Smart Education and e-Learning 2020 [1st ed.] 9789811555831, 9789811555848

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Smart Innovation, Systems and Technologies 188

Vladimir L. Uskov Robert J. Howlett Lakhmi C. Jain   Editors

Smart Education and e-Learning 2020

123

Smart Innovation, Systems and Technologies Volume 188

Series Editors Robert J. Howlett, Bournemouth University and KES International, Shoreham-by-sea, UK Lakhmi C. Jain, Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, Australia

The Smart Innovation, Systems and Technologies book series encompasses the topics of knowledge, intelligence, innovation and sustainability. The aim of the series is to make available a platform for the publication of books on all aspects of single and multi-disciplinary research on these themes in order to make the latest results available in a readily-accessible form. Volumes on interdisciplinary research combining two or more of these areas is particularly sought. The series covers systems and paradigms that employ knowledge and intelligence in a broad sense. Its scope is systems having embedded knowledge and intelligence, which may be applied to the solution of world problems in industry, the environment and the community. It also focusses on the knowledge-transfer methodologies and innovation strategies employed to make this happen effectively. The combination of intelligent systems tools and a broad range of applications introduces a need for a synergy of disciplines from science, technology, business and the humanities. The series will include conference proceedings, edited collections, monographs, handbooks, reference books, and other relevant types of book in areas of science and technology where smart systems and technologies can offer innovative solutions. High quality content is an essential feature for all book proposals accepted for the series. It is expected that editors of all accepted volumes will ensure that contributions are subjected to an appropriate level of reviewing process and adhere to KES quality principles. ** Indexing: The books of this series are submitted to ISI Proceedings, EI-Compendex, SCOPUS, Google Scholar and Springerlink **

More information about this series at http://www.springer.com/series/8767

Vladimir L. Uskov Robert J. Howlett Lakhmi C. Jain •

Editors

Smart Education and e-Learning 2020

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•

Editors Vladimir L. Uskov Department of Computer Science and Information Systems InterLabs Research Institute Bradley University Peoria, IL, USA

Robert J. Howlett Bournemouth University Poole, UK KES International Shoreham-by-sea, UK

Lakhmi C. Jain Faculty of Engineering and Information Technology Centre for Artificial Intelligence University of Technology Sydney Sydney, Australia KES International Shoreham-by-sea, UK Faculty of Science Liverpool Hope University Liverpool, UK

ISSN 2190-3018 ISSN 2190-3026 (electronic) Smart Innovation, Systems and Technologies ISBN 978-981-15-5583-1 ISBN 978-981-15-5584-8 (eBook) https://doi.org/10.1007/978-981-15-5584-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are solely and exclusively licensed 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

Preface

Smart education, smart e-learning, and smart universities are emerging and rapidly growing areas. They have a potential to transform existing teaching strategies, learning environments, educational activities, and technology in a classroom. Smart education and e-learning are focused at enabling instructors to develop new ways of achieving excellence in teaching in highly technological smart classrooms and smart universities, and providing students with new opportunities to maximize their success and select the best options for their education, location, learning style, and mode of learning content delivery. In accordance with the recent October 21, 2019, report “Gartner Identifies the Top 10 Strategic Technology Trends for 2020” by the Gartner company—the world leading research, strategic technology, and advisory company—“A smart space is a physical environment in which people and technology-enabled systems interact in increasingly open, connected, coordinated and intelligent ecosystems. Multiple elements—including people, processes, services and things—come together in a smart space to create a more immersive, interactive and automated experience.” In accordance with the recent November 25, 2019, report “2019 EDUCAUSE Top 10 IT Issues List” by the EDUCAUSE—the leading international professional community of IT leaders and professionals in higher education—“Security and student success are the two most pressing priorities of colleges and universities today. A modern IT infrastructure equipped with smart solutions can help in both of these areas.” We believe that our international professional research and academic communities—those who perform research in smart education, smart e-learning, smart universities, smart classrooms, smart pedagogy, and smart campus areas—made valuable contributions to those areas. From June 2014, the enthusiastic and visionary scholars, faculty, Ph.D. students, administrators, and practitioners from all over the world have an excellent opportunity for a highly efficient and productive professional meeting—the annual international conference on Smart Education and Smart e-Learning (SEEL).

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The KES International professional association initiated SEEL conference as a major international forum for the presentation of innovative ideas, approaches, technologies, systems, findings, and outcomes of research, and design and development projects in the emerging areas of smart education, smart e-learning, smart pedagogy, smart analytics, applications of smart technology and smart systems in education and e-learning, smart classrooms, smart universities, and knowledge-based smart society. The inaugural international KES conference on Smart Technology-based Education and Training (STET) has been held at Chania, Crete, Greece, June 18–20, 2014. The 2nd international KES conference on Smart Education and Smart e-Learning took place in Sorrento, Italy, June 17–19, 2015; the 3rd KES SEEL conference—in Puerto de la Cruz, Tenerife, Spain, June 15–17, 2016; the 4th KES SEEL conference—in Vilamoura, Portugal, June 21–23, 2017; the 5th KES SEEL conference—in Gold Coast, Australia, June 20–22, 2018; and the 6th KES SEEL conference—in St. Julian’s, Malta, June 17–19, 2019. The main topics of the SEEL international conference are grouped into several clusters and include but are not limited to: • Smart Education (SmE cluster): Conceptual frameworks for smart education; smart university; smart campus; smart classroom; smart learning environments; stakeholders of smart university; mathematical modeling of smart university; academic or institutional analytics; university-wide smart systems for teaching, learning, research, management, safety, and security; research projects, best practices, and case studies on smart education; partnerships, national and international initiatives, and projects on smart education; and economics of smart education; • Smart Pedagogy (SmP cluster): Innovative smart teaching and learning technologies; learning-by-doing; active learning; experiential learning, games-based learning, and gamification of learning; collaborative learning; analytics-based learning; flipped classroom; crowdsourcing-based learning; project-based learning; adaptive learning; badging-based learning; productive failure-based learning; smart learning analytics; research projects, best practices, and case studies on smart pedagogy; smart curriculum and courseware design and development; smart assessment and testing; smart university’s student/learner modeling; faculty modeling, faculty development, and instructor’s skills for smart education; university-wide smart systems for teaching and learning; learning management systems; smart-blended, distance, online, and open education; and partnerships, national and international initiatives, and projects on smart pedagogy; • Smart e-Learning (SmL cluster): Smart e-learning: concepts, strategies, and approaches; Massive Open Online Courses (MOOCs); Small Personal Online Courses (SPOCs); assessment and testing in smart e-learning; serious gamesbased smart e-learning; smart collaborative e-learning; adaptive e-learning; smart e-learning environments; courseware and open education repositories for smart e-learning; smart e-learning pedagogy, teaching, and learning; smart e-learner modeling; smart e-learning management, academic analytics, and quality

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assurance; faculty development and instructor’s skills for smart e-learning; research, design, and development projects, best practices, and case studies on smart e-learning; standards and policies in smart e-learning; social, cultural, and ethical dimensions of smart e-learning; and economics of smart e-learning; • Smart Technology, Software and Hardware Systems for Smart Education and e-Learning (SmT cluster): Smart technology-enhanced teaching and learning; adaptation, sensing, inferring, self-learning, anticipation, and self-organization of smart learning environments; Internet of things (IoT), cloud computing, RFID, ambient intelligence, and mobile wireless sensor network applications in smart classrooms and smart universities; smartphones and smart devices in education; educational applications of smart technology and smart systems; mobility, security, access, and control in smart learning environments; smart gamification; smart multimedia; and smart mobility; • “From Smart Education to Smart Society” Continuum (SmS cluster): Smart school; applications of smart toys and games in education; smart university; smart campus; economics of smart universities; smart university’s management and administration; smart office; smart company; smart house; smart living; smart health care; smart wealth; smart lifelong learning; smart city; national and international initiatives and projects; and smart society; • “Smart University as a Hub for Students’ Engagement into Virtual Business and Entrepreneurship (SmB cluster): Entrepreneurship and innovation at university; student role and engagement; student engagement with virtual businesses and virtual companies; virtual teams and virtual team working (technology, models, and ethics); university curricula for entrepreneurship and innovation (core and supplemental courses); new student goal—start his/her own business (instead of getting a job in a company); students and start-up companies (approaches, models, best practices, and case studies). This year, several subgroups in our international research and academic communities proposed in-depth discussion on a number of specific topics in smart education and smart e-learning. We strongly support those pioneering initiatives and are very thankful to the organizers and chairs of the following special sessions at SEEL-2020 international conference: • IS01: Smart University Development: Organizational and Managerial Issues (organizers and co-chairs: Prof. Anna A. Sherstobitova and Prof. Lyudmila V. Glukhova); • IS02: Smart Education and Smart Universities and their Impact on Students with Disabilities (organizer and chair: Prof. Jeffrey P. Bakken); • IS03: Mathematical Models in Smart Education and e-Learning (organizer and chair: Prof. Natalia A. Serdyukova); • IS04: Models of Professional Practice in Higher Education (organizers and co-chairs: Prof. Djordje Nadrljanski, Prof. Vladimir Simovic, and Prof. Mila Nadrljanski).

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One of the advantages of the SEEL conference is that it is organized in conjunction with several other Smart Digital Future (SDF) high-quality conferences, including Agent and Multi-Agent Systems—Technologies and Applications (AMSTA), Human Centred Intelligent Systems (HCIS), Intelligent Decision Technologies (IDT), Innovation in Medicine and Healthcare (InMed), and Smart Transportation Systems (STS). This provides SEEL conference participants with unique opportunities to attend also AMSTA, HCIS, IDT, InMed, and STS conferences’ presentations, and meet and collaborate with subject matter experts in those “smart” areas—fields that are conceptually close to smart education and smart e-learning. This book contains the contributions presented at the 7th International KES Conference on Smart Education and e-Learning (SEEL-2020), which being held as a virtual conference on June 17–19, 2020. It contains fifty-three high-quality peer-reviewed papers that are grouped into several interconnected parts: Part I— Smart Education, Part II—Smart e-Learning, Part III—Smart Pedagogy, Part IV— Smart Education: Systems and Technology, Part V—Smart Education: Case Studies and Research, Part VI—Smart University Development: Organizational and Managerial Issues, Part VII—Smart Education and Smart Universities and their Impact on Students with Disabilities, Part VIII—Mathematical Models in Smart Education and e-Learning, and Part IX—Models of Professional Practice in Higher Education. We would like to thank many scholars—members of the SEEL-2020 International Program Committee—who dedicated many efforts and time to make SEEL international conference a great success, namely: Dr. Farshad Badie (Aalborg University, Denmark), Prof. Jeffrey P. Bakken (Bradley University, USA), Dr. Elena Barbera (Universitat Oberta de Catalunya, Spain), Dr. Janos Botzheim (Budapest University of Technology and Economics, Hungary), Prof. Dumitru Burdescu (University of Craiova, Romania), Prof. Adriana Burlea Schiopoiu (University of Craiova, Romania), Prof. Nunzio Casalino (Guglielmo Marconi University, Italy), Prof. Michele Cole (Robert Morris University, USA), Prof. Robertas Damasevicius (Kaunas University of Technology, Lithuania), Prof. Jean-Pierre Gerval (ISEN, France), Prof. Lyudmila V. Glukhova (Volzhsky University, Russia), Dr. Foteini Grivokostopoulou (University of Patras, Greece), Assoc. Prof. Svetlana A. Gudkova (Togliatti State University, Russia), Dr. Karsten Henke (Ilmenau University of Technology, Germany), Prof. Alexander Ivannikov (Russian Academy of Sciences, Russia), Dr. Valery M. Kaziev (Kabardino-Balkarian State University, Russia), Prof. Aleksandra Klasnja-Milicevic (University of Novi Sad, Serbia), Prof. Natalya O. Mikhalenok (Samara State University of Railways, Russia), Assoc. Prof. Yana S. Mitrofanova (Togliatti State University, Russia), Prof. Andrew Nafalski (University of South Australia, Australia), Prof. Alexander D. Nemtsev (Volzhsky University, Russia), Prof. Toshio Okamoto (Kyoto College, Japan), Dr. Mrutyunjaya Panda (Utkal University, India), Prof. Ekaterina Prasolova-Forland (Norwegian University of Science and Technology, Norway), Dr. Isidoros Perikos (University of Patras, Greece), Dr. Danguole Rutkauskiene (Kaunas University of Technology), Prof. Demetrios Sampson (University of Piraeus, Greece), Prof. Dmitry L. Savenkov

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(Togliatti State University, Russia), Prof. Natalia A. Serdyukova (Plekhanov Russian University of Economics, Russia), Prof. Vladimir I. Serdyukov (Bauman Moscow State Technical University, Russia), Prof. Anna Sherstobitova (Togliatti State University, Russia), Prof. Cristi Spulbar (University of Craiova, Romania), Assoc. Prof. Ruxandra Stoean (University of Craiova, Romania), Prof Masanori Takagi (Iwate Prefectural University, Japan), Dr. Gara Miranda Valladares (University of La Laguna, Tenerife, Spain), Prof. Wenhuar Tarng (National Tsing Hua University, Taiwan), Prof. Dr. Toyohide Watanabe (Nagoya University, Japan), Prof. Yoshiyuki Yabuuchi (Shimonoseki City University, Japan), Prof. Larissa Zaitseva (Riga Technical University, Latvia), and Assoc. Prof. Vladimir N. Zhukov (Plekhanov Russian University of Economics, Russia). We are indebted to international collaborating organizations that made SEEL international conference possible, specifically: KES International (UK); InterLabs Research Institute, Bradley University (USA); Institut Superieur de l’Electronique et du Numerique ISEN, Brest (France); Science and Education Research Council (COPEC); and World Council on System Engineering and Information Technology (WCSEIT). Finally, we greatly appreciate the professional service for our research and academic communities completed by two senior students from the Department of Computer Science and Information Systems and research associates of the InterLabs Research Institute at Bradley University (USA), namely Marissa Ashley Anderson and Andrew D. Driscoll. They volunteered to proofread all chapters in this book and accomplished this gigantic work with a very high quality. We are very thankful to these young and motivated researchers for their excellent professional service. It is our sincere hope that this book will serve as a useful source of valuable collection of knowledge from various research, design, and development projects and useful information about current best practices and case studies, and provide a baseline of further progress and inspiration for research projects and advanced developments in smart education and smart e-learning areas. Peoria, IL, USA Shoreham-by-sea, UK Sydney, Australia June 2020

Prof. Vladimir L. Uskov, Ph.D. Prof. Robert J. Howlett, Ph.D. Prof. Lakhmi C. Jain, Ph.D.

Contents

Part I 1

Smart Education

Smart Learning Analytics: Student Academic Performance Data Representation, Processing and Prediction . . . . . . . . . . . . . . . . . . . Vladimir L. Uskov, Jeffrey P. Bakken, Kaustubh Gayke, Juveriya Fatima, Brandon Galloway, Keerthi Sree Ganapathi, and Divya Jose

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EIFEL—A New Approach for Digital Education . . . . . . . . . . . . . . Karsten Henke, Heinz-Dietrich Wuttke, and Johannes Nau

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FINMINA: A French National Project Dedicated to Educational Innovation in Microelectronics to Meet the Challenges of a Digital Society . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Olivier Bonnaud

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Effect of International Student Competition Experience on Smart Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Heather N. Yates, Sreemala Das Majumder, and Blake Wentz

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Knowledge-Based Model Representation for a Modern Digital University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tamara Shikhnabieva

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Effects on Girls’ Emotions During Gamification Tasks with Male Priming in STEM Subjects via Eye Tracking . . . . . . . . . . . . . . . . . Tabea Wanner, Tamara Wanner, and Veit Etzold

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Part II 7

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Smart e-Learning

Relevancy of the MOOC About Teaching Methods in Multilingual Classroom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Danguole Rutkauskiene, Greta Volodzkaite, Daniella Tasic Hansen, Madeleine Murray, and Ramunas Kubiliunas

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Contents

e-Learning Tools for Informal Caregivers of Patients with Dementia—A Review Study . . . . . . . . . . . . . . . . . . . . . . . . . . Blanka Klimova and Marcel Pikhart

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Automation of e-Workshop Project Control for Knowledge-Intensive Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Elena A. Boldyreva and Lubov S. Lisitsyna

10 Assessment of Student Work and the Organization of Individual Learning Paths in Electronic Smart-Learning Systems . . . . . . . . . . 113 Leonid L. Khoroshko, Maxim A. Vikulin, and Alexey L. Khoroshko 11 Implementation of Blended Learning into ESP for Medical Staff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Ludmila Faltýnková Part III

Smart Pedagogy

12 Providing an Ethical Framework for Smart Learning: A Study of Students’ Use of Social Media . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Michele T. Cole and Louis B. Swartz 13 “Product-Based” Master Program at ASCREEN Interactive Center . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Slavyana Bakhareva, Natalya Minkova, Irina Semyonkina, and Denis Yarygin 14 Developing a Conceptual Framework for Smart Teaching: Using VR to Teach Kids How to Save Lives . . . . . . . . . . . . . . . . . . 161 Tone Lise Dahl, Siw Olsen Fjørtoft, and Andreas D. Landmark 15 Blended Learning Technology Realization Using a Basic Online Course . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Lubov S. Lisitsyna, Marina S. Senchilo, and Evgenii A. Efimchik Part IV

Smart Education: Systems and Technology

16 Data Cleaning and Data Visualization Systems for Learning Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Vladimir L. Uskov, Jeffrey P. Bakken, Keerthi Sree Ganapathi, Kaustubh Gayke, Brandon Galloway, and Juveriya Fatima 17 Computational Linguistics and Mobile Devices for ESL: The Utilization of Linguistics in Intelligent Learning . . . . . . . . . . . 199 Marcel Pikhart, Blanka Klimova, and Ales Berger 18 Personal Generative Libraries for Smart Computer Science Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 Vytautas Štuikys, Renata Burbaitė, Ramūnas Kubiliūnas, and Kęstutis Valinčius

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19 The Virtual Machine Learning Laboratory with Visualization of Algorithms Execution Process . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 Vadim D. Kholoshnia and Elena A. Boldyreva Part V

Smart Education: Case Studies and Research

20 The Use of Students’ Digital Portraits in Creating Smart Higher Education: A Case Study of the AI Benefits in Analyzing Educational and Social Media Data . . . . . . . . . . . . . . . . . . . . . . . . 233 Svyatoslav A. Oreshin, Andrey A. Filchenkov, Daria K. Kozlova, Polina G. Petrusha, Lubov S. Lisitsyna, Alexander N. Panfilov, Igor A. Glukhov, Egor I. Krasheninnikov, and Ksenia I. Buraya 21 Using Smart Education Together with Design Thinking: A Case of IT Product Prototyping by Students Studying Management . . . . 245 Elvira Strakhovich 22 Application of Smart Education Technologies on the Disciplines of the Music-Theoretical Cycle in Musical College and University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 Svetlana A. Konovalova, Nataliya I. Kashina, Nataliya G. Tagiltseva, Lada V. Matveeva, and Denis N. Pavlov 23 Research on ‘Diteracy’ Measurement as a Smart Literacy Element . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 Seyeoung Chun, Jeonghun Oh, and Seongeun Lee 24 Internet Resource as a Means of Diagnostics and Support of Artistically Gifted University Students . . . . . . . . . . . . . . . . . . . . 283 Nataliya I. Kashina, Svetlana A. Konovalova, Anastasiya I. Suetina, Sergey I. Mokrousov, Elvira M. Valeeva, and Anastasia A. Gizatulina Part VI

Smart University Development: Organizational and Managerial Issues

25 Strategic Management of Smart University Development . . . . . . . . 293 Leyla F. Berdnikova, Irina G. Sergeeva, Sergey A. Safronov, Anastasia Yu. Smagina, and Aleksandr I. Ianitckii 26 Concepts of Educational Collaborations and Innovative Directions for University Development: Knowledge Export Educational Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 Svetlana A. Gudkova, Tatiana S. Yakusheva, Elena A. Vasilieva, Tatiana A. Rachenko, and Ekaterina A. Korotenkova 27 Project Management as a Tool for Smart University Creation and Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 Yana S. Mitrofanova, Valentina I. Burenina, Anna V. Tukshumskaya, and Tatiana N. Popova

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28 Human Resource Management System Development at Smart University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Leyla F. Berdnikova, Natalia O. Mikhalenok, Veronika A. Frolova, Victoria V. Sukhacheva, and Artem I. Krivtsov 29 Integration of Agile Methodology and PMBOK Standards for Educational Activities at Higher School . . . . . . . . . . . . . . . . . . 339 Anna A. Sherstobitova, Lyudmila V. Glukhova, Elena V. Khozova, and Raisa K. Krayneva 30 Intellectual Resources in the Development of Smart University . . . 351 Leyla F. Berdnikova, Natalia O. Mikhalenok, Svetlana V. Pavlova, Oksana G. Gortcevskaia, and Artem I. Krivtsov 31 VUCA-Managers Training for Smart Systems: Innovative and Organizational Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 Lyudmila V. Glukhova, Anna A. Sherstobitova, Elena N. Korneeva, and Raisa K. Krayneva 32 Economic and Organizational Aspects of University Digital Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 Tatiana N. Popova, Yana S. Mitrofanova, Olga A. Ivanova, and Svetlana B. Vereshchak 33 Security by Design Development Methodology for File Hosting Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 Ilya Danenkov, Daria Kolesnikova, Aleksandr Babikov, and Radda Iureva Part VII

Smart Education, Smart Universities and Their Impact on Students with Disabilities

34 Smart Universities: Gesture Recognition Systems for College Students with Disabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 Jeffrey P. Bakken, Nivee Varidireddy, and Vladimir L. Uskov 35 University Centers for Students with Disabilities: A Pilot Study . . . 413 Carrie Anna Courtad and Jeffrey P. Bakken 36 Face Recognition Systems for Smart Universities . . . . . . . . . . . . . . 423 Jeffrey P. Bakken, Nivee Varidireddy, and Vladimir L. Uskov Part VIII

