), internal test or assignment no (1st/2nd, etc.) (Fig. 6.3). Depending on internal test or assignment no, these information can be submitted multiple times. The teacher/faculty member has access to read/insert/update the question pattern that is the number of questions for each internal/assignment, its subparts, and the respective question paper versus CO (Each course can have generally six COs). In Fig. 6.4, it is seen that after the user submits the number of questions, the list of that many questions appears with fields to accept the number of subquestions. The whole process of entering marks for assignments/internals hence becomes easier and digitally more accessible. Now, after the subquestion fields are filled, the respective marks of the subparts and the CO number (which ranges from 1 to 6) are entered and submitted. As the teacher has access to read/insert/update the marks of his/her student for each internal/assignment, the teacher can display the CO mapping with the questions

Fig. 6.3 Faculty members are providing initial information regarding question paper

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Fig. 6.4 Faculty members are providing number of questions for each internal/assignment

given for each internal/assignment. Teacher can also display the attainment calculated by the total marks of the student for a course for any internal/assignment. Finally, the two modules, CO mapping and student details, contain their own fields of data (Fig. 6.5), that is, the CO mapping contains the details of the total marks and its respective CO number, and student details contain the marks for the students in

Fig. 6.5 Faculty members are providing subsubparts details of question with CO mapping

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their respective examinations. Moreover, all the data can easily be transferred to or retrieved from excel sheets, hence making the details available in printable format. 3. Administrator Admin can change the number of COs (if required) for any course. The admin has access to the details of the institution, that is, the admin can insert/update/remove the details of the department and courses of the institution. The admin has access the remove the data of the previous internals/assignments especially of the passouts of the institution. The admin can remove the internal/assignment details to accommodate further data. This work is based on relational database management system.

6.3.2.2

Direct Measures or Evidences for Attainment of Course Outcomes

Direct measures are those which are derived through the systematic analysis of faculty members of student projects, exams, or sets of specified course assignments. They can make a compelling case for the extent to which students have achieved expected learning outcomes. Teachers have to login to provide necessary information regarding these. This work considers the followings as direct evidence of student learning: 1. Theory courses (a) Internal assessment (i) Internal examinations (ii) Assignment/Quiz/Viva-Voce/Class Test (b) External assessment (i) End-semester examination 2. Practical/Laboratory-based courses (a) Internal assessment (i) Daily performance evaluation (b) External assessment (i) End-semester examination 3. Sessional courses (a) Internal assessment (i) Daily performance evaluation.

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Fig. 6.6 Flowchart of the system for calculating course outcome attainment of a course

6.3.2.3

Indirect Measures or Evidences for Attainment of Course Outcomes

Indirect evidence pertains to students’ self-perceptions of their learning and their perspectives on program structure and curricular content. This work considers course end survey as indirect evidence: 1. Course End Survey—At the end of a course, surveys are conducted to collect student input regarding their perception of attainment of course outcomes of the concerned course. A course end survey form is framed keeping in line with the respective course outcomes. Students have to login to fill up the form. 6.3.2.4

Attainment of Course Outcomes

Once data related to direct and indirect measures are provided by teachers or faculty members, the system automatically calculates the course outcome attainment as depicted in Fig. 6.6. The proposed software is configured this way—Direct method of assessment involves 40% weightage for internal assessments and 60% weightage for end-semester results. Then, in order to achieve total attainment, 80% of weightage has been given to direct method and 20% of weightage has been given to indirect method. After calculating attainment of course outcome for a single course, this software repeats the same steps for all courses in the department and records the attainment of course outcomes of all courses.

6.3.2.5

Attainment of Program Outcomes and Program-Specific Outcomes

This software assesses program outcomes (PO) and program-specific outcomes (PSO) using (a) direct assessment tools (for theoretical courses—theory internal

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Fig. 6.7 Flowchart of the system for calculating total PO and PSO attainment at program level

examination, theory assignments, theoretical quiz, and end-semester theory examination; for laboratory courses—daily performance evaluation and end-semester laboratory course examination; for laboratory courses—daily performance evaluation) and (b) indirect assessment tools (survey of Cell activities, exit student survey, alumni survey, and employer survey). This software calculates total attainment of PO and PSO in program level as depicted in Fig. 6.7.

6.3.2.6

Software and Hardware Requirements

Software requirements: Visual Studio Code, XAMPP. Hardware requirements: RAM: 2 GB and above, Processor: 2 × 1, 6 GHz CPU. Languages used: Front end: HTML, CSS. Back end: JavaScript, AJAX, PHP, SQL.

6.4 Case Study of CO, PO, and PSO Attainment Using Rubrics for a Set of Students Sample case study of rubrics-based CO and PO attainment for particular set of students of a certain program of a department is explained below.

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Fig. 6.8 Sample CO–PO and course–PO mapping

Fig. 6.9 Sample CO–PSO and course–PSO mapping

6.4.1 Generation of CO–PO, CO–PSO, Course–PO, and Course–PSO Mapping Every course of a program offered by a department of an educational institute has its own CO versus PO mapping which cumulatively generates a course versus PO mapping. This is shown in Fig. 6.8. Similarly every course of has its own CO versus PSO mapping which cumulatively generates a course versus PSO mapping. This is shown in Fig. 6.9.

6.4.2 Generation of Course–PO and Course–PSO Mapping at Program Level For measuring CO and PO attainment for a particular set of students for a program of the department (like B.Tech. in IT), initially course versus PO mapping for all the courses is required. This can be said as course–PO mapping at program level.

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Similarly, course versus PSO mapping for all the courses is required. This can be said as course–PSO mapping at program level. After this, based on scoring and non-scoring course, corresponding rubrics is applied to every course to find the course outcome attainment in which rubrics will be applied that is determined through machine intelligence as explained before.

6.4.3 Measuring Course Outcomes Attained Through University Examination (External Assessment) CO attainments from university examination for all the courses are measured. Sample CO attainment (CO1) for the course C214 is described next. Initially, student-wise university grade-equivalent score is listed from their obtained grade in university examination. Sample list for CO1 is depicted in Fig. 6.10. As in university examination, question-wise marks obtained is unknown, and equal weightage is paid for all COs. Same steps are repeated for all the COs. Next, student count with respect to achieved grade is calculated, and the same is depicted in Fig. 6.11.

Fig. 6.10 Sample university grade-equivalent score listing

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Fig. 6.11 Students count with respect to achieved grade

Fig. 6.12 Student percentage calculation

Based on scoring and non-scoring course, rubrics used either in Table 6.1 or in Table 6.2 is used. Previously mentioned sample course falls under non-scoring category. Hence, it uses Table 6.2 rubrics. Based on rubrics, student’s percentage is calculated which is depicted in Fig. 6.12. In next step, attainment level is calculated with the help of the help of the rubrics applied. As 87.037037%, students has scored more than 50% marks, as per rubrics attainment level which is 3. Based on the attainment level, CO attainment table for PO and PSO is formed for the course which are depicted in Fig. 6.13. Same steps are repeated for all courses. Course attainment table is then formed then which is portrayed in Fig. 6.14. This indicates Course–PO and Course–PSO mapping on the point of view of attainment level.

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Fig. 6.13 CO attainment table a for PO and b for PSO

Fig. 6.14 Course attainment table a for PO and b for PSO

6.4.4 Measuring Course Outcomes Attained Through Internal Examinations, Assignments, etc. (Internal Assessment) CO attainments from internal examination for all the courses are measured. Sample CO attainment (CO2) for the course C214 is described next. Initially, student-wise percentage mark obtained list is prepared against all the COs. Sample list is depicted in Fig. 6.15 for CO2.

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Fig. 6.15 Student-wise percentage marks obtained list against CO2

‘X’ indicates the question which is not attempted by a student. Same rubrics (mentioned in Table 6.2) is followed here. Based on rubrics, student’s percentage is calculated which indicates that 81.48% students has achieved more than 50% of marks which yields attainment level as 3. As attainment level is 3, CO attainment table and course attainment table will be similar to Figs. 6.13 and 6.14, respectively. These steps are repeated for all the COs of the course. For all CO, percentage of students achieve more than 50% marks is listed which is depicted in Fig. 6.16. Course-level attainment level: Finally, course-level attainment level for internal assessment is evaluated using the same rubrics. As 60.99% students has scored more than 50% marks cumulatively

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Fig. 6.16 Percentage of students achieve more than 50% marks for all COs

in all the COs, final course-level attainment level for this course is 3. Course-level attainment is calculated for all courses.

6.4.5 Course Outcome Direct Attainment Course outcome direct attainment is calculated by taking 40% of internal assessment and 60% of external assessment (as mentioned in Fig. 6.6). For this sample course, it is coming as 76.62% means attainment level is 3 (using rubrics mentioned in Table 6.2). This step is repeated for all courses.

6.4.6 Course Outcome Indirect Attainment Indirect attainment is measured through course end survey (mentioned in Fig. 6.6). Every feedback question is mapped to a CO, and finally, a course level attainment is obtained which might be fraction value also. This can be fraction value because rubrics is not used for indirect attainment. For the sample course, attainment level is 2.76. This survey is conducted for all the courses.

6.4.7 Total PO and PSO Attainment in Program Level Total Course–PO and Course–PSO attainment for a course is calculated by taking 80% of direct attainment and 20% of indirect attainment (as mentioned in Fig. 6.6). For this sample course, it is coming as 2.95. Based on this attainment level, a course– PO and course–PSO mapping is prepared which is similar to what is shown Fig. 6.17. This step is repeated for all the courses. Finally, a course–PO and course–PSO mapping for all the courses is generated. This forms program-level mapping which

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Fig. 6.17 Total course–PO and course–PSO attainment for a course

is referred as direct attainment through direct assessment. PO attainment from direct assessment process and PSO attainment from direct assessment process are shown in Figs. 6.18 and 6.19, respectively. PO and PSO attainment is also measured through indirect assessment process also. Indirect assessment process includes PO and PSO attainment through events organized by different cell, exit student survey, alumni survey, and employer survey. Figures 6.20 and 6.21 depict indirect PO attainment and indirect PSO attainment, respectively. Finally, total PO and PSO attainment in program level for the set of students is calculated by summing up 80% of direct attainment and 20% of indirect attainment (as mentioned in Fig. 6.7). Total PO attainment and PSO attainment is depicted in Figs. 6.22 and 6.23, respectively.

6.5 Comparative Analysis Performance of this software has been compared with some existing work already done in this domain. Bhatia et al. [3] have used ZeroR, OneR, IBK, J48, and Naïve Bayes classifiers to predict the attainment status using data mining tool named WEKA. They have treated attainment calculation task as a regression problem. Moreover usage of multiple classifiers has made the system not so much user friendly compared to this work as attainment is calculated in this work using fixed formula as per NBA guidelines. No computational intelligence is used to choose proper rubrics in their work, whereas this work employs computational intelligence to choose proper rubrics based on scoring and non-scoring courses. This approach has made the system more dynamic and simple. Dandin et al. [9] have made a computerized system which is based on spreadsheet. Use of spreadsheet may lead to various types of errors like selection of wrong cell while applying a formula, cell jumping or missing, etc. Moreover in case of spreadsheet, a user has to manually provide a lot of information which is also laborious.

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Fig. 6.18 PO attainment from direct assessment

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Fig. 6.19 PSO attainment from direct assessment

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Fig. 6.20 Indirect PO attainment

Fig. 6.21 Indirect PSO attainment

Fig. 6.22 Total PO attainment

Fig. 6.23 Total PSO attainment

Their work is also limited to attainment calculation of course outcomes and program outcomes only. But this work can generate attainment of not only course outcomes in course level and program outcomes in program level, but it can also generate attainment of program-specific outcomes in program level. They have done the work considering a fixed number of students for a certain year and program, whereas this work can be applied to any year and program having any number of students. This indicates that this software is more dynamic in nature.

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6.6 Conclusion This approach utilizes machine learning approach to classify courses into two categories—scoring and non-scoring which reduces loads of manpower as well as chances of possible errors. Based on that, this software automatically applies corresponding rubrics on a course to find attainment of COs. Moreover, this software considers program-level attainment calculation of both PO and PSO which is another important aspect of this software. This Web-based approach uses simple GUIs to make this software simple and easy to use. The venture based on RDBMS is a software using HTML, CSS as front end, JavaScript and PHP as back end, and MYSQL as the database which computerizes the whole processing of the calculation. Additional features and functions can be added to this software increasing its usage in different institutions. But the feasibility and processing of the additional features may need time to be incorporated in the system.

References 1. Kavitha, A., James, K.I.A., Harish, K.A., Rajamani, V.: An empirical study on assessment and attainment method of course outcome and programme outcome for NBA tier II accreditation in engineering colleges through outcome based education (OBE). Int. J. Pure Appl. Math. 117(22), 25–28 (2017) 2. Balasubramani, R., Chiplunkar, N.N.: Attainment of programme outcomes through course outcomes in outcome based education: a case study. J. Eng. Educ. Transform. 31(2), 26–30 (2017) 3. Bhatia, J., Girdhar, A., Singh, I.: An automated survey designing tool for indirect assessment in outcome based education using data mining. In: 2017 5th IEEE International Conference on MOOCs, Innovation and Technology in Education (MITE), Nov 2017, pp. 95–100. IEEE. 4. Namasivayam, S.N., Fouladi, M.H.: Utilisation of learning outcome attainment data to drive continual quality improvement of an engineering programme: a case study of Taylor’s University. Int. J. Eng. Educ. 34(3), 905–914 (2018) 5. Kavitha, A., James, K.I.A., Harish, K.A., Rajamani, V.: A empirical study on CO-PO assessment & attainment for NBA TIER-II engineering accreditation towards empowering the students through outcome based education. Int. J. Pure Appl. Math. 118(20), 2615–2624 (2018) 6. Ramesh, K., Ronak, A., Sai Prashanth, K., Shivogi, S.: Developing a software package for outcome based education. J. Eng. Educ. Transform. 31(3), 35–41 (2018). https://doi.org/10. 16920/jeet/2018/v31i3/120754 7. Khwaja, A.A.: A web-based program outcome assessment tool. In: 2018 21st Saudi Computer Society National Computer Conference (NCC), Apr 2018, pp. 1–6. IEEE 8. Rajak, A., Shrivastava, A.K., Shrivastava, D.P.: Automating outcome based education for the attainment of course and program outcomes. In: 2018 Fifth HCT Information Technology Trends (ITT), Nov 2018, pp. 373–376. IEEE 9. Dandin, S.S., Jinde, R., Kamble, N.: An attainment tool for measuring course outcomes and program outcomes. Int. J. Adv. Res. Dev. 3(3), 24–27 (2018)

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10. Rajak, A., Shrivastava, A.K., Bhardwaj, S., Tripathi, A.K.: Assessment and attainment of program educational objectives for post graduate courses. Int. J. Mod. Educ. Comput. Sci. 2, 26–32 (2019) 11. Poornima, S.N.: Outcome based education, need for the hour-NBA. Int. J. Adv. Res. Ideas Innov. Technol. 5(2), 1030–1033 (2019)

Chapter 7

Quality Issues in Teaching–Learning Process Habiba Hussain

Abstract Quality in the process of teaching and learning has been a matter of concern since long. Of late, this is being talked of all the more for certain leading factors like accreditation, OBE, challenges of a VUCA world, etc. This chapter deals with the quality issues mainly in the field of technical education system in the country. With more and more technical institutions being set up in the nation, quality has started taken a back seat. But, teaching cannot happen without a minimum threshold quality. Some major factors dealing with the quality aspects in the teaching–learning process have been discussed in the chapter. The recent initiatives taken by the government of India in boosting the quality in the education system are also highlighted here. Keywords VUCA · Accreditation · Pedagogy

7.1 Introduction If teaching happens, it cannot happen without quality. When one speaks about quality in the teaching–learning process, it is to move towards more and more subtlety. Quality is a never-ending journey and the only path towards achieving consistency in the education system. Quality in teaching–learning has been a subject of great concern, more so in higher education. Of late, there is a quality uproar in the education sector mainly because of the global competition and accreditation. This chapter is written mainly in context with the technical education system in our country. Quality, which is often discussed, but less practised, at least in higher education, has brought forward many challenges to the practising teachers. These issues are being explored in this chapter. The growth in technical educational institutes in the country has been phenomenal. As per the data provided by India Skills Report 2019, the total number of degree H. Hussain (B) Education and Management, National Institute of Technical Teachers’ Training and Research (NITTTR), Kolkata, West Bengal, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 A. Deyasi et al. (eds.), Computational Intelligence in Digital Pedagogy, Intelligent Systems Reference Library 197, https://doi.org/10.1007/978-981-15-8744-3_7

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and diploma-level engineering Institutes as approved by the All India Council for Technical Education (AICTE), the regulatory body of technical education in India, in the year 2016–17 are 3285 and 3925, respectively, while each of their approved intake is 1,553,360 and 1,244,778 respectively. India has a competitive edge over its neighbouring countries as more than 60% of its population is that of the youth (as per 2015 data). A major part of our workforce is being supplied by our Engineering colleges, Polytechnics, and ITIs. This technical manpower is the key to the industrial growth and development of our nation. Inspite of several initiatives, we find that our passouts are not sufficiently employable.

7.2 Rationale The Economic Times, Business Standard in April 2016, reported that only 7 per cent of the MBA graduates are ultimately employable, except those from IIMs. The Economic Times, June 2018, reported that 94% of engineering graduates are not fit for hiring. The Business Line reported in January 2018 that 80% of engineers in India are unemployable. Several other reports indicate such a discouraging scenario nation-wide. Key skills are unavailable among the present-day graduates. March 2019, Business Today reported that 80% of Indian engineers are not fit for any job in the knowledge economy as laid down by the new Annual Employability Survey 2019 report by Aspiring Minds. It also stated that there has been no change in the employability prospects of Indian engineering graduates in the past nine years, with only a handful of them possessing next-generation technical skills. When we talk of quality improvement in teaching, it certainly captivates our thought and leads us towards that kind of teaching which results in effective learning. In the 1980s, higher education became more and more abreast of TQM as a paradigm for improving every aspect of the functioning of Institutes of higher education right from fiscal administration to classroom instruction. Terms like “customer focus”, “employee empowerment”, “continuous assessment”, and “Deming’s 14 principles” got reflected in the educational research journals and gained greater popularity. Though it was well understood that TQM was developed by and for industry to improve, Deming himself suggested the linkage between quality management principles and education, claiming that “…improvement of education, and the management of education, require application of the same principles that must be used for the improvement of any process, manufacturing or service” [8].

7.3 VUCA and Quality in the LT Process It is understood that the teaching community is also not spared from Volatility, Uncertainty, Complexity, and Ambiguity (VUCA). All academic institutions now

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work under VUCA conditions. When we talk of the teaching community, it means a wider ambit of teachers, students, their parents, curricula as also the society at large. All of them are exposed to challenges, but the students are the ones who face greater risks. Our students are witnessing a rapidly changing world that offers lot of challenges and opportunities. It calls for building in them the ability to learn and a set of competencies that will be needed for life. The twenty-first century skills are different demanding greater agility and growth mind set in the youngsters. The present-day employers seek social skills, soft skills among their employees more than the subject knowledge, preferring the skill of learning to learn. If students are to develop essential life skills and the ability to think constructively and act wisely, the holistic approach must be understood and considered central to education for the twenty-first century. The VUCA world offers more challenges in the contemporary world by bringing in volatility, uncertainty, complexity, and ambiguity. The whole of technical education is now moving towards outcome based framework, very much required for providing a competitive edge to the education system. Hence, students have to be prepared to face the unknown and demonstrate themselves as lifelong learners. They need to come out as confident and self-sufficient individuals. Both teacher and taught are now exposed to a plethora of information as it is now a world driven by rapid changes due to overwhelming volume of information (volatility). Predicting outcomes become difficult due to uncertainty. Further, the several multiple aspects of any issue or event make it more complex with ambiguity due to different interpretations and diversity of thoughts. In this way, VUCA makes the environment all the more challenging. The changes in policies, emerging technology, job market, the scenario of employment, and employability of the passouts—all add to this VUCA situation. Therefore, we have to and we must now think of quality in education, not just to meet the threshold but to rise above that, i.e. to excel. It is also well understood that VUCA world needs a dynamic, continual improvement.

7.4 Characteristics of Quality Teaching Several descriptions of quality teaching can be found in literature. Teaching is a complex process and there are several inputs to it; hence, quality of teacher and the taught apart from the learning teaching process needs to be relooked into. The mushrooming of technical institutes in our country is quite alarming when quality of LT system is concerned. Based on all these considerations, there has been a paradigm shift in education. The technical institutes are now moving towards outcome-based education (OBE) from traditional one. OBE is an approach to education in which decisions about the curriculum are driven by the exit learning outcomes that the students should display at the end of the course. The process shifts from a content-based input approach

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to a competence-based output approach where certification validates the achieved competences. A quality teaching and learning can be characterised as follows (as retrieved from https://portal.uea.ac.uk/documents/6207125/8480269/cop-on-assuring-and-enhanc ing-teaching-quality.pdf). It • is based on a set of aims and objectives for each programme/course of study (via programme specifications) and each module of teaching • takes account of the needs, abilities, experience, and expectations of students and engages with students at an appropriate level of understanding, explaining the material plainly and helpfully • motivates students to learn by generating an enthusiasm for the subject • encourages students to study independently, taking responsibility for their own learning • uses teaching aids and techniques that are appropriate to the programme/course of study and of teaching • encourages and facilitates student participation in the learning process through classroom-based activities such as group discussions, presentations, problem solving, and collaborative project work • encourages, develops, and incorporates feedback from students on the programme/course of study/module of teaching • uses valid, appropriate, and fair methods for the assessment of students • provides constructive feedback to students on their work • enables students to acquire the necessary key, cognitive, and subject-specific skills • enables students to acquire generic skills • is linked to faculty research thereby making students aware of the continuing development of the subject and of the provisional nature of knowledge • is reflective and self-critical, thereby leading to enhancement • takes account of the requirements of relevant Professional and Statutory Bodies. Institutes of higher education (IHEs) are assessed in order to recognise the quality of educational practices and their performance. Excellence is intricately linked with quality enhancement and quality assurance. Quality has to be the essence and essential element of higher education. While the outcomes of institutional assessment are many, ultimately all are expected to lead to a sustained effort, primarily to improve the quality of teaching–learning. Accreditation is the measure of quality assurance in higher education. It is an independent appraisal of an institution during which the institution’s overall educational quality (including outcomes), professional status among similar institutions, financial stability, and operational ethics are self-evaluated and judged by peers.

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7.5 Teaching Methodology Teaching methodology encompasses the plethora of activities that a teacher performs to enable students learn. The teaching methodology is often found to remain the same over the years with the syllabus remaining unchanged as also the pattern of examination. The question papers also reveal the fact that memory is rewarded more than critical thinking. Our education system mostly focuses upon developing convergent thinking in the students rather than divergent thinking. Teachers need to connect engineering concepts to industry applications. Checking for students’ understanding while teaching is vital for learning. Students these days are examination-oriented learners, i.e. they learn, rather study to pass examinations with good marks or grades. Their learning is oriented towards qualifying the exams. Problem solving ability is rarely seen as the students want readymade solutions to questions. Decision making skill is very much lacking in today’s generation due to which students often fail to perform well in the selection tests/interviews. That is why, we have the concept of finishing school that make the pass-outs (engineering graduates) industry ready, equipped with the necessary skills of critical and analytical thinking and decision making. When India is competing globally to place its passouts at par with those of the other parts of the world, the following aspects of the LT process demand a major overhaul: • Methodology of teaching Teachers need to have a knowledge base of content, pedagogy, and technology and their combination so as to have a totality of TPACK framework. It is very well and widely known at least in the teaching fraternity that subject knowledge alone cannot make a good teacher. At the same time, just by the use of different technology, a lecture cannot be made impressive. The TPACK model provides a good understanding of the prerequisites to become an effective teacher. Methodology of teaching in the present era calls for a constructive alignment among the vital components of teaching, primarily the learning outcomes, learning methods, and assessment. The new millennium has been characterised by unprecedented breakthroughs in knowledge and technology. Twenty-first century challenges have called for new paradigms and “maps” of engagements in all spheres of life, especially in learning. One such step being taken is to involve students through active learning. Active learning involves providing opportunities for students to meaningfully talk and listen, write, read, and reflect on the content, ideas, issues, and concerns of an academic subject [19]. Active learning strategies are essential for enhancing student learning. The aim of “learning-to-learn”, as a major aim of engineering education relates to metacognition. In simpler terms, it can be described as the planning and management of one’s own learning. This will enable students to shoulder the responsibility of their learning. Once the students can do this, they will be better problem solvers and able to

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think critically. The inductive–deductive approach in teaching is also a step towards catering to the different learning demands of students. This has been well established by the research work of Felder and Silverman [10] for engineering students. Another tool that teachers can use for engineering students is the concept map. Students can visualise diagrammatically the different concepts and relate them. It can be used as a graphical tool to visually represent relationships between concepts and ideas. As an important aspect of digital pedagogy, teachers, today are being encouraged to plan a flipped classroom. The basic content is provided in online modules, the application part of which is carried out in the subsequent class. In a much simpler form, video(s) related to the topic to be discussed in the class is/ are supplied to the students beforehand, followed by some important points of discussion and/ or some related questions. Students need to watch the video and construct meaning of their own. In the following lecture, the students, then, discuss on the points and try to answer the questions. The teacher remains available here as a guide. The activity can be an individual task or a group work. This also encourages development of “learning-to-learn” skill among students, as discussed earlier. Teachers also can inspire students for team work through collaborative learning. It is highly effective in developing the much demanded twenty-first century skills. Learning is highly a social endeavour, and, therefore, when students learn from one another, they develop social skills. Collaborative learning is an approach to teaching and learning that requires learners to work together to deliberate, discuss, and create meaning. Further, collaborative thinking is the essential social ingredient of problem solving. Smith and MacGregor [21] define the term as follows: “Collaborative learning” is an umbrella term for a variety of educational approaches involving joint intellectual effort by students, or students and teachers together. All the methods discussed above take care of the different learning styles of students. Teachers also must be aware of the different teaching styles. They ought to know their preferences to choose some methods and approaches to teaching in a particular manner. An understanding of learning styles would enable the teachers to diagnose the learning difficulties and devise teaching strategies accordingly. These aspects are well considered by a reflective teacher. A reflective teacher is thoughtful about the learners and strives constantly to foster thinking in them. This way, it enhances professional competency in the teacher as she/ he detaches herself/ himself from the conventional teaching practices and continually tries out newer techniques. Reflective practice, itself, is a key component of lifelong learning. • Curriculum When we envisage outcome-based education (OBE) to provide an international foothold to our passouts, the heavily loaded traditional education can no longer provide the required support. Technical education in our country is all set for an overhaul. Accreditation for the technical institutes (degree and diploma levels) has now geared up. Accreditation is a process supposed to be designed for continual quality improvement. Once students graduate with an accredited qualification,

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they are supposed to be equipped with the graduate attributes (GAs). The GAs provide a common point of reference for stakeholders and bodies to describe equivalence across the globe. Graduate attributes are specific and clear statements expressing expected capability and competence of a passout. These attributes are clearly laid down in the curriculum as outcomes. In the outcome-based curriculum, outcomes are specified at different levels. • Assessment Formal education in India is examination-centred. Students often suffer from exam phobia. Assessment is not meant to declare pass or fail rather it should help in measuring learning achievement. OBE demands outcome-based assessment. Accreditation, especially the framework suggested by the NBA provides an integrated approach to assessment, planning, and improvement throughout the institution. Direct and indirect methods of assessment can serve the purpose, and it is already being practised in some of the colleges now. In industry, quality is relatively easy to assess. In education, even if a definition of quality can be formulated and agreed upon, devising a meaningful assessment process is a monumental task. But what are the measures of quality in education? Assuming that the mission of a university includes the imparting of certain knowledge, skills, and (perhaps) values, a meaningful assessment process must include measuring the degree to which the students have acquired those attributes. Assessing knowledge is relatively straightforward, but methods for assessing skills are complex and timeconsuming to administer, and valid means of assessing values do not exist.

7.6 Case in Point Students of present times cannot think of life without gadgets, at least a smartphone. This is the scenario in both urban and rural areas. This fact was utilized to undertake a small study in this context. As a snap study conducted in few polytechnics located in West Bengal, Information and Communication Technology (ICT) was used for giving assignments and feedback to students. The subject chosen was Communicative English. The teachers used concept maps to teach a part of English Grammar. For instance, the topics like “Tenses” and “Parts of Speech”, both of which are very essential for the students to be thorough with at least to speak correct English were taught using concept maps. Teachers first explained a few maps in the class. Students were later given assignments based on concept mams to check their understanding of tenses. For this, the students could use their mobile phones. The correct answer was supplied by the concerned teacher and peer feedback was sought. The learning achievement showed significant improvement, statistical analysis of which, is not being discussed here. In this example, many of the quality parameters discussed under teaching methodology have been taken care of. While students could learn from their peer group

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through collaborative learning, they were also actively involved all throughout. They could observe their own learning improvement which kept them intrinsically motivated as also involved in the task at hand. With simple access to Internet services, students could utilize ICT to make learning meaningful. Students could get a feel of self-paced learning. Students not only constructed their own learning but also utilized the new learning in their own ways instead of accumulating factual knowledge. This has been emphasised time and again by experts and also reiterated by Biggs [7] who declared that what is more important is the way learners structure information and how well that enables them to use it. Students become actively involved and they start enjoying the learning process itself, thereby transforming themselves to learners from students. The study was also an illustration of active learning by the students and new learning by teachers, as opined by the participating teachers. Besides saving teachers’ teaching time, use of concept maps could supply readymade data regarding learners’ progress. Active learning techniques used by the teachers further encouraged them to use ICT in assessment of learning. A few of them reported to have explored some C map tools available online. There are ample research studies related to use of concept maps and improvement in learning. However, the literature review is not within the scope of this chapter; hence, not being provided here. This snap study enabled the teachers to observe how digital pedagogy can promote independent learning among students even outside classroom settings. Of late, use of ICT in teaching–learning is becoming more extensive among teachers, but this demands more enhanced digital competence.

7.7 Quality Indicators Quality need not be discussed in theory as we now have to move from a twelfth century teaching model to that of a twenty-first century. The needs of the twenty-first century learners are also diverse and more complicated that yesteryears. Teachers (technical teachers) entering the teaching profession ought to have some orientation to teaching, specially the pedagogical strategies and methods. Hence, it needs to be implemented and practised in the classrooms so that quality need not be discussed in forums and meetings but actually reaches the learners. For any institute of higher education, there are certain factors that serve as quality indicators and these can be listed as follows. • • • • • • •

Curricular aspects Teaching, learning, and evaluation Research, consultancy, and extension Infrastructure and learning resources Student support and progression Governance, leadership, and management Innovations and best practices.

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Curricular aspects must reflect the vision and mission of the institute through the programmes being run. This would indicate that a systematic process is being followed in the design and development of the curriculum. Employability, innovation, and research have to be ensured through curriculum design and development. Teaching, learning, and evaluation serve as a vital indicator of quality as it directly reflects the quality and professionalism of the teachers. It is to be assured whether the teaching–learning process caters to student diversity. It goes without saying that the student enrolment and profile also affect the process and vice-versa. The success of learning–teaching process is ultimately envisaged through the attainment of learning outcomes. From time-to-time, there have been several reforms in the process of assessment and evaluation only with the objective of enhancing student performance. Research has taken a back seat over the years in many of the academic institutes of our country. The reasons for this are manifold. The next indicator of research, consultancy, and extension is very important in resource mobilisation for research. The quality of research publication in the country is also a matter of great concern. Institutes can collaborate for research activities, which also speak about their endeavour in this context. Outreach activities can be extended for the society as institutional social responsibility. Infrastructure and learning resources of any organisation speak about the quality service being provided by it. The different facilities for the well-being of the students and the employees also add to it. Maintenance of campus facilities portrays the concern for quality. A well-equipped library together with minimum IT infrastructure would also attract students and make the environment learning friendly. Another indicator of quality which stakeholders would search for is student support and progression. An effective measure in this connection by the AICTE is the introduction of induction training for the students. There are institutes where student mentoring is compulsorily done by the teachers. For the all-round development of students, students’ activities are encouraged. They are also made to participate in community activities and social work. Student progression is continuously monitored to boost their morale and confidence. A strong leadership is the crux of all the activities being discussed here. Without good governance and management, resource mobilisation becomes very difficult. An ambitious institutional vision and leadership would result in effective strategy development and deployment. Good leadership of any academic institute always nurtures a conducive learning environment and encourages faculty empowerment. An internal quality assurance system can work wonders for any organisation in terms of sustaining the quality. However, it is to be realised that the innovations and best practices are to be disseminated to the society in its best interest keeping in mind the environmental issues.

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7.8 Quality Initiatives As per AICTE 2017, only 16% of the programmes offered by Technical Institutions in the country are accredited by the NBA. Recently, some countries have even rejected the Indian technical degree holders. Therefore, one has to look into the overall academic quality of the institute. Academic quality refers to the overall performance of the institution in the context of its mission and as measured by the extent to which the institution achieves its intended student learning and student success outcomes. Student learning outcomes involve assessment of skill and competency attainment. AICTE envisions that by the year 2022, the percentage of technical programmes accredited would increase from 16 to 50%. Initiatives by the MHRD, Govt. of India, in boosting the overall quality of the education in India. However, providing education to all and maintaining quality in education at the same time are quite challenging. For higher education, more so for the sector of technical education, the GOI has opened up new avenues to enhance their teaching skills. For the technical teachers, several initiatives have been launched in the SWAYAM platform to update the pedagogical skills and teaching competency. There are several coordinators identified for this initiative, one of them being the National Institutes of Technical Teachers’ Training and Research (NITTTRs). Several initiatives have been taken by AICTE for nurturing quality in technical education under student development, faculty development, institutional development, research and innovations development schemes, besides several general schemes. Some of the efforts in this direction are National Doctoral Fellowship (NDF), Margadarshak scheme, Adjunct Faculty and Unnat Bharat Abhiyan, Student Startup Policy 2016, Smart India Hackathon 2018, a MOOCs platform SWAYAM, etc. Besides the model curricula of technical programmes both at degree and diploma levels have been designed keeping in view the latest market trends and employability factors.

7.9 Professional Development According to Guskey [12], the term refers to those processes, actions, and activities designed to enhance the professional knowledge, skills, and attitudes of teachers so that they might, in turn, improve the learning of students. Teachers need to develop a set of knowledge called technological pedagogical content knowledge or TPCK urgently to meet the needs of twenty-first century learners. To cope up with the challenges and demands of a rapidly changing society, the government of India has already taken several initiatives. However, it goes without saying that the process of selection of teachers has to be more stringent allowing only the deserving personnel into the noble profession of teaching. Teaching is possible only when the teacher is willing and loves the profession; hence, attitude of the teacher would be more important than aptitude in the journey of quality in the LT process.