Mathematical Models in Smart Education and e-Learning

37 Quasi-fractal Algebraic Systems as Instruments of Knowledge Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443 Natalia A. Serdyukova and Vladimir I. Serdyukov

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38 Model-Based Analysis for Smart University Development . . . . . . . 455 Lyudmila V. Glukhova, Svetlana D. Syrotyuk, Svetlana A. Gudkova, and Andrei Yu. Aleksandrov 39 University Financial Sustainability Assessment Models . . . . . . . . . . 467 Anna A. Sherstobitova, Maksim O. Iskoskov, Valery M. Kaziev, Marina A. Selivanova, and Elena N. Korneeva 40 Modeling of Residual Knowledge Estimation in Smart University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479 Yana S. Mitrofanova, Lyudmila V. Glukhova, Anna V. Tukshumskaya, and Tatiana N. Popova 41 Smart Algebraic Approach to Analysis of Learning Outcomes . . . . 491 Natalia A. Serdyukova, Vladimir I. Serdyukov, Sergey S. Neustroev, Elena A. Vlasova, and Svetlana I. Shishkina 42 Taxology in Smart University Economics: New Approaches to Teaching Taxation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503 Natalya V. Serdyukova and Ivan M. Kolpashnikov 43 Quality Assessment of Modular Educational Resources for Smart Education System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513 Yana S. Mitrofanova, Olga A. Filippova, Svetlana A. Gudkova, and Elena V. Ivanova 44 Soft Skills Simulation and Assessment: Qualimetric Approach for Smart University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527 Svetlana A. Gudkova, Tatiana S. Yakusheva, Anna A. Sherstobitova, and Valentina I. Burenina 45 Simulation for Evaluating the Feedback Effectiveness at e-Learning University System . . . . . . . . . . . . . . . . . . . . . . . . . . . 539 Olga A. Kuznetsova, Sabina S. Palferova, Svetlana A. Gudkova, and Oksana A. Evstafeva Part IX

Models of Professional Practice in Higher Education

46 Maritime Processes and Communications Management . . . . . . . . . 553 Djordje Nadrljanski, Mira Pavlinović, and Ante Sanader 47 Student Practical Training as an Education Factor . . . . . . . . . . . . 565 Djordje Nadrljanski and Kristina Vidović 48 Training of Student Practical Training Managers . . . . . . . . . . . . . 575 Mila Nadrljanski, Veronika Nemetschek, and Ante Sanader 49 Professional Preparation of Teachers for New Models of Student Practical Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585 Mila Nadrljanski, Mira Pavlinović, and Slavko Šimundić

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50 The Role and Importance of Digital Practical Training of Personnel Management Students . . . . . . . . . . . . . . . . . . . . . . . . 595 Sanja Frkić, Irena Mašće, and Kristina Vidović 51 Contributions of Digitization and Professional Practice to the Study of Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605 Goran Radović, Sanja Frkić, and Veronika Nemetschek 52 Practical Training of Nursing Students . . . . . . . . . . . . . . . . . . . . . . 615 Ana Roguljić and Ilija Guteša 53 Increase in Cooperation Between Industry and Postgraduate Education via Open Labs and Mobile Labs in South Africa . . . . . . 623 Momir Tabakovic, Marc-Oliver Otto, Walter Commerell, Csilla Csapo, and Herman Vermaak Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 631

About the Editors

Professor Vladimir L. Uskov, Ph.D., is a Professor of Computer Science and Information Systems and Director of the InterLabs Research Institute at Bradley University. He obtained his Ph.D. and M.Sc. in Computer Science from Moscow Aviation Institute – Technical University, Russia. He has previously worked at the University of Cincinnati and Michigan State University (USA), Moscow State Technical University and Moscow Aviation Institute – Technical University (Russia), and various other universities in Japan, Italy, Germany, the Netherlands and France. His current research is focused on engineering of software/hardware systems and tools for Smart University, Smart Education, Smart Classroom and design of innovative teaching and learning strategies for highly technological Smart Pedagogy. He has published 3 textbooks, 7 chapter books and more than 340 papers in international journals and conference proceedings. Professor Robert J. Howlett, Ph.D., is the Executive Chair of KES International, a non-profit organization that facilitates knowledge transfer and the dissemination of research results in areas including intelligent systems, sustainability and knowledge transfer. He is a Visiting Professor at Bournemouth University in the UK. His technical expertise is in the use of intelligent systems to solve industrial problems. He has been successful in applying artificial intelligence, machine learning and related technologies to sustainability and renewable energy systems; condition monitoring, diagnostic tools and systems; automotive electronics and engine management systems. His current research work is focused on the use of smart microgrids to achieve reduced energy costs and lower carbon emissions in areas such as housing and protected horticulture. Professor Lakhmi C. Jain, Ph.D., M.E., B.E. (Hons), Fellow (Engineers Australia), is with the University of Technology Sydney, Australia, and Liverpool Hope University, UK. Professor Jain serves the KES International for providing a professional community the opportunities for publications, knowledge exchange,

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cooperation and teaming. Involving around 5,000 researchers drawn from universities and companies worldwide, KES facilitates international cooperation and generates synergy in teaching and research. KES regularly provides networking opportunities for professional community through one of the largest conferences of its kind in the area of KES.

Part I

Smart Education

Chapter 1

Smart Learning Analytics: Student Academic Performance Data Representation, Processing and Prediction Vladimir L. Uskov, Jeffrey P. Bakken, Kaustubh Gayke, Juveriya Fatima, Brandon Galloway, Keerthi Sree Ganapathi, and Divya Jose Abstract Smart education requires design, development, implementation and active use of innovative systems, technologies, teaching and learning strategies and approaches that are based on various data sources in academia, modern mathematical methods in data statistics and data analytics, and state-of-the-art data-driven approaches and technologies. The availability of tools that measure, collect, clean, organize, analyze, process, store, visualize and report data about student academic performance in an academic course and/or student overall academic progress in the selected program of study has given rise to the field of learning analytics for student academic success. Student data representation, processing and prediction, as a central part of learning analytics system, are crucial topics for researchers and practitioners in academia. Our vision for the engineering of smart learning analytics—the next generation of learning analytics—is based on the concept that this technology should strongly support (a) various “smartness” levels of smart education such as adaptivity, sensing, inferring, anticipation, self-learning and self-organization, and (b) main types of data analytics of smart education such as descriptive, diagnostic, predictive and prescriptive analytics. This paper presents the up-to-date findings and outcomes of the research, design and development project at the InterLabs Research Institute at Bradley University (USA) aimed at application of a quantitative approach to student academic performance data representation, hierarchical levels of data processing, multiple quality evaluation criteria to be selected and used, and high-quality student academic performance data prediction in smart learning analytics systems.

V. L. Uskov (B) · K. Gayke · J. Fatima · B. Galloway · K. S. Ganapathi · D. Jose Department of Computer Science and Information Systems and InterLabs Research Institute, Bradley University, Peoria, IL, USA e-mail: [email protected] J. P. Bakken The Graduate School, Bradley University, Peoria, IL, USA e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_1

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1.1 Introduction Smart education is based on active use of main “smartness” features such as (a) adaptation, (b) sensing (i.e., getting data from sensors), (c) inferring (i.e., data processing and logical conclusions), (d) self-description and self-learning, (e) monitoring of incoming data and anticipation, and (g) self-organization and self-optimization. As a result, the institutional data, student data, academic and learning analytics and corresponding advanced software systems and technologies are crucial components of smart education. Importance of student data and analytics. In accordance with the recent EDUCAUSE Report “Top 10 IT Issues, 2020: The Drive to Digital Transformation Begins,” “… Institutions [of higher education] that haven’t implemented data governance and data architecture will need to do so in order to begin using AI [artificial intelligence] and analytics to deliver personalized, timely student services. Understanding the profile of students who would do well in their institutions is another key factor. The aim is to allow students to be the best judges of what they would like to achieve and where” [1]. The elevation of student success as a priority for higher education, coupled with the use of LMSs and tools that allow for cross-functional data integration, has led to increasingly diverse analytics. Over the past decade, institutions have employed analytics for functional support of enrollment management and general student progress, and less commonly for assessing student learning outcomes and individual student success. That is now changing, as the administratively focused measurement of institutional success is now being complemented by fine-grained analysis of student engagement and performance data [2]. Literature review. The performed literature review clearly shows that the researchers in student academic performance (SAP) data, educational data mining (EDM) and learning analytics (LA) area primarily concentrate on (1) computersupported learning analytics (CSLA), (2) computer-supported predictive analytics (CSPA), (3) computer-supported behavioral analytics (CSBA) and (4) computersupported visualization analytics (CSVA). For example, in [3], the authors examined 402 studies from 2000 till 2017. “The majority of the studies on CSPA (253 articles or 63.25%) focused on the use of predictive functions or continuous variables to suggest effective ways to improve students’ learning and performance, as well as evaluating the appropriateness of the learning materials. Most of the studies on the CSBA dimension (80 articles, 20%) discovered models of student behavior, actions and knowledge. Other studies on CSVA (38 articles, 9.50%) focused on methods to visually explore data (using interactive graphs) to highlight useful information and produce accurate and data-informed decisions” [3]. The completed analysis of designated and multiple additional publications (e.g., [4–9]) relevant to SAP data, EDM and LA areas clearly shows that, unfortunately, those publications do not provide a systematic approach to (1) possible forms of SAP data representation and (2) control flow for SAP data processing in LA systems.

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However, as it is shown below, these topics make significant contributions to the high quality, accuracy and effectiveness of LA systems.

1.2 Our Past Works Our vision. Our vision for the engineering of smart learning analytics (SLA)— the next generation of LA—is based on the concept that this technology should strongly support (a) “smartness” levels of smart education such as adaptivity, sensing, inferring, anticipation, self-learning and self-organization, and (b) main types of data analytics of smart education such as descriptive, diagnostic, predictive and prescriptive analytics [10–12]. Student data flow in the SLA system. In our past publications [10, 11], we developed and described the architectural model of the developed SLA system; it is given in Fig. 1.1. It presents the main SLA structural components and links between them. We would like to emphasize that it includes (1) engines for all four types of analytics

Fig. 1.1 Student data flow in the architectural model of the SLA system [10]

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as prescribed by the Gartner’s Analytics Ascendancy Model, including descriptive, diagnostic, predictive and prescriptive analytics engines, (2) hierarchical processing levels for various levels of LA (institutional, departmental, course, faculty, student) and (3) “smartness” levels as crucial and mandatory components of an SLA system, including (a) adaptation; (b) sensing (i.e., getting data from sensors); (c) inferring (logical conclusions); (d) system’s self-description and self-learning; (e) monitoring and anticipation; and (g) system’s self-organization and self-optimization. Sources of student data. In our past work [10], we identified the main sources of student data; they include but are not limited to: (1) student profile data such as lists of students’ current courses, courses taken so far and courses remaining in the program of study; (2) data about student major(s), minor(s), concentration(s), grade point average (GPA) score, etc.; (3) SAP data such as scores obtained for various learning assignments, tests, quizzes, laboratories, examinations, etc., in academic course(s); (4) student learning-related activities’ data such as a frequency of logs to learning management systems (LMS) and/or Web sites of online courses, time spent to watch video lectures or participate in online discussions, the number and quality of posted questions in discussion forums, etc.; (5) course syllabi, student program of study and required credits; (6) academic department-related data such as admission criteria, offered academic programs and courses, requirements to graduation, laboratory or technological fees, etc.; and (7) college- and/or university-related data such as constraints on credit hours per semester—min and max—to be taken by a student in one semester, constraints on the number of courses to be taken in January, May and summer terms, student-to-faculty ratios, max enrollment in a course and other possible inputs.

1.3 Project Goal and Objectives Project goal. The goal of the ongoing, multi-aspect research, design and development project at the InterLabs Research Institute at Bradley University (USA) is focused on SAP data representation, processing and prediction of outcomes in the SLA system. Project objectives. To achieve this goal, the project team concentrated on the following project objectives: • • • •

SAP data representation in the SLA system; Specifics of original SAP data; Hierarchical levels of SAP data processing at the SLA system; Benchmarking of the effectiveness of three groups of machine learning (ML) algorithms for SAP data processing; • Active use of various types of evaluation criteria for the quality and effectiveness of ML algorithms for SAP data prediction; • Active use of various libraries or systems with a collection of software implementations of ML algorithms and

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• Recommendations for researchers and practitioners in academia regarding the obtained quality of ML-based processing and prediction of SAP data in the SLA system. A summary of up-to-date project findings and outcomes is presented below.

1.4 Project Outcomes: The SAP Data Representation, Processing and Predication of Final Outcomes 1.4.1 SAP Data Representation Forms in the SLA System SAP data in the SLA system may be stored in at least five different forms. This is because (1) different universities may have different policies on the evaluation of learning assignments submitted by students and (2) different forms of SAP data may have an impact on the accuracy of SAP data processing and prediction in the SLA system. 1. “Original SAP input data” (ORI) form: The original SAP input data set (IDS) in various possible formats as delivered (sent) by the instructor to the SLA system; an example of SAP IDS in ORI representation is presented in Fig. 1.2. 2. “SAP data in Absolute Numbers” (ABS) form: This is the SAP data representation where each assignment is represented by the raw points of those assignments to a selected total course points. Each assignment then has its max and individual

Fig. 1.2 SAP data representation in the SLA system: an example of SAP data in ORI form

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Fig. 1.3 SAP data representation in the SLA system: a fragment of SAP data in ABS form

entries in whole numbers; a corresponding example of SAP data ABS representation in SLA is given in Fig. 1.3; for example, a maximum number of points available for a midterm are 100 points, for Laboratory 1–50 points, etc. It may happen that a student completed extra assignments in the course project and received extra points (as student #6 did for the course project presentation in Fig. 1.3). 3. “SAP data in Relative Numbers” (REL) form: This is the SAP data representation where each course learning assignment is represented by a percentage amount of its portion of the course final grade. In this case, each assignment has a max score of its percentage contribution to the final grade and each score entry is a further percent of that maximum. A corresponding example of SAP data REL representation in SLA is given in Fig. 1.4; for example, Test 1 contributes about 2.42% to the final grade, midterm—11.04%, etc. 4. “SAP data in Detailed Letter Grades” (DLG) form: This is the SAP data representation where each assignment is represented by the letter grade a student earned or would have earned on that assignment on a detailed +/− A/B/C/D/F scale, for example, A+, A, A−, B+, B, B−, C+, C, C−, etc. A corresponding example of SAP data DLG representation in SLA is given in Fig. 1.5; for example, for Test 1 students obtained A+, B, D−, B−, A, B−, and other detailed grades. 5. “SAP data in Regular Letter Grades” (RLG) form: This is the SAP data representation where each assignment is represented by the letter grade a student

Fig. 1.4 SAP data representation in the SLA system: a fragment of SAP data in REL form

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Fig. 1.5 SAP data representation in the SLA system: a fragment of SAP data in DLG form

would have earned on that assignment on the standard A/B/C/D/F scale, for example, A, B, C, D, etc. This SAP data form is close to the DLG representation of SAP data but without detailing of each grade. A disclaimer: All the data used in this research project and presented in this paper are anonymized; e.g., neither names of actual students nor students’ correspondence to those data was disclosed at any point in this research. Additionally, the input data presented in this paper have been proportionately modified in such a way that they are different from actual scores by any current or past student in that course. At the same time, the input data used in our experiments adequately reflect the learning outcomes of students in those courses.

1.4.2 SAP Data Specifics The effectiveness of SAP data processing in the SLA system and quality of processed outcomes (e.g., a prediction of a student’s final grade in a course after completion of about 50–70% of learning assignments in a course) significantly depends on SAP data specifics. It is necessary to note that SAP data specifics are different from SAP data inconsistencies. For example, in our case—the scores obtained by students for various course learning assignments—the specifics of SAP data are as follows: 1. A relatively small number of records in the ORI data sets—a total of about 300– 500 records are available (e.g., this is the case when 30–50 students take the same course every semester, and as a result, we have SAP IDS available for 8–10 most recent consecutive semesters); thus, ML algorithms that are focused on big data processing are expected to be ineffective for our case. 2. Different “weights” of the course learning assignments in different semesters (e.g., Test #1 may be out of 25 points in one semester and out of 22 points in another semester); as a result, SAP data REL form is a preferred form among all other forms for the effective updating, processing and predication of SAP data in the SLA system. 3. Multiple “bonus” assignments for extra points (e.g., students can get up to 25 extra points in the entire course); as a result, there will be multiple rows of data with “null” or 0 values in those columns in SAP ORI IDS (Fig. 1.2).

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4. Frequent changing of a total number of points available for a course (e.g., a total of 500, 505, 510, 495 points available in the same course but in different semesters); as a result, REL form is a preferred SAP data form among other forms. 5. Valid “null” and 0 values in ORI IDS (e.g., these are the valid cases when a student did not submit a required homework assignment or missed an in-classroom test, quiz or assignment). 6. Additional learning assignments may appear in the same course over the time, and as a result, in the future SAP IDS this is a consequence of natural evolution of the same course over multiple semesters or several years. 7. Different instructors of the same course in various semesters or instructors of different sections in the same semester may use different sets of assignments for students and/or different grading approaches similar to ABS, DLG or RLG forms.

1.4.3 SAP Data Processing in the SLA System: Hierarchical Levels In the general case, the SAP IDS in the SLA system may be consecutively processed on five hierarchical levels; the organization of those levels is similar to “nested loops” in programming, i.e., from the innermost loop to the outermost loop. The proposed levels are as follows: 1. Level # 1 (the base hierarchical level or the innermost loop in SAP data processing): The level of various possible forms of SAP data representation in SLA, including ORI, ABS, REL, DLG and RLG forms (see Sect. 1.4.1. above for details); in the general case, DLG and RLG forms of SAP data representation may use additional weight coefficients for every learning assignment(s)—this is an important feature because the same B+ grade for two different learning assignments that are out of 10 points and 50 points should make different contributions to the course’s final grade. 2. Level # 2: The level of various “training–testing” ratios for the training of ML algorithms in supervised learning; usually, those ratios are 90–10% (90–10), 80– 20% (80–20) and 70–30% (70–30); in the general case, a ratio may be arbitrarily up to a researcher. 3. Level # 3: The level of implementation of ML algorithms into at least three different groups of ML models, including (a) REG—regression models, (b) CLA— classification models, and (c) REG+CLA—combined regression + classification models. 4. Level # 4: the level of various possible implementations of ML algorithms, for example, (a) from well-known ML libraries such as the scikit-learn library, (b) built-in ML algorithms in various systems such as Weka, Microsoft Power BI,

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Azure ML, Google AutoML, SAS, Dataiku and other systems, and (c) selfdeveloped software implementations (codes) for ML algorithms. 5. Level # 5 (the top hierarchical level or the outermost loop in SAP data processing): the level of evaluation criteria in the success of both numeric prediction and classification, including: (a) correlation coefficients (CC) with numeric values in the range from 0 to 1, where “1” corresponds to the best result; (b) meansquared (MS) errors; in this case, the lesser value is better; (c) root-mean-squared (RMS) errors (lesser is better); (d) mean absolute (MA) errors meaning the average of the magnitude of the individual errors without taking account of their sign (lesser is better); (e) relative-squared (RS) errors; (f) root-relative-squared (RRS) errors; and (g) relative-absolute-squared (RAS) errors [13]. Additionally, the kappa coefficient is used to measure inter-rater reliability for qualitative (categorical) items.

1.5 Project Outcomes: Benchmarking of ML Models Benchmarking goal. The goal of each experiment was to predict the student final grade (or score) in the course based on a set of completed to-date course learning assignments by the student. The prediction is based on available SAP data (“knowledge”) for all course learning assignments for past students in that course. At the very beginning of the course, only a score for the first course learning assignment (or first feature) is available; as a result, the system predicts student’s final grade based on just that feature. As we move forward in the course, more features become available for data processing using various ML models. These additional features are used to predict student final grade more accurately based on the “more scores for course learning assignments we gain for a particular student—better quality of student final grade’s prediction we obtain” concept. The impact of the availability of the growing number of features on the quality of student final grade’s prediction can be gauged by monitoring calculated numeric values of various evaluation criteria (Figs. 1.6 and 1.7); the obtained trends are presented by graphs in Figs. 1.8, 1.9, 1.10 and 1.11. Benchmarking environment. We have actively used the Weka, Dataiku, scikit-learn and Google Cloud systems for a comprehensive benchmarking of ML algorithms’

Fig. 1.6 Obtained outcomes for quality of SAP (in “relative numbers” form) processing and prediction outcomes with the Weka system for regression ML models

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Fig. 1.7 Obtained outcomes for quality of SAP data (in “detailed letter grades” form) processing and prediction outcomes with the Weka system for classification ML models and multiple designated evaluation criteria

Fig. 1.8 Obtained outcomes for quality of SAP data (in “relative numbers” form) processing and prediction with the Weka system for regression ML models and calculated values of “correlation coefficient” evaluation criteria (Y-axis) for various course learning assignments (X-axis)

effectiveness and accuracy in terms of described SAP data representation forms, processing and quality of prediction of final outcomes (A note: the research team received a huge amount of reliable outcomes for ML models benchmarking in the given SAP IDS; however, due to the limited space available in this paper, we present in Figs. 1.6, 1.7, 1.8, 1.9, 1.10 and 1.11 just a few examples of obtained outcomes; other relevant obtained outcomes are available upon written request).

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Fig. 1.9 Obtained outcomes for quality of SAP data (in “relative numbers” form) processing and prediction with the Weka system for regression ML models and calculated values of “mean absolute error” (in %) evaluation criteria (Y-axis) for various course learning assignments (X-axis)

Fig. 1.10 Obtained outcomes for quality of SAP data (in “regular letter grades” form) processing and prediction with the Weka system for classification ML models and calculated values of “correctly classified instances” evaluation criteria (Y-axis) for various course learning assignments (X-axis)

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Fig. 1.11 Obtained outcomes for quality of SAP data (in “regular letter grades” form) processing and prediction with the Weka system for classification ML models and calculated values of “mean absolute error” (in %) evaluation criteria (Y-axis) for various course learning assignments (X-axis)

The Weka system [14] is an excellent open-source collection of plug-in algorithms for a machine learning workbench including artificial, neural network algorithms, and artificial, immune system algorithms. The research team tested various permutations of components from all five hierarchical levels (as they are described in Sect. 1.4.3 above) in conducted numerical experiments. We have used a large number of ML algorithms in conducted experiments from all three groups—REG, CLA and REG+CLA. The particular ML algorithms that are mentioned in Figs. 1.6, 1.7, 1.8, 1.9, 1.10 and 1.11, include (1) naïve Bayes (NB), (2) support vector machine (SVM), (3) random forest (RF), (4) random tree (RT), (5) linear regression (LiR), (6) logistic regression (LoR), (7) k-nearest neighbors (kNN), (8) multilayer perceptron (MLP), (9) J48 and (10) decision stump (DS) algorithms. SAP IDS were provided from one of the computer science courses. The number of rows in the “cleaned” SAP master data set of past students was equal to 114. The number of rows in SAP IDS data for current students is 21. Benchmarking outcomes. The examples of obtained outcomes regarding quality of SAP data processing and prediction with the Weka system, using designated ML models, are presented in Figs. 1.6, 1.7, 1.8, 1.9, 1.10 and 1.11. The obtained outcomes in Fig. 1.6 represented the quality of SAP data processing and prediction outcomes with the Weka system, using (1) the regression ML models and (2) multiple designated evaluation criteria. The research environment settings

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for this case are as follows: (a) SAP data are in “absolute number” form, and (b) “training–testing” ratio is set up to 90–10 for all ML models. The best outcomes for this case were produced by the SVM model and the worst ones by decision stump (DS) model for all evaluation criteria used. The obtained outcomes in Fig. 1.7 represent the quality of SAP data processing and prediction with the Weka system, using (1) the classification ML models and (2) multiple designated evaluation criteria. The research environment settings for this case are as follows: (a) SAP data are in “detailed letter grades” form, and (b) “training–testing” ratio is set up to 90–10 for all ML models. The best outcomes in this case were produced by (1) the SVM model for three designated evaluation criteria, including correctly and incorrectly classified instances and kappa statistics, (2) MLP model—for two evaluation criteria, including mean absolute and relative absolute errors and (3) RF model—for two remained evaluation criteria, including root-mean-squared and root-relative-squared errors. The worst outcomes for this case were produced by (1) J48 model based on calculated values of three evaluation criteria and (2) SVM and RT models—each based on the values of two evaluation criteria (Fig. 1.7). The obtained outcomes in Figs. 1.8 and 1.9 correspond to regression ML models, and in Figs. 1.10 and 1.11 classification ML models. The obtained outcomes in Fig. 1.8 represent the quality of SAP data processing and prediction with the Weka system, using (1) the regression ML models; (2) a single “correlation coefficient” evaluation criteria; the calculated numeric values of this criteria are in the [0, 1] range, where “1” is the best (Y-axis); and (3) multiple course learning assignments (X-axis). The research environment settings for this case are as follows: (a) SAP data in “relative numbers” form, and (b) “training–testing” ratio is set up to 90–10 for all ML models. The best outcomes in this case were produced by SVM, linear regression and random forest models and the worst ones by kNN model. The obtained outcomes in Fig. 1.9 represent the quality of SAP data processing and prediction with the Weka system using the same settings as in Fig. 1.8, but for “mean absolute error” quality evaluation criteria. The Y-axis represents the numeric values of MA criteria (in %), and the X-axis—multiple course learning assignments. (A note: For “mean absolute error” criteria, smaller calculated values are better— they represent a higher quality of SAP data processing and prediction.) The best outcomes were produced by SVM, linear regression and random forest models and the worst ones by decision stump model. The obtained outcomes in Fig. 1.10 represent the quality of SAP data processing and prediction with the Weka system, using (1) the classification ML models; (2) a single “correctly classified instances (CCI)” evaluation criteria; the calculated numeric values of CCI criteria are in the [0, 1] range, where “1” is the best case (Y-axis); and (3) multiple course learning assignments (X-axis). The cumulative values of CCI criteria (i.e., the most right values in Fig. 1.10) represent the quality of prediction of student final grades in a course; they were calculated in each ML model by adding “knowledge” about particular CCI numeric value for every course learning assignment (i.e., from left to right in Fig. 1.10). The research environment

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settings for this case are as follows: (a) SAP data in “regular letter grades” form, and (b) “training–testing” ratio is set up to 90–10 for all ML models. In this case, the best outcomes were produced by SVM, naïve Bayes model, multilayer perceptron, logistic regression and random forest ML models and the worst results by random tree and J48 ML models. The obtained outcomes in Fig. 1.11 represent the quality of SAP data processing and prediction with the Weka system, using the same settings, as in Fig. 1.10, but for “mean absolute error” (MA) evaluation criteria. The Y-axis, in this case, represents the calculated numeric values of MA evaluation criteria (in %), and the X-axis—multiple course learning assignments. (A note: For MA criteria, smaller calculated values are better—they represent a higher quality of SAP data processing and prediction.) The best outcomes for this case were produced by multilayer perceptron, logistic regression and naïve Bayes ML models and the worst results by the SVM model. A summary of the obtained research outcomes is presented in Table 1.1. The analysis of the obtained research data in Table 1.1 clearly shows that, in order to provide the highest quality of SAP data representation, processing and prediction of final outcomes in the SLA system, it is very important to select a proper (a) SAP data representation form, (b) a particular ML model and (c) evaluation criteria for quality of SAP data prediction. Table 1.1 SAP data processing and prediction with the Weka system: a summary of obtained outcomes SAP data form

Group of ML models

Best outcomes demonstrated by the following models

Ranking

Recommended for use in SLA systems

SAP data in relative numbers (REL) form

Group of regression ML models

SVM

Best

Yes

Linear regression

2nd best

Yes

Random forest

3rd best

SAP data in absolute numbers (ABS) form

Group of regression ML models

SVM

Best

Yes

Linear regression

2nd best

Yes

Random forest and multilayer perceptron

3rd best

SAP data in detailed letter grades (DLG) form

Group of classification ML models

Multilayer perceptron

Best

Yes

Logistic regression

2nd best

Yes

Random forest

3rd best

SAP data in regular letter grades (RLG) form

Group of classification ML models

Multilayer perceptron

Best

Yes

Logistic regression

2nd best

Yes

Naïve Bayes

3rd best

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1.6 Conclusions and Next Steps Conclusions. The performed research and obtained findings and outcomes enabled us to make the following conclusions: 1. SAP data representation, processing and prediction in the SLA system are complex multi-level hierarchical processes; its organization has a significant impact on the effectiveness and quality of SLA outcomes. 2. SAP data in the SLA system may be represented in various forms (Sect. 1.4.1); some forms are more effective for regression models of data processing, and the other forms—for classification models (Table 1.1). 3. SAP data have various unique specifics (Sect. 1.4.2); they should be taken into consideration by researchers and practitioners to provide effective SAP data processing and prediction in the SLA system. 4. SAP data should be processed consecutively on various hierarchical levels in the SLA system—those levels work like “nested loops” in programming (Sect. 1.4.3). 5. The SAP data representation in “relative numbers” form is strongly recommended to be used in the SLA system because it provides the highest level of flexibility in adding new SAP IDS and modification of existing master data sets. 6. Implementations of ML models from various ML systems and libraries can be integrated and used for SAP data processing and prediction in SLA system, including (a) well-known ML libraries, for example, the scikit-learn library, and (b) various systems with built-in ML algorithms such as Weka, Microsoft Power BI, Azure ML, Google AutoML, SAS, Dataiku and other systems. Based on our experience, we recommend highly effective Weka system and scikit-learn library for integration with the SLA system. 7. For the SAP data in “relative numbers” and “absolute numbers” forms, the following regression ML models demonstrated the best outcomes in our experiments in terms of quality of SAP data processing and prediction: (a) best—SVM, (b) 2nd best—linear regression, and (c) 3rd best—random forest (Figs. 1.6, 1.8 and 1.9). As a result, we strongly recommend SVM and linear regression ML models to be used for quality processing and prediction of SAP data in “relative numbers” and “absolute numbers” forms in the SLA system (Table 1.1). 8. For the SAP data in “detailed letter grades” and “regular letter grades” forms, the following classification ML models demonstrated the best outcomes in our experiments in terms of quality of SAP data processing and prediction: (a) best— multilayer perceptron, (b) 2nd best—logistic regression, and (c) 3rd best—random forest and naïve Bayes (Figs. 1.7, 1.10 and 1.11). As a result, we strongly recommend multilayer perceptron and logistic regression ML models to be used for quality processing and prediction of SAP data in “detailed letter grades” and “regular letter grades” forms in the SLA system (Table 1.1). Next Steps. Based on obtained research/design/development findings and outcomes, the next steps in this research project are aimed at comprehensive testing of the developed InterLabs SLA system in terms of quality of SAP data processing and prediction

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based on (1) anonymous SAP data about past students’ academic performance in various courses at our department and (2) anonymous SAP data from faculty of other departments at Bradley University.

References 1. Grajek, S.: Top 10 IT issues, 2020: the drive to digital transformation begins, EDUCAUSE Review (2019). https://er.educause.edu/articles/2020/1/top-10-it-issues-2020-thedrive-to-digital-transformation-begins 2. EDUCAUSE Horizon Report: Teaching and Learning Edition, https://library.educause.edu/ resources/2020/3/2020-educause-horizon-report-teaching-and-learning-edition 3. Aldowah, H., et al.: Educational data mining and learning analytics for 21st century higher education: a review and synthesis. Telematics Inform. 37, 13–49 (2019) 4. Ogor, E.: Student academic performance monitoring and evaluation using data mining techniques, electronics, robotics and automotive mechanics conference (CERMA 2007). Morelos 2007, 354–359 (2007) 5. Asif, R., et al.: Predicting student academic performance at degree level: a case study, I.J. Intell. Syst. Appl. 01, 49–61 (2015) 6. Hamza, H., et al.: Student academic performance prediction model using decision tree and fuzzy genetic algorithm. Procedia Technol. 25(2016), 326–332 (2016) 7. Guo, B., et al.: Predicting students performance in educational data mining, 2015 Int. Symp. Educ. Technol. (ISET), Wuhan, 125–128 (2015) 8. Halde, R.R., et al.: Psychology assisted prediction of academic performance using machine learning. In: 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) pp. 431–435. Bangalore (2016) 9. Pereira, F.D. et al.: Early performance prediction for CS1 course students using a combination of machine learning and an evolutionary algorithm. In: Proceedings of 2019 IEEE International Conference on Advanced Learning Technologies (ICALT), pp. 183–184. Maceió, Brazil (2019) 10. Uskov, V.L. et al.: Smart learning analytics: conceptual modelling and agile engineering. In: Uskov, V.L., Howlett, R.J., Jain, L.C., (eds.) Smart Education and e-Learning 2018, pp. 3–16, Springer. ISBN: 978-3-319-92362-8 (2018) 11. Uskov, V.L., Bakken, J., Shah, A., Hancher, N., McPartlin, C., Gayke, K.: Innovative InterLabs system for smart learning analytics in engineering education. In: Proceedings of the 2019 IEEE Global Engineering Education Conference (EDUCON), pp. 1363–1369. IEEE, Dubai, UAE (2019). https://ieeexplore.ieee.org/Xplore/home.jsp, https://doi.org/10. 1109/educon.2019.8725145 12. Uskov, V.L., Bakken, J., Shah, A., Byerly, A.: Machine learning-based predictive analytics of student academic performance in STEM education. In: Proceedings of the 2019 IEEE Global Engineering Education Conference (EDUCON), pp. 1370–1376. IEEE, Dubai, UAE (2019). https://ieeexplore.ieee.org/Xplore/home.jsp, https://doi.org/10.1109/educon.2019.8725237 13. Witten, I. et al.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn, Morgan Kaufmann Publishers (2011). ISBN 978-0-12-374856-0 14. WEKA: The workbench for machine learning. https://www.cs.waikato.ac.nz/ml/weka/

Chapter 2

EIFEL—A New Approach for Digital Education Karsten Henke, Heinz-Dietrich Wuttke, and Johannes Nau

Abstract The acceptance of digitally supported teaching has increased considerably in recent years. At the same time, however, the demands on availability, usability, and granularity of the offerings are also increasing. Especially in the study of STEM subjects, the combination of theoretically taught basics and their application and consolidation in the form of internships in the basic subjects is unfavorable in terms of time, as these are usually subject to a tight time schedule and do not provide opportunities for individual learning processes in terms of either space or time. The EIFEL project aims to develop and test the basis for the digital support of learning processes in computer science within the framework of a fellowship. On the one hand, this will allow new forms of teaching such as flipped classroom, problem-based learning with the help of virtual and remote-controlled laboratory experiments, and on the other hand, individual practice-oriented learning through flexible access to experiments in terms of time and place. It is one of eight projects supported by the Thuringian Ministry of Economics, Science and Digital Society and the German Stifterverband.

2.1 Introduction For the second time, the Thuringian Ministry of Science and the German Stifterverband Bildung, Wirtschaft, Innovation (Donors’ Association for Education, Industry, and Innovation) have awarded fellowships for new, digital teaching formats at Thuringian universities. The eight projects of the program “Innovations in Digital University Teaching” will start in January 2020 and run over a period of two years K. Henke (B) · H.-D. Wuttke · J. Nau Ilmenau University of Technology, Ilmenau, Germany e-mail: [email protected] H.-D. Wuttke e-mail: [email protected] J. Nau e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_2

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[1]. The fellowships are individual, person-bound grants that provide the fellows with the freedom and resources they need to implement the teaching innovations. “Universities must successfully prepare students for the digital world of living and working,” sums up Andreas Schlüter, Secretary-General of the Stifterverband. “With our support program ‘Digital Fellowships’ we are creating new formats for learning and teaching digital skills.” The Fellowship program is part of the “Thuringian Strategy for Digitization in Higher Education,” which was developed by the Ministry of Science together with the universities. The Free State of Thuringia has been providing more than 1 million euros annually since 2018 for its implementation. Creating a math app, learning in a virtual factory, or learning at home with a smartphone or tablet: Digitalization offers students and teachers alike great opportunities. Digital tools are increasingly being used in the projects. The Fellows’ concepts range from the digital development of the “Flipped Classroom” concept to virtual reality in the seminar room. For example, students of mechanical engineering can slip virtually into the role of a plant supervisor and interactively operate the plant. The fellowships are aimed at teachers who want to try out new forms of digitally supported teaching in their courses. A jury consisting of teachers and students from various disciplines, experts in digital university teaching, and representatives of university didactics selected the projects for funding. The decisive selection criteria were the expected contribution of the planned teaching innovation to the further development of digital teaching in the respective subject, the desired continuity, and the transfer potential. The aim of the program is to • create incentives for the development and testing of digitally supported teaching and examination formats (e.g., MOOCs, flipped/inverted classroom, games, simulations, e-examinations) or the redesign of modules and study sections with consistent use of digital technologies; • promote the exchange on (digital) higher education teaching and the dissemination of the teaching innovations developed by networking the Fellows across universities and countries; • contribute to the consolidation of digital university teaching in the universities themselves. In this article, one of the eight funded projects will be presented in more detail and the planned steps for successful implementation will be explained. The aim of the project “EIFEL—Entwicklung und Erprobung interaktiver Inhaltsobjekte für den Einsatz in digital gestützten Lehr- und Prüfungsszenarien” (which means: Development and Testing of Interactive Content Objects for Use in Digitally Supported Teaching and Examination Scenarios) is to develop and test the basis for the digital support of learning processes in the field of computer engineering [2]. On the one hand, this will allow the design of new forms of teaching such as flipped classroom and problem-based learning with the help of virtual and remote-controlled laboratory experiments. On the other hand, it will allow individual practice-oriented learning through access to experiments at flexible times and places.

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2.2 Planned Innovations in the Teaching Process The acceptance of digitally supported teaching has increased significantly in recent years, but at the same time, the demands on availability, usability, and granularity of the offerings are also increasing. In the study of STEM subjects, the combination of theoretically imparted basics and their application is important for the consolidation of knowledge. Particularly, practical teaching in form of hands-on training is unfavorable in terms of time, as these are usually subject to a tight schedule and do not provide opportunities for individual learning processes, which require more freedom in terms of space and time. Starting in the winter semester 2020, the class “Computer Engineering” will be redesigned. Up to now, it has been taught as part of the fundamental classes for all engineering courses. Currently, it is using a classical approach combining lectures, exercises, and practical training. The subject of the class is, among other things, the systematic design of digital circuits. In the class, theoretical knowledge is first taught in lectures, which will be deepened by means of tasks in the exercises. However, students will not come into contact with a circuit that works in practice until one semester later during the hands-on training. This organizational separation of theory and practice makes it considerably more difficult to understand the material. With the reorganizing of the class, we aim to closely integrate the theoretically taught and practically tested knowledge. The following procedures are planned, which are methodically based on the principles of problem-based teaching. Before the lecture date, students are given practical tasks to solve with the help of interactive content objects available in the learning management system (LMS) Moodle of the Ilmenau University of Technology. The content objects are designed in such a way that they can be used to solve these tasks, but also to record the activities of the students and to provide the lecturer with aggregated information about the difficulties in solving them. During the lecture, the results are used to address the difficulties in a dedicated way by explaining the associated theory and setting similar tasks for the preparation of the exercises. The exercises are then followed by a more detailed discussion of specific problems of the exercise group participants. By designing the interactive content objects accordingly, it should also be possible to use methods of learning analytics for further improvements to the class. For example, it could be used to determine which tasks are statistically the most difficult for students. Simulations and online experiments are suitable as interactive content objects, which are tailored to the respective thematic focus of the course. The following section describes an example of such an interactive content object.

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2.3 Remote Laboratory as an Interactive Content Object The subject of the course is, among others, the introduction of Boolean expression algebra. At first, the difference between constants and variables shall be recognized. For this purpose, the students get the task to control an elevator model in the remote laboratory GOLDi [3–6]—drive from the lowest to the highest floor without errors. The first step after the login is the experiment configuration. Based on the flexible grid structure of the GOLDi system, an experiment consists of two components: the various control units (e.g., FSM, microcontroller, FPGA) and the electromechanical physical systems (e.g., elevator, 3-axis model, or warehouse). Instead of using a real physical system, also, a simulated virtual model can be used. Figure 2.1 shows an example of the experiment configuration with available control units as well as physical systems. Let us assume that the student first tries to control the model with constants (0 = on, 1 = off). To do this, he informs himself about the actuators that come into question (see Fig. 2.2). For example, he decides to control the motor that moves the elevator upward (y00) and assigns it the constant 1. After he has started the experiment with this constellation, he will notice that the protection unit of the remote laboratory stops the experiment, because the cabin is still driven to go up, although it has already reached the top floor. A corresponding error message will be generated as feedback (see Fig. 2.3). The student will quickly realize that the elevator model cannot be controlled without errors by using only constants. Next, he will find out which sensors he might be able to use to drive to the top floor, stop there and not cause an error (see Fig. 2.4).

Fig. 2.1 GOLDi experiment configuration

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Fig. 2.2 Actuators of the elevator hardware model

Fig. 2.3 Termination of the experiment by the protection unit with a corresponding error message as feedback

He will most certainly decide for the sensor x26 (elevator on floor 4) and then control the motor for the upward control (y00) as long as the sensor x26 is not yet active (y00 = !x26). As soon as sensor x26 is activated, the motor (y00) will also stop. He managed to move the elevator to the top floor without triggering an error (see Fig. 2.5). After entering the appropriate Boolean expressions and starting the experiment, the movement in the animation can be observed and students receive immediate feedback on their actions. If the input is incorrect, either the animation will not work properly (e.g., different from the planned movement) or an error message will be

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Fig. 2.4 Sensors of the elevator hardware model

Fig. 2.5 Error-free control of the elevator model via Boolean expressions

displayed and the lift will be stopped. In this way, students can experiment with interactive content objects at any time and acquire practical skills. Results achieved by the students are to be used for evaluation in the future.

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2.4 Considered Aspects For the successful implementation of the planned concept and the corresponding extension of existing interactive content objects, the following aspects had to be considered more closely: • What is the motivation for the planned education innovation? Which problem should be addressed? The acceptance of digitally supported teaching has increased considerably in recent years, but at the same time, the demands on availability, usability, and granularity of the offerings are also increasing. The general availability of the Internet has led to the fact that it is now common practice, to rather search the Web using specific keywords related to a problem instead of reading a user manual for the device in question. A problem-oriented, digitally supported teaching program meets this working method. Fine-granular completed offers are expected, which one can use depending upon (previous) knowledge conditions purposefully for the acquisition of the demanded authority. The start-up financing of the follow-up program is to be used to prepare existing materials in such a way that they meet these new requirements and can be used in different learning scenarios such as “flipped classroom” and online laboratory experiments. • To what extent is this a central problem in teaching in the respective field of study? The central problem of training in the STEM subjects is the combination of theory and practice, i.e., the application of learned theory to practical examples, best achieved through active action in the form of practical training. For reasons of capacity, handson training is subject to strict time management and does not give students the opportunity to carry out these experiments according to their personal learning style. Strict time management means, for example, that some students have to carry out laboratory exercises before the content has been covered in the corresponding lecture or the practical training is delayed to a later semester in which other classes require full attention. The Fellowship aims to address this problem by implementing finely granulated, self-contained teaching units that include both theory and related practical applications. It is planned to develop new concepts of combining theory and practice on three selected examples and to test them in practice in the summer semester 2020. • What goals should be pursued with the planned teaching innovation? The planned teaching innovation aims to better meet the learning and working habits of a generation grown up using the Internet. The students should continuously deal with the teaching offer in order to avoid so-called bulimic learning. The closer linkage of theoretical knowledge and direct practical application on the basis of problems to be solved should contribute to better learning results in STEM subjects. Another objective is to also use the developed interactive content objects as finegrained teaching units for systematical repetitions or in preliminary classes.