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Teachers themselves have to be updated both in terms of pedagogy and content. If we refer to the TPACK model originally based on Lee Shulman’s construct of pedagogical content knowledge (PCK), we find that the three kinds of knowledge, namely technology, pedagogy, and content form an integrated whole [22]. This provides a theoretical base for understanding teacher knowledge required for effective technology integration as stated by Mishra and Koehler [20]. Both content and pedagogical knowledge are important for teaching to be effective. PK is nowadays gaining greater importance because of the quality of passouts being observed. Though content knowledge is essential, unless the message is comprehensively and meaningfully transferred across the audience, CK ceases to be effective, and therefore, the need for PK. As social networking takes a significant role in the learning-teaching (LT) process, teacher ought to be equipped with the necessary technological skills. So, any institution wishing to improve the quality of its instructional program should first make the necessary commitment to provide the necessary resources and facilities for faculty participation. Teacher training for technical teachers plays a vital role as has been reported by the teachers themselves. Trained teachers feel more confident in using different techniques in teaching. They feel in a better position to make their students think rather than leading them to rote memorisation. It would be noteworthy that National Institutes of Technical Teachers’ Training and Research (NITTTRs), formerly known as TTTIs are doing a commendable service to the nation in this regard for more than 50 years now. These institutes were established by the Government of India in the 1960s to train technical teachers across the country.

7.10 Conclusion When the VUCA has become the new normal, it is high time that we adopt a visionary outlook towards the learning–teaching process. This has been reiterated on several platforms, as recent as that of the Delor’s report, 1996. This conceptualization of education provided an integrated and comprehensive view of learning and, therefore, education quality [6]. Published by UNESCO in 1996, Learning: The Treasure Within, the Report to UNESCO of the International Commission on Education for the Twenty-first Century, chaired by Jacques Delors, former European Commission President, proposed an integrated vision for education. With the four pillars, the Delor’s report proposed an integrated vision for education. The four pillars described portray a holistic education for one and all. Education is a lifelong process and is based on four pillars, learning to know, learning to do, learning to live together, and learning to be. Formal education in our country largely believes in acquiring knowledge by an individual. Lately, with the Skill India initiative of the government, learners are being provided with hands-on experience emphasising on learning to do. When we speak about quality improvement in TL system, we must realise that it is not what we teach, it is what they learn which is more important. Learning

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requires time and space for thinking. To attain this, a teacher needs to be reflective. Reflective teaching develops a thinking teacher who will not be complacent and will certainly explore more and more. It will further provide a common framework to help teachers consolidate their experiences and guide them in systematically reflecting on their practices. Quality is not to be talked about, it is to be performed, carried out, to be practised.

References 1. https://economictimes.indiatimes.com/jobs/only-6-of-those-passing-out-of-indias-engine ering-colleges-are-fit-for-a-job/articleshow/64446292.cms?from=mdr 2. https://portal.uea.ac.uk/documents/6207125/8480269/cop-on-assuring-and-enhancing-tea ching-quality.pdf 3. https://www.aicte-india.org/sites/default/files/India%20Skill%20Report-2019.pdf 4. https://www.businesstoday.in/current/corporate/indian-engineers-tech-jobs-survey-80-percent-of-indian-engineers-not-fit-for-jobs-says-survey/story/330869.html 5. https://www.indiatoday.in/education-today/news/story/15-initiatives-taken-by-central-govern ment-to-improve-teaching-standards-in-india-hrd-minister-1556357-2019-06-26 6. https://www.unesco.org/education/pdf/DELORS_E.PDF 7. Biggs, J.: Teaching for Quality Learning at University, Society for Research into Higher Education/Open University Press, Buckingham (1999). Fry, S.M.: A Handbook for Teaching & Learning in Higher Education. Kogan Page, London (2003) 8. Deming, W.E.: The New Economics: For Industry, Government, Education. Massachusetts Institute of Technology, Center for Advanced Engineering Study, Cambridge, MA (1994) 9. Felder, R.M., Brent, R.: Teaching and Learning STEM—A Practical Guide. Wiley, USA (2016) 10. Felder, R.M., Silverman, L.: Learning and teaching styles in engineering education. Eng. Educ. 78(7), 674–681 (1998) 11. Guskey, T.R.: Rethinking mastery learning reconsidered. Rev. Educ. Res. 57, 225–229 (1987) 12. Guskey, T.R.: Evaluating Professional Development. Corwin Press, Thousand Oaks, CA (2000) 13. Guskey, T.R.: How classroom assessments improve learning. Educ. Leadersh. 60(5), 7–11 (2003) 14. Guskey, T.R.: Mapping the road to proficiency. Educ. Leadersh. 63(3), 32–38 (2005) 15. Guskey, T.R., Bailey, J.M.: Developing Grading and Reporting Systems for Student Learning. Corwin Press, Thousand Oaks, CA (2001) 16. Guskey, T.R., Passaro, P.: Teacher efficacy: a study of construct dimensions. Am. Educ. Res. J. 31, 627–643 (1994) 17. Guskey, T.R., Pigott, T.J.: Research on group-based mastery learning programs: a metaanalysis. J. Educ. Res. 81(4), 197–216 (1988) 18. Guskey, T.R., Sparks, D.: Linking professional development to improvements in student learning. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA (2002) 19. Meyers, C., Jones, T.B.: Promoting Active Learning: Strategies for the College Classroom. Jossey-Bass Publishers, San Francisco (1993) 20. Mishra, P., Koehler, M.J.: Technological pedagogical content knowledge: a framework for integrating technology in teacher knowledge. Teach. Coll. Rec. 108(6), 1017–1054 (2006) 21. Smith, B.L., MacGregor, J.: Collaborative Learning: A Sourcebook for Higher Education, pp. 9–22. National Center on Postsecondary Teaching, Learning, and Assessment (NCTLA), University Park, PA (1992) 22. Thompson, A.D., Mishra, P.: Breaking news: TPCK becomes TPACK! J. Comput. Teach. Educ. 24(2), 38, 64 (2007–2008)

Chapter 8

Digital English Language Laboratory: Roles, Challenges and Scopes for the Future Development in India Anwesha Basu

Abstract Digital English Language Laboratory has evolved in the last few decades across the world as an essential tool to learn English. The learners are facilitated to master the fundamental English communication skills with the help of multimediainfused visual, aural, audio-visual, and verbal communication devices. All the language learning tools are supposed to be developed to cater to the language learners of varying learning styles. Besides, research has shown that brain-wiring varies across cultures, socioeconomic conditions, and genders; the learning styles of individuals vary accordingly. This paper concerns to problematize the digital pedagogy of digital language laboratory to learn English and aims to ask the following questions: (1) Is the digital English language laboratory effective to facilitate language learners with varying and complex learning style and pace? (2) Is the self-directed, learnerautonomy-based digital language laboratory advantageous to the non-native English language learners of India? (3) What are the scopes for the future development of the digital English language laboratory in India? This paper intends to adopt the exploratory qualitative research method. The collected data will be thoroughly analysed and prospective recommendations will be provided at the end. Keywords Digital English Language Laboratory · English communication skills · Learning styles · Learning pace · Digital pedagogy

8.1 Introduction There has been a substantial utilization of technology in modern language teaching– learning around the globe since the inception of computers. English, being the global language has become an essential mode of communication across the continents. The Internet, whereas, being the boon of globalization, is connecting the entire planet to form a global village. Thus, the use of Internet through multimedia learning, online learning, web-based learning, Information and Communications A. Basu (B) RCC Institute of Information Technology, Kolkata 700015, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 A. Deyasi et al. (eds.), Computational Intelligence in Digital Pedagogy, Intelligent Systems Reference Library 197, https://doi.org/10.1007/978-981-15-8744-3_8

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Technology or ICT-based learning, Computer Assisted Language Learning (CALL), and Technology-Enhanced Language Learning (TELL) have shifted the paradigm of English teaching–learning a great deal in the new millennium [1]. However, the question lingers in our mind is, how effectual is the use of Information Technology in English language teaching–learning, especially in India, where, according to the 2011 Census Report of the Govt. of India, only 10.6% native speak English out of whom only 0.02% of speakers identify English as their first language [2]. The primary challenge of English Language Teaching (ELT) in India rests on the existence of multilingual and multicultural classrooms. In her M.Ed. dissertation proposal, titled, “A Study of Problems Related to Teaching–Learning of English in Government Schools”, Rehana Firdaus of Jamia Milia Islamia argues that in the Government institutions across India, following are the major issues in teaching the English language: 1. 2. 3. 4.

Lack of well-trained educators Conventional teaching and evaluation method Improper teacher–student ratio Lack of competent text-books and other reading resources [3].

Now, Digital English Language Laboratory essentially emerges to address the above-mentioned lacunae in ELT. Since English has become our lingua franca and even with 10% people of the total population speaking English, India ranks as the second-largest English speaking nation in the world, digital language laboratory has evolved to cater to the needs of English language learners of native tongues. In Digital English Language Laboratory, the learners are facilitated to master the fundamental English technical communication skills such as Listening, Speaking, Reading, and Writing (LSRW) [4] with the assistance of Information Technologyinfused visual, aural, audio-visual, and verbal communication-devices. A wellequipped Digital English Language Laboratory includes a Master Console that helps an instructor to supervise the operation of the laboratory, Student Units for the students to communicate with the Master Console, a Projector with a Screen for displaying interactive content for the learners and Computer devices with Internet facility and preloaded language-learning software. A language-learning technology is supposed to be developed to aid the learners of varying learning styles and learning pace. However, none of the digital English language laboratory devices mentioned before encompass to facilitate the language learners of all kinds of learning styles. A self-directed, learner-autonomy-based language laboratory makes the situation further hostile as it invalidates the presence of a human facilitator. Besides, research has shown that brain-wiring varies across the cultures, socioeconomic conditions, and genders; the learning styles of the individuals vary accordingly. So, is the digital English language laboratory effective for the learners of the Indian subcontinent emerging from varying class-caste-gender identities and multilingual, multicultural backgrounds? This paper will try to find that out.

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8.1.1 Motivation While elucidating the value of digital pedagogy in language teaching–learning in a short blog, Niall Curry (Senior ELT Research Manager, Cambridge University Press) kept championing the significance of pedagogy more than its digitization. Digital pedagogy, according to him, is a new concept and any new adoption is slow and consistent. Influenced by Scott Thornbury’s 12 principles on new technology adoptions, Curry raised the following questions to problematize the learning potential of the digital technology: • • • • • • • • • • •

Adaptivity—does the tool accommodate non-linear learning? Complexity—does the tool address language complexity on multiple levels? Input—is there rich, comprehensible input? Noticing—are users directed to notice useful elements of input? Output—are there regular opportunities for language production? Scaffolding—are learning tasks modelled and mediated? Feedback—do users get focused and informative feedback? Interaction—is there a way for users to interact and work together? Automaticity—are there opportunities for practice? Chunks—does the tool help with learning the formulaic language? Personalisation—does the tool encourage personal relationships with the material? • Flow—is the tool engaging? Challenging? With clear benefits? [5] And, he finally sums up that digital language learning must only be promoted given it helps to deliver a more complete learning process. “One in which learners can interact with authentic audiences and authentic tasks; produce varied and creative language; interact socially and negotiate meaning; receive feedback; develop noticing skills; are motivated and are autonomous”. And it is always “to see what technology can afford language learning and not the other way around” [5]. In a fast-changing scenario of educational technology where digital pedagogy or broadly speaking, “the use of electronic elements to enhance or to change the experience of education” [6] has turned out to be a hotcake, questioning its credibility, capability, utility, and feasibility in English as a second language learning in India seemed essential to me being an English language trainer in a multicultural, multilingual, polygendered classroom in a metropolitan city. The 12 parameter-framework discussed by Curry, the not so impressive statistics of English language learning in India as suggested by Firdaus (see Introduction), the recent COVID-19 pandemic [7] the entire world is fighting through when emergency remote teaching has literally become mandatory due to quarantine and on the contrary, the National Sample Survey as a part of National Survey Education [8] says only 12.5% of the Indian population has Internet access with a computing device; all these issues have compelled me to take up the present study.

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8.1.2 Novelty The concept of digital pedagogy is a new feather in the cap of educational technology. An extensive amount of research has been carried out all over the world on digital pedagogy since its prominence in the last decade of the twentieth century. The studies on the digital language laboratory are also going on across the globe. However, to the best of my knowledge, there has been no comprehensive research till date on the digital language laboratory applying the model of learning styles. No pedagogy can stand alone without educational psychology. Thus, every educationalist must understand every student’s need, which nevertheless will vary widely, before applying any new model of pedagogy. As the digital language laboratory is a comparatively new field yet to explore widely, at least in India, whether it can accommodate the varied and difficult learning styles of the students emerging from a wide array of backgrounds is a research worth working on. Besides, one must also explore the possibilities of new trends and scope for its future development. This paper will surely unfold a new arena which opens up future possibilities of research in this area.

8.2 Research Questions This paper concerns to problematize the digital pedagogy of language laboratory and aims to address the following questions: 1. Is the digital English language laboratory effective to facilitate language learners with varying and complex learning style and pace? 2. Is the self-directed, learner-autonomy-based digital language laboratory advantageous to the non-native English language learners of India? 3. What are the scopes for the future development of the digital English language laboratory in India?

8.3 Methodology At first, I will locate the Digital English Language Laboratory in various parameters such as learning styles and strategies and try to conceptualize the roles, effectiveness, and limitations of the digital language laboratory. I will then present the result of a survey administered by me on the students of the RCC Institute of Information Technology, Kolkata, and analyse the collected data to seek out the challenges of the digital language laboratory and also its scope for future development in India. After that, I will try to recommend the strategies to overcome the issues and highlight the possibilities to produce well-equipped, self-motivated English language learners ready to fight the challenges in the corporation and other sectors of work and life.

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My approach to this research is partly conceptual and partly exploratory. A part of this research is based on the theories of pedagogy and educational psychology; a part of this work is based on a case study with an ethnographic qualitative approach. The second part of the research qualifies the first part and thus makes it a substantial work. This paper will now categorically discuss each topic of our concern beginning with the concept of learning styles.

8.4 Learning Styles 8.4.1 Personality Types and Learning Styles Isabel Briggs-Myers, an American author, along with her mother Katharine Cook Briggs, created a revolutionary introspective self-report questionnaire, titled, MyersBriggs Type Indicator or MBTI [9], based on the famous Swiss psychiatrist and psychoanalyst Carl Jung’s theory of Psychological Types [10] to indicate that “much seemingly random variation in the behaviour (in individuals) is actually quite orderly and consistent, being due to basic differences in the ways individuals prefer to use their perception and judgment” [11]. According to Jung, individuals perceive the world using four principal psychological functions, viz., sensation, intuition, feeling, and thinking—and that one of these four functions is dominant for an individual most of the time. The following categories are derived from these four functions by Myers and Briggs in MBTI: 1. 2. 3. 4.

Introversion/ extraversion Sensing/ intuition Thinking/ feeling Judging/ perception.

Each person is thought to have one preferred quality from each of the four abovementioned categories, producing sixteen unique types [9]. Jenna Melvin, a researcher in the University of Rochester, published a paper in 2014 in the Centre for Excellence in Teaching and Learning, University of Rochester, titled, “Personality Type as an Indicator of Learning Style”, where she argues that a given preference in one’s personality type would determine a certain preference in one’s learning style [12]. She based her research on the theories of the MBTI and that of Felder-Soloman’s “Learning Styles and Strategies”. Richard F. Felder and Barbara A. Soloman divided the learners into eight distinct categories [13]. They are: 1. 2. 3. 4.

Active learners Reflective learners Sensing learners Intuitive learners

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Visual learners Verbal learners Sequential learners Global learners.

Jenna Melvin has established in her paper that each learner’s personality type may predict the learning style(s) of the same. Table 8.1 sums up her prediction. However, there can be no watertight demarcation to determine each personality type’s learning style and there can be more than one learning style suitable for each personality type as personality types change throughout one’s due course of life. Besides, learning styles are also determined by cultural and gender differences.

8.4.2 Impact of Gender and Cultural Differences on Learning Styles In their 2009 paper titled “Are There Cultural Differences in Learning Style?”, Simy Joy and David Kolb argued that culture that an individual lives in is a pervasive part of the environment in which one learns and values, behaviours, norms differ from one culture to another [15]. Culture acts like a socializing agent which impacts information processing and cognition. So, the differences in cultural socialization influences learning preferences and learning styles. They based their study on the Experiential Learning Theory, propagated by David Kolb [16] and drawn from the works of 20th scholars like John Dewey, Kurt Lewin, Jean Piaget, William James, Carl Jung et al. [17] Kolb’s Experiential Learning Theory defines experiential learning as “the process whereby knowledge is created through the transformation of experience. Knowledge results from the combination of grasping and transforming experience”. Kolb described two different ways of grasping experience: 1. Concrete experience 2. Abstract conceptualization 1. Reflective observation Table 8.1 Personality types and learning styles [14]

Personality type

Learning style

Extrovert

Active

Introvert

Reflective

Sensing

Sensing

Intuitive

Intuitive

Thinking

Verbal

Feeling

Visual

Judging

Sequential

Perceiving

Global

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2. Active experimentation [18]. Kolb’s ELT implies that learning is not only a cultural phenomenon but also determined by gender performance. In the survey-based research paper, “Review of Gender Differences In Learning Styles: Suggestions For STEM Education”, the authors Sadan Kulturel-Konak, Mary Lou D’Allegro and Sarah Dickinson [19] began their research assuming that learning style is gender-specific and that male students and female students approach differently to the course contents and teaching styles. However, after conducting a thorough online Learning Styles Survey, specifically, on the students enrolled at Penn State Berks, founding their study mostly on Kolb’s Experiential Learning Theory, they came to the conclusion that contrary to popular belief, females are better than males to favour abstract materials. Thus, rather than concrete experience, female learners rely more on abstract conceptualization and the result is just the opposite to the male learners. Besides, there is no such watertight distinction in learning styles between male and female students while learning new subjects and both the genders prefer similar modes of learning for a better learning experience. However, according to the postmodern feminist author-critic Judith Butler, gender is performative. Gender is not essential, rather, a constant reiteration of gender behaviours and gender role-plays under a grand-narrative [20]. So, it is needless to say, that gender is determined by the culture and that gender is also a sociopolitical construct. Besides, there exist more than two genders and each individual’s gender identity is culture-specific. Therefore, the result would have been different if a similar survey were conducted on the students from any of the Indian universities due to the cultural difference.

8.4.3 Learning English as a Second Language: The Role of the Digital Language Laboratory The Indian subcontinent is the breeding ground of languages. More than 19,500 languages are spoken in India as mother tongues including the dialects [21]. In such a country, learning English as a second language has always been a challenge. Since the learning styles are dependent and determined by the personality types, cultural, and gender differences, English language teaching–learning aids have to be designed accordingly. Before delving deeper into the topic, let us first see the difference between a traditional language laboratory and a digital language laboratory.

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8.4.4 Difference Between a Traditional Language Laboratory and a Digital Language Laboratory According to Dararat Khampusen, there has been a gradual shift from the traditional language laboratory to the present digital language laboratory system. Primarily, the language laboratory was monitored solely by an instructor and the contents, mode of teaching, pedagogy that happened to be administered single-handedly by the instructor. Thus, reliability and effectiveness were at risk most of the time. However, in those language laboratories, the students could actively perform in group activities such as role-plays, group discussions, and other speaking activities. Whereas, “[d]ue to advanced technology, today’s language labs can both work with this software only language laboratory solutions and deliver media synchronously. New language labs use the content that is much more affluent. These contents are self-authored or free. These include audio, video, flash-based games, internet, etc. Students and teachers are more engaged with a high speed and variety of materials and activities. A fixed network has gone and teachers and students can now access and work from these new “cloud” labs. These labs refer to the use of “network-based services, which appear to be provided by real server hardware and are in fact served up by virtual hardware, simulated by software running on one or more real machines. Such virtual servers do not physically exist and can, therefore, be moved around and scaled up (or down) on the fly without affecting the end-user -arguably, rather like a cloud”. Cloud allows students and teachers to work on their own devices at any time and anywhere” [22].

8.4.5 Roles of a Digital Language Laboratory In order to learn any modern language, a learner has to master the four fundamental communication skills, such as, 1. 2. 3. 4.

Listening Speaking Reading Writing.

A digital language laboratory boosts the listening skill the best with its advanced auditory system with the best possible contents. It teaches the students to learn phonology and phonetics that also aid in their speaking competencies. However, the lack of enough practice with the peer groups and unavailability of the classroom activities does not prepare a student to battle the speaking incompetencies in real-life scenarios. Although, reading comprehension is mastered adequately by a digital language laboratory, whether the learner masters the loud reading or not solely depends on the quality of AI programming and the utilization of apt technologies in the digital language laboratory, such as headsets and microphones, etc. However, to

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boost writing competency, the digital language laboratory takes a vital role. Unfortunately, the restlessness of the new generation has made the millennials less reflective and more lethargic towards writing. Motivating the students towards adding a flair for writing is also a challenge that modern technology has to fit in with.

8.5 A Survey A survey [23] was administered by me on the current First-year B.Tech. students (125 in number) (i.e. AY 2019–20) of RCC Institute of Information Technology, Kolkata on English Language Laboratory. The purpose of the survey was to find out the effectiveness and the setbacks of the language laboratory as a teaching–learning aid and whether the regular language laboratory of the institute can be upgraded to the digital language laboratory.

8.5.1 Language Laboratory at RCC Institute of Information Technology Presently, the RCC Institute of Information Technology possesses one Language Laboratory with two consecutive rooms dedicated to the laboratory. The first laboratory has one instructor’s console connected to the seats in the gallery through the LAN. There are headsets with mic for the students to communicate with the teacher. There is, however, a public address facility is also available. The other laboratory has altogether a different setup with long semi-circular couches for the students and a dais with the computer system for the teacher. The students may participate in quiz or poling using handsets connected to the system through LAN. Both the laboratories have projectors with large screens and sound amplifiers with loudspeakers for public address mode. The whiteboards are also used as an aid. The projectors are used to aid the visual and audio-visual learners. The second laboratory, however, is apt for kinesthetic learning along with visual and auditory learning. The survey was conducted on the first-year students of the departments of Electronics and Communication Engineering, Electrical Engineering and Applied Electronics and Instrumentation Engineering. In the current semester (i.e. 2019–20 Even Semester), they are taking a mandatory course framed by MAKAUT, titled, “Language Laboratory” that is supposed to prepare the students to acquire “basic proficiency in English including reading and listening comprehension, writing and speaking skills” [24].

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8.5.2 The Survey Questionnaire The following questionnaire was designed for the survey: Questionnaire 1: A Survey on the Effectiveness of the Language Laboratory • Your gender is a. Female b. Male c. Other… • Your age is a. 17 b. 18 c. 19 d. Other… • Your personality type is (you may choose more than one) a. Introvert b. Extrovert c. Feeling d. Judging e. Thinking f. Reflecting • Your learning style(s) is/are a. Visual (prefer video learning) b. Auditory (prefer audio learning) c. Text (prefer reading books) d. Kinesthetic (prefer learning through physical activities) e. Other… • Your learning pace is a. Slow b. Moderate c. Fast • You had a a. English-medium schooling b. Vernacular (first language)-schooling c. Other… • You’re strong in (you may choose more than one) a. Listening b. Speaking c. Reading d. Writing • Which communication skill is best-learned through the language laboratory, according to you? a. Listening b. Speaking c. Reading d. Writing • Is the language laboratory effective to improve communication skills? a. Strongly disagree b. Disagree c. Neutral d. Agree e. Strongly agree • According to you, which learning style is best catered through the Language Lab? a. Visual b. Auditory c. Text d. Kinesthetic • Is the language laboratory effective for slow learners? a. Strongly disagree b. Disagree c. Neutral d. Agree e. Strongly agree • Will you prefer having a digital language laboratory in the future? a. Yes b. No c. Maybe • Will you prefer having a software only language laboratory (without a teacher)? a. Yes b. No c. Maybe

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8.5.3 Data Collection and Analysis Datum 1: Personality types are varied and complex among the learners. The details are given in Chart 8.1 Datum 2: 72% of the total learners taking the survey prefer visual learning and 36.8% prefer kinesthetic learning. Since one may prefer more than one learning style, there are students who prefer mixed learning styles including all the learning styles. However, according to the survey, only 19.2% of students prefer the auditory learning style (Chart 8.2). Analysis of Data 1 and 2: The majority of the language learners prefer visual learning, however, there are learners too who prefer kinesthetic learning style. However, in a digital language laboratory, the facility of kinesthetic learning or

Chart 8.1 Survey result

Chart 8.2 Survey result

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learning through physical activities is not available. Which, on the contrary, is adequately catered in a traditional language laboratory set up monitored by a teacher. Datum 3: Only 20% of the learners consider their speaking skills to be strong and according to 68.8% of the students, in the traditional language laboratory, speaking skill is aided adeptly (Charts 8.3 and 8.4). Analysis: Only a few learners consider their speaking skills to be strong. To master any language, one has to master the speaking skill. Especially in global corporate work, communication in English is absolutely essential. However, as the personality types of the concerned learners are complex, varied, and multiple, a learner needs to be aided by their preferred learning styles accordingly. As Jenna Melvin has shown in her paper, each category of the personality type prefers a specific learning style. As well as, each gender and cultural category has its own preferences. A digital language laboratory is unable to cater to all kinds of learning strategies, which, on the contrary, a traditional language laboratory can cater to.

Chart 8.3 Survey result

Chart 8.4 Survey result

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Datum 4: The majority of the learners have a moderate learning pace and 14.4% of the total population are slow learners. According to 80% of students, though, the language laboratory is effective for slow learners (Charts 8.5 and 8.6). Analysis: There are slow to moderate-paced learners who think that the language laboratory can be useful to them. I prepared a short questionnaire to investigate the effectiveness of the digital language laboratory and circulated among a handful of language trainers in West Bengal [25]. According to Dr. Joydeep Banerjee, an Associate Professor of English at NIT, Durgapur, a digital language laboratory is quite effective for slow learners. It also helps the weaker section of the students as the “software (used in the digital language laboratory) is quite updated”, according

Chart 8.5 Survey result

Chart 8.6 Survey result

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to him. A similar proposition is given by Dr. Sumana Bandyopadhyay of Netaji Subhash Open University, Kolkata. Both of them are imminent ELT trainers and digital language laboratory instructors. Datum 5: 80% of learners would prefer having a digital language laboratory. However, 74.4% prefer having an instructor or a human facilitator instead of having a fully software-only or online digital language laboratory (Charts 8.7 and 8.8). Analysis: An overwhelming number of the students prefer having a digital language laboratory in future but very few of them want it to be digitally monitored without the presence of a human facilitator. The reasons may vary. But, probably from the learners’ perspectives, the dependence they have on their teachers can never be

Chart 8.7 Survey result

Chart 8.8 Survey result

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replaced by a bot or AI in general. The self-learning model of learning is yet to be a trend in India.

8.5.4 Challenges of the Digital Language Laboratory in India and Probable Recommendations 1. Socioeconomic and sociopolitical barriers—Ideally, the digital language laboratory is the need of the hour where English language learning has become essential. However, the scenario of the Indian education system and the overall socio-economic and sociopolitical conditions are quite adverse. Urban privatized institutions in metropolitan cities do possess digital language laboratories, however, in the small towns and rural areas, especially the areas occupied by minority groups, subalterns and other backward classes, having a digital language laboratory to learn English is almost an illusion. Where literacy means only the ability to read the alphabets, count numbers and sign one’s own name, learning and mastering English with a digitized system seems next to impossible. Recommendation—Thus, it is to considered how to program and code a digital language laboratory exclusively for varying purposes and learners coming from different socioeconomic and cultural backgrounds. 2. The “Burden” of Syllabi in Engineering and Management Studies—For STEM education and management studies, producing employable students has been a real challenge for the last two decades. With the rapid technological growth, advancement in AI, the digital language laboratory serves its role the best in this scenario. However, the vast ocean of syllabi to be “completed”, learned and mastered seems overwhelming to both the students and the teachers. English language learning thus takes the backseat and starts losing the prime focus. Apart from the institutions and the universities dedicating special courses on English Language Teaching and/or Linguistics, the digital language laboratory is not fully utilized. Recommendation—Therefore, the digital language laboratory has to be programmed in such a way that learners are not only interested to take the courses and lessons but also regularly motivated and mentored to give it prime importance. 3. Hindrances in self-directed model of teaching–learning—The self-learning model of teaching–learning is yet not a success yet to be “digital” India. Although, with the advancement in MOOCS, the institutions are slowly moving towards e-learning and digital classrooms, the utilization is yet to be fully discovered. According to both Dr. Banerjee of NIT Durgapur and Dr. Bandyopadhyay of Netaji Subhash Open University, Kolkata, a software-only language laboratory is more effective than instructor-administered traditional language laboratory. However, where, Dr. Banerjee is all championing the software-only language laboratory, Dr. Bandyopadhyay thinks that “some lessons are to be mastered with the help of an instructor”.

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Recommendation—So, it is better to assign a well-trained instructor to design the course modules, provide thoroughly researched and useful course materials and also teach the course at the digital language laboratory whenever necessary. 4. Absence of emotional intelligence—Besides, in a world full of hatred and violence, along with I.Q., one needs E.Q. as well. Emotional intelligence is what is required to save humanity. Whether digital pedagogy can replace a human facilitator in the early years of education is a million-dollar question. Where electronics are literally stealing childhood, can the replacement of human teachers ever be a machine or multimedia? In a paper titled, “The Study of Different Components of Teacher Competencies and their Effectiveness on Student Performance (According to Students)”, Dr. Asha Thakur and Monika Shekhawat write that, a teacher has to have: A. Professional competencies [26] B. Social competencies C. Personal competencies D. Pedagogical competencies. Recommendations—Thus, digital pedagogy has to embody all four abovementioned components for an effective teaching–learning program. Sadly, apart from the professional and pedagogical competencies, a digital language laboratory does not have social and personal competencies. Therefore, artificial intelligence has to be programmed accordingly so that along with intelligent quotient, a young mind is also equipped with an adept amount of emotional quotient. Empathy, compassion, love, and kindness are the key components of an emotionally mature mind. It is of utmost importance to inculcate these qualities in young minds for building a healthy society. 5. Ever-changing job market—In the ever-changing scenario of the job market in India, it becomes essential to adopt and accommodate such a curriculum that not only encompasses but also enhances the professional development of the students. Recommendation—Digital language laboratory certainly enables the students to access study materials already available in the portal; however, monitoring them to select the most suitable ones is not an easy task. The digital language laboratory should have been programmed accordingly.

8.6 Scope for the Future Development of Digital English Language Laboratory: Role of Artificial Intelligence Marina Dodigovic, in her seminal work Artificial Intelligence in Second Language Learning: Raising Error Awareness [27] argues that the adult language learners are capable of learning a second language other than their mother tongue with the help

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of AI or artificial intelligence. According to her, “the ‘artificial intelligence’ or AI can deal with new problems, once it has learnt the general principle” contrary to that of other “non-intelligent” data processors as these programs are “most of the time equipped with a finite number of alternative paths or procedures”. “Thus, given the data, we can easily predict which route the program will follow. Thus, artificial intelligence can be used to systematically correct a student’s typical errors to improve language. For example, in order to process a student’s erroneous sentence, a nonintelligent program would have to have the exact same erroneous form hard-wired into its system. For the same kind of error, committed in a different sentence, using different vocabulary, this program would again have to have the exact wording prestored in its memory. However, an intelligent program would only have to have a rule the student uses for such erroneous production. The program could theoretically recognize the same type of error in any context and with any vocabulary. For this very reason artificial intelligence could possibly become the student’s and the teacher’s best ally in dealing with second language errors” [27]. This book essentially deals with the error correction techniques through artificial intelligence. Installation of the suggested computational methods in the digital language laboratory will ensure the students self-learn through trial and error methods. Besides, this method may be beneficial for self-motivated adult learners but a major percentage of students depending solely on the educators or facilitators, this may not be that useful. On the other hand, AI-powered online language learning softwares like Gloossika and Duolingo, makes the learner a fluent speaker of the concerned language in no time [28]. Besides, these user-friendly, personalized exercise-based have proven much effective for the students of remote learning or virtual laboratory learning. However, given the present scenario of Indian education system, India is perhaps not yet ready for a fully digitized education. Blended learning or as Oxford Dictionary puts it, “a style of education in which students learn via electronic and online media as well as traditional face-to-face teaching” [29] can be more effective to accommodate the students from all sectors of the society, of all age groups and genders. Post-COVID-19 era will be altogether an unprecedentedly new experience for our planet Earth. Economists all over the world are catastrophizing the global economy [30]. The impact of the economic crisis will be massive on our lives. Unemployment may rise greatly. Given this situation, a third-world country like ours may need much longer time to heal completely. Restoration of the education system, rather progressing it towards betterment will need time. Meanwhile, the educationalists, researchers, teachers, and students together can work together tirelessly to adopt new educational technology that will be open-accessible, free or less-costly, extremely useful, inclusionary, and empowering encompassing everyone who engages themselves in language learning. Digital English language laboratory has a long way to go with a constant gradual upgradation in content and form with the help of AI and other thoughtful, sensible decisions made by the education sector of India will enable a new era of digital pedagogy in language learning.

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8.7 Conclusion Considering all the criteria, I would like to conclude that given the digital language laboratory is programmed to accommodate all the varying and complex learning styles of each learner, caters to all the linguistic and paralinguistic features of the language learning process, induces emotional intelligence into the young learners’ minds and prepares the undergraduates and graduating students to face the challenges of the job market in India—the digital language laboratory is the need of the hour in India. However, the self-accessing, software-only digital language laboratory cannot be completely introduced to all the sectors of Indian educational institutions at this moment. Blended teaching–learning could be introduced to both school and college levels assigning them with well-trained educators and course moderators constantly striving to excel in this field. However, in the new decade, with the rapidly changing socioeconomic and sociopolitical condition of the Indian subcontinent, with the fastapproaching status of “Digital India” [31], installation of AI-infused digital language laboratories in all the educational organizations beginning from the elementary level to the universities and advanced institutes will become inevitable. It is, however, to remember that “Digital Pedagogy is precisely not about using digital technologies for teaching and, rather, about approaching those tools from a critical pedagogical perspective. So, it is as much about using digital tools thoughtfully as it is about deciding when not to use digital tools, and about paying attention to the impact of digital tools on learning” [32].