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• In which degree program and sections should the planned education innovation be implemented? Is it a compulsory, elective, or optional area of study? The planned education innovation is to be used in computer science education in the first semester and second semester as part of the compulsory class “Technical Computer Science” and for preparatory classes in the introductory phase. • How can success and possible risks be assessed after testing the teaching innovation? The design of interactive content objects makes it possible to record detailed information about their use. This information can be used for an accompanying evaluation. In addition, many years of experience from examinations in recent years are available, which can be used as a comparison to the results achieved with the educational innovation. In the project “BASIC Engineering School,” methods of evaluation and competence measurement were developed, which we will also apply here [7, 8]. There are risks in the acceptance of the teaching methods by the students. However, there are positive experiences with the “BASIC Engineering School” project, in which methods of the flipped classroom and the closer integration of theory and practice have already been tested in smaller groups. The project results will show whether this can also be carried out in large groups of up to 500 students, as this is the usual number of students enrolled in the basic courses of engineering subjects. • How should the planned education innovation be sustained? The interactive content objects are placed in the Moodle platform of the Ilmenau University of Technology and are thus permanently available. With the introduction of the new curriculum in the winter semester 2020/21, they will be anchored as an integral part of the course in the pedagogical concept of the course design. • To which teaching-learning situations—also in other disciplines—can the planned education innovation be applied? The planned teaching innovation initially relates to computer science education but can be transferred to the other STEM subjects. Courses that include practical training or teach design and calculation methods that can be processed with digital support using practical examples are particularly suitable for this purpose. Examples are physical experiments in online laboratories, algorithm animations of practical computer science, 3-D models of mechanical engineering as well as calculations and simulations of signal behavior.

2.5 Planned Steps for the Implementation In order to successfully implement the planned project within the next two years, the following (coordinated) steps are planned:

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1. Requirements definition for interactive content objects (experiments/tasks) Based on many years of experience in the field of e-learning (online learning modules in Moodle as well as Web-based examinations) and remote engineering and remote laboratories, the requirements for the new interactive content objects are to be developed. Among other criteria, the following will be considered: • New, interdisciplinary practice-relevant tasks (in accordance with current technological developments, IoT), • Changed learning and user habits of students, • Interconnection of the system components involved (Moodle, RemoteLab, online test system), • Portability of the developed systematics to other teaching areas, e.g., physics, mathematics, electrical engineering (synergy effect). One focus here is that it should be possible in the future to automatically evaluate student activities (learning analytics). 2. Development of online test tasks For each individual content object, a corresponding LMS-based question catalog is developed in accordance with the teaching content of the course “Computer Engineering,” which is to be used both as an entrance test (i.e., a test for the knowledge required for a specific content object) and as a final test after editing the content object. Random questions are to be generated for the student from this catalog. 3. Development of the evaluation software All online test tasks answered by the students should be evaluated automatically. An analysis of the learning progress should provide the student with information about existing gaps and give him or her hints about content objects to be worked through again. The evaluation software must have appropriate interfaces for the individual content objects and to the online test tasks. The evaluation software should also allow evaluation and statistical comparison with previous years or comparison groups. For this purpose, the experience and scientific preliminary work of the department should be used. 4. Design of sample solutions For the interactive experiments, corresponding exemplary solutions are to be developed, which can be used for discussion in lectures and exercises. 5. Analysis of the reactions of the evaluation software using examples In this step, the behavior of the evaluation software to corresponding online test tasks shall be analyzed. 6. Test of the evaluation software with different, partly incorrect answers

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Hereby, the ability of the evaluation software to give the appropriate, individual hints for improvement according to the student’s learning progress is to be tested. 7. Connection of online tests with an interactive content object Here, a first prototypical implementation of a selected content object from the basics of the course “Computer Engineering” (course contents, online test tasks, corresponding evaluation software, inclusion of the Remote Lab) should take place. 8. Exemplary test of an interactive content object In this step, the results of the online test are to be analyzed in detail and, if necessary, further requirements for modification are to be derived. 9. Demonstration of the first interactive content object The results of the online test should be made available to a wider group of users. 10. Usage of the content objects for Problem-Based Learning (PBL) in the first 6 weeks of lectures in test group 1 (BASIC Engineering School) As support, especially during the introductory phase, students should be given the opportunity to deepen the basics in the course “Computer Engineering” during the first 6 weeks of lectures and seminars with the help of interactive content objects. 11. Preparation of online evaluation questions Online evaluation questions will be developed for later use, which support permanent quality control and further development of the entire system. 12. Proving of content objects for PBL in the first 6 weeks of lectures in test group 2 (regular engineering courses) As support, especially during the introductory phase, the students should be given the opportunity to deepen the basics in the course “Computer Engineering” during the first 6 weeks of lectures and seminars using the interactive content objects. 13. Demonstration of the evaluation and test results The results of the project are to be presented to a larger group of users within and across universities. For this purpose, internal workshops, as well as publications and conferences, will be used.

2.6 Conclusion After the project phase, it is planned to transfer the concept to regular teaching within the Joint Engineering Basic Studies GIG2020 at the Ilmenau University of Technology in the sense of a continuation. Due to the Web technologies used, cooperative usage and further development with external partners is also possible without any problems.

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Acknowledgements The work is supported by the Thuringian Ministry for Economic Affairs, Science and Digital Society (Thüringer Ministeriums für Wirtschaft, Wissenschaft und Digitale Gesellschaft) and the Stifterverband für die Deutsche Wissenschaft.

References 1. Fellowships für Innovationen in der digitalen Hochschullehre Thüringen, Homepage, https:// www.stifterverband.org/digital-lehrfellows-thueringen. Last accessed 01 Oct 2020 2. EIFEL Homepage: https://www.stifterverband.org/digital-lehrfellows-thueringen/2019/henke. Last accessed 01 Oct 2020 3. GOLDi-labs cloud Website: http://goldi-labs.net. Last accessed 01 Oct 2020 4. Henke, K., Vietzke, T., Wuttke, H.-D., Ostendorff, St.: GOLDi—Grid of online lab devices Ilmenau. Int. J. Online Eng. (iJOE) 12(04), 11–13 (2016). Vienna, Austria. ISSN: 1861-2121 5. Henke, K., Ostendorff, St., Wuttke, H.-D., Vietzke, T., Lutze, C.: Fields of applications for hybrid online labs. Int. J. Online Eng. (iJOE), 9, 20–30 (2013). Vienna, May 2013 6. Henke, K., Vietzke, T., Hutschenreuter, R., Wuttke, H.-D.: The Remote lab cloud goldi-labs.net. In: 13th International Conference on Remote Engineering and Virtual Instrumentation REV 2016, Madrid, Feb (2016) 7. Basic Engineering School: Homepage. https://www.tu-ilmenau.de/basic-school. Last accessed 01 Oct 2020 8. Petzoldt, J., Fincke, S., Hartl, K: Technische Universität Ilmenau, Rektorat: Schlussbericht Basic Engineering School—Neue Lehr- und Lernformen in der Ingenieurbildung—insbesondere in der Studieneingangsphase“ (in German), 1. Förderphase, FKZ: 01PL11102, Laufzeit: 01.01.201231.12.2016, Hrsg: TU Ilmenau, Rektorat, Technische Informationsbibliothek (TIB). https://doi. org/10.2314/GBV:102340625X(2017)

Chapter 3

FINMINA: A French National Project Dedicated to Educational Innovation in Microelectronics to Meet the Challenges of a Digital Society Olivier Bonnaud Abstract The national network for coordinating training in microelectronics and nanotechnologies, which brings together 12 joint microelectronics centers and the French professional union in this field, successfully responded in 2011 to the call for proposals for an innovative training excellence initiative. It thus piloted an eight-year project entitled “Innovative training in microelectronics and nanotechnologies (FINMINA),” which aims to meet the challenges of a digital society. This project deals with microelectronics in the broadest sense, providing future players with the knowhow capable of improving the connection capacities of the Internet of Things and extending the fields of application, while drastically limiting energy consumption. To these ends, the project aimed to:—promote scientific and pedagogical innovation in order to provide users with skills and know-how,—broaden the spectrum of skills to include multidisciplinarity,—attract more young people to scientific careers,—create a national portal for lifelong learning,—encourage cross-fertilization of actors through dissemination actions. After 8 years, 90 innovative platforms dedicated to training in know-how adapted to the issues at stake have been set up in the 12 centers. This paper details the organization of the project, its innovative pedagogical aspects, and its main results and gives examples of achievements oriented toward societal challenges.

3.1 Introduction: A National Program for Innovative Training In 2011, the French government launched a program to support educational innovation as part of an investment plan for the future. The French national coordination network for training in microelectronics and nanotechnology (CNFM) [1, 2], which O. Bonnaud (B) GIP-CNFM, Minatec, 38016 Grenoble, France e-mail: [email protected] Sensor and Microelectronics Department, IETR, University of Rennes 1, Rennes, France © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_3

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runs 12 joint microelectronics centers offering 82 platforms dedicated to technological practice and involving the French professional union in the field, responded to the call for proposals and thus managed an eight-year project entitled, FINMINA for Innovative Training in Microelectronics and Nanotechnology [3], which aims to meet the challenges of a digital society. This involves microelectronics on a massive scale. This field must improve the connection capacities of the Internet of Things and extend the fields of application while drastically limiting energy consumption. The project thus had different objectives:—to promote scientific and educational innovation in the platforms through innovative projects in order to provide users with the skills and know-how essential for their future careers [4],—to broaden the spectrum of skills and the scientific perimeter in order to move toward multidisciplinarity,—to increase awareness actions toward secondary education in order to attract more young people to training courses and scientific careers,—to create a national lifelong learning (LLL) counter for companies and academic institutions to enable skills to be constantly updated,—to increase the international attractiveness of French training by strengthening international cooperation,—to encourage crossfertilization of players through dissemination activities. After a presentation of the context, the document presents the different activities of the network and describes the main innovative and pedagogical results with the objective that future graduates meet the challenges of the highly digital future.

3.2 The Challenges in Microelectronics The field of microelectronics and nanotechnologies is part of a national and international context of evolution toward a hyper-digital and highly connected society [5]. Indeed, the rapid growth of the Internet of Things associated with the Fourth Industrial Revolution, Industry 4.0 [6], requires more and more connected objects and intelligent systems [7]. These objects apply to all of society’s needs and make it possible to revolutionize the design and manufacture of associated products, thanks to the development of robotics, the introduction of artificial intelligence and the optimization of the logistical management of production tools [8]. They contain sensors, actuators, systems, and connected objects. The fields of application are broad and cover all major societal activities such as the environment, energy, health, safety, agriculture, transport, and communications [9]. Figure 3.1 shows the evolution of connected objects, the number of sensors, and the Internet of Things over a period of 25 years [10, 11]. The growth is exponential in all three cases. Microelectronics is technically at the heart of all these systems via analog and digital electronics, heterogeneous multiphysical systems involved in sensors and actuators, communication electronics, and data storage and processing capacities in data centers. These connected objects have apparently low power consumption given the fabulous advances in microelectronic integration and communications, but the amount of information transported over very long distances and stored in data centers is gigantic and leads to energy consumption

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Fig. 3.1 Growth of the number of connected objects, sensors, and IoT is exponential. The number of IoT objects should reach up to more than 200 billion by 2030

that is growing exponentially as well [12]. This consumption is generally unknown to the general public. Loading a film onto a 5 GB digital medium (a common size) from a server on the Internet today consumes 25 kWh, of which nearly 50% is used by the data center, 15% by communications and 35% by the user [13]. The consequence is an exponential growth in consumption, as shown Fig. 3.2, which leads to a multiplication by 2 every 4 years. In 2020, the global consumption related to the IoT is three times higher than the global energy consumption related to air traffic. Without a profound transformation of microelectronics by 2030, Internet-related consumption will be equal to the global consumption of electrical energy in 2018. An extrapolation for 2040 leads to a global consumption equal to that of the planet in 2018. This evolution is neither realistic nor acceptable. The consequence is that an effort must essentially focus on microelectronics devices, circuits, and systems, which are the physical support for all these systems requiring many technical skills. This is only possible with technicians and

Fig. 3.2 Exponential growth of the energy consumption by connected object and IoT. An extrapolation gives by 2040 a consumption equivalent to that of the planet in 2018

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Fig. 3.3 Innovation in microelectronics represents the most important challenge for the next years. Education and training are the keys to meet the technical challenges

engineers capable of covering all these aspects. Industrialists are today aware of this need and are seeing a shortage of skills and know-how at all professional levels. This paper highlights these different aspects and shows the challenges that will have to be overcome in the case of the electronics sector and in its part dedicated to training, whether initial or continuing. Increase in the number of specialists able to be innovative becomes a real challenge that the French training network tries to meet. Figure 3.3 summarizes the related strategy governed by innovation in microelectronics. On the one side, we have the challenges for the IoT, and on the other side, we have the challenges for education and training.

3.3 The French National Network 3.3.1 The GIP-CNFM: Structure and Mission Higher education in microelectronics in France is driven by a 35-year national network, the National Coordination of Microelectronics and Nanotechnology Education (CNFM), recognized by the Ministry of Higher Education [1, 2]. Since 2001, the administrative structure of this network is the Groupement d’Intérêt Public (GIP) which is supported by the “Direction Générale de l’Enseignement Supérieur et de l’Insertion Professionnelle” of the Ministry of Higher Education. It is composed of fourteen members and a General Direction. Twelve are academic institutions in charge of twelve common inter-university centers spread throughout France as already described [4]. Two members are industrial unions, including the most representative association of the microelectronics industry in France, the ACSIEL Alliance Electronics consortium [14], and the electronics industry federation. The industrial partners consider training activities to be a priority factor for the continuation and

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development of the electronics industry in France. They provide the network with the valuable advice needed to train future actors within the microelectronics sector. The first pedagogical objective of the network is then to train graduates who are competent in their specialties, within the electronics industry, capable of meeting societal challenges, and adaptable throughout their careers thanks to knowledge and know-how [15]. It is in this context that the GIP-CNFM has applied for the IDEFIFINMINA project, which is primarily aimed at innovation in training and more particularly in the field of microelectronics and nanotechnologies.

3.4 Structuration of the Activities of the FINMINA Project In order to adapt the training, several work-packages have been structured. As the network offers users technological platforms, it is a question of offering activities that provide the necessary know-how to the future actors in the field, technicians, engineers or researchers, microelectronics being at the heart of all the connected objects families [7]. The first priority is to have platforms offering innovative contents and in line with the resolution of the mentioned challenges. Thus, the main workpackage has consisted in selecting themes in the microelectronics specialties that have become indispensable and in creating technological platforms open to the different training courses. These were proposed by the academic actors and validated by the network. Two other specific innovative work-packages were supported. These are digital security at the hardware level, which is becoming a necessity due to the explosive development of connected objects and the testing of mixed integrated circuits (analog and digital), which is indispensable for the same reason. The fourth work-package aims to develop the lifelong learning and related practice which are essential in a rapidly changing industrial sector. In order to develop a pool of young people attracted by science and technology, a globally threatened species that is becoming increasingly rare, a specific action to raise awareness among secondary school students has been launched at the national level by the network. The last three work-packages are more traditional, although necessary, as they concern the dissemination of results, internationalization, and management of the network. These eight work-packages are detailed below.

3.5 The Eight Work-Packages and Their Main Results 3.5.1 Work-Package on Innovative Projects This is the most important action of the network in terms of its objectives, funding, and innovation strategy. As the major action of FINMINA, it is aimed at all levels of training (from high school students to Ph.D. students, including masters and engineers), and must meet the scientific and practical training needs of the socio-economic

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world, on the one hand, and the requirements of pooling platforms and tools of high technicality and advanced, and therefore costly, on the other hand. The network’s strategy aims at innovation, mutualization, experience sharing, and dissemination of good practices. The network must also work on adapting learning and therefore the pedagogical approach to the nature of practical work. The selected topics bring to the content the multidisciplinary aspect, the development of connected objects, and new digital approaches, a way to meet the industry needs [16]. Since 2012, 90 innovative projects have been supported and set up in the network’s 12 common centers. The generic topics have been:—design of integrated circuits and embedded electronics (11),—physical and electrical characterizations of new elementary components and circuits (19),—component and system technologies (15),—technologies of the future (nanoelectronics, organic, and flexible) (17),—multidisciplinary systems for IoT including smart sensors (24),—new components and communication systems for connected objects (11). All these themes are therefore part of the evolution of a digital society and linked to the evolution of microelectronics devices and circuits [17], more especially in designs [18] and technological processes [19]. It may be mentioned that, in order to improve the effectiveness of learning, the teaching staff concerned has implemented several pedagogical approaches adapted both to the subjects dealt with and to the public concerned [4]. Since 2012, the innovative platforms corresponding to all these projects have hosted more than 33,800 students for more than 3.1 million student × hours, including the work of Ph.D. students.

3.5.2 Work-Package on Mixed Microelectronic Test The remote mixed microelectronics test consists in testing on an ADVANEST V93K industrial tester digital and analog-integrated circuits which are among the most advanced technologies in the development of a digital society [20]. The implementation of this platform intended for practical training has made it possible to train students in initial training as well as Ph.D. students, trainers, and company engineers and technicians in continuing education. Hosted by a network partner, the microelectronics CNFM center of Montpellier [1], this very expensive equipment is accessible via a remote connection from all the educational and research institutions members or partners of the network. Over the duration of the project, nearly 800 users have benefited from this original and unique training in France, corresponding to a total of 17,000 user × hours. This type of know-how is increasingly appreciated by industrialists in the context of the development of digital tools and IoT.

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Fig. 3.4 Digital security bench for analyzing the data content of an integrated circuit (left). Carrying case for a serious “Digital Security” game. This case can be used in many institutions, from high school to university (right)

3.5.3 Work-Package on Digital Security Platform The initial objective was to create a platform called SECNUM, resulting from research activities and intended to serve as a support for teaching digital security at all levels of students. The objective is to offer an open platform for the design of robust embedded circuits and systems that are immune to digital attacks [21]. A test bench became operational in 2014 (Fig. 3.4). Initially used by local students, the platform was then used to train national and international students in the context of mobility of teachers and remote experimentation. Throughout the FINMINA project, more than 3,000 users have spent more than 6,000 cumulated hours on the platform. In order to reach this level, the managers had to develop attractive approaches, in particular by developing a transportable case of a serious game (Serious Game), and by creating a video entitled “Serious Game Video on Digital Security.” This original platform, which is expensive and unique in Europe, has been set up with the support of FINMINA but also thanks to the substantial co-financing provided by several other programs.

3.5.4 Work-Package on Raising the Awareness of High School Pupils As already mentioned, this activity appears to be a first priority for the industrial partners. The objective is to maintain a high level, innovative, and competitive industry in the global market of the field through an increase in the number of future welltrained and highly qualified employees. Since the launch of FINMINA, this activity has greatly increased. The total number of high school pupils reached over 23,000 since 2012 on the platforms of seven CNFM’s centers [22]. The classes concerned have had practical experience in clean rooms as shown Fig. 3.5, in physical charac-

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Fig. 3.5 Secondary school students in the clean room of a joint microelectronics center (left). Schoolers on the nano-word platform including scanning electron microscopes (SEM) and AFM or atomic force microscope (right)

terization at nanoscale on nano-world platforms, and benefited from a “learning by doing” approach. Young people are made aware of microelectronics and nanotechnologies, key disciplines in the emerging digital society. These actions were also accompanied by clean room visits, awareness-raising via videoconferencing and the implementation of remote experiments.

3.5.5 Work-Package on Lifelong Learning National Portal A single national portal offering a consolidated continuing training offer has been developed thanks to the involvement of the 12 clusters, and its deployment has been achieved through strong collaboration with the continuing training departments of the project’s partner establishments [23]. Numerous training courses have involved differentiated approaches: e-learning, practical training on platforms, programs adapted to the needs of companies. Audio-visual supports have been produced to promote this continuing training offer during dedicated events and on social networks. While continuing education primarily concerns company executives, it is also applied to trainers at all levels, thus ensuring that the teaching staff are adapted to innovative platforms. Since 2012, training of trainers in specific sessions organized by the national services have dealt with CAD tools (CADENCE, SYNOPSIS, COVENTOR, MENTOR GRAPHICS), tools dedicated to FPGA type embedded electronics (XILINX, ALTERA, DIGILENT,…), cyber-security, and mixed industrial testing. The figures for all continuing education courses combined, amount to more than 4300 users who have completed more than 160,000 h.

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3.5.6 Work-Package on Dissemination During the 8 years of the project, all the actors working in the partner institutions have promoted their innovative work through conferences, seminars, and publications in journals of national and international audience. Presentations and articles have focused on the one hand on the originality of the scientific content and the nature of the platforms set up and on the other hand on the pedagogical approach adapted to the subjects and the users. Over the duration of the project, the output amounts to 694 items: 125 articles published in international (22) and national (106) journals, 46 invited lectures and keynotes, 1 book, 440 international (59) and national (381) conferences, 65 network meetings and industrial seminars, 21 conference and seminar organizations, and contributions to events for the general public.

3.5.7 Work-Package on Internationalization of the Approach The international activity is intense through the reception of many foreign students on the platforms. Around 50% of doctoral students and 20% of students in initial training are foreign. This represents more than 1300 foreign students graduating annually from institutions using the platforms of CNFM network developed in the frame of FINMINA project.

3.5.8 FINMINA Project Governance Work-Package Coordinated by the GIP-CNFM, governance is ensured by a Steering Committee which allows administrative, financial, and pedagogical monitoring, compliance with FINMINA’s objectives and indicators, self-evaluation, and the establishment of a sustainable strategy. Thanks to the dynamism of the project partners, significant co-financing (four times FINMINA funding) has been obtained via industrial, academic, and research partnerships and with related networks (IDEX, LABEX, Carnot, IRT, competitiveness cluster, and ERDF local authorities). The extent of the work carried out and the results obtained has earned our network national recognition. The new French “Filière électronique” (electronic sector recognition by the government) has called on the CNFM network to steer its “employment and skills” action and presents it as a model to be followed. This recognition has also been affirmed at the European level within the framework of future calls for projects, and internationally by the invitation to numerous invited conferences dedicated to both technical innovation and educational sciences.

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3.6 Examples of Achievements and Overall Outcome 3.6.1 Examples of Practice on Innovative Projects Due to the very large number of innovative projects (124) and the very many associated results, it is not possible to be exhaustive in this presentation. However, it seems useful to give the readers some typical examples of achievements made by many students. The selection proposed and presented in Fig. 3.6 gives an idea of the main orientation of the topics in the context of the evolution of IoT and with the goal to better control the energy consumption through new architecture of analog/digital mixed circuits, new devices with low leakage current, organic electronics with very low currents, and connected microsystems for many applications. The devices are fabricated in the seven clean rooms of the network or by industrial companies for the most complex circuits. All the centers are equipped of physical and electrical characterization tools adapted to their own specialty. In fact, the activities cover electronics from low to high frequency, devices from low to high power, the design of analog, digital, synchronous, asynchronous and mixed circuits, wired and wireless communications, flexible electronics, organic electronics and optoelectronics, photovoltaic generation, but also sensors and actuators for biological, chemical, and physical applications. The network organization promoting the mobility of students and even teachers gives access to high-performance platforms and design tools to the entire national and even international community.

Fig. 3.6 Examples of realizations on several of the 90 innovative platforms of the national network. The network activities cover all the facets of the microelectronics field

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Fig. 3.7 Innovative practice on dedicated platforms since the beginning of FINMINA project. More than 33,000 students had practice training and know-how acquirement on innovative platforms

3.6.2 Overall Pedagogical Outcome of FINMINA Project Figure 3.7 shows the evolution of the number of users on innovative platforms since the beginning of the FINMINA project. The topics include technological processes, computer-aided-design, characterizations, and tests. Thanks to the diversification of subjects, students from other fields such as mechanics, optics, biology, or medicine have been made aware of the field of microelectronics. They are thus better prepared to develop new applications for connected objects. Figure 3.8 shows the increase in the number of hours of initial and continuing training on innovative platforms. It is clear that doctoral students are much more quickly involved in innovative platforms and that they spend much more time on experimentation. This implies greater variability in the total number of hours depending on the theses in progress.

Fig. 3.8 Innovative practice on dedicated platforms since the beginning of FINMINA project. More than 1,800,000 h were spent by the 33,000 students as well in initial practice training as in lifelong learning and research experimentations

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3.7 Conclusion and Perspective After 8 years, 124 innovative projects focusing on practical activities and developing know-how adapted to the needs of the industry have been set up. They have hosted more than 33,000 students, raised the awareness of more than 25,000 high school pupils, developed the LLL, and increased international relationships and the dissemination of achievements. The extent of the results obtained has earned our network national and international recognition and has become an example for the scientific community. Even after having proven the importance of this approach for the microelectronics community (industry and universities), it will be difficult to maintain this strategy over the next few years without specific support from academic and ministerial authorities. In 2019, the French Minister of Industry recognized the field of electronics as a strategic industrial sector as a priority for the coming years. The fields of microelectronics and nanotechnologies are obviously included in this sector. This recognition appears very important for a new negotiation with the Ministry of Higher Education in order to extend financial support to innovative training activities. The electronics sector is increasingly important in order to face the new challenges of the digital society. Efforts must be made not only on initial training but also on lifelong learning. Indeed, many new activities that need to be carried out correspond to the new approaches in this field. They concern circuit designers, component, and circuit technologists, but also specialists in packaging, which is becoming increasingly complex, and specialists in information storage and transmission systems. As already mentioned, it should be possible to divide by a factor of at least ten the consumption of all connected objects and IoTs. It is thus possible to envisage the creation of new innovative platforms oriented toward new low-temperature technologies and generating very low consumption products such as devices based on organic materials. They can also enable the design of new very complex circuits using asynchronous modules, or new architectures controlling the standby of functions that are not essential for a given data processing. The optimization of communication protocols by avoiding as much as possible the dispersion of all data on the planet could be another strategic subject. In order to minimize the energy costs of transport, it will be necessary to return preferably to local data storage and therefore to small, efficient, and local servers. All these approaches will require skills, R&D know-how, and innovations that could be provided by this network. Acknowledgements The author wants to thank all the members of the French GIP-CNFM network for they contribution to this work, which is financially supported by French Education Ministry and by IDEFI-FINMINA program. A special thanks to L. Chagoya-Garzon, Secretary of GIP-CNFM for her fruitful advice for the redaction of this paper.

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References 1. CNFM: Coordination Nationale pour la formation en Microélectronique and nanotechnologies. Website: www.cnfm.fr. Last access 02 Feb 2019 2. Bonnaud, O., Gentil, P., Bsiesy, A., Retailleau, S., Dufour-Gergam, E., Dorkel, J.M.: GIPCNFM: a French education network moving from microelectronics to nanotechnologies. In: Proceedings of EDUCON’11, pp. 122–127. Amman-Jordan (2011) 3. FINMINA: Formations Innovantes en Microélectronique et Nanotechnologies, See website of CNFM, IDEFI project: ANR-11-IDFI-0017. Last access 12 Jan 2019 4. Bonnaud, O.: New Vision in Microelectronics Education: Smart e-Learning and Know-how, a Complementary Approach. In: Uskov, V., et al. (eds.), KES-SEEL-18 2018, SIST 99, pp. 267– 275, Springer, Berlin (2019) 5. Bonnaud, O., Fesquet, L.: Microelectronics at the heart of the digital society: technological and training challenges. In: Proceedings of SBMicro2019, IEEExplore, pp. 1–4 (2019) 6. Bortolini, M., Ferrari, E., Gamberi, M., Pilati, F., Faccio, M.: Assembly system design in the Industry 4.0 era: a general framework. IFAC PapersOnLine, 50–1, 5700–5705 (2017) 7. Bonnaud, O., Bsiesy, A.: Adaptation of the higher education in engineering to the advanced manufacturing technologies. In: Proceedings of International Conference on Advanced Technology Innovation, ICATI’2019, 15–18 July, Sapporo-Japon (2019) 8. Smith, R.: The future of manufacturing: Cobots in the factory, TCTMag, 4 Mar (2019) 9. Bonnaud, O., Fesquet, L.: Towards multidisciplinarity for microelectronics education: a strategy of the French national network. In: Proceedings of IEEE Microelectronics System Education Conference (MSE), p. 4, IEEE, Pittsburg MS-USA (2015) 10. Schütze, A., Helwig, N., Schneide, T.: Sensors 4.0—smart sensors and measurement technology enable industry 4.0. J. Sens. Sens. Syst. 7, 359–371 (2018) 11. Bonnaud, O.: New approach for sensors and connecting objects involving microelectronic multidisciplinarity for a wide spectrum of applications. Int. J. Plasma Environ Sci Technol 10(2), 115–120 (2016) 12. Fettweis, G., Zimmermann, E.: ICT energy consumption-trends and challenges. In: Proceedings of the 11th International Symposium on Wireless Personal Multimedia Communications (2008) 13. Source: International Energy Agency. https://www.iea.org. Last accessed (2019) 14. ACSIEL Alliance Electronique: Professional union bringing together all the actors involved in the electronics value chain, https://www.acsiel.fr. Last accessed 31 Jan 2020 15. Bonnaud, O.: Mandatory matching between microelectronics industry and higher education in engineering toward a digital society. In: Uskov, V.L., et al. (eds.), Smart Education and E-Learning 2019, Part of Springer Nature Singapore Pte Ltd. 2020, Chap. 24, pp. 255–266 (2019) 16. Bonnaud, O., Fesquet, L.: Innovation for education on internet of things. In: International Conference on Advanced Technology Innovation (ICATI’2018), Proceedings of Engineering and Technology Innovation, PETI, 9, pp. 01–08, Krabi-Thailand (2018) 17. Moore, G.E.: Cramming more components onto integrated circuits. Electron. Mag. 38(8), 114–117 (1965) 18. Bottoms, B.: System Level Design and Simulation for Heterogeneous Integration Electronic Design Process Symposium, SEMI, Milpitas, Sept 21–22, California-USA (2017) 19. Rao, R.T., Swaminathan, M.: System on Package: Miniaturization of the Entire System, McGraw-Hill Education; 1st edition, May (2008) 20. Pradarelli, B., Nouet, P., Latorre, L.: Industrial test project oriented education. In: Proceedings of EDUCON’2016, pp 119–124, IEEE, Abu Dhabi, United Arab Emirates (2016) 21. Bruguier, F., Benoit, P., Torres, L., Bossuet, L.: Hardware security: from concept to application. In: Proceedings of EWME’2016, pp. 1–6, IEEE, Southampton-UK (2016) 22. Bonnaud, O., Bsiesy, A., Martinet, E., Baptist, R., Basrour, S., Pernot, E.: Increasing attractiveness of electrical engineering for schoolers through experiments on microelectronics and nanotechnology interuniversity platforms. In: Proceedings 28th EAEEIE Annual Conference, Hafnarfjordur-Iceland, p. 4, IEEExplore (2018)

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23. Pradarelli, B., Bonnaud, O., Nouet, P., Benoit, P.: CNFM: innovative single national entrypoint for lifelong learning in microelectronics. In: Proceedings of EDULEARN 2019, Palma de Majorque-Spain (2019)

Chapter 4

Effect of International Student Competition Experience on Smart Education Heather N. Yates, Sreemala Das Majumder, and Blake Wentz

Abstract The advent of smart education has led many educators to focus on providing educational opportunities that foster smart learners in order to meet the needs of work and life in the twenty-first century. International experience and design has become more common in the engineering industry, and providing a cost-effective international experience is a positive way to give students this experience. This study surveyed students who participated in the ASC Region 5 International Competition to determine their perceived key factors for success. The students indicated that communication between teams, trust between team members, and proficiency between shared languages were the most important factors. The research shows there is a high value in providing a smart international competition experience to students as it is more cost effective than a study abroad program.

4.1 Introduction The development of new technologies provides students with new tools and techniques to learn more effectively and efficiently. Technology has been advancing at an exponential rate which affects students’ education. Smart education has become significant in all disciplines of engineering. There are many definitions of smart education and smart learning. A majority of students seeking an undergraduate education are digital natives and typically communicate and learn using technological devices. Smart education is a term used to describe learning in the digital age typically using technology. However on an broader scale, “The goal of smart education is to foster workforce that masters twenty-first century knowledge and skills to meet the need and challenge of society” [1]. In order to meet the needs of the global twentyfirst century society, students having experiences working a cross-cultural, global H. N. Yates (B) · S. Das Majumder Oklahoma State University, Stillwater, OK 74078, USA e-mail: [email protected] B. Wentz Milwaukee School of Engineering, Milwaukee, WI 53202, USA © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_4

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atmosphere is important. These experiences increase students’ employability as they promote soft skills that are difficult to teach in technical engineering curriculums. Traditionally, international experiences are gained through short- or long-term study abroad experiences; however, and alternate pathway to achieve smart education is through student participation in international competitions. Students gain international exposure which enhances their employability and adaptability in their future professional career. Additionally, the competition exposes the student to real-world problems in construction; they gain experience working through difficult problems and can apply what they have learned in the classroom to their competition project. The learning curve is extremely overwhelming for the students. However, the gained knowledge is something that is rarely matched in a typical learning environment. Domestic student competitions provide excellent learning opportunities but adding an international dimension with international collaboration enhances the students’ experiences enriching their global exposure. This paper discusses the benefits of international exposure for engineering students and reveals how the international competition facilitates smart education. A 2019 survey that was developed by Marutschke, Kryssanov, Chaminda, and Brockmann was used to identify the factors of success in the ASC International Competitions according to the student participants. The survey results when analyzed show the perception of the students about such competitions and compares the students’ perceptions of their experiences as part of an international competition team to Marutschke et al.’s 2019 research [2] that surveyed students who worked on international teams as a portion of a virtual collaborative course. Finally, the results and overall idea of such competition are presented to inspire other researchers and educators interested about a smart education option for future engineering professionals and preparing students for the skills needed in the twenty-first century workforce. The paper also provides support for international collaborative alternatives to students studying abroad.

4.2 Literature Review 4.2.1 Engineering Globalization and Smart Education In the age of globalization, there is a fundamental change in the educational paradigm. Smart education, smart devices and intelligent technologies are implemented in the classroom and beyond to help teach engineering [3]. Engineering as a profession seeks to solve global problems; therefore, it is very common for engineering employees to work on cultural and or geographically diverse teams. As identified over the last decade, employers are looking for engineering students who have both technical skills and soft skills along with international exposure. International experiences increase soft skills in students as they gain “intercultural skills, curiosity, flexibility and adaptability, confidence, and self-awareness” [4]. To understand and experience

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cultural differences and to adapt global working standards and ethics, students are encouraged to take part in study broad or similar international experience programs [4]. There is a high correlation between studying abroad and improved graduation rates, along with employability [5]. Several studies show that the number of US students studying abroad is relatively low. Forty-one percent of international students are studying under STEM programs in the USA, while only 16% of US STEM students are part of study abroad programs [6]. Marutschke et. al. [2] cited high travel costs and the need to work part time as deterrents for students to leave the university to gain international experiences. Where international experiences and smart education intersect is that international experiences can be offered as an objective to the overall smart education goal of fostering a workforce that is equipped with the skills to meet the ever-changing needs of the global society. There is no rigid definition of smart education. The concept of smart education and smart learning is constantly nurtured by multidisciplinary researchers and educational professionals. Smart education revolves around groupbased collaborative learning, active learning, and project-based learning. Educational projects based on smart education that have been integrated within coursework and have shown significant student knowledge gains [7]. Various universities and schools along with government support aims to improve and incorporate smart education to meet the challenges of the twenty-first century. Multinational companies such as IBM collaborated with Australia to develop a smart, multidisciplinary student-centric education system [1]. South Korea is promoting the idea of SMART education by reforming the educational system, improving physical environments, incorporating smart education projects and other infrastructures [8].

4.2.2 Smart Education Levels of Abilities Zhu et al. [1] proposed a smart education framework of specific abilities that students should master to meet the impending needs of modern society. These abilities include the following: 1. 2. 3. 4.

Basic knowledge and core skills Comprehensive abilities Personalized expertise Collective intelligence.

4.2.3 Smart International Student Competitions Weaving the smart education abilities with an international student competition experience, one can see how students who participate in the collaborative, context-based, active problem-based learning experiences gain valuable skills to meet the challenges

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of the twenty-first century engineer. It is important to pay more attention toward reallife problems in the real world and make these a part of smart learning process for students [9]. Merril explained how the correct smart learning environment is mostly engaging, efficient, and effective, where the students are considered the heart of the learning environment [10]. Smart learning environments help students to adapt themselves instantly at the required time and place. Student competitions challenge students on the mastery of basic knowledge and core skills that they have gained in the classroom prior to the competition. In the Associated Schools of Construction (ASC) Student Competitions, many times upper classmen are the students who compete as they have made it through more technical skills classes. The students’ comprehensive abilities are tested as they are challenged to solve a unique real-world construction problem by a construction firm who has had experience with the construction project. Oftentimes, there may not be one solution that the firm is looking for, as they are more interested in the thought processes that led the students to their proposed solution. Students are expected to have personalized expertise and have mastered the technology related to the construction field like scheduling software and building information modeling. Additionally, as a team member, students must be able to work collaboratively in real-time updating documents simultaneously. Creativity is also needed for applicable and innovative problem solving and collaboration with others. Collective intelligence is manifested through effective communication and collaboration. Successful teams learn to communicate clearly to each other and also clearly convey their solutions to the problems to the judges.

4.3 Associated Schools of Construction International Student Competition Each year, the Associated Schools of Construction hosts two international student competitions, one that is hosted outside of the USA and one hosted in Dallas, Texas as part of the Region 5 ASC Competitions. To compete as an international team, half of the six team members should be from an institution from a foreign country. The actual competition is an 8 to 16-h problem-solving timeframe where the teams are given a real-world construction problem to solve. Depending on the division the team is competing in, students could be asked to deliver pricing for a specific project, a solution to a problem, or an entire design and construction plan. The teams typically communicate prior to the competition via Skype or other collaborative means to meet and get ideas about team members’ areas of expertise, personalities, and to discuss ideas on how the team plans to work together. Additionally, the foreign team members arrive a few days in advance of the competition to meet and work on team building along with preparing any pre-competition documents and practicing presenting with the domestic team. The actual competition only lasts 8–16 h. The teams submit a copy of their work to the judges and then the teams

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are expected to prepare an oral presentation to offer or outline their solution. The winner of the ASC Student International Competition is determined by the judges’ perception of the presented solution to the construction problem. Points are awarded for both written and oral communication.

4.3.1 Benefits of Student Competitions There are many benefits of student competitions. These contests provide an opportunity for students to work in a collaborative environment with other students to develop a solution. Typically, the best solutions come from teams with diverse backgrounds. The students also have the opportunity to use the context of their previous years of higher education in construction to help them solve the problem, making the exercise relevant. The real-world project and unique solution add to the student’s learning, providing an active learning environment for students to excel. Student competition experiences boost communication and problem-solving skills. Adding the international aspect to the student competition team increases the benefits to the participating students. Students have the opportunity to work with people from other cultures to solve problems. Students report learning different solutions than they would have considered if not part of a culturally diverse team. Additionally, similar to a study abroad experience, students gain “intercultural skills, flexibility and adaptability, self-awareness, curiosity, and confidence” [4] through competing with an international team.

4.3.2 Benefits of International Student Competitions in Lieu of Study Abroad Research reveals that there is a strong association between studying abroad with employability [5]. Further research explains the positive impact of studying abroad that directly influences career possibilities, progression, and promotion [11]. Despite such positive outcomes, the statistics shows only a fraction of engineering and technology students opt for study abroad programs. Study abroad programs are extremely beneficial for engineering students. However, results indicate that many students cannot afford the time and financial commitment that these programs require. Employer’s look for engineering graduates with technical knowledge, effective communication, critical thinking, and the ability to perform in a group across diverse cultural backgrounds. In this age of globalization, engineers are expected to solve international problems. Today’s engineering employees excel when they have been exposed to working in diverse cultural situations. International engineering competitions are a cost-effective alternative to study abroad programs as they allow students to experience other cultures, practice engineering technical, and professional skills

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while engaged in a competitive project. Student competitions offer collaborative, context, and project-based learning. Our study and its results are rooted in benefits provided by alternate international experiences. Finding smarter ways for students to gain the much-required benefits of study abroad programs, without having to go through the extended financial and time commitments, provides built-in international proficiencies. If students can gain benefits from alternate international experiences, there is a high likelihood that more students will further expand their cultural perspectives. International student competitions and international virtual courses are not necessarily new ideas. However, what makes them different from study abroad programs are that many times the competition fees and other costs are completely or partially covered by the institution which the students are representing, where the study abroad expenses often fall completely on the students. Additionally, the time span of these competitions is usually less than that of the study abroad programs. Many times, in the STEM fields, there is little opportunity in undergraduate programs to take time off and study abroad. Students taking part in international competition teams benefit from several angles. They are exposed to diverse cultural backgrounds, gain experience solving real-world construction projects, develop new learning strategies, improve their soft skills and technical skills with a minimal financial burden and without an extended time commitment.

4.4 Research Method: Student Questionnaires For our research, we collected data from a selection of the 2019 ASC Region 8 international student competition participants. All students who were asked to complete the questionnaire were part of a competition team that was considered international as half of the six team members were from a different country. The countries represented at this year’s student competition included the following: the USA, England, Ireland, and the Czech Republic. All of the students were enrolled in undergraduate construction or construction related majors at a university. We received data from 15 participants on their perceptions of the factors that determined the success of the international student competition collaboration. Of the 15 participants, 5 data sets had to be removed due to inconsistencies in the data. The students were surveyed after their international competition team experience. Due to the European data privacy laws, all of the information was gathered anonymously. The researchers used the 2019 survey tool by Marutschke et al. where these authors looked at the factors perceived by students which determine the success of global software projects. Similar to our study, students were colloborating with other students who were culturally different, but received similar technical training and education. The students were asked to rank the following 11 factors with 1 representing the most important factor in their collaborative success and 11 being the least important: geographical distance; time zone difference; language difference; proficiency in shared language;

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cultural differences; familiarity between teams; trust between teams; transparency and accountability; communication between teams; software and hardware tools; and leadership. Each factor was to be assigned a unique rank, and the numerical rank could not be repeated among factors.

4.5 Results The data set in Table 4.1 shows the averages of the students’ perceptions of the factors that determined the success of the international student competition collaboration. From the means outlined in the table, lowest numbers reflect the factors seen by the students as most important while those factors with higher averages are seen as less important factors for success. The data is collected from students taking part in an international competition. As shown in Table 4.1, the students’ perceived communication between the teams was the most important factor followed by proficiency in the same language and trust among the team. Participants also ranked leadership and proficiency in a shared language to also be important factors. Geographic distance, software and hardware tools, and cultural differences were not deemed nearly as important as the other factors. The data set displayed as a box plot shows the variance of the responses along with the means. Interestingly, the range of responses on the factors of communication being important and geographical distance being not important are fairly small, meaning that most students surveyed agreed with the overall top and bottom rankings. Another point of agreeance was that transparency and accountability was not deemed most important but was notable for all students showing little range in the responses (Fig. 4.1). Table 4.1 Means of the students’ perceptions of the factors that determine the success of the International Student Competition Collaboration (lower numbers emphasize importance)

Factors

Average

Geographical distance

8.5

Time zone difference

6.7

Language difference

6.3

Proficiency in shared languages

3.8

Cultural differences

7.8

Familiarity between teams

5.6

Trust between teams

4

Transparency and accountability

6

Communication between teams

3.3

Hardware and software tools

7.9

Leadership

6.1

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Fig. 4.1 Box plot of results from International Student Competition Questionnaire

4.5.1 Comparison of Student International Competition Collaboration to a German Virtual Course Collaboration The following graph plots the median data from the International Student Competition Questionnaire with the data from European students collected by Marutschke et al. [2]. Both data sets represent the student perceptions at the end of the collaborative experience. The orange line plots the data from the internationalstudent competition participants that were a mix of both European and American students. The blue line plots the end rank of the German students who took a virtual global software engineering course that required international collaboration. The number of data points for the German students was 9, and there were 10 data points for the international student competition respondents. While the sample sizes are similar, they are both small which is a limitation of the study. Even with the limitations, there are some notable similarities. Both European and American students use English as mother tongue. The results reveal striking similarity and track relatively close to one another for all the factors except for cultural differences. The German students reported cultural differences as a more important factor, which makes sense as they were working with Japanese students. With the International Competition teams, the cultural differences were not as significant of a factor; this could be due to the fact that culturally Americans and Europeans do not see themselves as that different. The results show reproducibility as the questionnaire was given to two very different populations; however, the results on the perceptions of factors of successful collaboration are very similar between the international student competition participants and the virtual course participants (Fig. 4.2).