References 1. Shah, S.: Digital english language laboratory project: a critical evaluation. ELT Voices India 2(3) (2012) 2. https://censusindia.gov.in/2011Census/C-16_25062018_NEW.pdf. Accessed 20 Feb 2020 3. https://www.academia.edu/10095221/PROBLEMS_OF_ELT_IN_INDIA. Accessed 20 Feb 2020 4. Abilasha, R., Ilankumaran, M.: English language teaching: challenges and strategies from the Indian Perspective. Int J Eng Technol 7, 202–205 (2018) 5. https://www.cambridge.org/elt/blog/2018/10/05/putting-the-pedagogy-first-in-digital-pedago gies/. Accessed 15 Apr 2020 6. https://www.briancroxall.net/digitalpedagogy/what-is-digital-pedagogy/. Accessed 15 Apr 2020 7. https://www.who.int/emergencies/diseases/novel-coronavirus-2019. Accessed 15 Apr 2020 8. https://theprint.in/opinion/who-goes-online-to-study-in-covid-times-12-5-homes-of-indianstudents-have-internet-access/398636/. Accessed 20 Apr 2020 9. Huber, D., Kaufmann, H., Steinmann, M.: The missing link: the innovation gap. In: Bridging the Innovation Gap. Management for Professionals, pp. 21–41 (2017) 10. Siegel, P.H., Smith, J.W., Mosca, J.B.: Mentoring relationships and interpersonal orientation. Leadersh. Organ. Dev. J. (2001) 11. MBTI Basics: The Myers & Briggs Foundation (2014). https://www.myersbriggs.org/my-mbtipersonality-type/mbti-basics/home.htm?bhcp=1. Accessed 20 Feb 2020 12. Kamal, A., Radhakrishnan, S.: Individual learning preferences based on personality traits in an E-learning scenario. In: Education and Information Technologies (2018)

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13. Felder, R.M., Soloman, B.A.: Learning Styles, and Strategies. https://www.engr.ncsu.edu/wpcontent/uploads/drive/1WPAfj3j5o5OuJMiHorJ-lv6fON1C8kCN/styles.pdf. Accessed 6 Jan 2020 14. Melvin, J.: Personality Type as an Indicator of Learning Style (2014). https://urresearch.roc hester.edu/institutionalPublicationPublicView.action?institutionalItemId=27668&versionNu mber=1. Accessed 6 Jan 2020 15. Joy, S., Kolb, D.: Are there cultural differences in learning style? Int J Intercult Relat 33, 69–85 (2009) 16. Mayombe, C.: Enabling labour market entry for adults through non-formal education and training for employment in South Africa. Int. J. Lifelong Educ. 35, 1–20 (2016) 17. https://www.coursehero.com/file/17616606/Experiential-Learning/. Accessed 15 Apr 2020 18. Kitimbo, I.: Lessons learned: theory and practice. In: McIntyre S (ed) Utilizing Evidence-Based Lessons Learned for Enhanced Organizational Innovation and Change. IGI Global (2015) 19. Kulturel-Konak, S., D’Allegro, M.L., Dickinson, S.: Review of gender differences in learning styles: suggestions for STEM education. Contemp. Issues Educ. Res. 4(3) (2011) 20. Butler, J.: Gender Trouble: Feminism and the Subversion of Identity. Routledge (1990) 21. https://gulfnews.com/world/asia/india/census-more-than-19500-languages-spoken-in-indiaas-mother-tongues-1.2244791. Accessed 6 Jan 2020 22. Khampusaen, D.: Past, Present and Future: From Traditional Language Laboratories to Digital Language Laboratories and Multimedia ICT Suites. Semantic Scholar (2014) 23. https://forms.gle/i1FxLpUVED9t1ebLA. Accessed 6 Jan 2020 24. MAKAUT 1st Year Curriculum for B.Tech. Courses in Engineering & Technology. https://mak autexam.net/aicte_details/Syllabus/BTECH.pdf. Accessed 6 Jan 2020 25. https://forms.gle/PSvPATpaUd7gp1Yg9. Accessed 6 Jan 2020 26. Thakur, A., Shekhawat, M.: The study of different components of teacher competencies and their effectiveness on student performance (according to students). Int. J. Eng. Res. Technol. (IJERT) 3(7) (2014). ISSN: 2278-0181 27. Dodigovic, M.: Artificial Intelligence in Second Language Learning: Raising Error Awareness. SLA (2005) 28. https://www.telc.net/en/about-telc/news/detail/is-artificial-intelligence-the-future-of-lan guage-learning.html. Accessed 20 Apr 2020 29. https://www.teachthought.com/learning/the-definition-of-blended-learning/. Accessed 20 Apr 2020 30. https://www.undp.org/content/undp/en/home/news-centre/news/2020/COVID19_Crisis_in_ developing_countries_threatens_devastate_economies.html. Accessed 20 Apr 2020 31. https://www.digitalindia.gov.in/. Accessed 6 Jan 2020 32. Thomson, S.: Staff Guide to Lecture Capture [Teaching Resource]. Leeds Beckett University (2017). https://eprints.leedsbeckett.ac.uk/3639/1/lecture%20capture%20a6%20booklet. pdf. Accessed 6 Jan 2020

Chapter 9

Overview and Future Scope of SWAYAM in the World of MOOCS: A Comparative Study with Reference to Major International MOOCS Madhu Agarwal Agnihotri and Arkajyoti Pandit Abstract In the present competitive social structure characterized by multitasking, the need for additional qualifications apart from traditional degrees has become a necessity both for students as well as professionals. However, time being a limiting factor, gaining additional specializations is indeed cumbersome and calls for home schooling or schooling at the aspirant’s convenience. In this backdrop, the significant solution lies in Massive Open Online Courses (MOOCS). MOOCS are a host of courses delivered online with usually free access to anyone, anywhere, anytime and can be studied at the user’s own convenience and choice. In the world of MOOCS which started flourishing throughout the world from 2012, India is relatively a new player which has just significantly stepped in the scenario in 2017 with the introduction of SWAYAM (Study Webs of Active–Learning for Young Aspiring Minds). SWAYAM is an online academic resource platform sponsored and developed by The Ministry of Human Resource Development and based on the principles of “access, equality and quality”: [1]. The primary objective of SWAYAM is to deliver education to the remotest of places and the less advantaged groups of people who are yet not touched by digital revolution, [1]. However, with the advent and prosperity of digitization, when the world is becoming virtually smaller every second and quality resources are increasingly becoming freely accessible, it is necessary to have a comparative study of SWAYAM with other International MOOCS to understand the future viability, sustainability and further scope of the same. In this chapter efforts have been made to present a relative position of SWAYAM in the current context by comparing it with some of the major international MOOC players such as ‘Courseera’, ‘edX’ and ‘Future learn’. It is equally important to address the futures scope of SWAYAM and the limitations that it faces as compared to other MOOCS. SWAYAM being very new in such a venture, efforts are made to point out the areas of improvement with the help of comparative analysis on various parameters like ease M. A. Agnihotri Department of Commerce, St. Xavier’s College (Autonomous), Kolkata, India e-mail: [email protected] A. Pandit (B) University of Calcutta, NET-JRF, Kolkata, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 A. Deyasi et al. (eds.), Computational Intelligence in Digital Pedagogy, Intelligent Systems Reference Library 197, https://doi.org/10.1007/978-981-15-8744-3_9

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of access, time effectiveness, cost effectiveness to mention a few. The objective of this paper is to find ways in which SWAYAM can attain a position of being a major MOOC from a novice player. Keywords SWAYAM · MOOCS · Quality resource · Effectiveness · Ease of access · Digitization · Online · Education

9.1 Introduction Massive Open Online courses (MOOCS) are freely accessible online resource platforms consisting of various courses that can be availed by anyone, anywhere and at any time without necessarily having any bureaucratic obligations for course completion. They aim at building an interactive user network or forum that consists of not only scholars and students but also professionals, entrepreneurial vendors and most importantly elite universities. It’s no less than a revolution in the education system where the parcel of quality education is being freely and aptly delivered to the remotest of the sector riding on the wheels of digitization. India being the sixth largest economy in the world but with a striking figure of 23% of the world’s poverty dwelling in the country, the need for free MOOCS in India is of utmost significance. SWAYAM is a significant step to such a venture and hence the viability and its further expansion need to be done urgently not only to increase the quantity of education sphere but also develop the quality of the same. The rich diversity of the Indian democracy is not an exception to the Indian Educational scenario. This diversity can only be properly addressed by imparting education in a personalized manner. For the very reason it is necessary that Computational Intelligence mechanisms like Artificial Intelligence, Data mining be incorporated in the SWAYAM system. It will not only help in fetching greater degree of details of the user of the MOOC, but also design more interactive and automated courses as per the user’s need. The current paper after analyzing the major aspects of SWAYAM and other MOOCS, proposes a model of application of Computational Intelligence in the online courses. SWAYAM being a novice in the world of MOOCS, it is necessary that we compare it with other major players to find out the scope of improvement and increase the horizon of education from a national sphere to an international outreach. As once pointed out by Noble Laureate Dr. Amartya Sen, “Education brings social benefits that improve the situation of the poor”, the free and apt education reach of SWAYAM is a small step towards fostering the undiscovered values of education and consequently build an economy based on welfare, knowledge and self-sufficiency [2].

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9.2 Literature Review The significance of MOOCs in recent scenario is immense and substantive research has been done on the same. Jaganatthan and Sugundan have portrayed the growth of Indian MOOCs since its inception [3]. In 2019 World Bank has itself funded and structured 16 MOOCS. The Ministry of Human Resource Development (MHRD) of India has published an exclusive list of all the courses offered by SWAYAM in 2019 [4]. Nayek in his paper has efficiently done a survey on the awareness of SWAYAM among Library and Information Science professionals [5]. Samanta has made an analytical study of SWAYAM till its formation, concentrating on the quantum of enrollments in various courses in SWAYAM [6]. Shi et al. in their studies have provided a structure for pricing of MOOCS [7]. Srivastava and Yadav has discussed about the awareness of SWAYAM among the youth and the problems they face while undergoing a course in SWAYAM [8]. The 2018 OpenupEd trend Report on MOOCS show the recent trend and developments in MOOCS [9]. Jyoti Chauhan in her research paper tried to present an overview of the MOOCs in India [10]. The guidelines for developing MOOCS were published by UGC in 2017 which acts as the primary methodology for developing any MOOC in India. Notable to mention is the descriptive analysis of the trends and future perspectives of MOOCS provided by Organization for Economic Cooperation and development (OECD) in 2016 [11]. A significant insight regarding measures that could judge the success of any MOOC was provided by Jane [12].

9.3 Research Gap Researchers have been successful in determining the growth of SWAYAM along with MOOCS since their inception but none of them have pointed out the areas which contribute to the growth of any MOOC. The current research paper, first and foremost tries to identify these parameters which contribute to the success of any MOOC. It also provides an insight of the performance of SWAYAM in validating the parameters. The problems faced by users while undergoing a course in SWAYAM has been pointed out by researchers. There has also been a considerable amount of discussion on how these issues are not prevalent in major international MOOCS. However no research points out the remedy to such problems faced by SWAYAM. With the advent of SWAYAM in 2017, India has just stepped into the world of MOOCS and researchers have been able to trace its expansion till date. The research gap lies in the fact that though the horizon of expansion has been traced out but the steps necessary for SWAYAM to have an International outreach has not been pointed out. The challenges faced by SWAYAM under the current infrastructure have been highlighted in a number of researches. Neither of them discusses the reason for such challenges nor the remedy for the same.

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9.4 Need for Comparison of Swayam with Major MOOCS SWAYAM is one of a kind MOOC designed by the Government of India to increase the education sphere to the remotest of the villages. This was devised to ‘touch every single student with digital revolution and develop the habit of e-learning’. Also it was aimed at offering a variety of courses to a greater population. The illiteracy rate of India being at 26%, it is urgently necessary to provide quality education to a greater population at free of cost. This education must be of International Quality to fasten the development of the people and making them worthy enough to meet international standards when they arrive at the professional scenario. This is why it urgently required comparing the quality and structure of SWAYAM with other major International MOOCS to determine its capability of meeting International Standards and at the same time determining its role in sustainable development of the domestic society cohesively. Thus in this backdrop the significant reasons for comparison of SWAYAM with other MOOCS can be summarized as: 1. Assessing and developing the quality of prevalent educational system to meet International standards. 2. Increase the horizon and outreach of SWAYAM 3. Optimum utilization of education by fostering choice based education system. 4. To bring about diversity in choice of education. 5. To understand the revenue earning capacity of SWAYAM in comparison to other International MOOCS. 6. To assess the role of SWAYAM in sustainable development.

9.5 An Overview of Swayam in 2019 The concept of SWAYAM was perceived with the objective of ‘providing the best teaching and learning resources to all including the most disadvantaged and bridge the digital gap between the students who are untouched by digital revolution’ [1] and foster the habit of e-learning. This is in parlance with the objective of MOOCS which tends to provide free educational resources, mentoring and tutorial to users at their convenience. SWAYAM (Study Webs of Active–Learning for Young Aspiring Minds) is an online platform for providing teaching and learning resources mostly free of cost. This has been developed by the Human Resource Development [HRD] Ministry, Govt. of India and implemented in the year 2017. The platform offers courses ranging from 9th Standard to Post Graduation [13]. The courses are designed by eminent Institutions of the country and are maintained by nine esteemed National Coordinators. The National Coordinators are selected by the HRD Ministry. They are responsible for designing course content, update and maintenance of the same. These National Coordinators have local Chapters across the country. They act as an Intermediary

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between the users and the National Coordinators. These Local Chapters are famously known as SWAYAM-NPTEL Local Chapter [1]. The Local Chapters is a one stop centre for all information regarding the courses offered by the respective National Coordinators, the duration of the courses as well as the starting and end date of a course. Any technical glitch faced by a user while undergoing a course online can be redressed by the Local chapters. The necessary certification for completion of a course maintained by a National coordinator is provided by the Coordinator through the Local Chapters. At present SWAYAM—NPTEL local chapters are being set up in colleges [after being approved by the management] by the coordinators. These chapters are under the supervision of a faculty member of the college who is known as SINGLE POINT OF CONTACT [SPOC] [1]. The SPOC is informed by the coordinator about all the latest initiatives and updates of courses. The role of SPOC is to identify suitable mentors for a course who can track the student’s progress and clear doubts. The SPOC is also responsible for intimating the number of students appearing for an exam for gaining a certificate of completion from the National Coordinator. The SPOC also informs the National coordinators about the problems faced by the students of a particular chapter and seeks remedy for the same. The evaluation mechanism exclusively for courses that are paid up and certified is done by external examiners or distinguished educationist recruited by the National Coordinators. A SPOC cannot evaluate the papers of students of his or her local chapter even if he is recruited as an examiner by the National Coordinator. After evaluation, the successful candidates are handed over certificates by the National coordinator through the Local Chapters. The Local chapters either physically hand over the certificates to its enrolled students or dispatch the same to the student’s address by post. The Exam centers for conducting a particular exam are also designated by the national coordinator after taking into account the recommendation regarding the same from the local chapters. The invigilation process is also controlled by the Local chapter but the remuneration for the invigilation is provided by The National Coordinator directly to the invigilator’s bank Account. The credit facility of a course in SWAYAM is clearly laid down by University Grants Commission and the quantum of credit to be provided for a course is again decided by the National Coordinator [14]. The entire hierarchy of the SWAYAM operation is provided in a consolidated manner in Fig. 9.1. The entire learning process works in the form of four major aspects. These are the following: • Online Tutorial: Video and Audio content specific to courses are updated for students time to time or on a one time basis. Besides there are also animations and links to certain other domains so that students can be explained concepts in a lucid manner [3]. • Online Content:

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Fig. 9.1 Hierarchy of SWAYAM

The SWAYAM program offers its students mostly free of cost downloadable contents containing study material, exercise, research work, e-books, hypothesis, historical background and other anecdotal information. However there are certain contents that are exclusively available for downloading only when a user pays a certain amount of fee for acquiring a certificate of completion [1]. • Evaluation: Evaluation happens in two ways: – First there is a provision for Self evaluation where a user can appear in Mock examinations containing a host of questions from the well devised question bank of the course. – Secondly the user can pay a fee and opt for a certificate for which they have to appear in an Examination conducted by the respective National Coordinator who is responsible for maintenance of the user’s course. These Examinations are conducted in SWAYAM approved centers on certain pre mentioned dates. The user can appear for the examination only when he has completed the course in the prescribed duration and has paid a fee for availing a certificate of completion of his designated course. • Doubt Clearing session: There is no 24/7 system on doubt clearing. However on request or on certain intervals there remains a provision for clearing doubts where student can email their doubts to the mentor and the Coordinator and his team responds with an appropriate solution. However till date there is no provision for video conferencing or face to face doubt clearing.

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Since it is a government of India Initiative the HRD ministry has also provided certain pre-defined credit facility for the users of the courses. These are developed by the UGC and provide certain benefits to the users of the courses [14].

9.6 Research Objective 1. To collect details about SWAYAM, an Indian MOOC. 2. To collect details about major International MOOCS. 3. To identify parameters of comparison among SWAYAM and International MOOCS. 4. To do content analysis to understand the position of SWAYAM. 5. To propose a basic model for integrating computational intelligence with MOOCs.

9.7 Research Methodology The research is exploratory in nature. It is based on the secondary data available on the HRD ministry SWAYAM website [www.swayam.gov.in]. The research demands the data about the various MOOCS offered at National and International panorama. The details [like course name, subject duration] about MOOCS on offer have to be collected from relevant website and then content analysis is conducted on the data. The research will analyze the content of each of these MOOCS [preferably the largest players based on no. of users] [4], know about its course design and structure and compare it with SWAYAM. In order to achieve the objective of the study, the following steps have been adopted: 1. Collect parameters that describes SWAYAM, Indian MOOC 2. Collect parameters that describes major International MOOCS 3. Identify the parameters of comparison among SWAYAM and International MOOCS 4. Perform the Content Analysis of the selected parameters for the selected international MOOCS and SWAYAM whose details are available on websites to understand the relative position of SWAYAM. 5. Identify the scope for integrating the concept of artificial intelligence and machine learning in MOOCs for learners. The parameters of comparisons considered on the basis of the information available on websites about the MOOCS. Although the parameters selection may vary from researcher to researcher, the significant parameters identified in this study are mentioned below:

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Ease of access Ease of understanding Diversity of educational streams Availability of downloadable contents Evaluation mechanism for users Cost effectiveness on part of the user Time effectiveness on part of the user Mobile accessibility Career advancement Credit transfer framework Placement Assistance Dropout rate

The realm of courses analyzed is limited to its relevance at the Post Doctorate, Doctorate, Post Graduate and Under Graduate level. Credit facility of such courses in accordance with the prevalent professional courses is also taken into account. The entire analysis is done by considering how SWAYAM performs out of 40 marks in comparison with other MOOCS. Certain factors were identified that are exclusively responsible for the success of a parameter. The rationale for selecting the parameters and the underlying factors are discussed in detail in the ‘RATIONALE FOR SELECTING PARAMETERS’ section discussed just after the ‘RESEARCH METHODOLOGY’ section. Each underlying factor contributing to the success of a parameter is given a mark each. Only if a MOOC adhere to all the factors of these parameters then only the MOOC is awarded maximum marks. In case a MOOC does not satisfy a factor, the respective MOOC has not been awarded any marks for the same. Naturally not satisfying a certain factor will result in low scoring of MOOCS in that parameter. Vertical summation of all the parameters is done to understand the performance of a MOOC entirely after encompassing all the parameters. Horizontal summation is done to understand the factors and parameters that are provided by most of the MOOCS. It is assumed that the Parameters with value of horizontal summation greater than the Mean of Total scores of horizontal summation obtained by each parameter are CRUCIAL PARAMETERES as they are provided by all the MOOCS. In case of ‘Diversity of Educational streams’ and ‘Dropout Rate’ the mean of the parameters are found out and those MOOCs who are having a value greater than or equal to the mean have been provided a mark each and those below the mean have been provided 0.

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9.8 Rationale for Choosing Each Parameter and Its Contributing Factors The parameters that are chosen for measuring the success of each MOOC is based on the previous researches and founded on the following reasons: • Ease of Access For any MOOC to be successful the first and foremost is the easy accessibility of it, because until a MOOC platform is not found easily then the accessibility will be hindered. For ease of access it is utmost necessary that a MOOC has proper guidelines for access that will act as module for accessing the course, presence of frequently asked questions [FAQ] so that the common problems and doubts during access are cleared in real time and a Customer care helpline where adverse or emergency situations pertaining to individual users can be redressed easily and conveniently. Besides since MOOC has the objective of providing education ‘anytime and at anyplace’ [15] therefore 24 h registration is an utmost necessity. Any MOOC of International outreach must have a worldwide acceptable payment mechanism. This helps foreign students to have easy access to the online courses. This would also provide an impetus for devising courses of International standard and Global outreach. • Ease of understanding A study conducted in the year 2018 for understanding the reason for opting for MOOCS on 300 students of Punjab Central University shows that majority of the students feel that often the course content is ambiguous or sometimes difficult to decipher [8]. However another study conducted by Tony Bates proved that out of a sample size of 1500 users of MOOCS 50% believe that not all MOOCS add additional knowledge [16]. They believed it was a repetition of whatever they learned previously in their traditional degree courses. The two studies clearly reveal the pattern of students enrolled for MOOCS. There are basically three types of users who go for MOOCS. The first category wants to gain basic knowledge about the course because they don’t know anything about it. For them Basic level courses are necessary. Secondly there are users who want to add something extra to their knowledge. For them Intermediate level courses are important. Lastly there are users who want to be expert in that field and want to use their knowledge practically. For them professional level courses is a necessity. This proves that for easy understandability of a course it is essential that the courses are bifurcated into Basic, Intermediate and Professional category. Any new topic brings with it the inquisitive mind of doubts [2]. MOOC being an educational platform can’t be an exception. For this on the course question answer session and detailed information about course content mentioning the purpose of a course is essential. The world being a place of 6500 languages [16], It’s not possible for all to communicate only in English. So it’s utmost necessary

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that education in MOOC must be pertained in both English and a native language. Diversity in educational streams: As students have different preferences naturally the higher diversity of courses is necessary to meet the dynamic and varied choices of the student forum. Availability of downloadable contents: Dr. Jagannath Shankar in his research paper on comparison between Indian and International MOOCS has shown with evidence that till 2019 internet accessibility problems are a major challenge for Indian MOOCS [3]. For this it is utmost necessary that the course content can be downloaded and saved in device and students do not have to face the problem of logging in online again and again for studying, particularly at times when internet network is very poor. Downloading and saving content will not allow any hindrance in studies even at times of poor connectivity. However availability of downloadable contents cannot be always free as it requires huge capital investment from the MOOC. So the factors that contribute to the success of this parameter are availability of freely downloadable contents, availability of contents after paid up registration, availability of free downloadable contents for certain paid up courses. A combination of all three will help the students to gain knowledge at ease as well as the MOOC to have a cost effective model. Evaluation mechanism: Assessment in MOOCs is an effective tool for the user to understand the applicability of the knowledge gained. Dr. Marco Kalz in his conference proceeding highlights certain limitations of the evaluation approaches of MOOCS, particularly in the field of courses that require hands on training [17]. As discussed earlier, the three major types of students enrolled for a course requires different form of evaluation mechanism. For professionals willing to learn a course to become an expert, both self evaluation and terminal evaluation is necessary as he will be able to judge himself based on the everyday situation he faces and terminal examination will determine his level of expertise. For basic level users it is important that they go through a process of Continuous evaluation as for them practice is utmost necessary to be perfect in something they are learning new. Project completion, and Project presentation through web is also necessary for hands on training courses to show the excellence of students in practical life. Education encompasses interaction and for this video conference presentation is utmost necessary in MOOCS. Cost factor on part of the user: The cost factor is significant as students are often discouraged from availing a course with high course fee. It is thus necessary that we understand that the aspects a student has to pay for are. For a MOOC the basic four factors which usually need to be paid are availing a course, availing a certificate, availing credit facility, downloading course content even if paid for course registration. Time effectiveness:

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MOOC are online courses with the objective of studying anytime and at any where [4]. However it is often found that the courses offered by MOOC are only for a certain time of the year or days and not that a student can study a course any time. This is not in parlance with the objective of MOOCs. For time effectiveness of a course all the factors pertaining to the overall teaching through MOOCS must be available 24 h [2]. Thus the factors satisfying this parameter can be summarized as 24 h Video lectureship, 24 h course study access, 24 h mentorship availability. Mobile accessibility: In the era of digital revolution where mobile phones are becoming almost a replacement of desktops it is essential that MOOCS are easily accessible through mobile phones. People carry mobile phone almost everywhere and accessibility of MOOC through mobiles would mean that they will be able to carry their courses with them anywhere and at any time. This is in parlance with the main objective of MOOC. Thus for easy access it is desirable that a MOOC is available in the mobile version through APPs as well as access to the main website through phones. Career advancement: Any education sphere must have a practical application in the real world otherwise the need for such education in life is useless. However recognizing such applicability requires practical outlook. Development of this outlook can be easily made by career counseling by experienced veterans in the concerned field. This development of outlook regarding the applicability of a course will help in not only choosing a career but also progressing in a career by understanding the optimal usage of knowledge. Institutional recognition of courses is also important as they act as a standard for education level like undergraduate level, post graduate level etc. Credit transfer framework: By encouraging credit facility through MOOCs a student is able to pursue a choice based education system at his own convenience and get an institutional certification for the same. However the cost of availing such credit facility also matters and has to be affordable. We have assumed a model with freely available credit facility courses and paid up credit facility courses will be optimum both from the point of view of the MOOC providers and student. Placement Assistance: Placement is important in any sphere of education as that will be a way of applying the gained knowledge to practical life for enhancing the quality of life. A clear idea about the placement opportunities available after completion of a course is necessary as that will fasten the process of placement. This is best done through Placement Assistance and Placement guarantee. Dropout rate: The viability of a MOOC also depends upon the no. of persons completing a course. If the dropout rate is very high then there can be conclusions that the particular MOOC is not favored by most of the users.

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The entire parameters, the factors contributing to the success of each parameter and the numerical value assigned to each factor and summing up to each parameter are shown in Table 9.1. The Internationally prevalent major MOOCS with which the analysis has been made are chosen based on the Major MOOCS enlisted by Class Central, a website monitoring the pattern of International MOOCS. Based on their review the major MOOCS chosen for the analysis are shown in Table 9.2.

9.9 Analysis and Findings The research has been done based on the process mentioned above and the analysis is shown in the form of both numerical and graphical analysis. A detailed theoretical analysis has also been provided for each parameter of comparison and the areas of improvement or the areas where SWAYAM is ahead of all other courses is also mentioned in it. Thus to summarize, the entire analysis and findings, the section has been divided into four categories namely: • • • •

Numerical Analysis Theoretical Explanation Graphical Explanation Summarized position of SWAYAM with scope of improvement

9.9.1 Numerical Analysis: The entire numerical analysis is based on the research methodology and shows how much does each MOOC score in context to the parameters and the underlying factors as discussed in Table 9.1. The entire analysis is shown in Table 9.3. The entire Numerical Analysis has been summarized to form Table 9.4. Here the scores obtained by each MOOC after numerical analysis has been summarized. It is worthy to note that out of the 7 MOOCS chosen out of the world SWAYAM ranks 4th . This may portray that SWAYAM is far lagging behind. This image is partially true and the reasons for the same have been discussed in the ‘Graph of Numerical Analysis’and ‘Theoretical Explanation’ section. This also paves for understanding the future scope of SWAYAM and the areas where SWAYAM needs to improve.

9.9.1.1

Graph of Numerical Analysis

As discussed in detail in Table 9.4 the Numerical Analysis speaks volumes about the position of SWAYAM in the international context. It is clear that out of the

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Table 9.1 Table showing Parameters, factors underlying the success of each parameter and the numerical Value represented by them Parameters For comparison

Factors contributing to Numerical value Maximum numerical the success of the assigned to each factor value contributed by a parameter parameter

Ease of access

Presence of well defined guidelines for access to the course

1

6

Presence of Frequently 1 Asked questions (FAQ) Proper customer care helpline

1

Less than 4 steps 1 involved in registration

Ease of understanding

Provision for 24 h registration

1

Worldwide payment mechanism in any currency

1

Bifurcation of course into Basic, Intermediate and Professional category

1

Provision for on the course question answer session

1

4

Provision for 1 delivering lecture both in native language and English Detailed information regarding Course content and Credit facility offered by each course Diversity of educational streams

1

The mean of the 1 parameters are found out and those MOOCs who are having a value greater than or equal to the mean have been provided a mark each and those below the mean have been provided 0

1

(continued)

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Table 9.1 (continued) Parameters For comparison

Factors contributing to Numerical value Maximum numerical the success of the assigned to each factor value contributed by a parameter parameter

Availability of downloadable contents

Freely downloadable contents

1

Paid downloadable contents

1

Free downloadable contents only for paid up courses

1

Terminal evaluation

1

Evaluation mechanism for users For Courses which require hands on experience:

Evaluation mechanism for users For courses which do not require hands on experience:

Cost factor on part of the user (Analysis for determining the degree of freely available resources)

Time effectiveness

Mobile accessibility

3

5

Continuous evaluation 1 Project presentation through web

1

Project completion and showcase

1

Self evaluation

1

Terminal Evaluation

1

5

Continuous evaluation 1 Project completion and showcase

1

Video conference presentation

1

Self evaluation

1

Payment for availing certificate

1

Payment for availing credit facility

1

Payment for availing courses

1

Payment for downloading contents even if paid for the course

1

24 h video lectureship

1

24 h course study access

1

Mentorship availability 24/7

1

Mobile app

1

4

3

2 (continued)

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Table 9.1 (continued) Parameters For comparison

Factors contributing to Numerical value Maximum numerical the success of the assigned to each factor value contributed by a parameter parameter Website access from phone

1

Career advancement

Institution recognized courses

1

Career counseling

1

Credit facility

Freely available credit facility

1

Paid up credit facility

1

Placement assistance

Placement guarantee

1

Placement assistance

1

Dropout Rate

The mean of the 1 parameters are found out and those MOOCs who are having a value greater than or equal to the mean have been provided a mark each and those below the mean have been provided 0

2

2

2 1

Total numerical values of all the parameters taken together

Table 9.2 Table showing MOOCS chosen for analysis, their country and year of origin

40

Name of the MOOC

Year of origin

Country of origin

Coursera

2012

United States of America

edX

2012

United States of America

Future Learn

2012

United Kingdom

XuetangX

2013

China

MexicoX

2015

Mexico

MiriadaX

2012

Spain

SWAYAM

2017

India

seven MOOCS selected, SWAYAM ranks 4th and is lagging a lot behind the major players like Coursera and edX. The reason for such a position of SWAYAM has been discussed in detail in the ‘Theoretical explanation’ part of the chapter (Fig. 9.2).

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Table 9.3 Table showing Numerical analysis of the performance of MOOCS and SWAYAM Parameters with underlying factors

MOOCS

Total Coursera edX Future XuetangX MexicoX MiriadaX SWAYAM horizontal summation learn

Ease of access Presence of well defined guidelines for access to the course

1

1

1

1

0

1

1

6

Presence of frequently asked questions (FAQ)

1

1

0

1

0

1

1

5

Proper 0 customer care helpline

0

0

1

1

0

1

3

Less than 4 1 steps involved in registration

1

1

1

0

1

1

6

Provision for 24 h registration

1

1

1

1

1

1

7

1

(continued)

9.9.2 Theoretical Explanation The theoretical explanation provides a detailed analysis of the MOOCS and provides an in depth knowledge about the areas where SWAYAM lacks behind other courses and the reason for the same. It also proposes certain measures to overcome this hurdle. The explanation is divided based on the 13 Parameters that are considered to be mostly affecting the success of SWAYAM. • Ease of Access: In this category we find that SWAYAM has scored 5 out of total 6 marks. Though this is a good score but surely there is scope of improvement. SWAYAM lacks a worldwide payment mechanism which is very much an essential for foreigners to enroll in SWAYAM. Unless and until students from other countries are encouraged to join SWAYAM international exposure of SWAYAM is not possible and hence the impetus to develop courses of international standard will be missing. It is an urgent requirement that SWAYAM develops a worldwide payment mechanism and this international exposure will help SWAYAM get more foreign enrollments and increase the base of its users which it lacks very much. • Ease of understanding:

9 Overview and Future Scope of SWAYAM in the World of MOOCS …

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Table 9.3 (continued) Parameters with underlying factors

MOOCS

Total Coursera edX Future XuetangX MexicoX MiriadaX SWAYAM horizontal summation learn

Worldwide 1 payment mechanism in any currency

1

1

0

0

0

0

3

Total

5

4

5

2

4

5

30

Bifurcation of 1 course into basic, intermediate and professional category

0

0

0

0

0

0

1

Provision for on the course question answer session

1

1

1

1

0

0

5

5

Ease of understanding

1

(continued)

SWAYAM has scored just half of the maximum marks allotted for this category. Ease of understanding is inevitably a significant factor for success of a MOOC. SWAYAM does not provide a bifurcation of the courses into basic, intermediate and professional level. It must be noted that there are basically three types of users who go for MOOCS. The first category wants to gain basic knowledge about the course because they don’t know anything about it. For them Basic level courses are necessary. Secondly there are users who want to add something extra to their knowledge. For them Intermediate level courses are important. Lastly there are users who want to be expert in that field and want to use their knowledge practically. For them professional level courses is a necessity. SWAYAM does not offer such bifurcation and have to work on it. The personal touch to any course is important and verbal question answer session is a way to foster the same. SWAYAM does not have that in comparison to other MOOCS like Coursera and edX. • Diversity of educational streams: SWAYAM has a good amount of diversity in the courses they offer. However it does lag behind Coursera and edX. It may be suggested that SWAYAM increases its diversity a little more with more courses, particularly at the under graduate level. • Evaluation Mechanism for users:

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Table 9.3 (continued) Parameters with underlying factors

MOOCS

Total Coursera edX Future XuetangX MexicoX MiriadaX SWAYAM horizontal summation learn

Provision for 0 delivering lecture both in native language and English

0

0

1

1

0

1

3

Detailed 1 information regarding course content and credit facility offered by each course

1

1

1

1

1

1

7

Total

3

2

2

3

3

1

2

16

Diversity of educational streams

1

1

0

0

0

0

1

3

1

0

1

0

2

Availability of downloadable contents Freely 0 downloadable contents

0

0

(continued)

It has been found out that though SWAYAM has a pretty fair system of Terminal examination but in case of courses that require hands o training SWAYAM is far behind as it does not have a mechanism for Project Showcase or Project completion. These issues need serious attention. • Cost effectiveness on part of the user: This is a field where SWAYAM fairs well. Though SWAYAM does not have provision for free courses with both credit facility and certificate together but it does have a provision for free courses with certificates. It may be suggested that SWAYAM can introduce more free courses with credit facility to reach a greater population. • Time effectiveness: It is worthy to note that SWAYAM does not have a single course which can be taken up and completed any time or any place. The main aim of MOOC is to provide courses so that users can complete it at their own convenience. But such provision is lacking in case of SWAYAM. • Mobile accessibility:

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187

Table 9.3 (continued) Parameters with underlying factors

MOOCS

Total Coursera edX Future XuetangX MexicoX MiriadaX SWAYAM horizontal summation learn

Paid 1 downloadable contents

1

1

1

1

1

1

7

Free 0 downloadable contents only for paid up courses

1

0

0

0

0

0

1

Total

2

1

2

1

2

1

10

1

Evaluation mechanism for users for courses that require hands on experience Terminal evaluation

1

1

1

1

1

1

1

7

Continuous evaluation

1

1

0

0

0

1

1

4

Project presentation through web

1

0

0

0

0

0

0

1

Project completion and showcase

0

1

0

0

0

0

0

1

Self evaluation

1

1

1

1

1

1

1

7 (continued)

It is worthy to mention in an era where mobile phone are placing desktops Mobile accessibility is an important factor as it is more handy to carry and easy to access anytime. SWAYAM scores in parlance with most other MOOCS providing both an APP as well as mobile accessibility. • Career Advancement: In the century predominated by huge demand for multitasking, MOOCS are a savior as they provide ample opportunity for gaining additional degrees and expertise in parallel to traditional ones. SWAYAM though has certain courses that are recognized by a host of institutions but it does not provide any career counseling. Often students find it difficult to choose the career path after they have completed a course online from SWAYAM. • Credit Facility: SWAYAM scores relatively good in this aspect as UGC has given a proper framework for it. Since it is government sponsored and recognized no institution can decline the credits earned through SWAYAM.

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Table 9.3 (continued) Parameters with underlying factors

MOOCS

Total Coursera edX Future XuetangX MexicoX MiriadaX SWAYAM horizontal summation learn

Total

4

4

2

2

2

3

3

20

Evaluation mechanism for users for courses which do not require hands on experience Terminal evaluation

1

1

1

1

1

1

1

7

Continuous evaluation

1

1

0

0

0

1

1

4

Project completion and showcase

0

1

0

0

0

0

0

1

Video conference presentation

1

1

0

0

0

0

0

2

Self evaluation

1

1

1

1

1

1

1

7

Total

4

5

2

2

2

3

3

21

1

1

0

1

1

6

Cost factor on part of the user Payment for availing certificate

1

1

(continued)

However there must be certain scholarships to provide such credit facility and course completion expenses for financially poor students and the disadvantaged group. • Placement assistance: Any education must have a proper channel for its utility in the practical world. It must carter to the job sector. For this it is highly necessary that the educational institutions must have placement assistance scheme if not a placement guarantee. It is sad to say that SWAYAM offers none. • Dropout rate: The dropout rate is very high in case of students enrolled in SWAYAM. It was noted in 2018 that out of 1.8 million users only 0.6 million20 has completed the education. This can be due to language barriers, lack of 24 h course access or difficulty in understanding. A deeper analysis is required to understand the finer aspects contributing to the current position of SWAYAM and areas where it needs improvement or already ahead in the competition. This paves the way for a graphical representation of certain aspects of the MOOCS.

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Table 9.3 (continued) Parameters with underlying factors

MOOCS

Total Coursera edX Future XuetangX MexicoX MiriadaX SWAYAM horizontal summation learn

Payment for 1 availing credit facility

1

1

1

1

1

1

7

Payment for availing courses

0

0

0

0

0

0

0

0

Payment for downloading contents even if paid for the course

1

0

0

0

0

0

0

0

Total

3

2

2

2

1

2

2

14

Time effectiveness 24 h video lectureship

1

1

1

0

0

0

0

3

24 h course study access

1

1

0

1

1

1

1

2

Mentorship availability 24/7

0

0

0

0

0

0

0

0

(continued)

9.9.3 Graphical Representation In spite of the Numerical analysis covering a host of parameters influencing the success of MOOCS a graphical representation summarizes the same. Not only this, there certain other factors that may not have a quantitative influence but do qualitatively influence the success of a MOOC. In this backdrop this section represents three aspects of the MOOCs in the form of Graphical Analysis. They are the following: • • • •

Year on Year Trend Analysis for Revenue Earned Graph for total no. of UG and PG courses offered Graph for total no. of users as registered as on 2019 Summary Table and Graph for Horizontal summation analysis of Parameters discussed in Table 9.3

Under this category the revenue earning capacity for each of the MOOCS over a three year period starting from 2016 and ending on 2018 has been analyzed and a year on year trend analysis for the same has been given. We find that the highest earning capacity has been that of Coursera (140 million USD). SWAYAM has been earning revenues at 72 million USD in 2017 and 80 million USD in 2018 which is

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Table 9.3 (continued) Parameters with underlying factors

MOOCS

Total Coursera edX Future XuetangX MexicoX MiriadaX SWAYAM horizontal summation learn

Total

2

2

1

1

0

0

1

7

Mobile accessibility Mobile App

1

1

1

0

1

1

1

6

Website access from phone

1

1

1

1

1

1

1

7

Total

2

2

2

1

2

2

2

15

Career advancement Institution recognized courses

1

1

1

1

1

1

1

7

Career counseling

1

0

1

0

0

0

0

2

Total

2

1

2

1

1

1

1

9

Credit transfer framework Freely available credit facility

0

0

0

1

1

0

1

2

Paid up credit facility

1

1

1

1

1

1

1

7 (continued)

greater than MexicoX, Futurelearn and MiriadaX. The trend analysis of SWAYAM has also an upward slope that indicated that not only SWYAM is earning revenue above some major MOOCS but also it has been increasing the revenue earned each year starting from 2017 (Fig. 9.3). From this analysis we can say that SWAYAM being a novice in the world of MOOCS has relatively been doing well. However there is indeed room for improvement as there are other MOOCS that are doing hugely well. This improvement can be brought by reaching a greater sphere of users by overcoming language barriers and other internet facility problems (Fig. 9.4). The graph shows the amount of Undergraduate and Post graduate courses offered by the major MOOCS of the world and the position of SWAYAM in this regard. We find that SWAYAM offers a relatively fair amount of courses as compared to MexicoX and MiradaX is concerned but lacks way behind edX and future Learn particularly in the sphere of under graduate courses. In this regard we may say that though SWAYAM provides a fair amount of PG courses but it needs to increase the realm of Under Graduate courses to become a major international Player (Fig. 9.5). The total no. of users registered till 2019 represents a favorability of users towards a certain MOOC. This parameter enhances the viability of the MOOC because the more

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191

Table 9.3 (continued) Parameters with underlying factors

MOOCS

Total Coursera edX Future XuetangX MexicoX MiriadaX SWAYAM horizontal summation learn

Total

1

1

1

1

2

0

2

8

Placement assistance Placement guarantee

0

0

0

1

0

0

0

0

Placement assistance

1

0

1

1

0

0

0

3

Total

1

0

1

2

0

0

0

4

Drop out rate

0

1

0

1

0

0

0

2

Total performance of MOOCS out of 40 (sum total of grey cells)

29

28

20

24

16

18

23

Rank (based on performance out of 40)

1st

2nd 5th

3rd

7th

6th

4th

the no. of users the greater is the revenue earning capacity and the more sustainable it will be. We found out that SWAYAM accounts for the least number of registrations. This can be due to the recent launch of SWAYAM as compared to other MOOCS. But in order to be more viable in the industry of MOOCS it is desirable that SWAYAM expand its horizon. This can be done by proper and rigorous advertisement, mass awareness and introduction of more on demand courses.

9.9.4 Summary Table and Graph for Horizontal Summation Analysis of Parameters Discussed in Table 9.4 The Horizontal summation analysis is important because it is necessary to know how SWAYAM performs in respect to the CRUCIAL PARAMETERES as identified through Table 9.5 and Graph of Horizontal summation. It is assumed that the Parameters with value of horizontal summation greater than the Mean of Total scores of horizontal summation obtained by each parameter are CRUCIAL PARAMETERES. From the graph the Crucial Parameters identified are the following (Fig. 9.6): • Ease of Access [P1]

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Table 9.4 Summary table of scores obtained by MOOCS Sr. no.

Parameters

MOOCS Coursera edX Future XuetangX MexicoX MiriadaX SWAYAM learn

P1

Ease of access

5

5

4

5

2

4

5

P2

Ease of 3 understanding

2

2

3

3

1

2

P3

Diversity of educational streams

1

1

0

0

0

0

1

P4

Availability 1 of downloadable contents

2

1

2

1

2

1

P5

Evaluation mechanism for courses that require hands on training

4

4

2

2

2

3

3

P6

Evaluation mechanism for courses that do not require hands on training

4

5

2

2

2

3

3

P7

Cost factor on 3 part of the user

2

2

2

1

2

2

P8

Time effectiveness on part of the user

2

2

1

1

0

0

1

P9

Mobile accessibility

2

2

2

1

2

2

2

P10

Career advancement

2

1

2

1

1

1

1

P11

Credit transfer framework

1

1

1

1

2

0

2

P12

Placement Assistance

1

0

1

2

0

0

0

P13

Dropout rate

Total performance of MOOCS out of 40

0

1

0

1

0

0

0

29

28

20

24

16

18

23

9 Overview and Future Scope of SWAYAM in the World of MOOCS …

Fig. 9.2 Performance of MOOCS based on parameters discussed in Table 9.4

Fig. 9.3 Year on year trend analysis for revenue earned

Fig. 9.4 Total no. of under-graduate and post graduate courses offered

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Fig. 9.5 No. of registered users (in millions) Table 9.5 Summary table for horizontal summation analysis of parameters discussed in Table 9.4 S. No.

Parameters of comparison

P1

Ease of access

30

Crucial parameter

P2

Ease of understanding

16

Crucial parameter

P3

Diversity of educational streams

3

P4

Availability of downloadable 10 contents

P5

Evaluation mechanism for 20 courses that require hands on training

Crucial parameter

P6

Evaluation mechanism for courses that do not require hands on training

21

Crucial parameter

P7

Cost factor on part of the user 14

Crucial parameter

P8

Time effectiveness on part of 7 the user

P9

Mobile accessibility

15

P10

Career advancement

9

P11

Credit transfer framework

8

P12

Placement Assistance

4

P13

Dropout rate

2

Total

Total of horizontal summation Crucial parameter as per Table 9.3 identification

159

Mean of Total of Horizontal Summation =

 Total of Horizontal Summation 159 = = 12.230 No. of Parameters of Comparison 13

Crucial parameter

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Fig. 9.6 Graph for horizontal summation analysis of parameters discussed in Table 9.4

• • • • •

Ease of understanding [P2] Evaluation mechanism for courses that require hands on training [P5] Evaluation mechanism for courses that do not require hands on training [P6] Cost factor on part of the user [P7] Mobile accessibility [P9]

9.9.4.1

Summarized Position of SWAYAM with Future Scope of Improvement:

According to the research we believe that SWAYAM has relatively a fair position in the world of MOOCS as compared to other reputable MOOCS considering the fact that it has just set its foot marks in the year 2017. However the following improvements are utterly necessary for reaching an internationally acclaimed stage: • SWAYAM must provide a worldwide payment mechanism for encouraging and facilitating foreign students to enroll the courses and hence increase the user base as well as revenue. • It must differentiate its courses into Basic, Intermediate and Professional level with clear details mentioning the purpose of such bifurcation. • SWAYAM must include a proper live question answer session through video conferencing for better understanding of the subjects. • Number of under graduate courses are less as compared to other MOOCs and hence it must be looked into. • Evaluation mechanism must include project completion and showcase to foster practicality of a subject. • The system of continuous evaluation mechanism must be introduced for better learning.

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• SWAYAM must introduce more free courses with credit facility to reach out the financially backward class. • Scholarships for financially poor students can be introduced considering the falling GDP of the country. • Courses must have free access 24/7 in order to help the users complete it at his or her convenience. • Career counseling must be provided to the students on a mandatory basis irrespective of the course they undergo. • SWAYAM must look for sponsorships from elite institutions to increase its revenue base and also increase its user base by more accessibility towards the courses. • Placement assistance must be mandatorily given to the students and if possible placement guarantee. At least the 100 day work scheme of the Government of India must recognize SWAYAM courses and have provision for placement from the SWAYAM users. • Drop out ratio can be lessened by overcoming language barriers and internet facility. One must be made to understand the Economic benefit of SWAYAM.

9.10 Application of Computational Intelligence in MOOCS As per the Horizon Report 2019, the key technologies to have greater impact on teaching and learning practices from the year 2019 to 2022 includes mobile learning, analytics, Artificial Intelligence (AI), Virtual Assistants etc. [18]. AI comprises of the large number of technologies such as machine learning, data mining, neural networks, and algorithm. Machine Learning is the method to identify patterns and perform predictive analysis. Data mining is the way of performing deep analytics on data for better understanding and forecasting. The dynamic nature of AI has compelled the education sector along with other significant sectors to explore the relevant opportunities. Imparting education in personalized manner on the basis of learner’s abilities can bring revolution in the learning process [19]. The digital learning content can be made smarter by incorporating interesting features such as step-wise learning, complexity level feedback at every step, hints at every step, key points about the topic, audio and video demo explanatory lectures, virtual assistants to guide the learning etc. Intelligent tutoring system can play very effective role in making the learners grasp the concepts and master a subject as per their own learning speed and capabilities. This would result in better goal achievement that is imparting education that learners understand well. However, intelligent system design requires lot of data about the learner, course content designer, course details, learner’s academic background, performance record in earlier examinations as well as current progress rate, number of questions asked, number of times a chapter lesson has been repeated etc. Intelligent system can create a simulated environment for personalized learning experience. It does so on the basis of the data gathered about the learner, course, current learning path and previous performance in other courses with the help of

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computational intelligence techniques such as neural networks, content analysis of the learner’s data [20]. It also makes use of the concept of augmented reality to create the virtual environment for the teaching and learning process [19]. Such environment is very helpful for interactive and collaborative learning keeping the students in MOOCs engaged effectively with the course. With the use of such advanced technologies, predictive model for learner can be developed. Such model can be very helpful in predicting few important issues as mentioned below: (a) (b) (c) (d) (e)

Course engagement Understanding of the concepts Interaction rate Course completion probability Expected performance in examination

Intelligent systems for education can provide a boost to the effectiveness of MOOCs. The integration of Intelligent Tutoring system with MOOCs can make online learning more interactive, automated and adaptive as per the learner’s requirements making the system much more effective [21]. In the current scenario, MOOCs do not have the flexibility to adapt to the needs of individual learner [22]. Therefore, the drop out rate and dissatisfaction in learning outcomes is high. In order to make MOOCs more effective, it should implement AI technologies to build an inherent intelligent tutoring system with MOOC courses. Such system would require a front end environment that deals with the learner and an inner intelligent environment that can extract information from the data entered in run time, does data mining on available data and then integrates the outcomes to prepare the system dynamically ready for the next activity as per learner’s capabilities. Intelligent Tutoring System Integration with MOOCs—a basic model.

9.10.1 Stage I—Learner Enrolment Learner enrolls for a course Learner Profile created—includes general details along with data about current academic activities, previous courses attended, performance in courses, current occupation, work experience, work profile etc. Learner account (Log In id and password) is created.

9.10.2 Stage II—Proposed Model for Learning Process in MOOCs Considering the stages of recording data in the system mentioned in the above table, a dynamic predictive model can be developed to integrate intelligent tutoring system for overall improvement in MOOC courses and making it more effective. The data

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recorded in the system can then be used for auto assessment of learner and also to identify the probability of the learner completing the course (Table 9.6). Table 9.6 Integrating MOOC with intelligent system Front end

Inner intelligent system

Learner Logs In their account and MOOC user page opens ↓

→ Accesses Learner Profile and ↓

Course content list ↓

← accordingly starts data mining to prepare the contents to be presented dynamically ↓

Learner selects a module/task ↓

→ Task description can be created dynamically for selection of words, language complexity, block diagrams & images, keywords etc. on the basis of the Learner’s profile and prediction about the understanding level ↓

Task description is deployed ← Steps to solve the task are generated Learner starts the learning process and goes ↓ to steps of the task given ↓ Feedback about the task and the step may be → As per feedback, next step or hint for the asked next step will be generated dynamically ↓ ↓ Learner completes the task Gives rating for the task and the steps provided ↓

← Record the rating given by learner ↓

Learner takes the Test Question

→ Test question is generated/chosen from the question bank. The complexity level is decided on the basis of the number of hints taken by learner in learning task

Learner demands hints to solve the question ← Previous feedback and rating Acts as the ↓ basis to generate hint dynamically ↓ Learner completes the test

→ Evaluates and records learner’s performance in terms of time taken, number of hints used, reference to previous examples etc ↓ Records the whole activity in the Learner’s activity for developing better predictive model to assess next activity with more accuracy

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9.11 Limitations of the Study The current research tries to evaluate the position of SWAYAM based on quantitative analysis of course content. However not only the quantum of courses offered or the availability of essential parameters of comparison is important but the quality of such course content must also be taken into account. SWAYAM offers a host of courses but the quality of such courses offered are not analyzed in this research. Moreover the viability of a MOOC depends much on the economic environment of the country in which it prospers. Due to paucity of resources and first hand information it was not possible to compare the MOOCS in reference to the economic infrastructure in which they prosper. Time being a limiting factor the realm of comparison was limited to just 3 years of data. Application of latest technologies such as artificial intelligence and rigorous data mining can provide better insights into learner’s expectations and capabilities of learning on online environment. Thus, a system that integrates existing MOOCs with computational intelligence may yield better results. In this study a basic learning process model has been developed to show the integration. However, more study is needed to develop software in this line and implement in real life scenario to give more accurate inferences.

9.12 Future Scope MOOCs are extensively used by students. There is an utmost necessity that we study the viability of a MOOC from the perspective of a student as they are the main stake holders. For this a research based on the opinion of students as primary data regarding each parameter of a MOOC can be done. The Revenue earning capacity of a MOOC is an important parameter for its sustainability however no conclusions can me made regarding the favorability of MOOC if its earning is on the higher side. The high earnings can be due to the expensive course fees which may be a hindrance for a student availing a MOOC. Again quality maintenance of a course does require money which may lead to a high quality MOOC to be expensive. Thus there is also the need for researching on the cost effectiveness of SWAYAM in comparison with other MOOCS. Computational Intelligence based system software system needs to be developed to check the viability of integration of MOOCs with AI.

9.13 Conclusion With the advent and prosperity of digitization, when the world is becoming virtually smaller every second and quality resources are increasingly becoming freely accessible, it is necessary to have MOOCs. The importance of SWAYAM in this

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scenario is huge. However since SWAYAM is relatively a new player the sustainability of it is also a matter of concern. It is worthy to note that India has a population of 315 million students [the highest in the world]. Out of this population only 0.4 million have enrolled for SWAYAM and more importantly only 8% of the enrolled students have completed a course in SWAYAM. This data vividly proclaims the non popularity of SWAYAM. But that in no way can reduce the importance of free online courses in India. India being the sixth largest economy in the world but with a striking figure of 23% of the world’s poverty dwelling in the country, the need for free MOOCS in India is of utmost significance. SWAYAM is a significant step to such a venture and hence the viability and its further expansion need to be done urgently not only to increase the quantity of education sphere but also develop the quality of the same. The sphere of SWAYAM must be able to touch the remotest of the population to encourage education. The findings of our study proclaim that a huge layback of SWAYAM is the expanse of its horizon. India has got the least no. of users when compared to major MOOCS. The Indian economy at present is marked by the highest demographic dividend in the world. Such work force can easily be appointed after proper training for developing the course content of SWAYAM and increase its horizon to the deserted zone. This requires career counseling as well as placement assistance both of which are not present in SWAYAM. The main objective of MOOC; to foster learning at any time and at any place can only take place when the students are well aware what value addition to their life may happen if they avail a MOOC. They must have a clear picture of their prospective life once they have completed a MOOC. However such crystal clear vision can never be possible without awareness or placement assistance. This is an area where SWAYAM seriously needs to work. It must also be noted that it is the duty of all the citizens of a country to not only avail a bona fide initiative but also share such benefits with the fellow citizens through awareness, experience sharing and pointing out the flaws of the initiative. It is the users of SWAYAM on which rests the major duty of popularizing the open online education system. This endeavor will not only make a knowledge base for the country but will also add to the impetus of sustainable development.

References 1. MHRD, Government of India.: About Swayam (n.d.). Retrieved 10 July 2019, from www.swa yam.gov.in: https://www.swayam.gov.in/about 2. Sen, A.: Amartya Sen: The importance of basic education, 28 Oct 2003. Retrieved July 23, 2019, from www.theguardian.com: https://www.theguardian.com/education/2003/oct/28/sch ools.uk4 3. Jaganatthan, G.S., Sugundan, N.: MOOCs : a comparative analysis between indian scenario and global scenario. Int. J. Eng. Technol (2019) 4. MOOC LIST: The world bank MOOCs and free online courses (n.d.). Retrieved 10 July 2019. from www.mooc-list.com: https://www.mooc-list.com/university-entity/world-bank 5. Nayek, D.: A survey report on awarness among LIS professionals/students about SWAYAM. Researchgate (2018)

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6. Samanta, A.: Analytical Study of Swayam, July–September 2018. Retrieved 10 Aug 2019, from ijrar.com: https://ijrar.com/upload_issue/ijrar_issue_1471.pdf 7. Yingnan, S., Xinghao, L., Armin, H., John, C.: Knowledge pricing structures on MOOC platform—a use case analysis on edX. In: Twenty Second Asia Pacific Conference on Information Systems, pp. 1–13. Japan (2018) 8. Srivastava, P., Yadav, B.: SWAYAM the way of learning with special reference among the Users of Central University of Punjab 447–452, Aug 2018 9. EADTU: The 2018 OpenupEd trend report on MOOCs. European Association of Distance Teaching Universities, The Netherlands (2018) 10. Chauhan, J. (2018). Overview of MOOC in India. Int. J. Comput. Trends Technol. 49 11. Organisation for Economic Cooperation and Development: Massive open online courses: trends and future perspectives 12. Jane, E.K.: Measuring the success of scaleable open online courses. Perform. Measur. Metrices 15(3), 145–162 (2014) 13. Ministry of Human Resource and Development: Complete list of MOOC courses (2019) 14. University Grants Commission: UGC (Credit Framework for Online Courses through SWAYAM) Regulation, 2016 (First Amendment), 16 Mar 2017. Retrieved August 2, 2019, from www.ugc.ac.in: https://www.ugc.ac.in/pdfnews/6096053_1st-Amendment-UGC(Credit-Framework-for-Online-Learning-Courses-through-SWAYAM)-Regulation,-2016.pdf 15. MHRD, Government of India.: Guidelines for Development and Implementation of MOOCs, Jan 2015. Retrieved July 15, 2019, January, from www.aicte-india.org: https://www.aicte-india. org/downloads/MHRD%20moocs%20guidelines%20updated.pdf 16. Benton, R.G., Tony, B.: Strengths and weaknesses of MOOCs. In: Tony B (ed.) Teaching in a digital age. OpenEd (2015) 17. Kalz, M.: Evaluation approaches for massive open online courses. Retrieved 28 Aug 2019, from competen-sea.eu, November 8, 2017. https://competen-sea.eu/wp-content/uploads/2017/ 11/MOOC-Eval-OUNL-mkalz.compressed.pdf 18. EDUCAUSE: Horizon report. EDUCAUSE (2019) 19. Sudarshan, S.: The role of artificial intelligence in learning, 9 Aug 2018. Retrieved April 10, 2020, from eLearningIndustry.com: https://elearningindustry.com/artificial-intelligence-in-lea rning-role 20. Olaf, Z.R., Victoria, I.M., Melissa, B., Franziska, G.: Systematic review of research on artificial intelligence applications in higher education—where are the educators? Int. J. Educ. Technol. Higher Educ. 16 (2019) 21. Vincent, A., Jonathan, S., Octav, P., Micheal, R., Martin, V.V., Sandra, D. (2016). Embedding intelligent tutoring systems in MOOCs and e-learning platforms. In: Proceedings of 13th International Conference on the Intelligent Tutoring Systems, pp. 409–415. Springer International Publishing 22. Baker, R., Vincent, A., Nathan, B., Sewall, J., Andres, J.M., Wang, Y., Popescu, O.: Integrating MOOCs and intelligent tutoring system: edX, GIFT and CTAT (2017)

Chapter 10

Blending of Traditional System and Digital Pedagogy: An Indian Perspective Ishita De Ghosh and Satrajit Ghosh

Abstract Proper embedding of digitized facilities in traditional teaching–learning system is a salient characteristics of modern pedagogy. State-of-the-art computing is greatly influenced by computational intelligence. The chapter is a study on changing scenario of teaching–learning in India focusing on basic paradigm shifts. It explores blending of traditional system with digital technology in Indian perspective. At first, it gives a brief survey on blended learning. Then, it presents two teaching–learning models incorporating digital technology. Intelligent School Network for Research (ISNR) is a research-based teaching–learning model for school-level learners. It aims to instil inquisitiveness and research orientation in young minds with the objective that a number of socio-environmental problems can be resolved by indigenous ideas of young scientists. Intelligent Feedback System for Classrooms (IFSC) is a feedback model for formative assessment of learners in classrooms. It thrives on intelligent data analysis from CCTV-camera enabled classrooms. Though both models are designed for school education system, they can easily be extended for higher education systems. Keywords Blended learning · Digital pedagogy · Artificial intelligence · Computational intelligence · School network for research · Formative assessment · Intelligent data analysis

10.1 Introduction Due care of nature and her resources is essential for sustainability of human race and its socio-economic activities. This is equally true for one-sixth of the global population living in India, with its spectra of socio-cultural diversity and challenges. Recent I. De Ghosh (B) Barrackpore Rastraguru Surendranath College, Kolkata 700120, India e-mail: [email protected] S. Ghosh Charuchandra College, Kolkata 700029, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 A. Deyasi et al. (eds.), Computational Intelligence in Digital Pedagogy, Intelligent Systems Reference Library 197, https://doi.org/10.1007/978-981-15-8744-3_10

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spread of contamination caused by virus SARS-COV-2 and the related pandemic of COVID-19 have triggered a standstill to the movement of this huge mass, resulting in a deafening silence of on-site industrial production zones and vibrant academic campuses. Digitized mode of communication appears to be the sole saviour of the relentless endeavour of a dedicated teacher or a spirited learner to pursue teaching and learning from the safety of home. The influence and significance of digital pedagogy have never been so overpowering in India. Along with proficiency in new technologies, originality of thinking, experimentation with ideas and ability to optimize the efficacy of a team are essential qualities to survive and succeed in the ever-changing socio-environmental scenario of today’s world. It is a challenge to develop and instil these qualities among the learners. Reports show that 13 lakh government schools face difficulties like inadequate infrastructure, low teacher-student ratio, and poor learning outcome, whereas 3.2 lakh private schools are governed by examination boards that focus on rigid curricula and rote learning capability [1]. But critical thinking, cognition, and active involvement of learners are essential to learn science, mathematics, and any subject in general as an old proverb says ‘tell me – I forget, show me – I remember, involve me – I understand.’ Finally good amalgamation of ideas, experiments and innovations only can take a nation to new horizons [2, 3]. Current chapter aims to explore the changing scenario of teaching–learning in India in the light of digital technology. It focuses on basic paradigm shifts and not on technical details. At first, it presents a brief survey on blended learning emphasizing on developments in Indian context. Then, it presents two models of blended learning: (i) Intelligent School Network for Research (ISNR) and (ii) Intelligent Feedback System for Classrooms (IFSC). The first model ISNR is a research-based model for school students that encourages the learners to identify environmental, social, and economic issues and inspires them to come out with innovative measures to resolve them. The basic idea centres on intelligent networking among students, teachers, school authority and so forth. The second model IFSC is a feedback model for formative evaluation of students. The model thrives on intelligent analysis of data obtained from monitoring system enabled classrooms. It can also be used for estimating emotional intelligence competency of the teacher. Both models makes use of computational intelligence and supplements traditional pedagogy. Though they are related to school education system, they can easily be extended to higher education. The chapter is organized as follows: in Sect. 10.2, we give the basic philosophy of blended learning and a brief account of some significant recent developments with emphasis on Indian scenario; in Sects. 10.3 and 10.4, we present the models ISNR and IFSC, respectively, and finally, in Sect. 10.5, we give the conclusion with some future research directions.

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10.2 Blending of Traditional System and Digital Pedagogy: An Indian Perspective A brief survey on blending of traditional system and digital pedagogy and a brief discussion on the rising era of computational intelligence in India is given now. Digital Pedagogy (DP) is teaching and learning using contemporary digital technologies. Blended learning (BL) is a paradigm in which students learn through digital media as well as direct interaction with teachers. A pioneering scholar argued that mode of deliverance in BL can be classified into four dimensions, namely space (face-to-face or virtual), time (synchronous or asynchronous), sensual richness (high, all senses or low, text only materials) and humanness (high human, no machine or low human, high machine [4]. He also argued that flexibility enjoyed by the learners and, offered by the institution can be classified into the four levels: activity, course, program, and institution. He emphasized that depending upon its purpose, BL can be classified into three types: enabling blends (focuses on access and flexibility); enhancing blends (supplements traditional pedagogy); and transformative blends (aims to transform pedagogy). Recent developments in BL in respect of different countries are available in a number of work [5–7]. Digital Pedagogy initiatives in India is discussed now.

10.2.1 Digital Pedagogy Initiatives in India India’s social, economic, cultural, and environmental diversity often poses complex problems in education and other fields. Teaching–learning-related issues like (i) language barrier, (ii) lack of interest in science, (iii) difficulty in mathematics or physics teaching, (iv) shortage of laboratory equipments are often solved with indigenous ideas and local resources and some of them incorporate BL [8]. The influence of BL within a diverse learning environment was realized in India for the last couple of decades and government organizations have played important role in Information and Communication Technology (ICT)-enabled learning. There are substantive contributions of Ministry of Human Resource Development (MHRD), University Grants Commission (UGC) and its Inter-University Centres (IUCs), Information and Library Network (INFLIBNET), and Consortium for Educational Communication (CEC) which offer several digital platforms for this [9–11]. The e-learning materials available in these platforms are prepared by eminent scholars in relevant fields and are easily accessible to learners, researchers and teachers. The community depends heavily on these e-materials when little access is available otherwise such as the time of nation-wise lockdown period. Some prominent government initiatives are described in brief now. 1.

SWAYAM Online Courses were launched in 2014 to provide quality training courses by AICTE, CEC, IGNOU, IIMB, NCERT, NIOS, NITTTR, NPTEL, and UGC to more than three crores Indian students free of cost.

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2.

SWAYAMPRABHA is a group of 32 DTH channels providing curriculum-based course contents covering diverse disciplines to all learners, teachers, and citizens across the country interested in lifelong learning. These channels are free to air and can also be accessed through cable operator. The telecasted videos/lectures are also as archived videos on the Swayamprabha portal. 3. Massive Open Online Courses (MOOCs) platform hosts learning materials of the SWAYAM under-graduate (UG) and non-technology post-graduate (PG) archived courses. The platform has the potential to deliver quality education on a very large scale. 4. E-Pathshala hosts curriculum-based, interactive e-contents for school-level learners. E-PG Pathshala hosts high quality, e-contents containing 23,000 modules (e-text and video) in 70 PG disciplines of social sciences, arts, fine arts and humanities, natural, and mathematical sciences. 5. E-Content courseware in 87 UG courses with about 24,110 e-content modules is available on the CEC website. 6. CEC-UGC YouTube channel provides access to unlimited educational curriculum based lectures absolutely free. 7. National Digital Library is a digital repository of a vast amount of academic contents in different formats and provides interface support for leading Indian languages for all academic levels including researchers and life-long learners, all disciplines, all popular form of access devices, and differently abled learners. 8. Shodhganga is a digital repository platform of 2,60,000 Indian electronic dissertations for research students to deposit their Ph.D. theses and make it available to the entire scholarly community in open access. 9. E-ShodhSindhu provides current as well as archival access to more than 15,000 core and peer-reviewed journals and a number of bibliographic, citation, and factual databases in different disciplines from a large number of publishers and aggregators to its member institutions including centrally funded technical institutions, universities, and colleges that are covered under 12(B) and 2(f) Sections of the UGC Act. 10. Vidwan is a database of experts which provides information about experts to peers, prospective collaborators, funding agencies, policymakers, and research scholars in the country. Faculty members are requested to register on the Vidwan portal to help expand the database of experts. 11. DIKSHA-App was launched in 2017 to serve to teachers as a national digital infrastructure. It enables teachers to create training content, in-class resources, profiles, assessment aids, and connect with other teachers more seamlessly. It can be used by both public and private institutions as per their own requirements and capabilities. State-of-the-art digital technology is greatly influenced by computational intelligence, a discipline that falls under the purview of artificial intelligence. Its potential applications in teaching–learning are discussed now.

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10.2.2 Computational Intelligence: Applications in Pedagogy Computational intelligence (CI) is the theory, design, application and development of biologically and linguistically motivated computational paradigm [12]. Artificial intelligence (AI) is a more of a general idea used to conceptualize machines which can emulate human intelligence, behaviour, and actions. In this article, both the terms AI and CI are used according to the context. They are used in three major applications in education sector, namely (i) personalized learning, (ii) intelligent tutoring, and (iii) assistance in administrative tasks [13]. In personalized learning, the custom learning profile of every learner is created to identify and assess the affinity, ability, individuality, and deficiency of the learner with the help of AI. Then, this profile is used to customize the need-based learning material in an automated manner. AI is also helpful to design a personal conversational education assistant that can answer the query of the learner, assist him to complete assignments, and reinforce the concepts by providing supplementary materials. It can even be used to design intelligent digital tutors that allows every student to learn at his own pace by using adaptive learning features. AI-incorporated software can assist a teacher to perform tedious administrative tasks like processing papers for student-admission, building or modifying class schedules, grading activities for evaluation and dealing with various logistic-related matters. With its digital and dynamic nature, AI is progressing worldwide at an accelerated pace and a great impact is seen in education sector also. Till now, this sector in India has not been explored much in respect of implementation of latest developments adopted by its global counterparts. But it is predicted that in near future impact of AI will increase greatly in education and other sectors of India [14, 15]. A multilingual, multi-cultural country like India can be benefitted from AI applications in various ways [16, 17]. Some examples are given now. (a) AI can help teachers to create digital contents as per local needs of the students in different parts of the country. (b) Incorporating AI in tutoring apps may replace the dependency of students on their parents and teachers for guidance and tuitions. (c) Admission boards can use AI for automated classification of huge number of students who want to take admission in an institution, thus reducing admission-related burden on teachers. (d) AI-enabled grading systems will save valuable time and effort of the teacher. The systems are being designed to assess objective and subjective-type answers as well. Other administrative cores can be done in similar way. (e) AI-powered learning systems will enable students to access classrooms from anywhere in the world. (f) AI will be learned as a subject to explore its full potential. In fact, it has been introduced as a subject in class IX from the session 2019–20 in the schools affiliated with CBSE [18]. It is expected that AI will spawn new jobs related to logistics, Web and app design, system integration, customer experience, machine learning, big data, and predictive analytics. Now, two models of digital pedagogy incorporating CI are proposed one by one now.

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10.3 Nurturing Innovation Using Digital Technology We all have experienced joy in our childhood by making things (e.g. clay dolls or sand castles) or breaking things (e.g. a toy car, to see what is there inside). As we grow up, we get habituated to the fact that there will not be such joy anymore because a considerable part of our formal education remains examination driven. But such an education system can hardly provide the learner essential courage and confidence to explore and experiment. And for a learner, it is the creative activities that satisfy his over-enthusiasm and give him joy and excitement. Education scenario is changing in India with social and environmental needs. There are government initiatives like Atal Innovation Mission (AIM), Atal Tinkering Labs (ATL), and Rashtriya Avishkar Abhiyan (RAA) [19, 20]. The first two initiatives are for engineering students whereas RAA is a convergent framework for school students that aims at nurturing a spirit of inquiry and creativity. It focuses on connecting school based knowledge to life outside school and emphasizes on innovation. Our first model can be viewed as an extension of this model.