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Fig. 4.2 Comparing the student perceptions of factors of successful collaboration

4.6 Future Work International student competitions and simultaneous virtual courses are only two examples of international collaborations that increase global experiences for students. As stated before, most studies on the benefits of international experiences for students focus on study abroad. Our study strongly suggests that participation in the international competition is a perfect gateway for students to gain significant knowledge that helps them in the future, with employability and soft skills. The researchers also recognize that these skills can be improved through virtual collaborative international classes. More work can be done on additional experiences that provide students smart education experiences and prepare them for success in the twenty-first century. There is scope for further investigation to explore other outlets for international experiences that broaden students’ cultural experiences without costing as much time or money as a study aboard experience. There are also opportunities to implement various smart education techniques to international collaborative experiences. These opportunities could be adapting smart environments during student competition or virtual collaborative classes and then assessing how different models influence the students. Further, innovative models of international competitions and smart learning environments can be developed for future progress.

4.7 Conclusion Our study indicates that students perceive communication, proficiency in shared languages, and trust as the most important factors when working on an international team. These identified skills would fall into soft skills rather than technical skills and are important when considering fostering a future workforce to meet the everchanging needs of the global society is the overall goal of smart education. Employers

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are most interested in future employees with technical knowledge, effective communication, and critical thinking skills. When communicating and solving problems in engineering, the ability to perform in a group across diverse cultural backgrounds would prove to be beneficial to the workforce as many problems are not contained within a country’s borders. International student competitions offer an alternative for students to gain international experiences when they may not have the time or the money for a traditional study abroad experience. The gains and benefits of such competitions for the students and their university are of great value. Students gain valuable experiences in real-world context-based problem solving, while gaining experience working with students from and international country. The international context provides students practice in communication, trust, transparency, and leadership with other students who may have different priorities or values. All of these experiences lead to a highly sought after, more well-rounded, smarter future engineer.

References 1. Zhu, Z.T., Yu, M.H., Riezebos, P.: A research framework of smart education. Smart Learn. Environ. 3(1), 4 (2016) 2. Marutschke, D.M., Kryssanov, V., Chaminda, H.T., Brockmann, P.: Smart Education in an interconnected world: virtual, collaborative, project-based courses to teach global software engineering. In: Smart Education and E-Learning 2019, pp. 39–49. Springer, Singapore (2019) 3. Uskov, V.L., Bakken, J.P., Pandey, A.: The ontology of next generation smart classrooms. In: Smart Education and Smart E-Learning, pp. 3–14. Springer, Cham (2015) 4. Hovland, K.: Study abroad matters: linking higher education to the contemporary workplace through international experience (2018) 5. Fox, P., McIntyre, C.: Clear advantages to studying abroad: so why aren’t students enrolling? (2019) 6. Loveland, E., Morris, C.: Study abroad matters: linking higher education to the contemporary workplace through international experience (2018) 7. Goodman, A.E., Gutierrez, R.: The international dimension of US higher education: Trends and new perspectives. In: International Students and Global Mobility in Higher Education, pp. 83–106. Palgrave Macmillan, New York (2011) 8. Yates, H.N., Majumder, S.D., Pruitt, M.H.: Upgrading how technology is taught in undergraduate education, a case study. In: Smart Education and E-Learning 2019, pp. 375–388. Springer, Singapore (2019) 9. Choi, J.W., Lee, Y.J.: The status of SMART education in KOREA. In EdMedia+ Innovate Learning, pp. 175–178. Association for the Advancement of Computing in Education (AACE) (2012) 10. Hwang, G.J., Tsai, C.C., Yang, S.J.H.: Criteria, strategies and research issues of context-aware ubiquitous learning. J. Educ. Technol. Soc. 11(2), 81–91 (2008) 11. Merrill, M.: First principles of instruction [electronic resource]: identifying and designing effective, efficient, and engaging instruction/M. David Merrill (2013)

Chapter 5

Knowledge-Based Model Representation for a Modern Digital University Tamara Shikhnabieva

Abstract The intensive digital technologies’ penetration leads to significant changes in various fields of human activity, including the university background transformation. The Internet development’s new stage and up-to-date information technologies have contributed to the emergence of new educational institutions development tools served for smart education and e-learning. Some approaches to a meta-model design for the educational institution knowledge-base as well as the experience of its implementation in a digital university have been presented in the article.

5.1 Introduction In modern digital economy conditions, the matter of establishing the university that would provide professional development of a human and focus on training company leaders and specialists performing in the global marketplace is keeping up to date. Therefore, the development of new digital technologies will result in a significant university background change under the defined development strategy-related to a university’s features and specifics. It should be noted that modern digital technologies provide new tools for the development of educational institutions. Switching to a digital university is possible in the design and implementation activities aimed at using modern achievements in the field of information technologies, intelligent information systems, and innovative teaching methods.

T. Shikhnabieva (B) Federal State Budget Educational Institution of Higher Education, MIREA—Russian Technological University, Moscow, Russian Federation e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_5

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5.2 Literature Review The issues of improving the system for managing the educational processes of educational institutions have been investigated in a number of works [1–5]. So, work [1] is devoted to improving the system of managing the educational process of a university based on a process approach and a quality management system that uses ideas and methods of quality management in a university at all its levels. Improving the educational process, control system is possible through the use of modern digital technologies, safe, in the psychological aspect, for the personality of the student, the electronic educational environment, educational quality management system, innovative management approaches, etc. The use of advanced technologies in organizing electronic education and improving its quality has been the subject of a number of authors [5–7]. Technologies for ensuring information security of corporate educational networks and the development of informatization of education in the context of the information security of a student’s personality are reflected in [8–10]. Studies are devoted to the problems of a comparative assessment of the effectiveness of educational organizations of higher education [11]. Due to the increasingly active spread of digital technologies, the education sector is subjected to significant changes along with other industries. Modern digital technologies provide new tools for the development of educational institutions around the world. The issues of university transformation and the future of learning in the context of the digital paradigm of education are highlighted in the next works [12, 13]. The curricula and educational programs of higher education, which are the work of a number of authors [14, 15], as well as the knowledge assessment system, will be subjected to serious changes [16–18]. However, emphasizing the completeness and depth of the studies which have given above, they do not adequately present the issues of presenting the knowledge-base of a modern university in the context of the digital education paradigm. In addition, the educational system does not fully utilize the wide capabilities of modern intellectual technologies in the aspect of managing the learning process, adaptive assessment of knowledge, as well as analytical tools in the process of making managerial decisions.

5.3 Purpose and Objectives of the Study The paper is dedicated to the construction of a knowledge-base meta-model for a modern digital university, the use of which will improve the management of educational processes. To represent the knowledge-base of a modern educational institution, we propose using multi-level hierarchical semantic models [8]. Significant attention is currently paid to improving the education management. The necessary condition for the effectiveness of the entire education management system is the formalization and description of all its processes as well as the increasing use of modern ICT tools.

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The noteworthy feature here is that education management is a complex process that includes a number of the integral parts: information, psychological, and pedagogical as well as other ones [3]. When managing educational activities, as well as any other, the following questions arise: (1) who manages, (2) what is the object of management, and (3) what are the methods and effects of management, determination of the directions of management, etc. Furthermore, academic activities do not encounter any intellectual challenges, which modern distributed computer networks running on the software implementing complex systems research methods will readily respond to. As a result, ICT tools change their designated role as a managerial decision-making support to the category of technologies and systems that significantly reduce the part of the modern intelligent management systems. The main problem of modern education management is the most effective use of all available information resources as a timely response to changes both in the external environment and education system requirements. The problems specified are complex, poorly formulated scientific and technical problems that do not have simple solutions. Therefore, according to the authors [3], “in addition to the known types of support for the educational activities management (organizational, software, technical, etc.), it is necessary to introduce an intelligent type.”

5.4 Materials and Methods We consider that a promising direction for improving the university’s management system is to build an educational institution management model based on intelligent methods and models, as well as information systems intellectualization for teaching purposes. In order for information systems and technological processes as well as strategic management of an educational institution to function effectively in the field of education, we must solve a number of problems. This paper is devoted to some approaches for solving these problems based on the use of intelligent methods and models, including the adaptive semantic models. A distinguishing feature of the modern education stage is a search by teachersinvestigators ways to use formal techniques for describing learning processes based on the system analysis, cybernetics, and synergies with taking into account developing and expanding the concepts, principles, and achievements of didactics. At the present stage of evolution, pedagogical systems have approached the threshold beyond which we can expect the widespread use of semantic technologies and intelligent information systems for educational purposes (hereinafter referred to as IISON). Intellectualization of information systems for educational purposes is also due to the need of high-tech, automated information systems introduction and not only for the learning process but also directly for managing educational institutions in order to improve its quality.

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The analysis of the use of information systems for automating the educational process in Russian educational institutions has identified a number of contradictions that violate the two main trends of modern education—differentiation and integration [7]. Also, the existing intelligent information systems lack their targeted use of managing the educational process in accordance with the didactic systems’ required principles. The review of publications on the intellectualization of information systems in the last decade in Russia demonstrates an emergence of a number of information systems intellectualization areas for educational purposes [1–3, 6–8]. These areas are related to the development of various types of intelligent information systems for educational purposes. However, the problem of realizing interactivity and creativity in the teaching process when using intelligent systems and determining their quality has not been sufficiently studied [7]. For the effective functioning of IISON, it is needed to (1) develop methods and models of knowledge presentation and its structure in educational systems, (2) improve the educational process management based on model representations of the educational knowledge-base, (3) optimize and improve the quality of the management process in educational institutions, (4) develop new approaches to the knowledge formalization using various methods and techniques for educational institutions’ information environment’s intellectualization, and (5) form an information educational environment based on modern information systems of artificial intelligence technologies. The paper presents one of the options for developing a multi-level hierarchical model that makes up the digital university knowledge-base for switching to a new learning technology based on intelligent methods and models. We propose a multi-level hierarchical structure (a three-level one in our case) as a knowledge-base model for a digital university [8]. One can use frames, product models, semantic networks, and other intelligent models as well as a combination of them to create the first level of an educational institution’s knowledge-base. At the first level of the model proposed, we put information on directions and profiles of specialists’ training. The second meta-model level is designed to represent knowledge about specific academic disciplines studied in the course of training in accordance with the chosen specialty to reveal the first-level knowledge. The main sources of this level’s content are: data from academic programs of disciplines that reflect the structure of educational material, educational and guidance material recommended, materials of scientific and methodological conferences and exhibitions that provide advanced training for students. According to our experience of the development and use of intelligent tutoring systems as the base of the meta-model’s third level, it is convenient to choose the adaptive semantic network that reflects the logical structure of educational material and showing the cause-and-effect relationship between its concepts, which takes into account the dynamics and rapid development of the subject studied [19–21]. The source data for model presentation at this level are subject plans of academic disciplines and professional teachers’ knowledge.

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It should be noted that adaptive semantic models (ASM) are also used to control students’ knowledge [22]. The basis of this model is a knowledge system that has a certain meaning in the form of a complete network image, the nodes of which correspond to the concepts and objects of a certain domain area, and the links are the relations between them [22]. In practice, there are different types of semantic networks, depending on the meaning of vertices and arcs. Simple, homogeneous, hierarchical semantic networks, as well as functional semantic models, for which is effective in training systems are of interest to us. In this case, the vertices of the educational semantic network can contain either the object of knowledge or a student, as well as the main components of the learning process. Links between the vertices indicate the relationships between the objects. Among the semantic networks’ objects, the hierarchy is established in the relations “to be a subset” and “to be an element,” which are defined by links labeled SUB and E, respectively. The advantage of semantic networks as a model of knowledge representation and the learning process itself is the visibility of the subject area description, flexibility, and adaptability to the learner’s goal. However, the property of visibility is lost with size increasing and complicated links of the domain area knowledge-base. In addition, there are difficulties in handling exceptions of various types. To overcome the problems specified, we use the method of hierarchical description of networks to allocate local subnets at different levels (Fig. 5.1). It is worth noting that using semantic networks as a logical structure model of the teaching material grants an easy access to knowledge: moving from a notion along relation links allows revealing other notions of the domain area. It is known that mastering knowledge is a fairly complex problem. Semantic networks are unsophisticated and expressive and enable structuring and abstracting as well as transforming into a natural language. Moreover, teaching is a kind of a cognitive process, which takes place under specific conditions and implies the interaction

Fig. 5.1 General multi-level hierarchical model of knowledge representation

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between the teacher, learner, cognitive object, and phenomena of the reality as it is. The generally known models represented as graphs, matrices, logical equations, predicates, probabilistic, and deterministic automata are barely suitable for describing a learning process as they are aimed at analyzing and generalizing quantitative information. The objectives posed in this research are to do with processing semantic information presented as signs and some subjective factors. There exist numerous ways of presenting semantic information, one of which being a semantic network. Structurally, a semantic network is a directed graph, the vertices of which denote notions of the studied domain area while the links portray the relations between them. Using semantic networks for presenting and controlling the knowledge of the studied domain area facilitates, an information retrieval mode is resulting in a request for information according to classroom conditions [20]. One of the highlights of training systems based on semantic networks is a deep structuring of the studied notions of the domain area and their representation as a hierarchical structure. What is more, such systems possess the techniques which reveal a learner’s knowledge, his (her) personal traits and capabilities and personalize a learning process in order to suit a learner’s personal needs which increases the quality of training. Figure 5.2 shows an example of an adjustable semantic model. As a result of our theoretical research, we have developed and tested an intelligent learning system (IST) with a modular structure (Fig. 5.3), which is implemented using Delphi object-oriented programming language [21].

Fig. 5.2 Adjustable semantic model (called) ‘hierarchical structures’

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Fig. 5.3 Block diagram of intelligent tutoring system

It is worth noting that there are both stand-alone and network versions of the designed training and assessment system regardless of a particular academic discipline. The system allows for saving a user’s history including all the information about him (her) and his (her) marked answers. As one can see from the structured diagram of the designed system, all operations with the database are carried out through a database management module. The module contains an array of procedures and functions, which provide interaction with the database without using SQL instructions and accessing the database directly. The database management module is one of the main system modules. The other two system ones are the module of network editor management and the module of network object management. The remaining modules provide the user-friendly interface of system-to-user interaction. Modules 10, 11, and 12 of IST (Fig. 5.4) allow learners to choose their domain area (major (specialization)), a discipline within the chosen specialization, and then a particular theme of the course, which they are taking. In order to assess the effectiveness of the suggested approaches, we used a multidimensional analysis of academic achievements. The pedagogical experiments have resulted in the following quantitative effectiveness indicators (Fig. 5.4): ratio dependencies of teaching material acquisition in the experimental vs control groups.

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Fig. 5.4 Ratio dependencies of teaching material acquisition in the experimental vs control group

The designed intelligent system also allows revealing every student’s basic knowledge level providing him/her with a suitable teaching material. To sum up, using the above-mentioned approaches, we have practically accomplished the third level of the meta-model of digital university’s knowledge-base. The similar architecture employed for the model presentations of an academic knowledge-base leads to a groundbreaking academic approach and enhances its training capacity, especially when it refers to distance training.

5.5 Results of Testing the Proposed Approaches in Practice The principal result of the conducted theoretical research and suggested approaches on improving an education management system is the designed and practically tested “Kaspiy” intelligent training and assessment system; it is already used in a number of Russian educational institutions [20, 22]. This system facilitates the fulfillment of an adjustable user interface and the opportunity to use the action-related method in knowledge assessment. Furthermore, it allows revealing a learner’s basic knowledge level and, as a result, determining his (her) learning path. With a view to verifying the research hypothesis over a number of years, we conducted a set of experiments in three stages: summative, tracking, and educational assessment. The major objectives of the pedagogical experiments were: (1) to research the effectiveness of the suggested approaches for improving education management and (2) to study the influence of the designed training models and their use on students’ acquiring systematic, structural, and general knowledge. Moreover, we also sought to determine the model’s influence on fundamentals training in a general and algorithmic culture.

5 Knowledge-Based Model Representation for a Modern Digital … Table 5.1 Key indicators of research results

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Increasing the average student achievement score

(~20%) by 0.6 points

Less time spent on delivering instruction

1.5 times

Less time spent on assessment

1.4 times

*A note: accuracy of indicators is ±10%

According to the presented data, the experimental group acquisition ratio is on average 0.2 points higher than in the control group; thus, the major research effectiveness indicators are shown in Table 5.1. “Kaspiy’s” system-based learning enables a student to combine theory and practice. Moreover, the theory is structured (i.e., the basic definitions and general concepts of a domain area are highlighted, and the links between them are established). Such teaching material is easily acquired, remembered, and that is verified by the pedagogical experiment. This constitutes an important feature of the presented approach to designing the meta-model of a modern university’s knowledge-base. Furthermore, after introducing the designed methodology, the time spent on assessment is reduced by 1.4 times. What largely contributed to it was the personalized method of teaching tailored to suit an individual learner’s needs and cognitive activity management principles. In addition, the didactic capacity of “Kaspiy” defined by its user interface also played a major role.

5.6 Conclusions and Future Steps Conclusions. To conclude, employing intelligent technology to design a model as a knowledge-base of an educational institution allows: 1. revealing the leaner’s knowledge level in order to define a suitable personalized syllabus; 2. structuring of educational material to achieve academic goals; 3. perform adaptive assessment of students knowledge and increasing the effectiveness of education management; 4. personalize the process of obtaining the appropriate level of education. Our research to improve the management of the educational processes of a modern digital university through the use of intelligent technologies is ongoing. Currently, work is underway to refine the specific model of the first two levels of the meta-model of the knowledge-base of a modern university and to replenish the content with a view to its further practical implementation. Next Steps. To solve the tasks, it is necessary: 1. explore the possibilities of hybrid approaches to knowledge representation;

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2. on the basis of a comparative analysis of hybrid approaches to knowledge representation, select a specific model for the presentation of content for the corresponding level of a meta-model of the knowledge-base of a modern university in the context of the digital education paradigm; 3. practical implementation of the proposed approaches regarding the first and second levels of the meta-model of the knowledge-base of a modern digital university; 4. testing in practice the proposed meta-model of the knowledge-base of a modern university; 5. analysis of the results and refinement of the proposed approaches.

References 1. Abrosimov, V.K., Yablonsky, V.B.: Intellectual methods in the management of education: problems and prospects. Intell. Syst. 11(1–4), 21–54 (2007) 2. Shikhnabieva, T.: Model representation of a knowledge base in a digital university. Informatization Educ. Sci. 4(40), 54–60 (2018) 3. Shikhnabieva, T.Sh.: Methodological foundations for the representation and control of knowledge in the field of computer science using adaptive semantic models. Dissertation of a doctor of pedagogical sciences, p. 270. Moscow (2009) 4. Aksenov, A.N.: Improving the educational process management system of the university based on the process approach and quality management system. Young Sci. 6, 669–671 (2013). https:// moluch.ru/archive/53/7178/ 5. Uskov, V.L., Ivannikov, A.D., Uskov, A.V.: Promising Technologies for e-Education. Inf. Technol. Moscow. 2, 32–38 (2007) 6. Uskov, V.L., Uskov, A.V. Web-based education: 2006–2010 perspectives. Int. J. Adv. Technol. Learn. 3(3), 1–149 (2006) 7. Uskov, V.L., Ivannikov, A.D., Uskov, A.V.: The quality of e-education. Inf. Technol. (3), 36–43 (2007). ISSN 1684-6400 8. Uskov, V.L., Uskov, A.V. Promising technologies for corporate educational networks. In: Proceedings of the 4th International Scientific-Methodological Conference, pp. 341–344. New educational technologies in the university, Yekaterinburg (2007) 9. Robert, I.: Implementation of the internet for educational purposes. Smart education and eLearning. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Springer. Smart Innovation, Systems and Technologies, vol. 59 (2016) 10. Sklyamina, M.Y.: Ensuring information security of students in the general education system. Young Sci. 6.4, 52–55 (2015). https://moluch.ru/archive/86/16381/ 11. Kharlamova, E.E.: Modern approaches to assessing the effectiveness of the educational organization of higher professional education. Int. J. Exp. Educ. (8–1), 90–91 (2014). http:// expeducation.ru/ru/article/view?id=5809 12. Universities of the future. http://engtopic.ru/misc/universities-of-the-future 13. Digital-university-the-future-of-education-will-be-personalized. https://www.cognizant. com/perspectives/ 14. Roberts, P.: Higher education curriculum orientations and the implications for institutional curriculum change. Teach. High. Educ. 20(5), 542–555 (2015) 15. Barnett, R.: Knowing and becoming in the higher education curriculum. Crit. Engagem. Res. High. Educ. 34(4), 429–440 (2009)

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16. Anohina-Naumeca, A., Grundspenkis, J.: Concept maps as a tool for extended support of intelligent knowledge assessment. In: Proceedings of the 5th International Conference on Concept Mapping: 5th International Conference on Concept Mapping, pp. 57–60 (2012) 17. Anohina-Naumeca, A., Graudina, V., Grundspenkis, J.: Using concept maps in adaptive knowledge assessment. In: Advances in Information Systems Development: New Methods and Practice for the Networked Society. vol. 1, pp. 469–480. Springer, New York (2007) 18. Grundspenkis, J.: Concept Map Based Intelligent Knowledge Assessment System: Experience of Development and Practical Use. No: Multiple Perspectives on Problem Solving and Learning in the Digital Age, pp. 179–198. Springer Science + Business Media, LLC, New York, Dordrecht, Heidelberg, London (2011) 19. Shikhnabieva, T., Ramazanova, I.M., Ahmedov, O.K.: The use of intelligent methods and models to improve educational information systems. Monitoring. Science and Technologies. (2), 72–77 (2015) 20. Shikhnabieva, T., Beshenkov, S.: Intelligent system of training and control of knowledge, based on adaptive semantic models. smart education and e-learning. In: Uskov, V.L., Howlett, R.J., Jain, L.C.. (eds.) Smart Innovation, Systems and Technologies. 2016, vol. 59. pp. 595–603. Springer (2016). (*Web of Science, Scopus). http://link.springer.com/chapter/10.1007/978-3319-39690-3_53 21. Shikhnabieva, T., Brezhnev, A., Saidakhmedova, M., Brezhneva, A., Khachaturova, S.: Intellectualisation of educational information systems based on adaptive semantic models. In: 4rd International KES Conference on Smart Education and E-learning SEEL-2018, 20–22 June 2018, pp. 84–93. Gold Coast, Australia 22. Shikhnabieva, T.: Comparative characteristics of the main models of knowledge representation in intellectual systems of learning and knowledge control. Monit. Sci. Technol. 2(35), 61–64 (2018)

Chapter 6

Effects on Girls’ Emotions During Gamification Tasks with Male Priming in STEM Subjects via Eye Tracking Tabea Wanner, Tamara Wanner, and Veit Etzold

Abstract In the war of talents, there is great potential in female workers. It is important to gain the interest of female students in science–technology–mathematics–engineering (STEM) courses during their school time. One aim of our study was to find out which emotions teen girls (age 13–15) have while doing (block) programming gamification tasks and observe them while doing binary code calculation and building a Calliope mini piano (no gamification tasks). An eye tracker with an emotion software (based on Facial Action Coding System) measures their emotions during the gamification tasks. The girls were first divided into different groups, then primed in different gender stereotypes (Pro-STEM, Anti-STEM, and no priming). It was noticeable that the girls who were primed with Anti-STEM achieved better results in the programming game. This could be due to the age of the girls (puberty). It could be observed that the girls enjoyed the gamification tasks. The other tasks were hardly noticed. Gamification is one key to reach them.

6.1 Introduction and Literature Review In Germany, around 7.7 million STEM specialists (STEM = science, technology, engineering, mathematics) were employed subject to social insurance contributions in 2017. However, the number of STEM shortage jobs is rising. The shortage of skilled workers with vocational training could increase and widen in the future [1]. Where else can skilled workers be generated in Germany? Although the proportion of women in STEM occupations is slowly rising, it is still well below an average at T. Wanner (B) · V. Etzold Competence Center of Neuromarketing, University of Aalen, Aalen, Germany e-mail: [email protected] V. Etzold e-mail: [email protected] T. Wanner Course of Study Internet of Things, University of Aalen, Aalen, Germany e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_6

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15% in Germany. The proportion of women in the next generation of academics is also only 28% [1]. This could be counteracted, e.g., with the Girls’ Day. On Girls’ Day, schoolgirls can gain insight into occupational fields that girls rarely consider in the process of career orientation. Technical companies and departments as well as universities, research centers, and similar institutions primarily offer events for girls on Girls’ Day and register these in advance in the Girls’ Day Web site. By means of practical examples, the girls experience in laboratories, offices, and workshops how interesting and exciting this work can be [2]. Since 2001, there have been 1.8 million Girls’ Day places [3]. The Neuromarketing Competence Center together with the Internet of things course of Aalen University offer a course for girls. The theme was ‘Create your own dance party.’ Besides an interactive Internet game ‘dance party,’ and other games on code.org, a game with the programming language Python on codecombat.com, a Calliope mini (small minicomputer) and a mathematical problem (converting binary codes), there was an eye tracker study in which the students were observed in an Internet programming game named ‘Elsa.’ The aim of the study was to examine how gender stereotypical priming [4] affects girls. The eye tracker and the corresponding software iMotions, which recognizes the basic emotions by facial analysis, are intended to recognize the implicit impressions of the girls. The explicit impressions are queried by two questionnaires. Women are more often implicitly associated with humanities, family and domesticity, equality, or flat hierarchies than men. Men are more often implicitly associated with natural sciences, mathematics, career, hierarchy, and great authority [5–7]. In 2008 study by Ortner and Sieverding, female test subjects had to complete a task of spatial imagination. Women performed better in the task if they imagined themselves to be a stereotypical man compared to the idea of a stereotypical woman [8].

6.2 Research Goal Can male priming possibly be used to make women better in these areas? We have put forward three hypotheses: • H(1): If the participants are primed to ‘Anti-STEM,’ the fear of programming is greater than with ‘Pro-STEM Priming.’ No particular reaction occurs with any priming. • H(2): The priming reference to Anti-STEM serves a typical cliché and distracts the participants from the actual task. • H(3): Anti-STEM Priming causes the participants to perform the programming task worse than the ‘Pro-STEM’ primed or those who were not primed at all.

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6.3 Research Environment. Girls’ Day Program and Eye Tracking—System and Software A total of nine schoolgirls, aged between 13 and 15 from the 8th and 9th grades, came to the Girls’ Day. They all come from the same local high school. In advance, they have received a consent and privacy statement from their parents to bring on Girls’ Day to participate in the study. One schoolgirl was not allowed to take part in the study because her parents did not agree. After the greeting of the participants, the girls had to complete the following tasks in 3.5 h: Dance party, Elsa, and Angry Bird (browser-based programming games from code.org), Calliope mini piano and binary code calculation. The girls could organize their time freely. The girls wanted to work in groups, so they formed three teams of two and one of three. At the beginning, each participant received a number for the eye tracker study (no. 2–10). They were called one after the other to participate in the study. The Tobii Pro X2-30 system from Tobii Technology was used to record the eye data. This eye tracker can be attached directly to the laptop and is primarily used for non-contact measurements. Consequently, the subject is in a fixed position, but after a successful calibration he can move freely within a certain radius. The Tobii Eye tracker measures the subject’s eye position with an accuracy of 0.4 degrees and a sampling rate of 30 Hz. The eye tracker has several infrared lamps and a high-resolution video camera [9]. The iMotions software was used to record, process, and analyze the eye tracking data. This software is particularly suitable for usability studies for the investigation of Web pages, because the software has extensive analysis and visualization possibilities of eye tracking data. It offers the possibility to capture and process the data quickly. In addition, meaningful visualization options and statistics are provided. The software was used in version 7.1 [9]. Structure of the Study. The aim of the study was to see whether priming texts, i.e., texts that can influence the person, have an effect on the result of the person. The pupils were divided into three groups—independent of the group formation for the tasks: • First group Pro-STEM (test subjects no. 2, 3, and 5) • Second group Anti-STEM (test subjects no. 6, 7, and 8) • Third group no priming (subjects no. 9 and 10). It was not told to them that they had been divided into these groups and that they were primed. The first group received a text in which priming should have a positive effect on the result [10, 11]. The text shows that a girl is successful in the STEM subjects (mathematics, engineering, science, and technology). For comparison, the second group received a text in which priming should have a negative effect on the result [8]. The text shows that a girl has no interest in STEM subjects because “boys are better.’

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The third group received no priming text. The procedure of a study is as follows: 1. 2. 3. 4. 5. 6.

Welcoming the test person and checking the declaration of consent Queries: glasses/contact lens, eye problems Adjustment of the monitor position on the test persons Calibration of the eye tracker on the test persons Conducting the eye tracker study with subsequent questionnaire Farewell of the test person.

When asked whether the test subjects needed seeing aids, it came out that four test subjects wear glasses and one of them suffers from an eye disease. All four pupils wore glasses during the study. The quality of the eye tracker recording was between 56 and 91%. For those who did not wear glasses, the quality was between 95 and 98%.

6.4 Research Outcomes In the following, the emotions are compared before and during the task.

6.4.1 Emotions of the Respondents on the Introduction Texts Before the Task Pro-STEM Text. Three schoolgirls, also called test subjects (no. 2, 3 & 5), were given a Priming Text—Pro-STEM (Fig. 6.1) [11]—to read at the beginning of the study.

Fig. 6.1 Cutout of Pro-STEM text

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The three test subjects mostly showed neutral or positive emotions when reading the text. In respondent no. 2, there is a higher value at the beginning for the emotion ‘fear.’ At the end of the text, the diagram shows a small negative emotion, but this has nothing to do with the test, since the respondent did not look into the camera but spoke to the study director. • All three have recurring high values for ‘joy.’ The values are about 80 to 99.9 out of 100. • Each of the group members is ‘surprised’ but has different values (values between approx. 54 to 98 of 100). • Toward the end, higher values accumulate for negative emotions (‘disgust,’ ‘contempt,’ ‘sadness’). These values lie between about 30 and 95 out of 100. This could also be due to increasing time pressure at the end of the task. Task at Anti-STEM. Three test subjects (no. 6, 7 + 8) have been given a Priming Text—Anti-STEM (Fig. 6.2)—to read at the beginning of the study. The three test subjects showed neutral, negative, or positive emotions when reading the text. Test person no. 6 shows few positive emotions, but rather negative emotions such as ‘contempt’ and ‘disgust’ at the beginning. In summary, the emotions of the priming group Anti-STEM (Fig. 6.3) show that

Fig. 6.2 Cutout of priming text Anti-STEM

Fig. 6.3 Priming texts Anti-STEM summed

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Fig. 6.4 Emotions no priming text summed

negative emotions occur more frequently than positive emotions. No Priming Text. The last group of the study consists of two probands, no. 9 and no. 10. This group had no priming text The two test subjects showed different emotions while reading the text. Proband no. 9 felt ‘fear,’ ‘contempt,’ ‘sadness,’ and ‘disgust.’ Proband no. 10 only showed the emotion ‘fear’ at the beginning of the text (Fig. 6.4). With this background H(1) can be seen as confirmed.

6.4.2 Emotions of the Test Persons During the Task All test persons of the three groups played a ice-skate programming game after the introductory text. It is a block programming game with Elsa and Anna from the Disney film ‘The Ice Queen—Completely Frozen.’ The game is available at Code.org [12] ‘Code.org® is a nonprofit dedicated to expanding access to computer science in schools and increasing participation by women and underrepresented minorities.’ [13]. The game consists of different levels. For each level, the respondents must complete a task. Predefined blocks have to be put together to solve the task. Either the figure Elsa or the figure Anna shows the result on the left side of the browser. The levels consist of Elsa or Anna skating a certain figure, e.g., a snowflake, through the block programming with the, so that the figure is created. The respondents have twelve minutes to complete as many levels as possible. There are two explanatory texts (at the beginning and in the middle) to explain block programming. After twelve minutes, the browser is closed, and the task is finished. Then, the test subjects have to fill out a small questionnaire. Task at Pro-STEM. Test subjects took approximately one to two minutes to read the first explanatory text for the block programming. Until then, they had hardly shown any emotions, except subject 2 with ‘joy.’

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• All three have recurring high values for ‘joy.’ The values are about 80 to 99.9 out of 100. • Each of the group members is ‘surprised’ but has different values (values between approx. 54 to 98 of 100). • Toward the end, higher values accumulate for negative emotions (‘disgust,’ ‘contempt,’ ‘sadness’). These values lie between about 30 and 95 out of 100. This could also be due to increasing time pressure at the end of the task. Task at Anti-STEM. Results of the Web site recording of the Anti-STEM group: • Proband no. 6 has predominantly ‘joy’ in the task (values approx. 93 to 99 of 100). There are only a few outliers with contempt (values approx. 70 to 86 of 100) and disgust (values approx. 7 to 14 of 100). • Proband no. 7 shows hardly any emotions. If then short sequences of ‘surprise’ (values 14 of 100), ‘fear’ (values 30 to 43 of 100), ‘contempt’ (value 4 of 100), and ‘disgust’ (values 34 to 44 of 100). • Proband no. 8 shows mixed emotions. In addition to almost consistently high values for ‘joy’ (values: approx. 60 to 99.9 of 100), she also shows almost consistently medium–high values for ‘fear’ (values approx. 32 to 83 of 100). There are in a few places with high values of ‘sadness’ (values approx. 80 to 99 of 100), ‘surprise’ (values approx. 70 to 90 of 100), and ‘disgust’ (value approx. 75 of 100). Task at No Priming Text. Results of the Web site recording of the probands with no priming. • Test person no. 9 was often ‘angry’ (values between approx. 19 and 93 of 100). However, she also had ‘joy’ with higher values (values between 96 and 99 out of 100), a recurring ‘sadness’ at a lower level (values between 15 and 23 out of 100). From time to time, there were higher values for ‘contempt’ (about 72 to 98 out of 100) and ‘disgust’ (about 44 to 94 out of 100). • Test person no. 10, on the other hand, did not show so many emotions. There were higher values for ‘joy’ (values between 76 and 98 of 100), ‘contempt’ (values between 69 and 96 of 100), and ‘disgust’ (values between 31 and 99 of 100).

6.4.3 Analysis of the Attention Distribution by Heatmaps During the evaluation of the attention distribution, the mean values on the respective AOI areas of interest (AOI) from the eye tracking analysis are calculated and illustrated by heat maps. The red and orange colors indicate a focus of attention, the green color, which is more widely distributed, indicates a reduced cognitive load on the test subjects. The heatmap of the Pro-STEM group (Fig. 6.5) shows that the respondents are primarily focused on the text describing the subsequent task (bottom paragraph). The

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Fig. 6.5 Heatmap of the priming text Pro-STEM

focus of the eyes is less on the lower part of the priming section (middle section), which states that the former Girls’ Day participant uses the programming languages in her daily work. The heatmap of the Anti-STEM group (Fig. 6.6) shows that the respondents are primarily focused on the priming text (top paragraph). Here is the description of a former Girls’ Day participant who is not responsible in the STEM area and prefers ‘female-associated things.’ The focus is then on the text for the subsequent task.

Fig. 6.6 Heatmap of the priming text Anti-STEM

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Fig. 6.7 Heatmap of the text with no priming

The heatmap of the text without priming (Fig. 6.7) shows a focus at the end of the text. Therefore, H(2) can also be seen as confirmed.

6.4.4 Comparison of the Results of the Task The Pro-STEM group did not achieve more than level 6. In the group Anti-STEM, the test subjects no. 6 and no. 7 have passed the 6th level and have mastered the 8th or 9th level. Test person no. 8 did not complete the 6th level. In the group without priming, subject no. 9 reached the 7th level, but subject no. 10 did not complete the 6th level. When comparing the priming groups, it is noticeable that the Anti-STEM group successfully completed more levels in the same time than the Pro-STEM group.

6.4.5 Questionnaire After the Girls’ Day, the test subjects were asked about their experiences with technology, their hobbies, the work of their parents and siblings in a questionnaire. These values were compared with the data of the study. The following results were obtained: Girls who indicated that they had already had something to do with technology successfully completed more levels than other test subjects. Test person no. 7, who sees herself in the future to 90% in a technical, mathematical, scientific, or IT-related occupational field (highest value of all test persons) also reached the highest level of all participants (successfully completed level 9).

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A high average daily use of the Internet does not necessarily lead to a good result in the test. 62.5% of the participants said that their interest in technology increased after the Girls’ Day. In no case did the Girls’ Day negatively influence their interest in technology. Knowledge of block programming had no effect on the success of the test. None of the participants did like the task, but only 37.5% of the participants would like to learn more about block programming.

6.5 Discussion The organizers were able to make the following observations during Girls’ Day: • Questions related to math were not dealt with precisely, simple tasks could not be solved (e.g., dividing a three-digit number by two). • Inaccurate reading of tasks: The Calliope mini piano was only tested to see if things conduct electricity. The girls said, they already know the answers. However, their answers were only partly correct. The following parts were provided for testing: a paper clip, a banana, a wooden clothespin, a wire, a plastic wire, a piece of cloth, and an eraser. They found it exciting that the banana was conductive and then took a closer look at the piano. The others simply plugged the things into the crocodile clips and looked to see if there was any sound coming. • A positive example is the dance parties of Code.org: the girls were very happy to program their own dances with blocks. They use the time intensively to combine their hobbies like dancing or movies/cinema with programming. • Most of the time they spent programming the dance party than the other programming games. Less time were spent for the Calliope mini and only little time was used for the calculation with the binary code. • The questions were cribbed from each other, so the question cards could not be included in the evaluation. • Block programming codes could be reproduced, and questions could be answered right. Python codes could also be applied. In order to get the girls to program, they need something playful in which they can participate themselves, such as dancing. The tasks should not look technical; with ‘real’ interactions, the girls could be persuaded to participate. Like, ‘I enter a code and then something happens.’ In the priming texts, the test subjects predominantly showed the following emotions: • Pro-STEM: Positive emotions • Anti-STEM: Negative emotions • No priming: negative emotion. The Anti-STEM group has completed its tasks most successfully. Therefore, the H(3) cannot be regarded as confirmed. Accordingly, in this study, it could not be

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found that male priming improves women in certain areas [8]. However, this may also be due to the age of the test subjects and priming at that age is the opposite. The girls in the study are in their puberty and behave accordingly. The reaction to the Anti-STEM text could be a defiant reaction (‘I’ll show them how it works’). This may also indicate that young women at this age can no longer be primed so easily and that priming takes place earlier, e.g., in childhood. For this, a separate test series would be promising.

6.6 Conclusion In order to bring programming with less negative emotions closer to the girls, they must be brought to the STEM subjects earlier, as it becomes more difficult during puberty to get them enthusiastic about these topics. Only hip things like Disney movies or current charts in connection with games (keyword gamification) can make it enjoyable for girls to deal with the topic. In addition, learning must take place in schools, as they are not motivated to learn programming in their free time. The STEM programs of education in schools should become ‘smart,’ and thus, be more attractive for female students by using the main features of smart education and smart universities, including (1) adaptation, sensing, inferring, self-learning, attractiveness/anticipation, and self-optimization [14, 15].

6.7 Next Steps The future steps are (a) research with a larger group of females, (b) compare with males in the same age and (c) research with younger females (and males) and compare it with the other results. Also, one can analyze at what age which methods for STEM subjects are better.

References 1. Statistik der Bundesagentur für Arbeit Berichte: Blickpunkt Arbeitsmarkt–MINT— Berufe, Nürnberg, Sept 2018 https://statistik.arbeitsagentur.de/Statischer-Content/ Arbeitsmarktberichte/Berufe/generische-Publikationen/Broschuere-MINT.pdf. Access: 26 May 2019 2. Girls’ Day.: Was ist der Girls’ Day (2019). https://www.girls-day.de/Footer/Haeufige-Fragen Access: 16 May 2019 3. Reker, J.: Girls’ Day-Auftakt 2019: Mädchen für MINT-Berufe begeistern (2019). https:// www.girls-day.de/Footer/Presse/Pressemitteilungen/Girls-Day-Auftakt-2019-Maedchenfuer-STEM-Berufe-begeistern. Access: 26 May 2019

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4. Chatard, A., Guimond, S., Selimbegovic, L.: ‘How good are you in math?’ The effect of gender stereotypes on students’ recollection of their school marks. J. Exp. Soc. Psychol. 43(6), 1017–1024 (2007) 5. Fine, C.: Die Geschlechter Lüge. Die Macht der Vorurteile über Frau und Mann. /Delusions of Gender. The Real Science behind Sex Differences. How Our Minds, Society, and Neurosexism Create Difference. W.W. Norton & Company, New York, London (2010) 6. Mast, M.S.: Men are hierarchical, women are egalitarian: An implicit gender stereotype. Swiss J. Psychol. 63(2), 107–111 (2004) 7. Rudman, L.A., Kilianski, S.E.: Implicit and explicit attitudes towards female authority. Pers. Soc. Psychol. Bull. 26(11), 1315–1328 (2000) 8. Ortner, T., Sieverding, M.: Where are the gender differences? Male priming boosts spatial skills in women. Sex Roles. 59, 274–281 (2008). https://doi.org/10.1007/s11199-008-9448-9 9. iMotions.: Tobii X2-30, https://imotions.com/tobii-x2-30/. Access: 23 May 2019, p. 1 10. Stajkovic, A.D., Locke, E.A., Blair, E.S.: A first examination of the relationships between primed subconscious goals, assigned conscious goals, and task performance. J. Appl. Psychol. 91, 1172–1180 (2006) 11. McIntyre, R.B., Lord, C.G., Gresky, Dana, Ten Eyck, L.L., Frye, G.D.J., Bond Jr., C.F.: A social impact trend in the effects of role models on alleviating women’s mathematics stereotype threat. Curr. Res. Soc. Psychol. 10, 116–136 (2005) 12. Code.org Frozen (2019). https://studio.code.org/s/frozen/stage/1/puzzle/1‘. Access: 26 May 2019 13. Code.org (2019). https://code.org/international/about Access: 26 May 2019 14. Uskov, V.L., Bakken, J.P., Pandey, A.: The ontology of next generation smart classrooms. In: Proceedings of the 2nd International Conference on Smart Education and e-Learning SEEL2016, 17–19 June 2015, pp. 1–11. Springer, Sorrento, Italy (2015) 15. Uskov, V.L, Bakken, J.P., Pandey, A., Singh, U., Yalamanchili, M., Penumatsa, A.: Smart University taxonomy: features, components, systems. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and E-Learning 2016, pp. 3–14, Springer (2016). ISBN: 978-3-31939689-7

Part II

Smart e-Learning

Chapter 7

Relevancy of the MOOC About Teaching Methods in Multilingual Classroom Danguole Rutkauskiene, Greta Volodzkaite, Daniella Tasic Hansen, Madeleine Murray, and Ramunas Kubiliunas

Abstract Over the last couple years, migration between European countries and immigration to Europe from non-European countries has increased a lot. Due to this increment, the situation in language diversity and multilingualism in schools has also changed. Now, teachers face a lot of difficulties related with teaching in multilingual classes in Europe. The authors present the research on MOOC delivery for embracing language diversity in the classroom and results from surveys completed voluntary before and after MOOC courses. The research revealed the 97% of participants would recommend the course to a colleague or a friend.