10.3.1 Proposed Model 1: Intelligent School Network for Research (ISNR) Intelligent School Network for Research (ISNR) originates from the idea that young minds can help the country to identify and resolve some baffling socio-environmental problems. Given below are a list such problems: consumption of fossil fuel and non-renewable resources, depletion of ground water level, deposition of bio-excrete, generation of non-biodegradable plastic waste, and execution of essential tasks at hazardous/deplorable condition. Learners are encouraged to come out with prototype solutions like generation of green energy or renewable energy, groundwater recharge, transformation of bio-excrete into bio-fertilizer, construction of durable road with non-biodegradable plastic, generation of biodegradable plastic, use of remote-controlled automated navigation system to perform hazardous tasks and likes. Promotion of organic farming and eradication of chemical fertilizer, cultivation of medicinal plants, design and development of drone for various ethical applications like environment monitoring and emergency supply of medical aids are also of interest. The objective of ISNR is to create a suitable ambiance to impart inquisitiveness and independent thinking among school students and also to nurture originality and innovation among them through school-level research. This requires blending the traditional system with a new pedagogy in such a way that completing specified syllabus in a small span of a trimester or semester does not become a hindrance. This in turn requires a systematic planning and intensified effort. The model suggests a self-contained framework for the same. It also includes a methodology to monitor and assess the progress of the work.

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The basic framework of the model is described now. In the beginning of an academic session, each student is asked to identify two pertinent socio-environmental issues of their surroundings in accordance with (but not limited to the) abovementioned guideline. Once the problems are identified, they are sorted out by the teachers and merging or splitting is done according to their difficulty level. Feasibility study is performed to explore the practical prospect of the project. Students are divided into groups according to their interests, and once the problem-list is finalized, each group is assigned a problem to work on throughout the year. After a problem is allotted to a group, a teacher-mentor is assigned as the supervisor and a group-member is selected as a project-leader. Supervisor divides the work among the group-members. Project-leader coordinates the day-to-day progress of the project and interacts with the supervisor as and when needed. The group can also take help from any other teacher or senior student of the school through the supervisor. The allotted work can be carried out in school or at home as per the convenience. Daily progress is discussed among the group-members by direct interaction in school or by online platform such as a social media group. This platform is also used to report to the supervisor or to exchange ideas with group-members of similar projects via their supervisor. The network of various groups working on similar projects are shown in Fig. 10.1. Here, kth project consists of a group of students designated by S 1 to S 4 . One of them is selected as the group-leader. This project is monitored and supervised the teacher T jk . Teachers of a school have connections among themselves. And teachers of different schools can connect via nodal officers N i . Project-related experiments are designed by the group-members and approved by the school authority. Equipments required for experiments are provided by the school. School may procure them or use them from a tool-bank on rent. A tool-bank ensures supply of essential tools at a small rent. This is conceptualized to ensure reusability and bring down the cost of research. Experiments can be scaled up or down or upgrade to a modified form as suggested by the supervisor. After completion, the project will be assessed by the school authority by a project-report and a presentation or demonstration by the group-members. Best project from each participating school gets a chance to showcase their project by an audio-visual media in a district-level online exhibition. Good performance in these exhibitions enables a group and its mentor to showcase their project in state-level and then in national-level platforms. Best groups at each level can be given scholarships for their sincere effort and hard work. Transmission and refinement of ideas are possible through a hierarchical networking among the learners, their mentors, and the governing authority as shown in Fig. 10.2. The digital framework can be implemented by using a part of budgetary allocation for ‘Innovation, Technology Development and Deployment’ of DST, Government of India [9]. Digital technology and CI and can be used in various steps for implementing the ISNR model. These are stated one by one as following. (i)

Each school provides an online platform for information exchange between group-members, mentors, and school authority. (ii) Scientific experiments can be demonstrated to a large audience by recording and uploading of them in an online platform. Thus, the cost of organizing an

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Fig. 10.1 Project monitoring and coordination in ISNR using mesh structure Country

State

State

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School

District

School

School

District

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School

Fig. 10.2 Hierarchical structure of the ISNR model

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exhibition for evaluation purpose is reduced considerably. Moreover, it gives the opportunity of viewing an experiment more than once. (iii) Huge number of problems submitted by students in the beginning of the academic year may be sorted out by using CI-based technology.

10.3.2 Case Study A case study is described now where the lowest level of the model as shown in Fig. 10.2 has been implemented. It has been implemented for under-graduate (UG) project-work and post-graduate (PG) dissertation work in the computer science department of the higher education institution one of the authors is affiliated to. Students are grouped according to their interest, a supervisor is assigned to each group and a group-leader is selected for each group. The maximum number of students allowed is 2 and 4, respectively, for PG and UG groups. Several groups under one or more supervisors can collaborate, even UG and PG groups may do collaborative work if the supervisors agree. The assigned task is divided among the group-members. Interaction and data sharing among the group-members take place via social media platform facilities like WhatsApp, Skype, and conference call via telephone network. Regular reporting of progress of work to the supervisor is also done via these facilities. This is particularly helpful during the lockdown period when physical movement is restricted. Finally, for interdisciplinary work, groups from more than one department can collaborate. And the collaboration can be extended to other institutions as well.

10.3.3 Benefits of ISNR Model Benefits of ISNR model are manifold. They are stated below. (i)

Learners are benefitted through understanding a subject by active learning, gaining confidence through contributing solutions to problems, and getting early exposure to research and development. (ii) Teachers are benefitted by incorporating innovation in mundane teaching, earning rewards for sincere efforts, and contributing continuous development of the subject taught. (iii) School is benefitted by using existing infrastructure for future extension and getting appreciation for good work. (iv) Nation is benefitted by (a) getting a young pool of future scientists, engineers, and technologists by giving them early exposure to planning, implementation, and presentation of research ideas and (b) obtaining a probable simple and cost-effective solution for some persistent global problems. In next section, we discuss on assessment methods and our model for formative assessment.

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10.4 Computational Intelligence for Formative Assessment In a teaching–learning environment, there are two types of assessment, summative and formative. The first one sums up the achievements of a learner at the end of a period of time, in respect of the learning goals and the relevant national standards. The period of time may vary depending on initiatives of the teacher or the directives of the school authority. There may be an assessment at the end of a topic, in the middle or at the end of a semester, or at the end of an academic session. Summative assessment essentially summarizes attainment at a particular point in time. It is recorded in the form of individual and cohort data useful for tracking progress. And it is also produced as a report to students, parents, school authority, and administrative bodies like school or examination boards. Formative assessment takes place on a day-to-day basis, helping the teacher and the students to assess attainment and progress more frequently [21]. At the start of a unit or a topic in a particular subject, it begins with assessing whether the students possess prerequisite skills or knowledge or there are gaps to be filled in. And it continues till the end of the topic in the form of questions, quizzes, worksheets, computer-based or other tasks, and oral or written tests. These provide direct feedback to the teacher and the students. The feedback is then used to take appropriate corrective measures for students who have not yet mastered the topic. Each of the phases (teaching, assessment, and remediation) are planned, prepared, and managed by the teacher who attempts to ensure that all the students masters the objectives of the unit. Sometimes formative assessment is recorded to be reflected in the endsemester evaluation, or to decide whether the teaching plans need to be amended to reinforce or extend learning. Formative assessment is carried out informally by observing the students in the classroom during teaching, interaction among students, and during the whole-class discussions that allow students to present different ways of understanding a task or of carrying out an activity. An experienced teacher can monitor mental states of his students by observing gaze pattern, facial expressions as well as by their physical activity, or inactivity. Mental abilities like power of observance, perseverance, initialization, inquisitiveness, concentration, and the ability to work in a team/group are important in learning and they help to create an enjoyable learning experience. The student learns while he enjoys and vice versa. And the realization that ‘I can learn’ generates self-confidence and responsibility in the student. This in turn reduces distracting behaviour and improves the relationship between the student and the teacher and also among the students. And all these together create a positive atmosphere in the classroom and make a great impact on progress of the learners. Therefore, in addition to evaluation methods like quizzes, worksheets and so on, monitoring mental abilities and maturity of the learners help in the formative assessment. But huge number of students, insufficient class-time, and the urge to complete the syllabus in a specified time often prevents the teacher to do this effectively. Digital technology can help the teacher in this.

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A model called Intelligent Feedback System for Classrooms (IFSC) using digital technology incorporating CI is proposed now.

10.4.1 Proposed Model 2: Intelligent Feedback System for Classrooms (IFSC) The objective of the model is to assist the teacher in formative assessment of the students. It can also be used for assessing cognitive capabilities and learning affinity of the students. Finally, it can be used for detection of unhealthy behaviour leading to an unwanted situation in a classroom and thus preventing such situation. The model can be of use to evaluate emotional intelligence competency of the teacher which is an important quality for effective class management and proper use of valuable class-time [22]. Affective and social cues in Intelligent Tutoring Systems (ITS) is well explored [23, 24]. Ekman established that, human emotions (or affects) like happiness, sadness, surprise, fear, disgust, anger can be accurately detected across cultures [25]. Frequently used affective states for student monitoring are engaged concentration, frustration, boredom,, and confusion [26, 27]. Affects like confusion may be beneficial for learning, but prolonged confusion may lead to frustration [28]. There are studies which explore whether there are subtle differences in the way affects are facially expressed in different cultures. A study shows the differences in facial expressions of the students from eastern and western cultures in respect to affect confusion in a game based learning environment [29]. Affective and social cues in Human Robot Interaction (HRI) is of interest to the researchers. Increase in pace of learning is possible with the help of ‘engagement cues or interaction events’. Automation of the same is possible ‘supervised classifiers trained with social, physiological, or task-based interaction features’ [23, 30–32]. IFSC is a feedback model which thrives on intelligent data analysis from CCTVcamera-enabled classrooms. At the heart of the model is the assumption that in a teaching–learning environment a learner’s engagement state can be inferred by affective and social cues like gaze patterns, facial expressions, prosody, body pose, proxemics, and physiological information (e.g. skin conductance), as well as taskspecific behaviours. Video-image sequence captured by CCTV-camera installed in the class-room is the input to an intelligent system. The system supplies this data to different sub-systems to perform tasks like face recognition, gaze detection, facial expression detection, activity detection, and proximity detection. Output of these sub-system will be fed to the main system which will use multi-modal fusion to fuse the information and get a concise decision. The number of sub-systems can be adjusted according to the requirement. Figure 10.3 shows the main system at the centre and the sub-systems at the periphery. Each of the nodes use CI-based methods to extract information from video-image sequences captured by the cameras. This model could not be implemented due to infrastructural limitations.

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Fig. 10.3 Main system and the sub-system of IFSC model

10.4.2 Benefits of IFSC Model Benefits of IFSC model are stated below. (i)

Formative assessment of the students is done with the help of automation. Students are benefited because the assessment is not teacher-specific. The teacher is benefitted because this saves valuable time of the teacher. (ii) Identification of unhealthy behaviour for ensuring safety and security during learning-process is done easily. (iii) Emotional intelligence competency of the teacher can be assessed by the authority to enhance progress of learning.

10.5 Conclusion The ability to explore innovative solutions for diverse types of problems is the defining characteristics of the human civilization and its socio-economic activities. And for a child or teenager learner, it is the creative activities that satisfy his overenthusiasm and give him joy and excitement. These qualities can be best used to instil scientific temperament in young minds. Pedagogy is undergoing through a

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paradigm shift in Indian perspective and digital platforms are playing a crucial role in it. There are a number of areas to explore regarding this. The pace of learning gets optimized, when it is learnt with joy, splendour, and excitement. This essentially requires nurturing of over-enthusiasm and innovation of the young learners as well as their mentor to create a favourable ambiance. Cognitive capabilities and learning affinity of the students can be assessed effectively by an intelligent system.

10.5.1 Contribution of the Work Teaching, especially science teaching should ideally be based on ‘do it yourself’ philosophy implemented by hands-on experiments, projects, and micro-teaching. Inadequate laboratory facility, poor infrastructure, and huge number of students pose a challenge to this philosophy. This chapter presents interesting models of pedagogy to implement the philosophy using CI suitable for digital age. It proposes two models ISNR and IFSC. ISNR is research-based teaching learning model that may be helpful to address some socio-environmental problem through involvement of fresh minds. Transmission and refinement of the ideas are possible through a hierarchical research network amongst the learners, their mentors and the governing authority. IFSC is a model to automate formative assessment of the students with the help of CI.

10.5.2 Future Work Digital pedagogy initiatives by private sector organizations will be explored. Feasibility study and cost–benefit analysis for proposed models are under development. Case studies of both the models will be done. Possibility of ISNR model in an automated learning network to reach the learners in remote areas of the country will be explored. Possibility of IFSC model to assess the attention level of the learners learning through a digital platform from home will be explored. It can also be modified as a surveillance system at home to ensure security of learners.

References 1. Nath, A., Yasmeen, S.: India’s nascent homeschooling revolution. In: ParentsWorld, March. Available at: www.educationworld.in/indias-nascent-homeschooling-revolution (2019). Accessed 22 Apr 2020 2. Rothstein, D., Santana, L.: Make Just One Change: Teach Students to Ask Their Own Questions. Harvard Education Press, Cambridge (2011) 3. Wang, Y., Li, B., Xie, B.: Constructing of research-oriented learning mode based on network environment. US-China Educ. Rev. 4(9) (2007)

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4. Graham, C.R.: Blended Learning Systems: Definition, Current Trends, and Future Directions. The Handbook of Blended Learning: Global Perspectives, Local Designs, pp. 3–21. Pfeiffer Publications, San Francisco (2006) 5. Barbour, M., Brown, R., Waters, L.H., Hoey, R., Hunt, L.H., Kennedy, K., Ounsworth, C., Powell, A., Trimm, T.: Online and blended learnings: a survey of policy and practice of K-12 schools around the world. International Association for K-12 Online Learning, Vienna (2020). Available at: www.inacol.org.iNACOL_a-surveyof-policy-and-practice.pdf. Accessed 22 Apr 2020 6. Bowyer, J., Chambers, L.: Evaluating blended learning: bringing the elements together. Research Matters: A Cambridge Assessment Publication (2017). Available at: https://www. cambridgeassessment.org.uk/research-matters. Accessed 22 Apr 2020 7. Bryan, A. & Volchenkova, K.N.: Blended learning: definition, models, implications for higher education. Bull. South Ural State Univ. 8(2), 24–30 (2016). 10.14529/ped160204. Accessed 22 Apr 2020 8. NTSC: Abstracts of selected papers. National Teachers Science Congress, Vikram A Sarabhai Community Science Centre, Ahmedabad, India, Dec 2018. Available at: https://www.ntscin dia.in/Downloads/Abstracts%20of%20selected%20papers%209%20NTSC.pdf. Accessed 22 Apr 2020 9. DST: Annual Report 2018–19. Website: dst.gov.in/about-us/annual-reports (2019). Accessed 22 Apr 2020 10. MHRD (2015). All India survey on higher education report. Available at https://mhrd.gov.in/ statist. Accessed 22 Apr 2020 11. Murthy, S., Iyer, S., Warriem, J.: ET4ET: a large-scale faculty professional development program on effective integration of educational technology. J. Educ. Technol. Soc. 18(3), 16–28 (2015) 12. IEEE: https://cis.ieee.org/about/what-is-ci (2020). Accessed 22 Apr 2020 13. Schmelzer, R.: AI applications in education. Forbes. Available at: https://www.forbes. com/sites/cognitiveworld/2019/07/12/ai-applications-in-education/#1819f22a62a3 (2019). Accessed 22 Apr 2020 14. Arora, M.: Artificial intelligence is changing the teaching-learning process in education. Web article, India Today Web Desk, Aug 2019. Available at: https://www.indiatoday.in/educationtoday. Accessed 22 Apr 2020 15. Dwivedi, Y.K., Misra, S.K., Hughes, L.: Artificial intelligence: challenges and opportunities for India. Yojana 64(2), 16–20 (2020) 16. Chanda, A.: Artificial intelligence, machine learning spawn new jobs in eCommerce. News article, ET Contributors. Oct, 2019. Available at: https://economictimes.indiatimes.com/jobs. Accessed 22 Apr 2020 17. Roy, D.: Artificial intelligence can empower our education system: here’s how. In: India Today Web Desk, July 2018. Available at: https://www.indiatoday.in/education. Accessed 22 Apr 2020 18. CBSE: Available at: https://cbseacademic.nic.in/ai.html (2019). Accessed 22 Apr 2020 19. MHRD: Website: Rashtriya Avishkar Abhiyan (2020). Available at https://mhrd.gov.in/rashtr iya-avishkar-abhiyan. Accessed 22 Apr 2020 20. Seshadri, S.: Innovation in higher educational institutions. Yojana 64(2), 23–26 (2020) 21. Voinea, L.: Formative assessment as assessment for learning development. J. Pedagogy 1, 7–23 (2018). Available at: https://doi.org/10.26755/RevPed/2018.1/7. Accessed 22 Apr 2020 22. Kaur, I., Shri, C., Mital, K.M.: The role of emotional intelligence competencies in effective teaching and teacher’s performance in higher education. Higher Educ. Fut. 6(2), 188–206 (2019) 23. Ali, S., Shah, M.: Human action recognition in videos using Kinematic features and multiple instance learning. TPAMI 32(2), 288–303 (2010) 24. Bilen, H., Fernando, B., Gavves, F., Vedaldi, A.: Action recognition with dynamic image networks. TPAMI 40(12), 2799–2813 (2018)

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25. Ekman, P.: Facial expressions of emotion: new findings, new questions. Psychol. Sci. 3(1), 34–38 (1992) 26. Calvo, R.A., D’Mello, S.: Affect detection: an interdisciplinary review of models, methods, and their applications. IEEE Trans. Affect. Comput. 1(1), 18–37 (2010) 27. D’Mello, S.: A selective meta-analysis on the relative incidence of discrete affective states during learning with technology. J. Educ. Psychol. 105(4), 1082–1099 (2013) 28. Bosch, N., Chen, Y., D’Mello, S.: It’s written on your face: detecting affective states from facial expressions while learning computer programming. In: Intelligent Tutoring Systems. Springer International Publishing, pp. 39-44 (2014) 29. Tabanao, E., Rodrigo, M.M.: A comparison of the experience of confusion among Filipino and American learners while using an educational game for physics. In: Proceedings of ICCE 2016 Doctoral Student Consortium, pp. 17–20. Asia-Pacific Society for Computers in Education, TX (2016) 30. Gordon, G., Spaulding, S., Westlund, J. K., Lee, J.J., Plummer, L., Martinez, M., Das, M., Breazeal, C. (2016). Affective personalization of a social robot tutor for children’s second language skills. In: 30th AAAI Conference on Artificial Intelligence 31. Park, H.W., Grover, I., Spaulding, S., Gomez, L., Breazeal, C.: A model-free affective reinforcement learning approach to personalization of an autonomous social robot companion for early literacy education. In: Proceeding of 33rd AAAI Conference on Artificial Intelligence (2019) 32. Rudovic, O., Park, H.W., Busche, J., Schuller, B., Breazeal, C., Pichard, R.W.: Personalized estimation of engagement from videos using active learning with deep reinforcement learning. In: IEEE CVPR-AMFG W, June 2019

Chapter 11

Application of Internet of Things in Digital Pedagogy Monu Bhagat, Dilip Kumar, and Sushma M. Balgi

Abstract The Internet of Things (IoT) is a novel idea in computing. The purpose of IoT is to connect the objects that surround us through network. The main aim of IoT is to access information from anywhere and at anytime. The development of IoT in the pedagogy helps users to learn effectively, efficiently and flexibly. Smart education depicts learning in the digital era. Learners can access resources through wireless network using application in their mobiles. These days a huge number of electronic devices are associated with the web. In this paper, discussion is made to significantly improve education field by using IoT. A system is proposed that provides students to interact with the neighboring objects. An experiment is conducted with the aim to illustrate the use of IoT in education which improves the learning outcome. Keywords IoT · Smart education · Digital pedagogy · Health care · IoNT

11.1 Introduction The emergence of the World Wide Web (WWW) has impacted many fields and caused many disciplines to revise their vision. The Internet has impacted our lives by connecting the people and objects to build smart environment. The thought of Internet is rationalized to the thought of Internet of Things. The idea of connecting devices occurred since 1970s. At that time, the idea was called “embedded Internet” and “pervasive computing”. Kevin Ashton used the term Internet of Things (IoT) in 1999. Kevin Ashton said “We need to empower the M. Bhagat (B) · D. Kumar · S. M. Balgi Computer Science and Engineering, National Institute of Technology Jamshedpur, Jharkhand, Jamshedpur, Jharkhand 831014, India e-mail: [email protected] D. Kumar e-mail: [email protected] S. M. Balgi e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 A. Deyasi et al. (eds.), Computational Intelligence in Digital Pedagogy, Intelligent Systems Reference Library 197, https://doi.org/10.1007/978-981-15-8744-3_11

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computer with data, so that it can observe and listen the world itself. RFID and sensor device technology allow computers to monitor, recognize and be aware of the world, without the limitations of human entered data” [1]. Internet of Things is defined as a system of interconnection of computing devices, mechanical machines, digital machines and entities which has unique identifiers (UIDs) and is able to transfer data over the network without the intervention of humans [2]. IoT devices are linked to the Internet with the purpose of controlling them from anywhere. The device collects information from its surrounding and sends it the base station [3]. The connectivity helps to capture more data from many places which ensures increase in efficiency and improvement in safety and security. It helps to improve performance through IoT analytics and security [4]. IoT finds its applications in every field such as agriculture, health care, education, business management and transportation [5] (Fig. 11.1). Cisco has taken the Internet of Things concept to the next level called Internet of Everything (IoE). According to Cisco, organizations have implemented networked connections of things, i.e., Internet of Things (IoT) from many years, so some additional capabilities such as context awareness, energy independence and increased processing power should be added to make Internet of Things as Internet of Everything. IoT is changing the world. It has the ability to perform device-to-device communication. Cities can be transformed into smart cities by the use of IoT. Smart traffic lights, smart surveillance and so on help to improve the city. More the data collected, smarter the devices. Using IoT, car owners can operate their cars such as preheating the car before the driver arrives or remotely summoning a car by phone [5]. People are adopting smart devices in their day-to-day life, and IoT connected devices are becoming an element of the electronics culture. The rate of use of connected device

Fig. 11.1 Internet of Things [6]

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has increased from 5 million to billion in just one year. The devices are becoming smarter due to integration of machine learning and artificial intelligence [7]. In current situation, more than 12 billion devices are connected to the Internet, and researchers at IDC approximated that there will be 26 times more connected devices than the number of people by 2020 and they have estimated that there will 21 billion connected devices in 2020 [8]. According to Gartner, consumer applications will drive the number of connected things, while enterprise will account for most of the revenue. IoT adoption is growing, with manufacturing and utilities approximated to have the largest installed base of Things by 2020.

11.2 Motivation and Contribution Internet of Things is gaining rapid advancement in every field. We can use IoT to implement smart learning environment that enhances the learning. In near future, the Internet of Things will be further incorporated into the education system. Schools in-corporate IoT in education system to store huge amount of data, save money and prepare students to become tech literate. Our perceptive of education must change if we want to assimilate IoT into the education. The intention of smart education is to ameliorate student’s quality of learning. The encroachment of IoT in education helps to develop individual in terms of skill, knowledge and experience. Also students learning exposure could be intensified and extended. Thus, it helps individual’s development in an all-round way in terms of affection, intellectual and physical. Students can learn flexibly during their free time and working collaboratively in smart learning environments, and as a result, they could advance the progress of personal and collective intelligence of learners. The main aim of IoT is to access information from anywhere and at anytime. The development of IoT in the pedagogy helps users to learn effectively, efficiently and flexibly. Smart education depicts learning in the digital era. Learners can access resources through wireless network using application in their mobiles. In this paper, discussion is made to significantly improve education field by using IoT. A system is proposed that provides students to interact with the neighboring objects. A case study is done to depict the usage of IoT in education, which has good improvement in terms of learning, skill and knowledge.

11.3 Literature Survey The study of the present smart systems, technology and their applications in education proves that application of smart learning and Internet of Things in education is dominant in 5–15 years. On the basis of research, we expect that IoT will actively

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improve the various types of innovative pedagogy like teaching based on smart technology, learning and pedagogy, combined learning, learning by performing, learning based on games, flipped classroom learning, pedagogy and so on [9]. Experiment is conducted with the aim to describe the usage of IOT in education which improves the learning outcome. This experiment is conducted on 50 students, among which 25 students are assigned to control group and remaining 25 students to experimental group. Control group received conventional lectures and experimental group worked in tools created using IoT. Based on various tests and analysis, it is concluded that Internet of Things improves teaching process and learning outcome [10]. The Internet of Things has increased the opportunity, usage of Internet, applications to expand technology and became Internet of Everything. The Internet of Things has used various devices and applications to create a platform for sharing knowledge and resources to students present in various parts of the world. Some universities are providing distance learning environment. The Internet of Everything is gaining importance, and it is carried out by the combined effort of government and non-governmental organization [11]. The fast improvement in information technology paves a way to the introduction of ubiquitous learning and ubiquitous age. For studying various technologies, learners need the ubiquitous learning environment to get the needed learning contents at the right place and at the right time so that they can achieve self-learning. Because of IoT tools, knowledge can be gained from any place and at anytime. IoT plays an important role in ubiquitous learning. This learning environment enables seamless learning from anywhere and at anytime [12]. IoT is advancing very rapidly, and it has huge impact on every field. In the education flied, usage of IOT has taken e-learning to the next level. The improvement in smart learning helps to inculcate good skills and knowledge in the individual and hence to create a better person. RFID chips are used to track objects, gather information about them and store it in cloud. This is helpful to get conclusion of data. This is mostly used by researchers. IoT devices are helpful for interacting and controlling the system [13]. Lamri et al. authors have given the design of the BBA ELab platform. It provides students to pursue their courses online and to carry out their experimental works through IoT devices. This development in technology will add a new dimension of teaching collaboration and convenience to more pedagogical tools especially those associated with the use of laboratory resources. Administrating and operating of ELabs in real time need more access to IoT [14]. The prototypes developed in context of MOSAIC project are touching campus, touching cabinet, touching note and NFC interactive panel. Touching note uses near field communication tag. This is located on the door of instructor’s office to give the message whether the instructor is present or not. A place or entity in a space can be tagged to give some relevant information to the learner by touching in touching cabinet. For touching campus, the application presented is campus recommender. This application gives suggestions to new people in the campus [15].

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Maintaining machinery is one of the important tasks in manufacturing. Detecting faults and predicting of equipment failure can be done using the concepts of IoT and data science. For this task, sensors, communication networks and a small-scale IoT test bed called IoT Platform for Engineering Education and Research (IoT PEER) are required. The experiment is intended to gather sensor data from machines wirelessly and store and analyze the data at Microsoft Azure cloud. Artificial intelligence (AI) is applied on these data, and analysis is done to identify the failures. This technology is beneficial to maintain the machines and recover from the failure when fault is detected [16]. An experiment was done on students of humanities course. Students were given training regarding the construction of IoT. Through step-wise construction, students were able to think ideas for applications pertaining to their field. The result obtained from this experiment is students who do not have science background can be trained to the level at which they are able to create applications and can find solution to the problems. These results were not possible in conventional way of teaching using books. This method is predicted to expand application field in each area of expertise involving people remotely related to the ICT field [17]. The authors presented a learning framework, which combines Internet of Things and hardware and software technologies to build a new paradigm of learning. Internet of Things (IoT) is growing rapidly. It is rising as the next generation of communication infrastructure where sensors and various types of devices are connected to provide computing and communication. Because of the fast growth in IoT, the demand for experienced professionals in IoT field has increased. IoT course is offered in few fields such as science, technology, engineering and mathematics (STEM). So students have very less exposure to IoT field until graduation. There is a less hope for adding additional courses into existing STEM curriculum. So authors have presented a way to transform STEM core courses by integrating IoT-based learning framework into their corresponding laboratory projects [18]. Development in technology demands changes in education which requires new methods to computer science education. Adding IoT as the core course of the first year computing curriculum and opening it up for combined experimentation is a vast improvement in conventional computer science curriculum. Educational initiatives focus on graduate education. The conventional undergraduate education in computer science begins with basic ideology, and it gradually levels up for experimentation. Current online educational courses focus on topics that can be easily self-studied in leisure time. By eliminating constraints, My Digital Life course provides a new way for undergraduate computer science education [19]. Enhancement in Internet connection speed, development of technology, reduction of equipment and software cost made learning resources available to everyone and at anytime. These improvements have helped to make learning interactive and gain experience compared to the existing one. Ubiquitous computing has impacted on everyone. Anyone can store a vast amount of data, access data from anywhere and can share data with anyone from anywhere even during traveling [20]. Study was conducted on 17 students. The aim was to familiarize students with the basic Internet of Things concept and help them to solve problems. Training was given

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to the students in three phases: introduction, details regarding tiles inventor toolkit and the student review. The students were assessed based on the analysis, classroom observation, test and review. The result of this study is students gained concrete experience, reflective observation, active experimentation and abstract conceptualization [21]. Developing countries are concentrating more on smart cities. This causes requirement to educate engineers regarding Internet of Things. To solve this problem, IoT is introduced as open elective course for eighth semester computer science undergraduate course. The outcome of this course is the students are able to build smart waste management, smart irrigation, smart lightning and so on. These pedagogy techniques enhance the designing skill, implementation methodology, domain knowledge and communication skills of the learners [22]. Flipped classroom method is used in some universities. In flipped classroom method, teacher shares the video regarding the course to the students beforehand. Students have to learn from the videos before attending the class. In class, teacher discusses or gives test regarding the subject. This method is helpful because students can watch video at his convenient time and space. Student can replay video any number of times. Flipped classroom approach gave better results than the traditional method [23]. Devices such as sensors, actuators and transponders are fixed to the entities such as libraries, in and out gate, laboratories and classroom. These devices collect information about the surroundings and send them to the database for storing. The information is sent in wireless medium. To protect data from intruders, encryption is done. Detailed analysis is done on these data. System controller which is in charge of data analytic unit processes the user request and gives command to the devices in an intelligent way [24]. To improve learner’s ability for gaining knowledge. (a) Classroom-based differentiated instruction, in this approach students with different abilities are taught together; (b) in group-based collaborative learning, students are grouped with each group containing three to four students, and students learn by discussing and experimenting within the group; (c) in individual-based personalized learning, learning is done based on learner’s interest, experience and learning ability; and (d) in mass-based generative learning, students are able to link new information with the old and can build their knowledge [25]. The authors have introduced a new approach for educating students in computer science. It is based on collaborating the concepts of Pervasive interactive Programming, the Internet of Things, iCampus, Living Labs and a ‘Smart-Box’ model. IoT is distributed system, so programming in IoT is a difficult task. To overcome this, the authors have proposed a new framework to teach programming to the novice persons. The framework is Pervasive interactive Programming (PiP). It has been evaluated on 18 students, and the result of evaluation in individuals can master the skill and understand the concepts quickly even if they have diverse background [26]. To overcome the problems of short duration of course, the authors have proposed a study plan that combines IoT and technique of learning analytics to establish an Environment for Lifelong Learning using Learning Analytics (ELLLA). The characteristics of ELLLA are as follow students can access physical resources using

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student ID, student’s records are stored and analyzed, teachers can obtain student’s real-time activity and performance, discussions can be made online, information can be shared and so on [27]. English pronunciation can be learnt using IoT without the need of instructor. The image sensor takes pictures of learner’s mouth’s shape, and then the software compares this image with the standard image which is stored in the database. Voice sensors are used to analyze tone, frequency and voice when the person pronounce. If the learner pronounces wrongly, then software corrects the learner’s pronunciation [28]. Intelligence of Learning Things (IoLT) is learning platform. It is based on Internet of Things (IoT). IoLT has increased the higher education quality with its creative learning method and technologies in developing countries. IoLT gathers information from contributor’s (such as teachers and students) smartphone, laptop, wearable electronic gadgets to generate instructions that combines and share their ideas and data using IoT. Hardware and software modules like web, mobile and embedded system are used to improve quality of education [29]. The characteristics of mobile learning are authenticity, collaboration and personalization. The authenticity attribute highlights opportunities for contextualized, participatory, situated learning. Collaboration attribute captures the often-reported conversational, connected aspects of mobile learning. Personalization attribute has strong implications for possession, organization and self-learning. The authors have contributed a framework that highlights distinctive, current sociocultural qualities of mobile pedagogy [30]. Ubiquitous learning is reinforced by ubiquitous computing. The goal of ubiquitous learning is to make learning environment available in various circumstances. To achieve this goal, an integrated approach of web-based education is needed; in this way, a digital learning space can be utilized in different circumstances. Plasticity is defined as the ability of digital learning space to be available for learning in varying situations. Plasticity includes various aspects of context and adaptation [31]. The Internet of Everything concept can be extended to Internet of Nano-Things (IoNT). This concept can be realized by integrating nano-sensors in different objects and by using nano-networks. One of the applications of IoNT is in the field of medical science. IoNT is able to access data from the areas where it is impossible to sense it by means of instrument due to its size. This data collected improves the medical field significantly by refining the existing information, new innovation and better medical diagnosis [32].

11.4 Advantages of IoT in Education [6, 7] 11.4.1 Data Collection IoT provides enormous data of the students to the teachers and the management at the fingertips. IoT stores students’ performance in its database by which instructors

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Fig. 11.2 Toward smart IoT-based classroom at Bradley University [9]

can track the progress of the student. Wearable RFID device records the student’s attendance automatically, and it sends the notice to the parents if the student has less attendance.

11.4.2 Personalized Learning One of the problems in conventional education system is the lack of flexibility in the course work. Every student is bound to take the same course even if the student is not interested. In the classroom, the interaction is collective and does not consider the needs of the individual. Using Big Data with IoT technology, each student can be assessed on an individual basis. Weaker students are permitted to change the course work which helps them to learn based on their interest and abilities (Fig. 11.2).

11.4.3 Security Sometimes schools are the targets for attackers. So to avoid this, robust security system is required. IoT can be used to provide security. The application of facial recognition, GPS tracking devices, remote RFID checks and biometric can detect and deny the trespassers. Using artificial intelligence (AI) in IoT, the devices can even identify student’s or staff’s suspicious intents. It disallows them before any potential damage is done. Students can be supervised using various technologies such as 3D positioning. Their presence can be reported at anytime. IoT contains button which alarms and

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informs to the concerned persons if anything unusual happens. Smart cameras can be used to monitor students.

11.4.4 Interactive Learning In conventional education system, the learning is limited to textbooks and notebooks, but nowadays, many textbooks are coupled up with web-based sites that provide additional images, videos, PDF materials, animations, etc. to aid the learning process. This helps to gain a broader vision on new things with a better perceptive and interaction with their friends and teachers. Using these resources, students can learn in their flexible timing at their own place. Distribution of learning material from teachers to students can be done using interconnected devices like smart pens, tablets and smart boards.

11.4.5 Increasing Efficiency In schools and colleges, instructors and clerks spend a lot of time on extra activities which is not related to learning process such as marking attendance. Attendance of students is taken for every subject, and attendance register is to be submitted to the main office for various reasons. IoT can be used for this purpose in an efficient way. Intelligent camera captures the presence of the student automatically based on the students seating arrangement. This data is sent to the central office server, and message will be sent to the parents if the student is absent. Due to IoT, the work of instructors and clerks is reduced. This allows them to pay more attention toward teaching and learning which is the core function of schools and colleges.

11.5 Implementation The objective of suggested system is to enhance the result of the students learning by considering their interactivity with resources that encloses them in their learning space and their interactivity with those executed programs. Here, the resources are complemented using the Internet of Objects idea. Objects are embedded with QRCODE and NFC with unique tag. Each tag has exclusive information that identifies the physical object uniquely, and it connects to the virtual device. The physical objects can be accessed through mobile which has the interface that contains QRCODE and NFC technology. The system supports video, text, speech, audio, GIF, animations, image and so on. The server is accessed via web socket with the purpose of minimizing the latency problems between the HTTP, server and client.

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Fig. 11.3 System architecture of IoT [33]

The database contains learning materials which are maintained by teacher via Internet. The students access server through application which is installed in the mobile (Fig. 11.3). According to the instructor’s settings, the resource management interface provides information regarding the augmented objects which is concerned to the learning activity. To provide the data to the students, two databases are required. First database will contain log in details, profile of the student and other related student information. Second database contains data about augmented learning objects. Physical objects contain interface based on QRCODE and NFC. It permits the interactivity with physical object. When mobiles are near the tagged resources, the students can log in to database through NFC or QRCODE. If NFC is chosen, then the device collects the data present in the resource in NDEF format. An algorithm decodes the NDEF and directs it to the server. As a result, the virtual object will be displayed in the mobile using graphical user interface (GUI). QRCODE reading is similar to NFC in terms of performance. The reader translates the tag data, and then the user interface displays the learning material. The students know their performance which will be stored in the database (Fig. 11.4). The case study was done on the students who had taken the course Introduction to Systems Engineering, provided by the University of Cordoba, Colombia. The duration of this course was one term. The study was done on an activity called identifying computer operations and hardware. The task was designed with the aim to learn about the basic operation and to know about the hardware related to the computer. The laboratory is embedded with the tag containing NFC, which transmits the data to the mobile. The system will get to know that the student is already in the location. So the student can obtain information about the physical objects in the laboratory.

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Fig. 11.4 Structure of IoT in education sector

Students formulate a set of readings of QRCODE or NFC tags attached to the computer. The system and the learner interact with the different objects and send it to the mobile devices which is associated with the augmented objects. It might use video or animations engineered to illustrate the device at work. Thus, each augmented object will explain how each component of the hardware operates, how it should be installed for normal operation, etc.

11.6 Results The experiment is done on 50 students who were enrolled in Introduction to Systems Engineering course. Experiment is conducted with the aim to illustrate the use of IoT in education which improves the learning outcome. This experiment is conducted on 50 students, among which 25 students are assigned to control group and remaining 25 students to the experimental group. Control group received conventional lectures and experimental group worked in tools created using IoT. Various types of tests were given to both the groups by the teacher. The scores indicate the knowledge gained by the student. To evaluate the knowledge of the student before the experiment, a pre-test was taken. To know the knowledge gained by the student after the experiment, post-test was taken. The below figures show scores obtained by the students in the test. Graph is drawn based on the test score. From the graph below, we can conclude that the usage of Internet of Things in learning has increased the knowledge gain. From the data analyzed, the average progress in learning for the control group is 1.1276 and the average learning progress for the experimental group is 2.0716. In terms of score, good improvement is seen in students who secured terrible results in the pre-test (Fig. 11.5).

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Fig. 11.5 Graph based on pre-test and post-test scores [10]

A parametric experiment hypothesis was applied because there is proof of normality of the data in the pre-test for the two groups. Table 11.1 provides the statistical summary of the data. To get the mean scores of two groups, T-tests were done. In this experiment, null hypothesis indicates that there is no difference between the scores of the control and experimental group. The student’s t-test will indicate the data is deviating or undeviating from this expectation. Presuming equal variances, we get t = −0.281507 for p-value 0.77953. P-value is not less than 0.05, so we cannot discard the null hypothesis. Post-test evaluation scores are given in Table 11.2. Here, a non-parametric hypothesis test was used for evaluation in terms of the academic Table 11.1 Pre-test score obtained by the control and experimental group [10]

Control group

Experimental group

Recount

25

25

Average

20,476

21,028

Confidence intervals (95.0%)

17,902; 23,050

179,050; 24,151

Standard deviation

06,235

0,756,607

Coefficient of variation

304,522%

359,809%

Minimum

103

103

Maximum

34

35

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11 Application of Internet of Things in Digital Pedagogy Table 11.2 Post-test score obtained by the control and experimental group [10]

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32,752

41,744

Standard deviation

0,834,387

0,527,479

Medians

35

439

Coefficient of variation

254,759%

12,636%

Minimum

153

296

Maximum

459

49

Range

306

194

performance. We use W-Test Mann–Whitney (Wilcoxon) to compare medians. This test is conducted by merging both the samples. Sorting is done in increasing order on the merged data, and then the average of the two samples is compared. By evaluating the values, we get W = 517.5 and p-value = 0.000071; therefore, the variation between experimental and control groups is considerably static using W-test with a confidence level of 95.0.

11.7 Conclusion Internet of Things (IoT) is gaining rapid advancement in every field. We can use IoT to implement smart learning environment that enhances the learning. In near future, the Internet of Things will be further incorporated into the education system. Schools incorporate IoT in education system to store huge amount of data, save money and prepare students to become tech literate. Our perceptive of education must change if we want to assimilate IoT into the education. The intention of smart education is to ameliorate student’s quality of learning. The encroachment of IoT in education helps to develop individual in terms of skill, knowledge and experience. Also students learning exposure could be intensified and extended. Thus, it helps individual’s development in an all-round way in terms of a section, intellectual and physical. Students can learn flexibly during their free time and working collaboratively in smart learning environments, and as a result, they could advance the progress of personal and collective intelligence of learners.

References 1. https://internetofthingsagenda.techtarget.com/definition/Internet-ofThings-IoT. 2. https://en.wikipedia.org/wiki/Internet_of_things. 3. https://www.happiestminds.com/Insights/internet-of-things.

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4. Hassija, V., Chamola, V., Saxena, V., Jain, D., Goyal, P., Sikdar, B.: A survey on iot security: application areas, security threats, and solution architectures. IEEE Access 7, 82721–82743 (2019) 5. https://www.edureka.co/blog/iot-applications. 6. https://images.app.goo.gl/F8uonLUDD9XWG8Tk6. 7. Chen, S., Xu, H., Liu, D., Hu, B., Wang, H.: A vision of iot: Applications, challenges, and opportunities with china perspective. IEEE Internet Things J 1(4), 349–359 (2014) 8. https://www.webopedia.com/TERM/I/internet_of_things.html. 9. Uskov, V., Pandey, A., Bakken, J.P., Margapuri, V.S.: Smart engineering education: the ontology of internet-of-things applications. In: 2016 IEEE Global Engineering Education Conference (EDUCON), IEEE, pp. 476–481 (2016) 10. Gómez, J., Huete, J.F., Hoyos, O., Perez, L., Grigori, D.: Interaction system based on internet of things as support for education. Procedia Comput. Sci. 21, 132–139 (2013) 11. Barakat, S.: Education and the internet of everything. Int. Bus. Manage. 10(18), 4301–4303 (2016) 12. Xue, R., Wang, L., Chen, J.: Using the iot to construct ubiquitous learning environment. In: 2011 Second International Conference on Mechanic Automation and Control Engineering, IEEE, pp. 7878–7880 (2011) 13. AjazMoharkan, Z., Choudhury, T., Gupta, S.C., Raj, G.: Internet of things and its applications in e-learning. In: 2017 3rd International Conference on Computational Intelligence and Communication Technology (CICT), IEEE, pp. 1–5 (2017) 14. Lamri, M., Akrouf, S., Boubetra, A., Merabet, A., Selmani, L., Boubetra, D.: From local teaching to distant teaching through iot interoperability. In: 2014 International Conference on Interactive Mobile Communication Technologies and Learning (IMCL 2014), IEEE, pp. 107– 110 (2014) 15. Gonzalez, G.R., Organero, M.M., Kloos, C.D.: Early infrastructure of an internet of things in spaces for learning. In: 2008 Eighth IEEE International Conference on Advanced Learning Technologies, IEEE, pp. 381–383 (2008) 16. Guo, T., Khoo, D., Coultis, M., Pazos-Revilla, M., Siraj, A.: IoT platform for engineering education and research (IoT peer) applications in secure and smart manufacturing. In: 2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI), IEEE, pp. 277–278 (2018) 17. Akiyama, K., Ishihara, M., Ohe, N., Inoue, M.: An education curriculum of iot prototype construction system. In: 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE), IEEE, pp. 1–5 (2017) 18. He, J., Lo, D. C.-T., Xie, Y., Lartigue, J.: Integrating internet of things (iot) into stem undergraduate education: case study of a modern technology infused courseware for embedded system course. In: 2016 IEEE Frontiers in Education Conference (FIE), IEEE, pp. 1–9 (2016) 19. Kortuem, G., Bandara, A.K., Smith, N., Richards, M., Peter, M.: Educating the internet-ofthings generation. Computer 46(2), 53–61 (2012) 20. Sarıta¸s, M.T.: The emergent technological and theoretical paradigms in education: The interrelations of cloud computing (cc), connectivism and internet of things (IoT). Acta Polytechnica Hungarica 12(6), 161–179 (2015) 21. Mavroudi, A., Divitini, M., Gianni, F., Mora, S., Kvittem, D.R.: Designing iot applications in lower secondary schools. In: 2018 IEEE Global Engineering Education Conference (EDUCON), IEEE, pp. 1120–1126 (2018) 22. Raikar, M.M., Desai, P., Naragund, J.G.: Active learning explored in open elective course: Internet of things (IoT). In: 2016 IEEE Eighth International Conference on Technology for Education (T4E), IEEE, pp. 15–18 (2016) 23. Zhamanov, A., Sakhiyeva, Z., Suliyev, R., Kaldykulova, Z.: IoT smart campus review and implementation of iot applications into education process of university. In: 2017 13th International Conference on Electronics, Computer and Computation (ICECCO), IEEE, pp. 1–4 (2017)

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24. Ahad, M.A., Tripathi, G., Agarwal, P.: Learning analytics for ioe based educational model using deep learning techniques: architecture, challenges and applications. Smart Learn. Environ. 5(1), 7 (2018) 25. Zhu, Z.T., Yu, M.H., Riezebos, P.: A research framework of smart education. Smart Learn. Environ. 3(1), 4 (2016) 26. Chin, J., Callaghan, V.: Educational living labs: a novel internet-of-things based approach to teaching and research. In: 2013 9th International Conference on Intelligent Environments, IEEE, pp. 92–99 (2013) 27. Cheng, H.-C., Liao, W.-W.: Establishing an lifelong learning environment using iot and learning analytics. In: 2012 14th International Conference on Advanced Communication Technology (ICACT), IEEE, pp. 1178–1183 (2012) 28. Wang, Y.: English interactive teaching model which based upon internet of things. In: 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), vol. 13, IEEE, pp. V13–587 (2010) 29. Satu, M. S., Roy, S., Akhter, F., Whaiduzzaman, M.: Iolt: An iot based collaborative blended learning platform in higher education. In: 2018 International Conference on Innovation in Engineering and Technology (ICIET), IEEE, pp. 1–6 (2018) 30. Kearney, M., Schuck, S., Burden, K., Aubusson, P.: Viewing mobile learning from a pedagogical perspective. Res. Learn. Technol. 20(1), n1 (2012) 31. Bomsdorf, B.: Adaptation of learning spaces: supporting ubiquitous learning in higher distance education. In: Dagstuhl Seminar Proceedings, Schloss Dagstuhl-Leibniz- Zentrum fr Informatik (2005) 32. Miraz, M.H., Ali, M., Excell, P.S., Picking, R.: A review on internet of things (IoT), internet of everything (IoE) and internet of nano things (IoNT). In: 2015 Internet Technologies and Applications (ITA) (2015), IEEE, pp. 219–224 (2015) 33. https://images.app.goo.gl/Aa9rjV3yFqEw4XNK8.

Monu Bhagat is currently working as a research scholar in National Institute of Technology Jamshedpur. He did B.Tech (2014) and M.Tech (2017) in Computer Science and Engineering from WBUT and Kalyani Government Engineering College respectively. He wrote many papers. His area of interest includes Image processing, Machine Learning, Internet of Things, Security etc.

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Sushma M. Balgi is currently pursuing her M.Tech degree from Computer Science and Engineering Department at National Institute of Technology Jamshedpur. Her area of Interest includes Cloud Computing, Optimization, Machine learning, IoT etc

Chapter 12

An Innovative Step for Enhancement in Student Results and Teaching–Learning Process Using Educational Technology Sudhanshu S. Gonge, Ratnashil N. Khobragade, Vilas M. Thakare, Vivek S. Deshpande, and Manikrao L. Dhore Abstract An education is an important part of daily lifestyle of student. A knowledge can be gained through newspaper, television, Internet, books, etc. This formal and informal way of learning education can also be done with the help of Internet communication technology. There are various ways of delivering knowledge, viz. (i) an ancient method of classroom teaching, (ii) journal and magazines and (iii) education technology using varieties of applications. In this paper, a novel and different methods and functions used for teaching by AI are being explained for enhancement of student results, and outcome-based teaching–learning process using educational technology and its statistical result analysis is described using T-test. Keywords AI · Education technology · Teaching methods · T-test

12.1 Introduction Teaching is a skill of teacher. Every teacher has different skill set. These skill sets utilized by teacher are different for various subjects. Education technology helps to teach and work in paperless format and helps in tracking records related to their marks, attendance and various curricular, co-curricular and extra-curricular activities

S. S. Gonge (B) · V. S. Deshpande · M. L. Dhore Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, India e-mail: [email protected] R. N. Khobragade · V. M. Thakare Department of Computer Science and Engineering, Sant Gadge Baba Amravati University, Amravati, India © Springer Nature Singapore Pte Ltd. 2021 A. Deyasi et al. (eds.), Computational Intelligence in Digital Pedagogy, Intelligent Systems Reference Library 197, https://doi.org/10.1007/978-981-15-8744-3_12

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[1–3]. Teaching methodology is also used based on the knowledge and quality of delivering contents of subjects. It also depends upon ethics and utilization of modern tools for subject. Many times teaching faculty does their student’s assessment and evaluation in two types, i.e., formative assessment and summative assessment [1–6]. Flowchart shown in Fig. 12.1 shows the teaching process and assessment method used for improvement of teacher as well as student. Teacher progress as well as student progress evaluation is a continuous process. There are two methods, i.e., (i) internal quality assurance cell and (ii) external quality assurance cell. The internal and external qualities of student outcomes are based on the teachers’ quality teaching and their assessment [5–7].

Start

Teaching Method Used by Teacher

Exams Taken by Teacher at College / University Level

Assessment Methods

Summative Assessment Methods

Formative Assessment Methods

Result Declaration

Result Analysis

Stop

Fig. 12.1 Flowchart of teaching process and assessment methods

Continue Process

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12.1.1 Formative Assessments In formative assessment, the student evaluation is done for improvement in their skill that they have been learned in the teaching–learning process. This process is being carried out after external exams are over and the results of that exam are being declared [6–9].

12.1.2 Summative Assessments The summative assessment is applicable for the practical evaluation to identify the student skills’ effectiveness. Summative assessment is basically associated with the evaluation of practical exams, co-curricular and extra-curricular activities [6, 9–14]. Assessment procedures vary based on the subject and the way of methodology adopted at the class by teacher/professor. There are different methods used by teachers/professors used in India as shown in Fig. 12.2. There are two main methods of delivering knowledge used by teachers, i.e., one is formal type also known as supervised teaching and other is informal type also defined as unsupervised way of teaching [1, 3, 6, 8, 10]. Formal type of teaching pedagogy has different methods like classroom teaching, blended learning, flipped classroom, ICT teaching, flash card, books, magazines, journals, case studies, etc. which are being used [2–9]. Informal type of teaching pedagogy has more varieties of methods as compared to that of the formal way of teaching. Informal type of teaching includes forum discussion, distance learning, debate competition, conferences, exhibitions, seminars, workshops, symposium, expert lectures, virtual laboratories, online courses, libraries, etc. It also provides education through radio, television, industrial visits, etc. which is totally connected to the outside world of classroom teaching [1, 11–13]. There are huge numbers of students who are learning through various modes. This raised the problem of result analysis after examination. To overcome this issue, there is need of AI-based computational intelligence s/w for doing result analysis. This also helps teacher for concentrating on their teaching skills and can deliver knowledge to students and achieve the outcomes. It also reduces the manual calculation and physical work of teachers.

12.2 Literature Survey Koehler et al. (2005) [1] explain knowledge content delivered by teacher in technical way. It helps teacher to understand complicated content associated with different educational technology used for delivering knowledge deeply. Chai et al. (2010) [2] have done analysis of the technical knowledge and pedagogical content description used for understanding the subject through ICT teaching methodology of teachers.

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Formal Type / Supervised

Informal Type/ Unsupervised

Classroom

Blended

Flipped

ICT

Distance

Teaching

Learning

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Learning

Forum Discussion Flash Card

Magazines

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Books

Debate Competitions

Conferences

Seminars

Case Studies Workshops

Symposium

Radio

Television

Expert Lectures

Industrial Visits Exhibitions

Online Courses

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Libraries

Fig. 12.2 Tree chart for method of delivering knowledge by teachers

Archambault et al. (2010) [3] explain teacher pedagogy content and its knowledge model used for judging teachers across the USA. It also explains about the benefit gained by teachers and students including staff working in the administration of teaching field. Various teaching–learning principles based on the content knowledge explain teaching methods and cognitive thinking of teachers’ principle used for evaluation [4]. Campbell et al. (1995) [5] define and explain the distance learning concept through interactive way. Many researchers work on making enhancement in content delivery to student for improving their knowledge and skills [6, 9]. The skill sets were statistically interpreted with the help of education technology used for teaching in various ways [6]. Brown et al. (2006) [7] introduced a novel way of teaching through pedagogical knowledge for delivering the fundamental concepts of electrical and computer engineering. Ismaili et al. (2008) [8] implement higher education ethics for the development of teachers and students. Researches also works on a different way of teaching–learning, which works for the different processes used to maintain the quality of accreditation and excellent assessment of student [6, 9].

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Schmidt et al. (2009) [10] developed an instrument for assessing the quality improvement in teachers with respect to their subject knowledge, content delivery and way of pedagogy used in the classroom. The Internet and education technology also helps to reduce the physical work of teachers by conducting exam online. This improves an exam conducting system in teaching–learning field [11]. Harris et al. [12] tell about curriculum-based technology design for teachers and students. Korthagen et al. (1999) [13] proposed the changing methodology of teachers with respect to their curriculum subject and different trends in education technology used for teaching. However, many researchers are also working on the artificial intelligence and its role in the outcome-based teaching and improvement in the student skills. It is found that AI provides the facility in terms of information based on the requirement of teachers and students. AI has improved teaching–learning facilities and has great impact on the education field [14]. Kandlhofer et al. (2016) [15] explain role of AI and their functions used in education for developing skills of computer science students in the field of AI and computer science. It also explains about the development and utilization of AI-based softwares for all classes used for delivering the lectures. Popenici et al. (2017) [16] describe the emerging technology developed by using AI for teaching–learning process. Safiuddin et al. (2018) [17] clear the concepts of engineering education. It also deals with technological development for data processing using AI and innovative implementation for feedback control system. Ciolacu et al. (2018) [18] explain the Education 4.0 by using AI and early recognition system for the development of student ability. It also deals with various machine learning algorithms like SVM and neural networks used for the analysis of student performance. Fauvel et al. (2018) [19] describe the agents requirement in AI research for developing the massive open online courses. It tells about the way of student involvement with full dedication. Muheidat et al. (2018) [20] proposed different strategies used for teaching–learning process, which is implemented in US education system. It describes different ways of learning methods in group or team for making team formation. Ciolacu et al. (2019) [21] describe the Education 4.0 system and AI role in IoT subject taught for higher classes and challenges in higher education. Tich Phuoc et al. (2019) [22] explain the learning system, which is used for delivering knowledge to student. Furse et al. (2020) [23] explain flipped classroom teaching and QCF model used for designing the instruction. Moreno et al. (2020) [24] explained about assessment method for mathematics course using AI. Ali et al. (2017) [25] explain the method of ensemble learning based on decision making. It also explains its future scope and status of this technique. Rojarath et al. (2016) [26] describe the improvement in classification of dataset feature based on voting technique. Verma et al. (2015) [27] explain the classification of data using ensemble learning with random forest tree algorithm. From this survey, it is observed that AI can play an important role in education system.

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Artificial Intelligence in Education

Automatic Grading

Diverse Learning

Identifying Weakness & Strength of Class

Support Student

Motivation to learn

Customized Help in Learning

Support Teacher

Fig. 12.3 Function of AI in education system

12.3 Artificial Intelligence in Education Field There are various functions of AI in education system as shown in Fig. 12.3. AI works on basic principles of environment culture, cognition and its system behavior [14]. The seven functions are being used by AI, for delivering lecture content to the class. There is AI classroom s/w service called as Google Classroom developed by Google [14–24]. It facilitates teacher to give online teaching material, assignments, multiple choice questions and to-do list for teaches as well as for students. It helps teacher to take exam and avoid physical work of monitoring class as well as reduces the utilization of paperwork. It also provides support to student to upload assignments and study anytime and anywhere in the network. It motivates student toward learning the subject as compared to that of traditional way of teaching. An automatic grading system of Google Classroom is handled by AI system, which facilitates good services to teachers for providing grades to students [17–19]. It also helps in identifying the slow learner and fast learner in the classroom based on auto-generated result analysis. It also provides the diverse learning facilities for studying different subject for different students at same time. Based on these, the statistical T-test is applied and explained for the improvement of student skill set and teaching–learning process.

12.4 Role of Computational Intelligence in Result Analysis A computational intelligence is subbranch of AI. It consists of various algorithms used for computing based on the available dataset [20–24]. It is based on the type of machine learning components, which work on three different learning concepts, viz. (i) supervised, (ii) unsupervised and (iii) reinforcement. It is explained in the following sub-sections.

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12.4.1 Supervised Learning The supervised learning is the function used for performing approximation task based on the input and output values [18, 19]. It is used for two different methods, viz. (i) classification and (ii) regression. The classification methods are used for result analysis of the student after their exam as well as student who have re-attempted the exam. It can be implemented using various algorithms, such as support vector machine, discriminant analysis, Naïve Bayes and nearest neighbor used for classification of data. The regression method is used by different kinds of algorithms, viz. (i) linear regression, (ii) support vector regression, (iii) Gaussian process regression, (iv) generalized linear model, (v) ensemble methods, (vi) decision tree, (vii) neural network, etc. [15–20].

12.4.2 Unsupervised Learning In unsupervised learning, the data do not have any target attribute; by using this learning function, there is a need to enhance the data for obtaining required structure from the dataset. It is used for clustering purpose. It can be implemented by using different algorithms, viz. (i) k-means, (ii) k-medoids, (iii) fuzzy c-means, (iv) Gaussian mixture, (v) hierarchal clustering, (vi) neural network, etc. Generally, deep learning uses the unsupervised learning function for clustering the data of similar attributes, for prediction of image patterns and for unlabeled dataset [18–24].

12.4.3 Reinforcement Learning It is a behavior of an agent that learns through trial and error method used in the dynamic environment. It is an orthogonal learning function used for evaluating the learner’s performance and providing the standard of correctness in form of behavioral target [18–24].

12.4.4 Ensemble Learning Method It is one of the machine learning techniques, which develop multiple model and combined them to produce enhancement in the output results. Ensemble learning method is used for the result analysis of students. It can be implemented on huge dataset. It provides better result as compared to that of the single machine learning algorithms. It provides classification as well as regression methods [25]. Ensemble learning is implemented using two steps. (i) The same or different machine learning

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algorithms are used. These base models of machine learning algorithms are implemented for generating multiple machine learning model. (ii) These models are used to perform basic task like classification, prediction, regression, etc. There are different techniques, which use ensemble learning, such as (i) voting and averaging, (ii) stacking, (iii) bootstrap aggregating and (iv) boosting [25–27]. In this chapter, voting and averaging technique is used with ensemble learning method. However, voting technique is used for classification of student data, whether he/she is passed/failed. Averaging technique is used in regression, to display and compare the student result using regression analysis. In this research, parameters like pass, fail, ATKT, above 60%, above 55%, below 55%, etc. are considered as dependent parameters, whereas the number of students appearing for exam is independent variable. By using these techniques, result analysis is done as shown in Fig. 12.4. It shows the classification of student with respect to pass and fail count, whereas regression analysis is used for predicting percentage scores after successful completion of classification process. This is required because there are huge numbers of student appearing for exam and need to identify the results of each subject for every class. The chapter also explains the fundamental concepts of teacher that are required for the teaching–learning process described in Sect. 12.5. There are different pedagogies used by teacher for delivering the knowledge to the student as explained in Fig. 12.2. The evaluation of the student assessment is necessary which is described in Sect. 12.1. Hence, there is a need of result analysis for the summative assessment and formative assessment. Result analysis is very important to identify the student. There are three types of students, i.e., (a) fast learner, (b) average learner and (c) slow learner. These student can be evaluated and identified after result analysis. The workflow of result analysis is shown in Fig. 12.4. This flowchart also helps to evaluate course outcome and program outcome. The steps for result analysis are as shown below:1. Declaration of the theory practical results of exams by college/university teacher. 2. Depending upon the condition of passing, the students are declared as either pass or fail. 3. If students are passed again, then the percentages of students are check based on the condition put for result analysis. 4. If students are failed, then Allowed to keep terms (ATKT) condition is checked for further evaluation of result analysis. Suppose the students fall in ATKT grade or year down, then the remedial coaching classes are being conducted to improve the skill sets of students. Otherwise, students’ percentages are calculated with respect to class wise and subject wise, and grades are allocated to students. These steps are implemented using ensemble learning method [25]. The advantages of this method help to combine one or more machine learning models, which can be used for result analysis, and improve the performance.

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Start

University/ College Theory and Practical

Declaration of University/College Result

Yes

No

Condition for Passing

Student Fail

Student Pass

Percentage of Student

No

Condition for ATKT

Yes

Above 60%

Grade ATKT

Year Down Required Remedial Coaching Condition for

Above 55%

Distinction

Calculate Second Class Count

Yes

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Below 55%

Calculate Distinction Count

Calculate Pass Class Count

Calculate First Class Count

Calculate Distinction Count Subject wise

Calculate Count Subject Wise

Calculate Count Subject Wise

Calculate First Class Count Subject wise

100% Student Passed

Stop

Fig. 12.4 Flowchart of result analysis

12.5 Fundamentals of Teachers’ Teaching To get an enhancement in students’ skills, four parameters are important: • • • •

Teaching model, Teaching skills, Teaching relationship with student, Reflection of student.

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12.5.1 Teaching Model A teaching model is an active process to improve the skill of the students and convert it into outcome-based teaching–learning process. It is a real-time process conducted by the teacher in classroom environment to interact with the student and increase the learning interest about the subject [1, 6, 9, 11–13].

12.5.2 Teaching Skills Teaching skills are the activities carried out by instructor in class either in direct method or in indirect method. There are five properties of teaching skills that every teacher may possess that are (a) patience, (b) creativity, (c) good communication, (d) personality and collaboration and (e) self-discipline. These properties help teaching skills to make good relationship with students related to their academics. Due to this, student increases their interest in learning for that respected subject where these five properties are present in the teaching skills [6–13]. Figure 12.5 shows the teaching skill and teaching model relationship. The student reflections are based on the fundamentals of teachers’ teaching skills related to the subject. There are four fundamentals of teacher teaching used for student reflection which are described as follows:1. Focus—Teacher should be focused on curricular, co-curricular and extracurricular aspects with respect to academic syllabus and current scenario of science and technology. The working and relationship of focus are associated with teaching relationship with student and subject. It is also directly proportional to the relation between student reflection, teaching models and teaching skills [1, 3, 7]. 2. Fundamental Outcomes—The outcome of teaching methodology must be correct and chosen perfectly with respect to subjects and syllabus [1, 4, 8, 10].

Teaching relationship with student and subject

Teaching Skills

Teaching Model

Fig. 12.5 Teaching skill and teaching model relationship

Student Reflection

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3. Social Systems—The teacher should implement his/her skill which differentiates from above-described techniques. The concepts of learning should be positive attitude with ethics [1–9]. 4. Practical Support Systems—The teacher should demonstrate his/her skill through practicals, such as demonstration of computer programming and its execution. This improves the skills of computer engineering student. This method of teaching also helps teachers to get the honest feedback of his/her teaching skill [6–9].

12.5.3 Teaching Relationship with Students There are basic five fundamentals of teaching relationship with student. They are:1. Teacher should able to provide activities that make them safe and fearless toward their curricular and extra-curricular activities [1–9]. 2. Teacher must able to make and maintain discipline with them and also respect student [1–9]. 3. Teacher should able to create an interest for their subject in their classroom [10]. 4. Teacher should be passionate about his/her subject during delivering the subject contents [11–13]. 5. Teacher should able to solve their problems regarding class work and home assignment assigned for them [13].

12.5.4 Reflection with Students 1. Teacher should provide study material like notes, books and content through lectures punctually along with course plan and syllabus [6–9]. 2. During teaching–learning process, students must have to be already prepared with prerequisite for novelty learning in lectures [6–9]. 3. Students reflecting toward teachers during practical, this can create a sign of an outcome-based student development for that subject [6–9]. During this Covid-19 pandemic, the fundamentals of teachers’ teaching have played an important role for delivering lectures. Many professors and teachers have taken online lecture through Zoom, Cisco meeting s/w and AI-based software like Google Classroom. It was found that the student’s attendance was more as compared to the traditional way of teaching carried out in classrooms. It also found that the time of teachers was saved and used by them for enhancing their skills. Many quizzes were conducted through Google quiz. This helps teachers for completing their syllabus through combination of Internet technology and AI-based softwares. These lectures were recorded and shared with student through Google Classroom for enhancing their computer science and engineering skills. This results in the improvement of the student skills which is explained in Sect. 12.6.

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12.6 Statistical T-Test Analysis of CSE Students for Outcome-Based Teaching–Learning Process To evaluate OBE teaching methodology, based on the exam taken before pandemic using classroom teaching and after pandemic using educational technology. A random sample of 10 students are being selected from a class based on their attendance in traditional classroom and online learning process as shown in Table 12.1. The formative and summative assessment is consider for mid-semester marks obtained by student after attending classes through traditional way and end-semester marks of students achieved by using educational technology after attending the classes by student through online lectures conducted through Zoom, Google Meet and Google Classrooms. The ten samples of student from a class of computer science engineering student are taken for analysis. Let us consider X score of students scored in the mid-semester through traditional classroom teaching and Y is the end-semester marks scored by the students using educational technology. With respect to the samples taken in Table 12.1, the null hypothesis is considered for the improvement of teachers’ teaching skills along with enhancement of students’ learning approach toward the subject. Mathematically, hypothesis can be written as:H 0 : μ1 = μ2 which is equivalent to test H 0 : D = 0. H p : μ1 < μ2 (as case study explained in this paper is consider for the novel and different methods used for teaching are being explained for enhancement of student results obtained by teaching–learning process using educational technology.) Since after mid-semester, the assessment is done by the students and their evaluation is carried out after result analysis process. Table 12.1 Statistical T-test calculation Students

Mid-semester score (X) out of 30 marks

End-semester score (Y ) out of 70 marks

Difference D = (X − Y )

Difference square D2

1

12

42

−30

900

2

14

36

−22

484

3

15

35

−20

400

4

15

29

−14

196

5

5

44

−41

1681

6

7

42

−35

1225

7

9

46

−37

1369

8

6

40

−34

1156

9

8

39

−31

961

10

5

36

Total n = 10

μ1 = 96

μ2 = 389

−31  D = −295

961  2 D = 9333

12 An Innovative Step for Enhancement in Student Results …

 √  t = D  − 0/σ diff n

247

(1)

To perform T-test analysis for above samples, first calculate the value of ‘t’ given in Eq. 1. Equation 1 has deviation and the mean difference. The value of the ‘D’ can be calculated by using the equation of deviation of difference as shown below: D =



D/n = −295/10 = −29.5

(2)

The value of σ diff, i.e., the mean can be calculated by using the equation of deviation of difference as shown below:    σdiff = (3) D 2 − (D  )2 ∗ n /(n − 1) diff =



(9333 − (−29.5)2 × 10)/(10 − 1) = 99.614256

 √  t = (−29.5 − 0)/ 99.614256 10 = −29.5/315.007936 = −0.093648

(4) (5)

The degree of freedom can be computed as:d f = (n − 1) = (10 − 1) = 9

(6)

As H p is consider as one-sided, thus one-tailed test is applied on the left side. Since H p is not greater than type i.e. (μ1 < μ2 ) for evaluation and determination of the region which can be rejected at 10% level of significance which comes under t-distribution table of 9° of freedom as: R: t < −1.383. The computed and observed value of t is − 0.0093648 which is less as compared to the value of t-distribution table. Thus, H p at 10% level is rejected because it falls in the region of rejection. This concludes that there is improvement in teachers’ teaching and enhancement in students’ learning of computer engineering student skills using educational technology.

12.7 Conclusion In this paper, the role of AI and computational intelligence in education system is explained. The paper described the ensemble learning method used for the result analysis. It explains AI, and computational intelligence reduces the manual work of teachers and provides 24 × 7 learning service to students. It explains about the AI-based softwares used for delivering lectures. The paper describes the teachers’ teaching models and different innovative ways to improve the learning skill of computer science and engineering students with the help of educational technology. It deals with the case study of the teaching–learning model used for the improvement

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of teachers’ teaching style and enhancement in student skills based on the assessment methods used by teacher. It also explains the result obtained after using educational technology has increased as compared to that of results achieved by students in traditional teaching–learning process. This shows that there is enhancement in outcome-based teaching–learning process. It also proved that there is increment in student results by doing result analysis and statistical analysis with the help of T-test.

References 1. Koehler, M.J., Mishra, P.: What happens when teachers design educational technology? The development of technogical pedagogical content knowledge. J. Edu. Comput. Res. 32(2), 131– 152 (2005) 2. Chai, C.S., Koh, J.H.L., Tsai, , C.C.: Facilitating preservice teachers’ development of technological, pedagogical, and content knowledge (TPACK). J. Edu. Technol. Soc. 13(4), 63–73 (2010) 3. Archambault, L.M., Barnett, J.H.: Revisiting technological pedagogical content knowledge: exploring the TPACK Framework. Comput. Educ. 55, 1656–1662 (2010) 4. Kagan, D.M.: Ways of evaluating teacher cognition: Inferences concerning the goldilocks principle. Rev. Edu. Res. 60(3), 419–469 (1990). https://doi.org/10.3102/00346543060003419 5. Campbell, J.O.: Interactive distance learning: issues and current work. J. Instru. Deliv. Syst. 9(3), 32–35 (1995) 6. Gonge, S.S., Ghatol, A.A.: Education technology used for improving learning skills of computer science and engineering students. In: 2013 IEEE International Conference in MOOC Innovation and Technology in Education (MITE), pp. 100–103. Jaipur, India (20–22, December 2013) 7. Huettel, L., Brown, A., Collins, L., Coonley, K., Gustafson, M., Kim, J., Ybarra, G.: A novel introductory course for teaching the fundamentals of electrical and computer engineering. In: Proceedings of the 2006 American Society for Engineering Education Annual Conference and Exposition, Chicago, IL (19–21, June) 8. Ismaili, M., Hamiti, M.,Pajaziti, A.: Implementation of ethics in high education. In: Conference of Social Sciences, ISBN 978-99943-47-80-3. Tirana, Albania (September, 2008) 9. Gonge, S.S., Ghatol, A.A.: An art of teaching in teaching-learning process: an important part for educational accreditation, quality and assessment. In: 2014 IEEE International Conference on MOOC, Innovation and Technology in Education (MITE), pp. 17–21. Patiala (2014). https:// doi.org/10.1109/MITE.2014.7020233 10. Schmidt, D.A., Baran, E., Thompson, A.D., Mishra, P., Koehler, M.J., Shin, T.S.: Technological pedagogical content knowledge (TPACK): the development and validation of an assessment instrument for preservice teachers. J. Res. Technol. Edu. (JRTE) 42(2), 123–149 11. Gonge, S.S., Kandalkar, G.M.: Combination of combination of internet technology and education technology used for improvement in examination system of engineering. In: 2015 IEEE International Conference on MOOC, Innovation and Technology in Education (MITE), 978-1-4673-6746-2/15© IEEE (2015) 12. Harris, J., Mishra, P., Koehler, M.: Teachers’ technological pedagogical content knowledge and learning activity types: curriculum-based technology integration reframed. J. Res. Technol. Educ. (JRTE) 41(4), 393–416 (2009) 13. Korthagen, F.A.J., Kessels, J.P.A.M.: Linking theory and practice: changing the pedagogy of teacher education. Educ. Res. 28(4), 4–17 (1999). https://doi.org/10.3102/0013189X0280 04004. 14. Tuomi, I.: The impact of artificial intelligence on learning, teaching, and education policies for the future. In: Cabrera, M., Vuorikari, R., Punie, Y. (Eds.) EUR 29442 EN, Publications

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Chapter 13

Digital Pedagogical Paradigm in Language Lab-Based English Teaching for Higher Technical Education Sadhan Kumar Dey and Alice Dey

Abstract National Board of Accreditation (NBA) of India in compliance with the policy decisions of Washington Accord has created a landmark event in the current decade as envisaged in the emphasis on ‘digital pedagogy’ in higher technical education in India today. NBA in compliance with the basic tenets of the Washington Accord has made accreditation of ‘professional courses’ compulsory across the length and breadth of India. Therefore, ‘Digital Pedagogy’ is sine qua non of the present situation in higher technical education. The proposed book chapter is a serious attempt to present a model for teaching of English for Technical Communication using Digital Pedagogy in higher technical education as prescribed by NBA (India). In spite of the prescribed course planner of NBA (India), English faculty members of various technical universities of India still continue with the traditional pedagogy of ‘chalk and talk’ due to lack of insightful training in digital pedagogy. The proposed digital pedagogical model specifies how to handle Digital Language Laboratory to develop English Communicative Competence of UG students in higher technical education in India and abroad. Digital Language Laboratory is often misunderstood and misused as an alternative smart classroom. The proposed book chapter would focus on how NBA—prescribed norms can be maintained adeptly using Digital Language Laboratory as a teaching aid or grand digital Realia. The proposed book chapter would also showcase digitized strategies for English language teachers so that they can handle digital pedagogical paradigm by following the legacy of ‘continuity in change’ through proper application of digital pedagogy. Keywords Digital · Pedagogical · Paradigm · Language lab-based · English teaching · Higher · Technical · Education S. K. Dey (B) Department of Engineering Science and Management, RCC Institute of Information Technology, Kolkata, India e-mail: [email protected] A. Dey Department of Law, Kalinga University, Raipur, Chhatisgarh, India e-mail: [email protected]; [email protected] © Springer Nature Singapore Pte Ltd. 2021 A. Deyasi et al. (eds.), Computational Intelligence in Digital Pedagogy, Intelligent Systems Reference Library 197, https://doi.org/10.1007/978-981-15-8744-3_13

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13.1 Introduction Digital pedagogy is the central pivotal concept that is attached to all Online Courses offered by most advanced universities of the world through Coursera1 MOOCs. NPTEL2 Online Courses of several Indian Institutes of Technology/universities or research institutes of India that are offered through SWAYAM3 also use the digital pedagogical norms for successful implementation of these Online Courses. During the first two decades of the current century, traditional pedagogy has faced a plethora of mega challenges and dramatic changes in the field of English Language Teaching for Higher Technical Education across the globe. Driven by innovations in the field of Information and Communication system, Electronic advancement and Information and Communication Technology (hereafter referred to as ICT), classroom pedagogy has adopted a new paradigm shift in higher education. Technical education has experienced these innovations in the field of ICT and introduced new technologies that are evolving in great speed. These cutting-edge educational technologies have not only altered the constraints of space and time but also reshaped the way students of higher education learn, think and communicate in Academia and Industry today. Rapid advancement in Information Technology and digital system has started reshaping the digital ecosystem of innovations in ICT. Now, people can transmit information widely within a second propelling the growth of new urban communities through linking distant places and diverse areas of endeavour in productive new ways. Few decades ago, this was quite unimaginable. The present chapter has primarily focused on the two interrelated issues as pointed out below: a. How technical education in India under the provisions of Washington Accord has executed the transition from the traditional pedagogy to the digital pedagogy? b. How two overriding principles known as the principle of ‘variety’ and the principle of ‘flexibility’ have become integral parts of digital pedagogy of English teaching? Recent world wide turmoil due to global Lockdown4 on account of COVID-19 set glaring example in front of all doubtful eyes that Digital Pedagogy based on www along with Web Tools Weblog and Appliances are the only saviour in Academics and Administration be it an issue of teaching–learning of Law, Literature or of English Language.

1 Coursera

Inc. (Stanford, USA) which is famous for launching Massive Open Online Courses on different subject domain across the globe. 2 National Programme on Technology Enhanced Learning (India). 3 Study Webs of Active–Learning for Young Aspiring Mind (India). 4 That inter alia started the culture ‘work from home’ across the globe.

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13.2 Language Lab-Based English Teaching Across Time and Clime The first experiment of using Language Laboratory for language teaching was undertaken at the University of Grenoble, France in 1908. USSR and Germany also used Language Laboratories in 1950s and 1970s, respectively. CELT5 recommended the use of Language Laboratories for focussed learning/acquisition of English as the Second Language. But these primary attempts failed to earn reputation due to frequent breakdowns and limitations of technology. At present, language labs are well equipped with advanced technologies such as ICT and IT-enabled Web tools. The English Language Lab is a technological breakthrough for imparting almost all needful language skills and sub skills of English due to application of Digital Pedagogy. It offers an exclusive result-oriented and efficient way to enrich English language learning process. Multimedia-based language lab helps to learn and enhance language proficiency by sharing course materials within seconds. These are developed on the methodology of LSRW skills. The language laboratory is a very helpful tool for practising and assessing one’s speech, testing four skills, learning at their own pace without teachers. At the same time, it builds the motivation of learners reducing fear, and it makes students feel comfortable [1]. Language lab software, in the present century, is one of the essential tools used in teaching and learning Communicative English to the students. Language lab is a practical approach which boosts the self-learning by providing self-guided and at the same time well-structured training in order to achieve the objective set by the education body. It functions as complementary to the classroom teaching through which the students can reinforce the material learned in class by putting them into practice. It is a great help to the teachers in monitoring and evaluating the progress of the students and mentor them about their weaknesses. It has been observed that the students learn much faster in the language lab compared to the classroom where they are taught traditionally. Language lab allows the teachers to bring more diversified activities in order to keep the classroom interesting. They also offer a great tool to foster the communication in the classroom as it allows to chat—send a message and interact in the group. Language lab software devices are mainly used for the following purposes: Multiple options for communication: There are multiple ways a teacher can communicate with the student. There are various inbuilt features which make communication one to one either through audio, video or text. Intercom feature lets you send text messages, or through audio–video, one can directly communicate with the student. There are unique tools in the software lets the teacher communicate with the individual student, selected students in a group or to a whole class. It is oneway communication provided only to teachers. Instant messaging, audio and video chatting are the different forms that the teacher communicates with the students.

5 Communicative

English Language Teaching Method is popularly known by that name.

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Incognito monitoring: As the name suggests, the teacher can monitor student activity in real-time discreetly. Incognito supervision of students’ activities takes place through the supervisor feature. Inbuilt applications: The teacher can make customized e-lessons with the help of specially designed tools to make the lessons interesting and engaging. There are various inbuilt applications like Lesson Studio, e-Reader, Billboard, e-Writer, Net Flick, X-Play, Video Streaming, Conferencing, Live Classroom, e-Assignments to create customized lessons creation and implementation. Inbuilt mechanism for lab class: The list of implementation tools include incognito monitoring, customized e-lessons creation, instant evaluation and feedback, listening comprehension practice and pronunciation practice. Personalized, interactive video quiz, proctored assignments and progress tracking will safeguard students’ engagement and involvement in English Language Lab class. Instant evaluation and feedback facility: Assessment of speaking, listening and all the other assignments addressed to the students are corrected, and the evaluation and feedback are given instantly at a single click of a button. Listening comprehension practice facility: Language lab provides a platform for the teachers to skilfully use the resources available in the software to the best fit. The technology provides various comprehension modules for different levels and helps the students gradually move to the tougher lessons successfully. Pronunciation6 practice facility: Language laboratory lay stress on pronunciation, stress, intonation and expression in the guided mode where the student can monitor their progress report on each stage. This helps to fill the loopholes in the ability to understand the right way to spell a particular word or the expression. Personalized interactive video quiz facility: Implementing a personalized and interactive video quiz in the learning module develops self-learning and enables the students to reach the expected progress for the module quickly. Visual aids play a vital role in bringing out the best in the students that make the learning exciting and fun. Assignments and progress tracking facility: Each student is continuously monitored through his completed tasks and assignments. There are alerts set in the software to remind the students to expedite the backlogs quickly. Progress report for each assignment is available to the teacher on the main screen. The teacher can set deadlines for a particular assignment and offer help based on his progress chart. Students’ active involvement: The most important output the teacher receives at the end of each session is the student’s participation and active involvement in the assignments in every stage of her/his progress. Collaborative learning and interaction enhances the student’s active involvement in the lessons and stays concentrated 6 Mainly

of RP (Received Pronunciation of Standard British) variety or ARP (American Received Pronunciation).

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on completing the given assignments. The language laboratory is very useful for assessing students’ speech. It provides students with the technical tools to get the best samples of the pronunciation of the language. The electronic devices used in the laboratory will stimulate the eyes and ears of the learner to acquire the language quickly and easily. The laboratory’s collection is designed to assist learners in the acquisition and maintenance of aural comprehension, oral and written proficiency, and cultural awareness. Integrated facilities: The language laboratory offers broadcasting, television programmes, web-assisted materials and videotaped off-air recordings in the target language. In short, a learner can get the experience of having interaction with native speakers through the laboratory. Hence, the language laboratory has become the need of the hour in any language learning process for communication.

13.3 Digital Lesson Plan for Lab-Based English Teaching Technical Education in India has been brought under the provisions of Washington Accord since 2010 which has prescribed the transition from the traditional pedagogy to the digital one. Two overriding principles for standard course planning as well as lesson planning are known as the principle of ‘variety’ and the principle of ‘flexibility’ [2].7 The term ‘Variety’ in the context of Digital Lab-Based English Language Teaching refers to involving technical students of higher education in a number of various types of Internet-based or Web tools-based Digital Learning Activities (DLA). It necessitates formal introduction of key functionalities to the students of higher education so that they can opt from available wide selections of relevant Digital Study Materials through a number of Realia (i. e. Teaching Aids) as displayed in Fig. 13.1. Side by side the term ‘Flexibility’ refers to non-rigid approaches that come into application when Engineering English teachers deal with the NBA—designed Course Planner in classroom teaching. The NBA course plan is based on Major Graduate Attributes of English Communication that is included in the above mentioned Washington Accord. Lesson planning for digitally programmed Engineering English Teaching in a Lab situation should take up the challenges of the recently designed NBA Course Planner (See Annexure 1 and Annexure 2 for details) so that learning Technical Communicative English becomes ever—interesting and never—monotonous one for the Engineering Students.

7 International

Journal of English Literature and Language Skills.

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Language Lab Rialia

INTERNET

CD /DVD Player

Digital Graphics or Charts

Digital Cue Cards / Flash Cards

Projector

Digital InteracƟve Smart Board

LCD Projector

LAN/WAN

Over Head Projector (OHP)

Digital Ac vity Sheets

Teacher’s Talk /A V Lectures

Flip Audio – Video Materials

WEBINAR

Podcast

MAN

Computer – Aided Language Laboratory

Fig. 13.1 English language lab realia at a glance

The Lesson Plan for a particular day or a session in the Engineering English Language Lab class may not be appropriate for that class. There may be a number of reasons behind the failure of a lesson plan in a Digital English Language Lab class. Some probable reasons are (Fig. 13.2). The flexible Engineering English teacher must be able to change the Lesson Plan in such a situation even if it requires minor adjustments with the prescribed NBA Course Planner. Flexibility at the implementation level is the prime requisite that a genuinely adaptable and dynamic teacher may never disregard as shown in Fig. 13.3. Recent surveys, done in the field of English teaching, have highlighted the fact that technical students are hyperactive. Most of the Engineering students need to know the purpose behind a certain task for they need to know why they are doing some language activity and what they will achieve after completing the given task/s. The Lab-based Lesson Plan should specify the purpose which may be ‘Communicative Activities’ or development of receptive skills (listening skill and reading skill) and productive skills (speaking skill and writing skill) or orientation to technical and business situations in real life. Engineering English faculty members of engineering institutions must have a purpose for all the activities they organize in a class or in Digital Language Laboratory for that matter throughout a particular session or semester. They should communicate that purpose in their Session Plan which should also be available as an appendage to the syllabus and curriculum of the university concerned. One must not forget that a technical class of higher education may include a number of intellectually advanced tech-savvy personalities with diverse learning

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Among the students/faculƟes

Odd RouƟne Timings

Lack of Proficiencfy

Lack of preSession MoƟvaƟonal AcƟviƟes

Lack of coordinaƟon

Lack of instrucƟon in Flip materials

Lack of CooperaƟon

Non – Contextual Texts

Lack of organizaƟon

Fig. 13.2 Probable reasons for failure of lesson plan in lab-based teaching

experiences and stylistic preferences based on their different world views and their respective diverse socio-economic and ethno-cultural backgrounds. As a result, the language activity of English that is specifically appropriate for a fresher Undergraduate may not be ideal for a Diploma holder with certain reallife industrial job experience. Therefore, Engineering English faculty members who vary their teaching approaches from time to time may be able to satisfy most of their Engineering students [1]. The doctrine of Variety8 as it is used in the field of Educational Psychology refers to a pedagogic principle that applies especially to a series of advance level English language classes. For example, English faculty members will try to implement a series of digitized learning activities (see Conclusion for details) in the technical class by following the doctrine of variety during a particular semester.

8 The

doctrine of variety has its origin in B. F. Skinner’s concept of “Operant Conditioning” for maximum learning within minimum time frame [3].

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Fig. 13.3 Sub skills in digital pedagogy of English teaching

The principle of variety applies to a lesser extent to a single class period also. Although there are some activities that can last for fifty minutes, it seems generally true that changes of activity during that time are advisable. An additional introductory session of a new language item or an aspect of English language that requires another period of fifty minutes would probably be counter-productive. It is noticeable how an over-long accurate reproduction stage creates fatigue in technical students and fails to achieve any effective outcome. We would not expect, either, to ask the students to engage in ‘Reading Comprehension’ for the entire period of the digital class. We might, however, be able to use the entire class on one reading passage if we introduce various facets and planned activities that we can intertwine with the reading exercise. Thus, we might get students of higher education to read to extract specific information. This reading for comprehension may be followed by some topic-based thematic discussion, some intensive tasks and some kind of written or oral follow-up integrated tasks.9 Adult learners of technical education, especially, need to do a variety of tasks

9 Based

on Task-Based Language Teaching Approach of Communicative English Language Teaching.

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in fairly quick succession as they will generally not be able to concentrate on one activity for a longer stretch of time than 8–10 min. The teacher who believes in the doctrine of variety will have to be simultaneously flexible as the only way to provide variety is to use a number of different techniques. All of these may not fit into one method or approach and may not be synchronous all the time. Teacher administrator should be immediately suspicious of anyone who says all the students have been able to answer to language-related problems. This sort of generalized over-positive feedback will imply a lack of flexibility and variety in digital pedagogy. Digital Session/Lesson Planning is a pedagogic art of mixing teaching techniques, digital tool-based activities and deployment of TLM/LTM10 in such a way that an ideal balance is maintained for the ELT11 class. In a general language course, there will be work on the four skills. Although a teacher will probably come to a decision about the relative merits of each skill, there will be presentation and controlled practice, roughly tuned input of receptive skill work and communicative activities. Different student grouping will be used. If the lab-based English faculty members have a large variety of techniques and activities that they can use with students, they can then apply themselves to the central question of session/lesson planning: ‘What is it that Engineering English students will feel, know or be able to do at the end of the session/class that they did not feel or know or were not able to do at the beginning of the session/class?’. It may be presumed that the technical students will feel more positively involved in active learning of English at the end of the class than they did at the beginning as a result of activities that were enjoyable. One may say that the students will know some new language usage that they could not learn before their involvement in Digital Labbased English class. For example, one may presume that the target technical students will be able to write a type of Business Letter that they were not able to write before they got exposure to digital pedagogy which entails digital tool-based peer-reviewed practice sessions. (See Session Plan for details). It goes without saying that English teacher will create the objectives for the class at the outset. Students may be involved in a game-like activity because the teacher’s objective is to have them relaxed so that they may feel more positive about their digital English Lab classes. The students may be given a reading passage to work on because the teacher’s objective is to improve their ability to extract specific information12 from written text/s in context. Storytelling activity may be introduced if the objective is to enhance ‘speaking skill’ of the students by enhancing speaking sub skills called reference skill of the past and the present in context.

10 Teaching

and Learning Materials/Learning and Teaching Materials. Language Teaching. 12 By using the Reading sub skill known as ‘Skimming Skill’. 11 English

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Fig. 13.4 Sample session plan of digital English teaching

Let us look at a sample Session Plan designed upon English Language Laboratory syllabus of an advanced technical university13 as shown below (→marks denote digital focus) (Fig. 13.4). Under Outcome-based Education (OBE) scheme, introduced by NBA, technical students need to know the purpose behind a certain task. The technical students need to know why they are doing some Digital Lab activity and what they will achieve after completing the given task.

13 Maulana Abul Kalam Azad University of Technology (formerly known as WBUT), West Bengal,

India.

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13.4 Pedagogical Progress of English Teaching Across the Globe Let us very quickly review the global progress of pedagogical paradigm of English teaching from traditional classroom pedagogy to digital pedagogy. The first five features were mostly practiced in higher education from the Eighteenth century to the twentieth century. A. Teacher/s → Communication → Learner/s Classroom communication played an important role in traditional classroom situation. The mono-directional arrows clearly show that the Lecture Method is followed in such classroom practice. The focus of teaching English was ‘learning English’ for Reading and Writing. A number of technical colleges all over the world follow this model for teaching Technical Communication in English till today. B. Teacher/s → TLM/Content + Communication → Learner/s In continuation with the traditional classroom pedagogy as shown above, teacher played an important role by preparing Teaching Learning Material (TLM). The mono-directional arrows clearly show that the Authoritarian14 practice is followed in such situation. The classroom remained teacher-centric and teacherdependent. The students still practiced ‘rote learning’ as a widely practiced method of learning. A number of technical colleges all over the world follow this model for teaching Technical Communication in English till today. C. Teacher/s → Method/s TLM/Content + Communication → Learner/s In most cases, faculty members decided the teaching method/s to be used for making the Business Communication class interesting. The mono-directional arrows clearly show that the Method was solely decided by the concerned faculty. Moreover, scientific method was hardly followed in such classroom situation. All over the world, most of the technical colleges and universities including MIT15 follow this model for teaching Technical Communication in English till today. D. Teacher/s → TLM/Content + Communication LE + Realia → Learner/s Authoritarian English Communication faculty members maintain strict discipline, and Learning Environment (LE) is hardly student-friendly. Students are allowed to follow teacher’s dictation from the TLM16 or old notes of the concerned faculty. Unlike digital pedagogy Realia are hardly used. Few technical colleges follow this model for teaching Technical Communication in English till today.

14 The

practice of Teacher-centered teaching when the teacher is considered as infallible authority. Institute of Technology, USA. 16 Teaching Learning Material, i.e. the material that is shared in between the language teacher and the technical students of higher education. 15 Massachusetts

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E. Teacher/s → Test → Learner/s English Communication faculty member played the role of Test Administrator and Evaluator. The mono-directional arrows clearly show that the Authoritarian practice is followed in such situation where the students had no say regarding Test Design and Evaluation process. Most of the virtual technical colleges and universities across the globe including Harvard University, USA and Leeds University, UK, follow this model for teaching Technical Communication in English till today. F. Teacher/s → Interaction ← Learner/s Behavioural psychology was brought in the domain of technical education as a pedagogical paradigm during the last century. At present, English Technical Communication is taught using Interactive Discussion Method. The classroom interaction takes place in a student-friendly Learning Environment (LE), and technical students find their place equal to the faculty member. The face to face directions of the arrows clearly prove this point. A handful of technical colleges and universities in India and other developing countries across the globe follow this model for teaching Technical Communication in English till today. G. Teacher Variable ← → Teaching Style Digital pedagogy depends on the capacity of the Technical English Communication faculty. Teacher variability has been regarded as one of the key factors for progress of the Management study. Therefore, standard Management colleges arrange for FDP17 to en-skill and improve the teaching style of the concerned faculty member/s. Very few technical colleges and universities across the globe including Australian and Canadian universities follow this model for teaching Technical Communication in English till today. H. Learner Variable ← → Learning Style Digital pedagogy has paved the way for the Technical English Communication faculty to emphasize the diverse learning style of Management students. As a matter of fact, learner variability has become the buzz phrase and one of the key factors for progress of the Management study. Therefore, standard Management colleges arrange for Faculty Development Programme/s FDP to en-skill and improve the teaching style of the concerned faculty member/s on the basis of different learning styles of Management students. Few technical colleges follow this model for teaching Technical Communication in English till date. I. Learner/s ← → Evaluation → ← Teachers Digital pedagogy has established the norm that technical students are primary stake holders of their education. Technical Communication is not an exception. Technical Communication students are aware of the Evaluation criteria from the very beginning of the course. Moreover, they may consult the concerned faculty member/s to adopt digital evaluation as a norm for better implementation of Evaluation yardsticks. Few technical colleges follow this model for teaching Technical Communication in English till today. 17 Faculty

Development Programme/s.

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DigiƟzed Realia DigiƟzed Teaching

ICT

DigiƟzed Learning

Language Lab

Fig. 13.5 Displaying digital pedagogy

J. Digital Pedagogy in Focus Digital pedagogy believes in Constructive Method of English Language Teaching and believes in the proposition that learning is student’s responsibility and teacher is a facilitator of student’s learning (Fig. 13.5). Driven by ICT18 digital pedagogy is primarily concerned with Digitized Teaching which includes proper use of digitized Realia in student-friendly labbased Digitized Learning Environment for teaching English Technical Communication. Lab-based digital pedagogy faced a shock wave of crisis during the recent lockdown phase due19 to pandemic COVID-19 health hazard round the globe. But when the practice of digital lab-based English teaching got an online launching tool like Zoom or CISCO Webex, it could easily adapt to digitized Web tools and proved its worth. Digital pedagogy must ensure the Digitized Learning in digitized process in higher technical education. Only a handful of technical colleges follow this model for teaching Technical Communication in English. The Digital content may be focused through the following (See Conclusion for details) (Fig. 13.6). Digital lesson plan should specify the target outcome/s which may be ‘Communicative Activities’ or development of receptive skills (listening skill and reading skill) and productive skills (speaking skill and writing skill) or Technical-cum-Business Orientation. Technical English Communication faculty members of higher technical institutes must have a specific purpose for all the activities they organize in a class or in Digital Language Laboratory for that matter, and they should communicate that purpose in their lesson plan. Lesson plan should also be available as an appendage of NBA course plan. 18 Information 19 Started

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i. Select CD /DVD Player

ii. Audio –video Lectures

iii. E Graphs / Charts

iv. Language Lab Class

v. E-language Lab -based AcƟviƟes

Fig. 13.6 Digital tools of English language laboratory

13.5 Teaching English for Technical Communication A paradigm shift has been observed globally in the pedagogy of English for its extensive use for technical communication along with the application of IT in the process of teaching and learning of the English language. Digital literacy has been added as one of the essential components of general literacy. Every20 passing day brings new technology, innovative means and tools of teaching and learning of the English language. The problem remains Have we got enough training for the matured use of digitized technology in English Language Lab?

According to the global reports of Digital 2019, there have been more than 400 million Internet users recorded in November 2019 in which there were 34% users’ penetration was registered for the said year. The report further states that there are 250 million active social media users and mobile users have touched 230 million milestones as active mobile users. The facts reflect that the people of India have more usage of hand-held devices compared to other countries. The figures also advocate that there has been significant research work carried out in the field of education technology in the last decade. Many private and public universities have made use of language lab mandatory for students. The ICT tools have enhanced English language teaching and could remove the limitations of time, space and pace of traditional teaching. ‘Integrating ICT tools in teaching can lead to increased students’ learning competencies and increase opportunities for communication’. Scientific advancements in the field of Artificial Intelligence and systematic use of Web tools have produced several innovative products to assist the self-learning process. Innovative products such as digital multimedia control, wireless headsets 20 Information

Technology.

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and microphones and the interactive response pad are very useful for students learning languages for communication. These interactive tools are designed to enhance not only language teaching but also classroom grading and distance learning. With the focus on language, communication and culture, English language teachers are continually searching for better ways of accessing authentic materials that will improve their students’ knowledge and skills in the targeted areas. As the technology of the Internet has transformed communication around the world, it is natural that it should play a major role in a multimedia language laboratory for developing English language skills. BBC21 had started providing regular online lessons on English language learning. However, the crucial factor that leads to the failure of Computer-Aided Language Lab (CALL) or any other technology used in language education is not the failure of technology, but the failure to invest adequately in the growth of a person as a teacher and teacher training [4]. ICT22 is the demand of the time. ICT has a revolutionary role in modern communication. In each field, the use of ICT has boomed for digital literacy. In India, particularly, we are in a transition phase, moving from conventional to innovative, and that is why the older generation is yet not ready to cope with this rapid shift. In the field of education too, the same is the situation. For most young teachers, it is more comfortable to use ICT compared to the older ones. Government is also taking many such initiatives where through training and upgrading people’s skill this gap between users and non-users of technology can be reduced. ICT helps to make the globe easy to reach, easy to access. Anyone sitting in any corner of the world can generate, share and retrieve any information through just one click. ICT enables to share social, cultural, economic and scientific knowledge worldwide. Television, cell phone, Internet, video conferencing, teleconferencing, digital database are examples of ICT being used at each and every walk of life. We are living in the age of ‘Information Revolution’ and ICT being a powerful tool brings rapid upgrades and updates in the society. The field of education is also not an exception. Every discipline, every subject does have its digitally stored bulk of information worldwide, which is open to easy access. So far as language learning is concerned, most school going kids are now very much aware of using the mobile phone and tablets to learn and to play with. Language learning has always remained a common stratum for each and every person as per one’s own need and requirement. ICT has entered into this phase too. Today, many software and applications are available for learners of different age groups, starting with school children to adult learners. A paradigmatic shift has recently been globally observed towards the fact that English language must be learnt and not taught. Teaching English as the Second Language in higher technical education has become quite challenging for the teachers. Higher Technical Education teachers do not have enough time to prepare lesson plans.The teacher–student ratio is too high to follow the traditional pedagogy

21 British

Broadcasting Corporation. and Communication Technology.

22 Information

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of ‘Chalk and Talk’ Technical students do not get enough time and opportunity to get their queries solved in the traditional classroom. Moreover, the learning material which is provided is not updated and not apt to address the heterogeneous class. As a result, the students fail to stay connected and attached to the traditional classroom. A thorough, result-oriented assessment and analysis of the students is difficult when the number of students is more than 60 in a technical UG or PG class. Under these circumstances, one can find English teaching is going on in the language classroom, but unfortunately, the learning is found missing in such traditional class.

13.6 Digital Pedagogy and Teaching Strategies in Operation With the advent of technology in education, one can feel the paradigm shift in pedagogical innovations. The experts, in their research, could find the gap and vacuum in the area of teaching language through technology rather technology-enabled language teaching. Many creative teachers and corporates have come up with plenty of platforms which suffice the needs of the learners and teachers concerning language learning. One can find and filter the language lab software as per the needs and wants from the huge list of available options for language lab software. Let us be acquainted with some language lab software for the future reference (Fig. 13.7). Activity-based English Language Learning (ABELL) is the new adaptation of Task-based English Language Teaching (TBELT) introduced first by the Communicative English Teaching during the fag end of the twentieth century. Let us discuss how digital pedagogy is operated through some language lab software samples that are available in global market. The highlighted ones in the table above have been taken up for detailed discussion for hands-on operation of Digital Pedagogy. AI-based language lab software: FluentU (Ref: iii, Fig. 13.4): FluentU is a product of FluentFlix Limited based in Hong Kong. A team of passionate language teachers have come up together and designed this application which can assist the language teachers in teaching language. FluentU deals with nine different languages, i.e. Chinese, Spanish, French, German, Japanese, Italian, Russian, Korean and English. FluentU offers one of the richest libraries of 6000+ videos, which helps in broadening the learning experience of students. These videos have been selected from the various categories like Music, Movies, Television Commercials and News Clips. Inbuilt Features: FluentU has been uniquely designed language laboratory software which has many distinguished AI-based features: Amazing Video Player: FluentU offers an amazing video play which provides the scripts as subtitles, and on hovering the word, it shows the concerned image along with the word promptly. Authentic Content Commensurate—in-Class Platform: The learners can find the phrasal verbs, collocations, contextual explanations, examples for each word so that

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DIGITAL LAB APPLIANCES / SOFTWARE i. ii. iii. iv. v. vi. vii. viii. ix. x. xi. xii. xiii. xiv. xv. xvi. xvii. xviii. xix.

CACM Smart English Lab: IIT, Kharagpur ETNL Language Lab Software AI26-based Language Lab Software: FluentU Sanako Language Lab India (Comp-Point) Orell Interactive Digital Lab Studio CaLabo EX i-LOTUS Zybro Words Worth Digital Language Lab SANSSpace OpenSim. Multimedia –Based English Language Lab DiLL Skill Junction Interactive Smart Device Repeat After Us Global Arena AI-based Virtual English Communication Lab

Fig. 13.7 Language lab software at a glance

A B C

• Online Home Work • Offline Home Work

• E-Reader and E-Writer. This feature helps the student to do their assignments/homework from the college itself when they are logged into the network.

• Offline Home Work This opƟon helps the teacher to create homework and to assign it to students. The students can download these files and work at their convenience and upload to the teacher aŌer finishing their work

Fig. 13.8 Displaying software-based home work facilities

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they can be aware of all the dimensions of using the same word. FluentU also offers the option of managing personal vocab lists. Effective Personalized Video Lesson: FluentU offers an interactive platform ‘for videos which can be converted into an effective personalized video lesson. The learners can watch the video, learn the words by hovering on the progress bar of the video and can test his/her knowledge by answering the multiple choice questions mentioned on the canvas of the platform. Assignment Tracking: One of the most useful features of FluentU is the language teaching activities like whether the learner has watched the video, learnt the lesson, his/her accuracy and overall progress for the assignment/s.cher can assign the tasks to the learners and can track the progress of the learners’. Oréll Interactive Digital Lab Studio (Ref: vi, Fig. 13.4): Oréll is digital language software that helps the student’s accents and dialects effectively synonymous to native speakers. The headquarters of Oréll TechnoSystems (India) Private Limited is at Cochin. Overseas offices are in Sharjah, UAE and Colorado, USA. It is an ISO 9001:2008 certified company with 1 million delighted benefactors worldwide. There are more than 3000+ installations across 40 countries. Software Features: Oréll is comprehensive language lab software that gives multifaceted benefits to teachers and students alike. An overview of the software is given below for better understanding. Function Tools: The Function Tools contain multiple functions that help the teacher to manage learning activities in language labs. Function Tools include the following operations: a. Class View: This feature allows the teacher to view the total number of students in a class. The students who have logged into the application are enabled with their picture and name as icon, whereas the absentees are indicated by a shadow dark profile. b. Screen Viewer Intercom: Grouping if the teacher wants to communicate something to an individual student, they can make use of the INTERCOM feature. Here, the teacher has an option to conduct three modes of communication, i.e. through audio/video/text c. Class View: Communicator: The Communicator function is a one-way communication for the teacher. The teacher can communicate with selected students, a group or a whole class. The teacher can communicate with one or more students through instant messaging and audio and video chatting d. Sharing and Class View: Class View: Class View: Supervisor: This is one of the most interesting tools which can be used by the teacher for real-time monitoring of students’ activity. Incognito supervision of students’ activities takes place through the Supervisor feature. Besides, the teacher can listen discreetly to the voice of the selected students and view the webcam image of the student e. Interactive Teaching Tools: The interactive teaching tool includes various teaching aids. Interactive Teaching Tools control the operations as given below: i.

Lesson Studio

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The teacher can create her/his own audio/video lessons with the help of this feature. Even if a particular faculty member wants to go out on leave, s/he can assign the pre-recorded lesson, and the normal class working hours will never get interrupted. ii. E-Reader iii. Billboard iv. e-Writer v. Net Flick vi. X-Play vii. Video Streaming viii. Conferencing ix. Live Classroom x. E-Assignments: This audio recording can be sent to the Board for evaluation. The e-Assignments feature enables the teacher to view the assignments submitted by the students. Here, there are two windows, the first one is called as Feedback to be sent that contains the lessons that have to corrected and to be sent back to the student/s, whereas the Feedback sent contains the previously corrected assignments of various students. The following types of assignments deserve special mention: xi. Assessment of Speaking: Here, the teachers can group the students and then assign topics to them for discussion. The discussion can be recorded and then downloaded. xii. Assessment of Listening: Here, online exams can be conducted based on the assigned audio/video lessons. The teacher has an option to create questions in the form of MCQ, True or False, Fill in the blanks or Match the following. The submitted exams can be corrected, and the scores can be generated as per the CBSE format at a single click of a button. f. Generating Reports: English Language Lab teacher can generate performance report of the students concerned. g. Alert Function: The Alert function enables you to view the list of students who tried to call the teacher. Besides, when the student tries to call the teacher, an alert message will appear on the teacher’s desktop. If the teacher does not like to respond immediately, then he can click on the button ‘Not Now’. Else he can click on ‘Go To Alert’. h. The e-Board: Digital Board better known as e-Board is another exciting feature that helps the Teacher to emulate an ordinary classroom whiteboard, where the teacher can type or draw on the board and send that to a single, a group or all students. It lets the teacher write or draw on the board, and the students selected by the teacher can view this. i. The ‘Controller’: The ‘Controller’ allows the teacher control the student’s computer from their desk. The English Language Lab teacher can perform the following functions: a. Shutdown students’ system/s b. Reboot students’ system/s

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c. d. e. f. j.

Log off students’ system/s Lock students’ screen/s Enable Input (keyboard and mouse) in students’ system/s Disable Input (keyboard and mouse) in students’ system/s Home Work: The feature enables the teacher to set homework to students. The following functions are available in Homework feature (Fig. 13.8). k. Things-To-Do: The Things-to-do feature enables the teacher to create and assign lessons/works to selected students, a group or to a whole. Oréll provides complimentary study materials in English worth more than 5000 h, and the lessons here are arranged into 25 modules. Multimedia–Based English Language Lab (Ref: xiv, Fig. 13.4): K–Van solutions is an ISO 9001:2008 certified company which offers various range of products like Application development, Education, Web solutions, Data Solutions and Graphic Design. Multimedia English Language Lab is one of its products which come under the umbrella of educational services under which the company offers two labs; i. Multimedia English Language Lab and ii. Advanced Communication Skills Lab. Both English and Communication Skills labs have their distinct features, respectively. The labs are cost-effective, easy to install, and it has got a user-friendly interface. The advanced communication lab has been divided into two parts, i.e. Students’ Module and Teacher’s Console for effective and impactful management of learning. Functional Tools23 are as smart as depicted above under Oréll Interactive Digital Lab Studio. AI-based Virtual English Communication Lab (Ref: xix, Fig. 13.4): Designed by experts in the field, AI-based Virtual English Communication Lab deals in the linguistic and functional grammatical areas that require random practice sessions for the technical students of higher education. The areas that have achieved recurring emphasis in Virtual English Communication Lab are listed below (Fig. 13.9).

13.7 Adapting Digital Pedagogy in Technical Education Technical students of higher education will be able to write a type of Business Letter or Technical Report that they were not able to write before attending the digitized lab-based Technical Communication class.

23 See

Oréll Interactive Digital Lab Studio → Functional Tools (a.-K.) for details.

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English Vocabulary Skill i. English vocabulary skill is accepted as the requisite knowledge and skill of using English words and understanding of English word meanings. This skill is considered as a prime tool for gaining command over English communication. ii. English Grammar Skill English Grammar Skill refers to the methodical study and description of the structure of English language. This skill is considered as a primary tool for gaining command over English communication. iii. Common Errors in English Communication The Common Errors in English is intended to provide guidelines to technical students of Higher Education so that they can avoid the common mistakes made in their day to day use of the English language for formal and informal communication. iv. Business Communication Skill Business Communication refers to context-based communication that promotes a product, a service or an organization in order to enhance future prospects of the commodities or services of the company. Understanding ‘Business nuances’ and ‘Business Communication’ is essential for technical students who are budding enginers. v. Communication Skills Communication is the activity of exchanging meaningful information. Technical students require the following communication skills in English for abademic excellence and professional success: a. Intrapersonal Communication Skill b. Interpersonal Communication Skill c. Extra-personal Communication Skill d. Group Communication Skill e. Intra-organizational Communication Skill f. Inter-organizational Communication Skill g. Local Communication Skill h. Global Communication Skill i. Online Communication Skill j. Digital Communication Skill vi. Active Technical Listening Skill Active technical listening is a form of listening that emphasises on focussed listening to what a person or system says and involves an understanding of the context of the message as well as the emotions underlying a particular talk or speech. This module aims at focussed listening of the budding engineers to different situations and place –bound announcements. vii. Active Technical Reading Skill Active technical reading for comprehension involves a measure of how well an individual grasps what s/he has read as focussed item. viii. Active Technical Writing Skill Active technical writing is required for technical written communication in English. It provides guidelines to adopt a style of writing which enables technical readers to understand a technical process or a scientific concept. ix. Active Technical Speaking Skill / Presentation Skill Active technical speaking is required for technical oral communication in English. It provides guidelines to adopt a style of speaking which enables technical audience to understand a technical process or a scientific concept by paying heed to a SOP or LOP.28

Fig. 13.9 Virtual English communication lab in operation

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In answering the moot question, Technical English Communication faculty members are required to create the outcome-based objectives for the language labbased Technical English Communication class. Students may be involved in gamelike communication activities because the teacher’s objective is to have the Technical English Communication students highly motivated to adopt digitized lab-based English class. TCA24 and BCSA25 may be introduced for advanced technical English communication if the objective of the concerned faculty member is that technical students should acquire the following sub skills of technical English communication skill through proper adaptation of digitized Web tools and application of digital pedagogy: Enriching English Vocabulary Skill: The ‘Vocabulary Module’ is to be designed to enhance the repertoire of technical English vocabulary of the students. The visual vocabulary is one of the essential features of the Web tool-based language lab which is essential in digital pedagogy. Providing Inbuilt Multi-lingual Dictionary: As English is still treated as a foreign language in certain remote parts of India, many students feel shaky at the beginning of their technical class in the first few semesters. Therefore, the digital lab provides multilingual English dictionary which is inbuilt as virtual/visual dictionary along with pronunciation key so that the students can learn, remember and use the words appropriately. English Grammar Skill: Special technical lessons should be designed under the Grammar Module that can guide the technical students in enhancing the grammar consciousness of the students. The provision of providing automated grammar usages for random practice is one of the essential features of Digital Language Lab. Reading Comprehension Materials: Reading comprehension is the best way to gauge the learners’ understanding the proficiency level of English language. The digitized language lab provides ample examples and practice materials so that the technical students can master the reading comprehension skill within a very short time. The skill set that the students would acquire after random practice of technical Reading Comprehension Passages are as listed below: Global Comprehension Skill: This Reading/Listening sub skill guides to figure out the main format-related understanding of the Reading/Listening passage. i.

Scanning Skill: This Reading/Listening sub skill guides to figure out the main points and major issues through Critical Reading/Listening ii. Skimming Skill: This Reading/Listening sub skill guides to elicit examples and facts from the passage. iii. Reference Skill: This Reading/Listening sub skill guides to refer to the past anecdotes.

24 Technical 25 Business

Case Analysis. Case Study-based Activities.

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Integrated Skill Practice: Skill integration is highly beneficial for digital labbased pedagogy. The following skills will get enriched if digital pedagogy is applied in digitized lab: i.

Comparison/Analogical Skill. This Speaking/writing sub skill guides to compare between two or more business situations. ii. Arguing/Debate Skill. This Speaking/writing sub skill guides to put forward logical argument/s in favour of the sustainable decision using proper digital platform and debate style. iii. Phonetics/Phonological Skill. Language Lab facilitates the learners with different types of phonetics like Articulatory Phonetics, Acoustics Phonetics and Auditory Phonetics for guiding the students for picking up standard pronunciation of RP variety of English for technical communication. iv. Group Discussion Skill. This compound skill includes a number of ‘Listening/Speaking sub skills’ for grasping fundamental concepts and details of group discussion and to integrate the ability to master many interesting topics on which the learners can ponder and present their views for 15–20 min. v. Resume and Interview Skill. Preparing the students for placement requires mastery over the sub skills of Resume/CV writing and facing Interview. Multimedia English Language and Advanced Communication Skills lab enable the students in writing their Resumes and also prepare them with common interview questions. vi. Telecommunication Skill. In this fast-moving era, when telecommunication is essential for day-to-day communication, the students get hands-on practice for telephonic conversations and tips for effective telecommunication. vii. Technical Writing Skill. This module assists the students in developing their writing skills as it covers Official/Formal Letter writing, short/long Technical Report Writing and preparing Presentation Slides for Business Presentation. viii. Presentation Skill. For many technical students, the ‘presentation’ has become the matter of present ‘tension’ due to lack of practice and confidence. Hence, there is a special module on presentation, which involves the careful planning and tips for effective presentation of one’s best self.

13.8 Conclusion Dynamic lesson planning is the art of mixing and matching digital techniques, digitized learning activities and learning and teaching materials in such a way that an ideal balance is maintained for teaching Technical English Communication in a flipped class or online class in real time. During lockdown phase26 due to COVID-19, realtime digital online classes using different platforms like Google Duo, Zoom or Cisco WebEx became sine qua non in global academics including India. In other words,

26 From

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lockdown phase in India ushered in the boom of digital pedagogy, and it opened the lock gates of AI-based Virtual Lab for Higher Technical Education in India. In a general English language teaching course, faculty members often emphasize on teaching the basic four skills namely Listening, Speaking, Reading and Writing (LSRW). Digital pedagogy of Technical English Communication focuses on presentation skill and digitally controlled practice sessions of roughly tuned input/receptive skill activities or communicative activities that centre round the issue of real-life problem solving. Different student grouping models may be used for ensuring group dynamics and team work in digital framework based on AI-based Web tools. If the technical English communication faculty members have the scope of using a large variety of digital techniques and SBA27 , then the former can apply their honest effort for upgrading lesson plan to cope up with the requisite standard of digital pedagogy. In a digitized learning environment, Technical English Communication learner may feel positive about learning Technical English Communication in digital mode. They can finish their digital learning activities and take part in digitized evaluation process at their own pace and own urge. The technical students do never feel good about Humanities and Social Science classes as well as Law and Management classes due to theoretical approach followed in classroom situation under traditional pedagogy. As a result of DCA28 or SBA that are enjoyable, the technical students of higher education will learn useful communication nuances of English for technical communication within the scheduled period of time that they could never learn before in a traditional setting. Finally, we must not forget to boot that Digital Pedagogy will work effectively only if the control of operation is in the hands of a well-trained English faculty who is well-trained in both traditional and digital pedagogy and who is in possession of teaching sub skills as displayed in Fig. 13.2. To exemplify the statement, we may share the analogy of ‘leaving the students with e-study materials and not guiding them how to extract those for solving real life problems’ will have the same effect as ‘supplying the digital tools to the students without specifying the outcomes and providing training of effective handling of these Web tools’. English can be taught most effectively as technical English communication by using SBA and DCA. Digital tech-savvy learners of higher Technical Education need to be continuously monitored with the digital tools, and proper measures are to be taken by using control tools for effective application of digital pedagogy (Fig. 13.10).

27 Software-Based 28 Digitized

Activities. Communicative Activities.

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Abbreviation ABELL AI CALL CELT DCA DLE ELT ICT IT LSRW LE MCQ NBA RP SBA TBELT TLM

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Full Form Activity –Based English Language Learning Artificial Intelligence Computer –Aided Language Learning Communicative English Language Teaching Digital Communicative Activities Digital Learning Environment English Language Teaching Information & Communication Technology Information Technology Listening Speaking Reading and Writing Learning Environment Multiple Choice Question National Board of Accreditation Received Pronunciation Software-Based Activities Task –based English Language Teaching Teaching Learning Material/s

Fig. 13.10. Abbreviations-used in the context of digital pedagogy

References 1. Dey, S.K.: Teaching of English, 2nd Ed. 2014. Pearson Education, New Delhi (2013) 2. Dey, S.K.: Lesson Planning for Engineering English: NBA Course Planner Reviewed in the Light of Digital Lab-Based English Teaching in Engineering, vol 5, Issue 1, pp. 137–140. IJELLS. April 2016. 3. Skinner, B.F. The Behavior of Organisms: An Experimental Analysis. New York, London (1938) 4. Hirsch, B.D.: Digital Humanities Pedagogy: Practices, Principles and Politics. Open Book Publisher, New York (2012)

Chapter 14

A Novel Outcome Evaluation Model Blended with Computational Intelligence and Digital Pedagogy for UG Engineering Education Arpan Deyasi, Arup Kumar Bhattacharjee, and Soumen Mukherjee Abstract According to the guideline of Washington Accord, accreditation of every engineering curriculum is necessary in order to reach the minimum criteria set as the benchmark. In this context, data analysis is required based on the several input parameters which will ultimately lead to satisfy the program outcome. In this context, a novel model is proposed which reveals the importance of computational intelligence applied over specific input data so that the required benchmark can be achieved. Different blocks are shown for identification of phase-wise analysis, and corresponding to relevant blocks, specific soft computing techniques are applied so that the desired input data can be extracted which will be further processed in order to obtain desired human resources fit for both academia and industry. Novelty of the proposed model also lies in the fact that it also considers the change of didactic for benefit of the students, if necessary, whereas other literatures made their analysis based on specific pedagogical techniques. Role of academic and psychological counselor is taken into consideration as a part of feedback mechanism, and meaningful outcome may be obtained through proper implementation. Keywords Outcome-based education · Computational intelligence · Digital pedagogy · Engineering education · Program evaluation parameters

14.1 Introduction Technical education has become a topic of experiment in last few years owing to the demand of new generation’s choice of learning methods [1] as well as the requisite A. Deyasi (B) · A. K. Bhattacharjee · S. Mukherjee RCC Institute of Information Technology, Kolkata 700015, India e-mail: [email protected] A. K. Bhattacharjee e-mail: [email protected] S. Mukherjee e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 A. Deyasi et al. (eds.), Computational Intelligence in Digital Pedagogy, Intelligent Systems Reference Library 197, https://doi.org/10.1007/978-981-15-8744-3_14

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of new-age industries as well as academics. Several bodies are formed worldwide to combat with this requirement, and different outlines are proposed [2–5], which ultimately produced Washington Accord [6] and is now widely accepted across different countries due to its overall established significance. In the juncture of changing educational scenario for input–output-based system to outcome oriented system though the introduction of pedagogy following Washington Accord, data analysis becomes an integral part for evaluation for the students and corresponding upgradation of curricula. Because of large volume data generated in every academic semester, it is not possible to analyze it manually, and in this context, different soft computing and machine learning approaches become essential to analyze, synthesize, and evaluate data. Outcome obtained for every student can be classified for different categories which are related with course outcome, program outcome and moreover, can be mapped with institutional vision and mission. Thus, implication of modern data analysis approaches is extremely crucial in this point of view, and also relevant in present day context.

14.1.1 Digital Pedagogy: Significance in Present Education Scenario Pedagogy is now become the essential part of education system, replacing the conventional input–output-based techniques, where sophisticated technical tools and inventive schemes are intermixed with the obtained data for further upgradation to produce better quality of human resources. It is a truly a scientific approach where didactics is revolutionarized by introduction of technology. Teaching methodologies are changed through introduction of active/flipped/blended/adaptive/authentic learning methods for producing better outcome. As per the guideline of Washington Accord, every student has to satisfy the program outcome as dictated before initiation of the course, at least partially in each of them. Therefore, strategic principle has to be adopted at the beginning of each course where the planning or more precisely, program educational objective, will critically depend on the geographical, financial, and socio-economical class of the students they have received [7]. In this context, analysis of data should be considered as a very important part of redefining program outcome and also of objective [8]. Pedagogy is a modern science where engineering didactics is conceptualized by introduction of technology through proper amalgamation of experimental, computational, and self-learning methodologies in information-based teaching for producing better outcome. With introduction of appropriate pedagogical technique, it has been observed in different published reports that outcome and moreover, acceptance of the students are increasing in both academia as well as industrial sectors [9]. There exists the relevance of pedagogy, and when it is perplexed with digital mode [10], learners will be provided added flexibility where they can set their time and pace to adopt the

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concepts. Thus, learning becomes out of the classroom binding, and effective utilization of time with the availability of Internet creates a different level of environment, which suites the present-age students. Here lies the success of digital pedagogy, and that gives the concept of outcome-based education (OBE) model, accredited by all the educational Institutions, to measure the performance of students at the end of course. This will be discussed in the next part.

14.1.2 Outcome-Based Education Outcome-based education (OBE) is a new model of learning which is adopted to renew the conventional educational policy. OBE comes with lot of advantages as well as challenges for the academicians. OBE is implemented worldwide to support policy reform in education and has gained recognition internationally. The main objective of OBE is to determine whether students learn successfully or nor and what they learn rather than when and how they learn [11]. In this process, course curriculum is designed to emphasis more on the learning outcomes, that is, to determine the gap in learning of the students and what necessary skills are expected from the students before they join work force. And in this process, backtracking takes place of the entire system to redesign the curriculum, mode of instructions, delivery modes, and assessment techniques. Hence, the total course and program design are evaluated. Reform of education sector is in high demand for fulfilling the gap between industry needs and skill developed by the fresh graduates coming out of the colleges. Countries like USA, United Kingdom, South Africa, New Zealand to name a few who implemented OBE irrespective of much criticism [12–15]. Even there are reports, where universities have successfully implemented OBE and they have used the learning outcomes to design their curriculum. In fact, there are few cases where OBE has failed and was abandoned [16]. One term which is highly associated with OBE is Washington Accord. As per Washington Accord, the signatory countries must update and maintain their curriculum in such a way graduates of all accredited programs in the member states must met the academic requirements for entry in any member nation. OBE approach is strongly associated with e-learning approach which is conducted online. Study material, assessment, feedback, and grading all are performed with the help of Internet and in recent time, computational intelligence is also added to make the system more strong.

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14.1.3 Role of Computational Intelligence in Output Prediction Recently, several works are done on student learning outcome predication using computational intelligence (CI). These can be achieved in more perfect way when learning occurs in online instead of traditional classroom-based teaching– learning methodology. In traditional classroom teaching–learning, identification and recording are somewhat difficult. Online educations are nowadays taking more concrete form due to emergence of Massive Open-Online Courses (MOOCs) and Virtual Learning Environment (VLE) [17]. Student engagement predication is easy if some input variables are present like student educational pre-requisite and qualification, intermediate and final assessment score, number of assignment submitted, number and frequency of page viewed the number and frequency of clicks in item, time and frequency of study hour, etc. Machine learning (ML) is a useful tool in student output prediction. In machine learning, support vector machine (SVM), artificial neural network (ANN) [18], decision tree (DT), etc., are some of the useful algorithms. Some of the issues in this research are, is it possible to model student output prediction using ML algorithms, is it possible to predict most important features of student activity, what is the relationship of the student score with these features. Other than the different machine learning approaches, educational data mining (EDM) also holds a key role in student learning and engagement output prediction [19]. Several online open-source dataset are present to validate the model and result. Researchers are also generating their own dataset for their research work. Under graduate educational systems generally have three community components: student, instructor, and degree administrator [20, 21]. Pervasive computing-based software agent can be used to convert student passive learning skills to active learning skills. In some research work, big data technology is coupled with EDM to predict actual student learning patterns and student success rate [19]. As the world is living in a digital era, so, possible connection in between e-education and computational intelligent is to be identified properly to make a better model [22]. Educational data mining contributes a major role in student performance predication [23–29]. In last decade, due to Washington Accord and standardization in education system throughout the world and emergence of the terms like PO, CO, and PEO, the usefulness of EDM and CI is growing exponentially. The proper mapping in between PO and CO with PEO can be modeled using EDM and CI.

14.1.4 Why Outcome Measurement Is Important in Today’s Perspective? Educational area is not an exception where soft computing approaches are not used. Pedagogy in the digital era also explores soft computing techniques for the upliftment of education system [30]. Related to this context, the question arises about

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measurement of outcome and its significance in the big picture. It has been shown by the survey of various industries that engineering students after completion of undergraduate course are not suitable enough for the job (www.nasscom.in), and that creates huge amount of unemployment. However, it was noticed that only change in curriculum does not provide the desired result at all. Therefore, need of the hour is to change the approach of teaching–learning, as the existing input–output-based system does not satisfactorily map the performance of the learners. Henceforth, through involvement and incorporation of all the stakeholders [31] and the prolific utilization of the knowledge in real world, new pedagogical methodologies are adopted where conventional system is replaced by outcome-based model, defined as OBE model. Here, we have tried to focus the different explored methods already utilized by various researchers and educationists for the said purpose. Artificial intelligence is essential for large data set analysis, where human intervention is not necessary. In online activity-based learning system, several students get enrolled, which is much larger than traditional class room teaching. The different features of student and faculty members are automatically being recorded in the system. This dataset can be of huge dimension, which is manually not possible to analyze. Computational intelligence can be used for student’s performance and faculty feedback analysis. Here, comes the significance of measurement, and the didactical methods are reported in the next section.

14.2 Literature Review With progress of time, teaching–learning procedure and corresponding didactics have undergone a lot of research, primarily due to the poor quality of human resource generation which are not suitable with change in technology [32, 33], requirement [34, 35], or with the need of the hour for a short span of time-period. Didactics in conventional input–output system is not at par with the invention of scientific progress, different innovative teaching methods are recently proposed like activity learning [36] where project-based curriculum design is prioritized, flipped learning [37] where classrooms are used for solving assignments and doubt clearing sessions through peer-share method, blended learning [38] as a part of modified flipped learning on constraint-specific scenario. In the next section, details of different approaches are discussed wit their relevance in Indian technical education context.

14.2.1 Related Works on Digital Pedagogy Among different pedagogical methods, flipped learning is most popular where classroom and assignment submission procedure are reversed, and students can set their own time for learning. Assignments are solved in the class after their acquaintance of knowledge [39] and ‘think-pair-share’ methodology is applied for the solving

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design problems. However, the hindrance of this pedagogy is requirement of interrupted Internet at every learner’s home, which is a severe constraint. Also statistically, it is considered that every earner grab the concepts from the video lecture, which is nothing but a over-simplistic assumption, and therefore, expected outcome will grossly vary depending on the socio-economic status of the students. A much-needed solution is provided by active learning where over-dependence of internet is eliminated through project-oriented curriculum [40]. A mix of both the didactics is also implemented as experimental basis, called blended learning [41] where part of the flipped learning is considered through online elarning, and assignments are mostly converted into short-term projects, considered as active learning-based approach. Authentic learning is recently proposed which is a modified active learning methodology [42] where instructions are pre-set for execution of certain type of work. Different outcomes are reported by implementing various pedagogical techniques where geographical location of the learners, facility and infrastructure, resource availability make profound impact.

14.2.2 Related Works on Computational Intelligence Applied to Digital Pedagogy Even though application of Artificial Intelligence (AI) in Education (AIEd) is not a new term, it is there for more than 30 years and AI is adding various advantages to pedagogy, especially in the recent years. And still there is lot of scope where AI applications can make lot of contribution to teaching at higher education. In fact, like role of AI on every other field viz. engineering, medicine, literature, science, etc., application of AI in education is also on rise and is receiving lot of attention in current years. Different subparts of AI [43] are applied to education, namely (a) Collaborating learning with the help of AI-based software, (b) AI-based software which behave like private tutor known as intelligent tutorial systems (ITS), (c) software which assist learners by generating virtual reality. AI-based collaborative learning is a bit complex where some model is prepared that support group interaction or some discussion forum that helps the students on those topics which requires some group activity kind of work instead of one to one interaction [44, 45]. ITS can interact with a student in the form of dialogue and make certain decision based on certain algorithms to prepare a model to decide about the learning procedure of a student. This model is found to be very successful where oneto-one student, facilitator interaction is not possible like in distance education or open school environment [46–48]. Finally, intelligent virtual reality (IVR)-based education involves virtual agents like remote laboratories, etc. Even teachers or students may act like virtual agent. Nowadays, game-based learning is becoming very popular which is also a form of IVR environment [49]. In addition to these three categories, AI has

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huge role in assessment and feedback system. Various machine learning algorithms like neural network can accurately predict the student’s achievement [50]. When developing AI-based application for pedagogy, the orientations of these apps are either learner centric or facilitator centric or system centric [51]. Learner centric tools are developed considering the students needs like assistance in learning subjects where as in facilitator centric tools are developed to assist teachers in their teaching, administrative jobs, or any other role they play in their institution. System centric software is for the institution authority or administrators who monitor the entire organization including teaching, assessment, feedback, etc. Since AI has huge influence on developing applications that support and maintain academics and administration in teaching, data mining in academics and research associated with it, is very popular now a day’s [52]. Researches in this domain concentrate mostly in the area of teaching, students’ achievements, admission, feedback, and dropout. Content creation for the text or reference books to support customized learning known as ‘Smart content’ are introduced at all levels starting from elementary to post graduate level [53, 54]. This concept is extensively used and it is possible because of AI. Such smart content-based guide books supports creating chapter contents, summaries as well as content which can be archived for future use. Some of the well-known smart content developers are Cram101, Netex Learning. These platforms support facilitators to master new technical skills as well as to assistant those to improve the performance of their students based on continuous assessment and feedback, thus increasing the productivity [55]. Hence, computational intelligence tools offer students an environment which is judgment free and thus supports trial and error learning. This computational intelligence-based digital pedagogy tools have made a radical change in education industry regardless of experience and expertise of teachers [56]. So these new technology-based teaching helps in managing and improving the standards of education. AIEd have also made a huge contribution in laboratory-based courses, popularly known as virtual laboratories [49].

14.3 Measurement of Outcome As per the guideline of Washington Accord, every student has to satisfy the program outcome as dictated before initiation of the course, at least partially in each of them. Therefore, strategic principle has to be adopted at the beginning of each course where the planning or more precisely, program educational objective, will critically depend on the geographical, financial and socio-economical class of the students they have received. In this context, analysis of data should be considered as a very important part of redefining program outcome and also of objective. The outcome expected is that the learner, after completing the curriculum, should be considered fit for respective industry or academia, and that measurement should be accepted globally. Henceforth, measurement of outcome becomes a prioritized task, and computational intelligence should be deeply involved for near-accurate estimation [57, 58].

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Analysis of the data leads to predictive outcome, and also suggests the essential modifications required. With advancement in the field of artificial intelligence, machine learning, and deep learning propelled by the growth in hardware technology like GPU and with the availability of huge primary and secondary data, it is easy for the data scientist to work on them. This also substantiates the need of curricula development/modernization/restructuring. Based on the data received from the outcome of conventional educational system, intelligent diagnostic and feedback system are developed which will change the teaching strategies and corresponding teaching– learning process. Interactive teaching dashboard is therefore proposed in this purpose, from which one can easily map the relation between Program Outcome (PO), Program Specific Outcome (PSO), Course Outcome (CO), and Program Educational Objective (PEO).

14.3.1 Relation Between PO, PSO, CO, PEO for Outcome Evaluation Evaluation of curriculum is the result of achievement of different outcomes as defined by ABET. In this context, we have to think about the rubrics formed between PO, PEO, PSO, and CO. These rubrics are made in departmental level, and some of the findings are communicated to the institute level to set the vision and mission of the institute for next few years. In this chapter, we have proposed a model for outcome measurement in the present engineering curriculum in India, as described in Fig. 14.1, and use of computational intelligence is incorporated in later stage. In this section, we will define the complex relationship between the objectives and outcome, and in the next section, we will define the role of input parameters for the process. According to the Fig. 14.1, all the outcomes and corresponding objectives are defined for any arbitrary department. Program outcome for any specific discipline should be limited to 12 as per the guideline framed by Washington Accord, followed by AICTE (All India Council for Technical Education). The variable is represented by ‘i’. Objective can be set in order to satisfy the outcome, which is a case of reverse engineering process. The variable for PEO is defined by ‘j’, which can be limited to 6. Any program or discipline should consists of several courses, s by further splicing the PEO, we will get the courses. Every course should have some defined objectives, indicated by the variable ‘k’, and that can also be varied between ‘3’ and ‘6’. As per the pedagogical process, learner has to achieve a meaningful output at the end of every class, be theoretical, practical, or sessional. Henceforth, unit-level objective (UO) setup is very much required which will be limited per unit class. The corresponding variable is defined by ‘q’. It may be better if we can define the module objective which will be comprised of a few units, and that will help to structure the course. The block consists of PEO, COb (Course Objective), UO and MOb (Module Objective) sets the guideline and the direction of teaching-learning process, as indicated by Block 6.

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Program outcome is obtained after SWOT analysis by department, and that is considered as input variable for Block 6. It has also received data from Program Specific Outcome (PSO), which is nothing but the outcome learners will get just after completing the course, defined under Block 8. Henceforth, an optimization procedure is run to set the PO, after the initial setup. PSO is obtained from CO, only when learners are satisfied. Otherwise, a feedback loop is proposed for both learners and facilitators separately. These are separately discussed in the next section. In this context, it may be defined that number of PSO’s should be less than number of PO’s owing to the fact that POs are generalized outcome, and valid over a longer time duration, whereas PSOs are instantaneous measurement.

14.3.2 Relevant Parameters Required for Estimation For outcome measurement, we have considered various input parameters. Block 1 represents input from academia and industry, which, according to the feedback of the institute, can also actively take part in curriculum design. This block has a bi-directional relationship with the Institute. Block 5 consists of input from alumni, Guardian, and DAB. These inputs are vital for SWOT analysis of department, and therefore, may be considered as critical input parameters. They play a pivotal role in setting mission and vision of the department. Block 7 comprises of input parameters for teaching–learning process. The major component of Block 7 is the pedagogical process, which controls the TLP. Initially, one pedagogy is adopted, however, if found not suitable, then it is modified for better outcome. Role of academic counselor and psychological counselor is very crucial in this regard, as also mentioned in the National Assessment and accreditation council (NAAC) process, in the Indian scenario. Summative assessment of mission and vision of different departments set the vision and mission of the Institute, respectively, and henceforth, these variables w.r.t department may be considered as dependent inputs. Consisting all these functional blocks and inputs, now, we add the computational intelligence for proper evaluation of outcome, which is discussed in details in next section.

14.3.3 Role of C.I. for Outcome Evaluation Every under graduate and post graduate programme under university curriculum has several PO and CO. The PO and CO are derived from the Mission and Vision of the educational institution like University and colleges, which are also, derived from the current industry needs. The definition and mapping between Mission, Vision, PO, CO and PEO as per the input from government, industry, academician, psychologist,

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economist, teacher, parents, alumni, and student community are not an easy task. It requires several efficient machine learning algorithms and computational intelligence designed by the domain expert and data scientist. Educational data mining holds the key in designing those machine learning algorithms. Different machine learning (ML) or computational intelligence (CI) application can be used to the different section of the proposed model, shown in Fig. 14.2. In SWOT analysis, machine learning algorithm like decision tree (DT), support vector machine (SVM), artificial neural network (ANN), etc., can be used to properly classify between strength (S), weakness (W), opportunity (O), and threat (T). There might be some fuzzy overlapping between the strength and weakness or opportunity and threat, which can be easily identified by fuzzy logic tool. In case of Industry/Academia, input text mining can be use to find the important terminology from the input from industry expert and academician. Data cleaning which is a useful tool in data mining can be used to eliminate erroneous input from alumni/student/guardian feedback. Reasons of student’s failure can be estimated by using intelligent learning management software (LMS) which is directly related to teaching–learning process. Program/course outcome and teacher performance can be used to classify excellent, good, average, and below-average performing student and teacher using supervised and unsupervised algorithms like classification and clustering. Selection of proper pedagogical method and psychological counseling can be done by different computational intelligent educational feature selection methods (e.g., forward feature selection, backward feature selection) and feature ranking method (e.g., ReliefF, minimum correlation etc.). It may be mentioned in this context that inter-relations between different components are not shown in Fig. 14.2 in order to avoid mere repetition.

14.4 Application of Model for Institute-Level Accreditation The present model involves a comprehensive feedback mechanism from the student’s point of view as well as the rectification methods for both students as well as faculties. As per the guideline of Washington Accord, feedback mechanism is a mandatory part of the teaching–learning system, which can be used at the end of semester. However, the present model incorporates the same at any ongoing session as well as the endsemester mechanism so that it can eradicate the drawback when the system runs. For that purpose, role of academic counselor and psychological counselor are pivotal. They can provide the expert advice as a part of the continuous process for the students which effectively enhance the outcome fulfillment, and therefore, employability of the batch increases. Actually, role of these two counselors is mandatorily invoked in the present accreditation procedure, but normally Institutes seem a little bit reluctant for implementing these things. It can be visualized from the proposed model that proper incorporation of counselors at the appropriate level can help the institute to

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achieve its vision and mission. Here lies one of the novelties of the proposal for accreditation purpose. Obviously another major novelty of the model is the incorporation of machine learning techniques at different levels of data analysis. Normally, when an institute seeks for accreditation, they have considered all the available data for measurement. However, different soft computing approaches can be adopted at different levels to find out the correlation matrix, which will not only help to found the meaningful information, but can also help to achieve the desired goal in a scientific way. In Fig. 14.2, those approaches are vividly mentioned, and therefore, can rightly be considered as an important aspect of the proposal.

14.5 Setting Guideline for Future TLP Teaching–learning process, as per the modern pedagogical process, is always learner– centric. Henceforth, special care is to be required for learners who cannot cope up with the ongoing processes, owing to various reasons, may be psychological or socioeconomical. But it is not generally possible by teachers due to large number of batch sizes. Here comes the role of academic counselor which can suggest the change of pedagogical processes for those small numbers of students, and faculties will be

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helped to identify them and arrange different learning methods. This is one possible guideline may be espoused by the institute. Another major guideline lies in the proposal is the flexibility in terms of incorporating pedagogical techniques. Normally, one institute can adopt a single pedagogical method for TLP, so that they can set a definite outline and reference all the measurements. However, in the present socio-economic scenario, it may not be possible for all the faculties to adopt the same due to geographical locations or infrastructural availability to their residence. As a consequence, outcome of those faculties will be different from others. The present model suggests a change of didactics for those faculties so that they can overcome their non-performance. This is crucial in terms of computing overall outcome, and has direct impact on the accreditation process. Henceforth, new TLP method an also implement it, subject to the provision of the institute within its existing constraints.

14.6 Conclusion A novel model is proposed for evaluation of outcome in present technical education scenario for undergraduate students. The uniqueness of the model is that at different levels of data input, help of computational intelligence is taken so that outcome will be more authentic by minimizing the errors. Blocks of different levels are considered for ease of computation, and parameters, which will form the rubrics, can successfully be evaluated. The model is completely in accordance with the Washington Accord, and henceforth, can safely be applied in the ongoing technical education system. Though results are not provided, however, it may be stated that choice of pedagogical method critically affects the final outcome. Henceforth, for best output, proper choice of didactic as well as input from different stakeholders and counselors are vital, and there lies the success of this proposal.

14.7 Future Scope The proposed model can be validated by sufficient input data, and can be collected from any educational institution, better for any Indian universities. The model will give best-fitted output with the proper application of computational intelligence, and therefore, can effectively help any institute/department for accreditation purpose. A few more complex loops can be added depending on the situation, and that is why diagnostic survey at the beginning and end-semester survey will play the pivotal role. One very crucial factor is that the roles of academic counselor as well as psychological counselor become very important for attainment of outcome, and thrust should be given at this juncture also. The process is optimizing, and can only be improved with rerun in consecutive semesters.

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