7.1 Introduction Massive open online courses have become popular not so many years ago. Mainly, they are used to provide education for non-traditional audiences, which include people from different cultures. However, as the number of different cultured students in all the classrooms are increasing, teachers are still not prepared and need additional lessons and seminars. The authors stated [1] that new teachers begin their career without any or with little preparation about multilingualism. For example—the training programmes for teachers in England. The authors highlighted that the system overfocuses on English as an additional language and does not pay the required attention for students from different cultural backgrounds [2]. It is not a surprise that 35% of people under 35 years old in Europe have an immigrant background [3]. This caused the effect of linguistic diversity in schools of almost in all the biggest cities in whole Europe. This mainstream attracted the D. Rutkauskiene (B) · G. Volodzkaite · R. Kubiliunas Kaunas University of Technology, Kaunas, Lithuania e-mail: [email protected] D. T. Hansen Aarhus Business College, Viby, Denmark M. Murray Dublin City University, Dublin, Ireland © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_7

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attention of scientist, and the number of researches increased automatically. For example, researches explore the challenges of teaching students with the national language and other additional languages [4]. These kinds of researches show the opportunities and challenges in the process of course delivery.

7.2 Methodology The article goes as follows: in the first part of the paper, there is the identified problem and the need for the research area. The literature overview on teaching in multilinguistic classes using MOOCs methodology is discussed and presented. The discussion of the research is carried out by presenting a MOOC which was delivered to the Teacher Academy by School Education Gateway. ‘The Teacher Academy on the School Education Gateway was launched by the European Commission to help teachers’ access relevant professional development activities across Europe. The Teacher Academy is a single point of access to on-site and online in-service courses as well as a selection of teaching materials [5]’. Experimental evaluation of the course delivery challenges is presented and the effectiveness of the presented model is shown in the paper. The conclusions are provided at the end of the paper based on 1272 pre-responses and 245 post-responses.

7.3 Literature Review The key challenge when preparing future teachers is to find ways to implement study about diversity and teaching fast and with greater value [6]. When analysing the case of multilingualism in USA, the authors highlighted that diversity in USA is described as ‘fragmented and superficial’. The authors claimed that there still are programmes for teachers in which diversity learning is ignored, despite the growing number of students [7]. The authors claim that there is a need to revise study programs of all teachers to include multilingualism the problem where it is not included [3]. A lot of researches have analysed culturally and linguistically responsible and responsive teaching [8]. It also includes discussions on what is the required response and how it should appear in teachers’ education. Some authors [9] have discussed that the education of all teachers should include spheres like ‘transformation of trainees multicultural attitudes’ and increment of skills and knowledge that are vital for teachers when teaching in diverse classroom. Other authors [10] think that all teachers in bigger schools and bigger cities should know several different languages (language learning feature should be one of their learning modules). Another group of the authors suggest to teach teachers about the majority of students from other country backgrounds. Hey, do you think that combining cultural knowledge and language knowledge will provide great benefits [11].

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However, there are researchers who are worried about overprotective and superficial treatment of the diversity and multilinguistic problems in classrooms [12]. This approach started to gain popularity very fast and got the name of ‘stomp and chomp’ [13]. Later on, it started new activities which were related with diversity awareness and teacher education. Such an example is the EUCIM-TE project [14]. A lot of researchers started to analyse the problem of skills needed to work in a diverse classroom [15]. The majority of the authors have claimed that [16] skills and knowledge are not the only ones needed. All of the teachers should demonstrate their knowledge through practice which should be responsive and will empower a teacher to act in creative ways [17]. The authors have identified that creative ways [18] mean that teachers should be given the basic information and interpret it in their own ways, to control and teach class with the best approach. After the study of the researches, it is clear that there is no one sustainable practice on how to prepare teachers for teaching in the diverse and multilinguistic classes. More research needs to be done and all of the actions should be implemented in practice [19].

7.4 Discussion on the Pedagogical Model to Plan MOOC The analysing course has been developed for primary and secondary school teachers. Also, it includes teacher trainers who want to improve their competencies development and teachers who work in bilingual and CLIL schools regardless of the subject they usually teach. This course was built to encourage teachers of all age groups and from all subjects, to more deeply realise the importance of teacher language awareness and to better understand the multilingualism in their classroom. This course provides teachers with tools and resources to deliver subjects in different languages and to plan lessons. The main purpose of this MOOC (‘Embracing language diversity in your classroom’) is to ‘increase teachers’ awareness of the language competencies development of their students and how to benefit from them, as well as to provide them with different ICT tools and resources to support them in delivering curricular subjects in different languages’. The platform for MOOC implementation was by chosen School Education Gateway. Later on, the outcomes which will describe the preparation of the teachers were described and raised. The outcomes: (1) come to know the importance of teachers’ awareness of the students’ language diversity; (2) understand and turn language diversity into an asset for their teaching; (3) learn to empower language teaching with innovative technologies (CLIL, Multilingualism and Translanguaging) and apply them in the classroom; (4) explore freely available online tools and open educational resources for language teaching and learning to integrate in the classroom; (5) become familiar with multilingual classroom projects; and (6) learn how to build learning activities using resources for CLIL. Based on these outcomes and reachable results, coordinators created 5 different modules for this course: the

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importance of language awareness; turning language diversity into an asset for your teaching; content and language integrated learning; multilingual classroom projects. After the completion of the course, students receive digital module badges for every completed module of the course as well as a course badge and a course certificate upon completion of the full course. All badges can be exported to the Mozilla badge backpack. Full scheme of MOOC planning process is seen Fig. 7.1. This MOOC is especially valuable to teachers who are new to working in bilingual and CLIL projects or have little experience with these, and it will serve to provide participants with various tools and resources they can easily integrate in their lessons for more efficient and innovative teaching and learning. In this course, students who enrol in the course are not left alone and a support process assures their easier participation in the course. Also, a key principle of the

Fig. 7.1 Planning MOOC design processes

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Fig. 7.2 Role of tutor and actor in the MOOC

MOOC moderation on courses in Teacher Academy by School Education Gateway is about community building so that the students can help each other, rather than get full 1:1 support from a tutor all the time. The main functions of the tutor are listed in the Fig. 7.2. Raising awareness about how having students from diverse nationalities and speaking different languages in the same classroom can actually be used as an asset providing a benefit and added value to the said classroom. Besides, the MOOC will help teachers to build learning scenarios for content and language integrated learning (CLIL) in a framework of twenty-first century skills. Looking from the student (actor) perspective, when actively participating in the course, it should help to develop such competencies as scaffolding language, task design to support communication, anticipating problems and selecting tools to support understanding, checking learners’ understanding, and providing relevant and personal feedback.

7.5 Experimental Evaluation The research data is based on participants feedback/reflection on the course (started/completed) data, and on the data collected via two surveys completed voluntarily before (pre) and after (post) the course, providing information about the profile of course participants, the participants’ course impressions, and participants’ self-assessed knowledge of course topics. Results are based on 1272 pre-responses and 245 post-responses.

86 Table 7.1 Top 10 countries by a number of participants that started and completed the MOOC

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Completed the MOOC

Italy (639)

Italy (234)

Turkey (75)

Romania (35)

Romania (73)

Turkey (31)

Portugal (56)

India (20)

Greece (48)

Portugal (19)

India (41)

Greece (19)

Spain (37)

Spain (18)

Croatia (15)

Croatia (6)

Germany (14)

Germany (4)

Poland (13)

Serbia (4)

7.5.1 Participants’ Profile Totally, 1272 participants have started the MOOC and just 363 participants finished it and got the certificates. Top 10 countries in which MOOC was started and completed are listed in the Table 7.1. The table shows that the leader was Italy with 639 participants who started the MOOC and 234 participants who have completed it. Talking about the situation in the Baltic countries, Latvia was the leader with four participants who have started the MOOC and all of them completed it. In Lithuania, six persons started the course, but just one of them has finished it. In Estonia, one person started the MOOC but did not finish it. Data collected via the pre-course survey indicates that the majority of course participants are secondary school teachers (64% of respondents) and the second place is taken by primary school teachers (28% of respondents). The minority of the participants were school counsellors and policymakers (see Fig. 7.4). However, the survey identifies that the majority of participants were females (88% of all respondents). The age of the participants is from 25 or younger to over 55 years old. The majority (85%) of the participants were 36 years of age or older (see Fig. 7.5) To sum up, the majority of the participants were females who are 36 years of age or older and are working as secondary school teachers.

7.5.2 Course Impressions Results for course evaluations were revealed from 245 post-course surveys. The first question was to evaluate the overall value of the course. 99% of post-survey respondents rated the overall value of the course as ‘Good’ or ‘Very good’ (see Fig. 7.6).

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Fig. 7.4 Professional background of the participants

Fig. 7.5 Age of the participants

Fig. 7.6 Evaluation of the overall value of the course

The second question was to evaluate the extent to which participants do agree to the statements below (see Fig. 7.7). Results revealed that 96% of survey respondents agree or agree strongly that the course has made them more confident to use the methods addressed in the course. Also, 97% of the survey respondents said that they would recommend this course to a colleague or friend (agree or agree strongly).

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Fig. 7.7 Extent to the agreement with statements

The participants were asked to rate how confident they feel in effectively benefiting from the language competences of their students after taking this course (5 = high level of confidence). 83% participants reported that they feel confident in benefiting from the language competences of their students after taking this course. Also, following the MOOC, 95% indicated that they learned how to build learning activities using content and language integrated learning (CLIL) and 98% agreed that they have a better understanding of language diversity.

7.6 Conclusions 1. 2. 3. 4.

In general, 35% of people under 35 years old in the whole Europe have the immigrant background and requires learning language in MOOC. Studies across Europe explore the challenge of teaching students with the national language and additional one. The key challenge is to find ways in which diversity can be given much greater prominence and time. Teacher education needs to be revised to facilitate student success in the best way.

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5.

There has not been sufficient empirical research on how best to prepare teachers for diversity. 6. MOOCs have started to gain popularity for opening up education to nontraditional student audiences. 7. 2437 people from 55 countries registered to take part in the MOOC. 1218 participants started following at least one course module. 436 participants passed the MOOC and received the course certification. 8. The majority of course participants were secondary school teachers, female, and 36 years of age or older. 9. 99% rated the overall value of the course as ‘Good’ or ‘Very good’. 10. 97% would recommend the course to a colleague or a friend. 11. 83% feel confident in benefiting from the language competences of their students after taking this course.

References 1. Whittle, A., Lyster, R.: Focus on Italian verbal morphology in multilingual classes. Lang. Learn. 66(1), 31–59 (2016) 2. Kurzer, K.: Dynamic written corrective feedback in developmental multilingual writing classes. TESOL Q. 52(1), 5–33 (2018) 3. Jessner, U.: Metacognition in multilingual learning: a DMM perspective. In: Metacognition in Language Learning and Teaching, pp. 45–61. Routledge (2018) 4. Kim, E.K., Choi, K.S.: Identifying global representative classes of DBpedia Ontology through multilingual analysis: a rank aggregation approach. In: International Semantic Web Conference, pp. 57–65. Springer, Cham (2016) 5. School Education Gateway Homepage. https://www.schooleducationgateway.eu/en/pub/ teacher_academy.htm. Last accessed 25 Jan 2020 6. Wang, C.: Why are my chinese students so quiet? A classroom ethnographic study of Chinese students’ Peer review activities in an American multilingual writing class. INTESOL J. 13(1) 7. Ünsal, Z., Jakobson, B., Molander, B.O., Wickman, P.O.: Language use in a multilingual class: a study of the relation between bilingual students’ languages and their meaning-making in science. Res. Sci. Educ. 48(5), 1027–1048 (2018) 8. Yusuf, H.O.: 20. Teaching reading comprehension in large multilingual classrooms at the basic education level in Nigeria: the present Scenario. Issues Contemp. Afr. Linguist. Festschrift Oladele Awobuluyi 11, 283 (2016) 9. Canagarajah, S.: Multilingual identity in teaching multilingual writing. Reflections lang. teacher identity Res. 67–73 (2017) 10. Rebele, T., Suchanek, F., Hoffart, J., Biega, J., Kuzey, E., Weikum, G.: YAGO: a multilingual knowledge base from wikipedia, wordnet, and geonames. In: International Semantic Web Conference, pp. 177–185. Springer, Cham (2016) 11. Garrido, M.R., Oliva, X.: A multilingual, collaborative and functional approach to nongovernmental Catalan classes. In: Adult Language Education and Migration, p. 112124. Routledge (2015) 12. Tian, S., Bhattacharya, U., Lu, S., Su, B., Wang, Q., Wei, X., Tan, C.L.: Multilingual scene character recognition with co-occurrence of histogram of oriented gradients. Pattern Recogn. 51, 125–134 (2016) 13. Chikiwa, C., Schäfer, M.: Teacher code switching consistency and precision in a multilingual mathematics classroom. Afr. J. Res. Math. Sci. Technol. Educ. 20(3), 244–255 (2016)

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14. Nilsson, J., Axelsson, M.: “Welcome to Sweden”: Newly arrived students’ experiences of pedagogical and social provision in ıntroductory and regular classes. Int. Electron. J. Elementary Educ. 6(1), 137–164 (2017) 15. Dobinson, T., Buchori, S.: Catering for EAL/D students’ language needs in mainstream classes: early childhood teachers’ perspectives and practices in one Australian setting. Aust. J. Teach. Educ. 41(2), 32–52 (2016) 16. Lasagabaster, D.: I always speak English in my classes. Reflections L 1, 251–267 (2017) 17. Li, B.: Discovery and collaborative learning through the development of a multilingual and multipurpose resource pool (2018) 18. Miller, L., Habib, A.S., Michiels, P.: Dynamic written corrective feedback: a tool to improve multilingual student writing. In: Innovations in Teaching & Learning Conference Proceedings, vol. 8, p. 206 (2016) 19. Webb, L.: Conflicting perspectives of power, identity, access and language choice in multilingual teachers’ voices. In: Selected Regular Lectures from the 12th International Congress on Mathematical Education, pp. 843–857. Springer, Cham (2015)

Chapter 8

e-Learning Tools for Informal Caregivers of Patients with Dementia—A Review Study Blanka Klimova and Marcel Pikhart

Abstract Currently, due to the changing demographic trends, there is a growing number of older people suffering from aging diseases such as dementia. Dementia is a neurological disorder, which develops gradually, but progressively. Especially in the last stage of dementia, patients are fully dependent on caregiver’s help since they are not able to take care of themselves. The purpose of this article is to explore the use of e-learning as an informal caregiver’s tool for persons with dementia and discuss its benefits and limitations in order to improve the quality of life of caretakers of people with dementia. The methodology is based on a literature review of the research topic, i.e., the use of e-learning tools for informal caretakers of patients with dementia in the period of January 1, 2015 to June 30, 2019. The findings show that the informal caregivers in all identified articles were satisfied with the e-learning platform, program, or the course whose content tried to help them acquire more information and knowledge about the disease, as well as educate them how to cope with the perceived distress and enhance empathy for their patients. One of the main limitations of this review is a lack of studies found on the research topic. However, this fact has been confirmed by other research studies, which reveal that technologies evaluated for informal caregivers are rare. Therefore, more research should be done in this area.

B. Klimova (B) · M. Pikhart Department of Applied Linguistics, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic e-mail: [email protected] M. Pikhart e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_8

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8.1 Introduction Nowadays, due to the changing demographic trends, there is a growing number of older people suffering from aging diseases such as dementia. According to the World Health Organization [1], there are about 50 million people living with dementia worldwide, mostly in low- and middle-income countries. It is estimated that in 10 years, this number should reach 82 million. Dementia is a neurological disorder, which develops gradually, but progressively [2]. It firstly affects cognitive functions (e.g., process of thinking, memory, or orientation). These foremost signs of dementia can be accompanied with other symptoms that include depression, agitation, anxiety, aggression, sleeping or eating problems [3]. Dementia develops in three stages: mild, moderate, and severe. Especially in the last stage of dementia, patients are totally dependent on caregiver’s help since they are not able to take care of themselves [4]. At present, there is a paucity of professional/formal caregivers and therefore it is the informal/family caretakers who usually take care of their loved ones [5]. However, these family caretakers have to balance caregiving with other activities such as child raising, career, and relationships. Thus, they are at increased risk of depression, stress, social isolation, cognitive aging, physical and economic burden [6, 7]. For example, 36% of informal caregivers spend 100 h a week taking care of a person with dementia. In addition, 60–70% of caretakers for patients with dementia are women, and they provide 2.3 times more care for a person with dementia for over 5 years than male caregivers [8]. Research shows that the informal caretakers particularly require help on how to cope with symptoms of dementia, how to address behavior issues, and on how to be provided with knowledge and information about the disease itself, as well as relevant support [5]. Therefore, there is ongoing effort to reduce burden of the informal caregivers and provide them with some support, either instrumental (e.g., helping with household chores) or informative (e.g., providing information and knowledge by health professionals or by other tools). Furthermore, especially interventions focused on caregiver’s needs have appeared to be effective [6]. In this respect, e-learning programs/courses seem to be a promising tool in the improvement of caretaker’s quality of life while taking care of a person with dementia. For instance, Eisdorfer et al. [9] reported that there had been a significant decrease in depression symptoms among white and Cuban Americans when they had had access to technology-based interventions. The key advantage of e-learning is that a user has the opportunity to access it at any place and at any time. Learning is also more interactive [10]. The purpose of this article is to explore the use of e-learning as an informal caregiver’s tool for persons with dementia and discuss its benefits and limitations in order to improve the quality of life of caretakers of people with dementia.

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8.2 Methods The research issue, i.e., the use of e-learning tools for informal caretakers of patients with dementia, was searched in three recognized databases Web of Science, Scopus, and PubMed from January 1, 2015 to June 30, 2019. Furthermore, reference lists of the detected studies were checked in order not to omit other important studies on the research topic. The authors used the following keywords: e-learning AND caregivers, e-learning AND caretakers, e-learning AND dementia, e-learning AND dementia AND caretakers, e-learning AND dementia AND caregivers. Altogether 248 peer-review journal articles are written in English. The largest share of them was identified in Scopus (91), followed by PubMed (82) and Web of Science (75). After a thorough review of the titles and abstracts (61) and their duplication (16) of the selected studies, 45 studies were screened, and after that, 30 studies remained for the full-text analysis. These full-text articles were then analyzed and evaluated on the basis of the following inclusion and exclusion criteria: • The articles had to be published between January 1, 2015 and June 30, 2019. • Only peer-reviewed journal articles written in English were included. • The articles, which involved older patients with dementia and their informal caregivers, were included. • Only randomized controlled trials, experimental studies, or survey quality studies were included. • The primary outcome concentrated on the use of e-learning as a support tool for informal caregivers. The exclusion criteria were as follows: • The studies, which focused on a different target group and disease, were excluded such as [e.g., 11–15]. • Descriptive studies depicting the e-learning course for dementia caregivers, not empirical studies [e.g., 16, 17], posters [18], or protocol trials [19]. • Review articles dealing with the research topic [20–23]. In addition, a backward search was also performed, i.e., references of detected studies were evaluated for relevant research studies that authors might have missed during her search. After this, another article has been identified. Thus, altogether six research articles were eventually analyzed and evaluated. Figure 8.1 then describes the selection procedure of the detected studies.

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Identification

Records identified through database searching - Scopus (n = 91), PubMed (n = 82), Web of science (n = 75), (keywords in title) (n = 248)

Records after irrelevant content of the abstract (n = 61), duplicates removed (n = 16)

Screening

Records screened (n = 45)

Records excluded (n = 16)

Full-text articles excluded, with the following reasons: •

the

studies,

which

focused on a different target

group

and

disease, were excluded (n = 13) •

Eligibility

Included articles through the backward search (n = 1)

Full-text articles assessed for eligibility (n =29)

descriptive studies (n = 4), posters (n = 1),

•

protocol trials (n = 1),

•

review articles dealing

Included

with the research topic Studies included in qualitative synthesis (n = 6)

(n = 4).

Fig. 8.1 An overview of the selection procedure

8.3 Results The authors identified six articles discussing the research issue. Two articles were quality survey studies [24–26], two were randomized controlled trials [27, 28], one article was a qualitative case study [25], and one was an experimental study [29]. The key research issues focused on the usability and acceptability of e-learning programs/courses by informal caregivers, as well as the reduction of caregiver’s stress

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and enhancement of their empathy and receiving relevant knowledge and information about dementia and its management. The topics of each e-learning program were similar. For instance, the STAR e-learning course included the following topic [24]: • • • • •

What is dementia? Living with dementia. Getting a diagnosis and why it is important. Practical difficulties in daily life and how to help by best practice. The emotional impact of dementia: how adaptation and coping influences behavior and mood. • Support strategies to help people cope with consequences of dementia. • Positive and empathic communication. • Emotional impact and looking after yourself. The specific e-learning tools were as follows: • A multilingual e-learning portal—the European Skills Training and Reskilling (STAR). This portal provides dementia care training both for informal and formal caregivers [24]. • A dementia e-learning educational program (ADCarer.com), which provides dementia education and psychological and emotional help to informal caregivers [24]. • An e-learning platform (understAID application) providing information about dementia care, as well as taking care of oneself as a caretaker [28]. • A guided self-help Internet intervention “mastery over dementia” (MoD), focusing on decreasing caretaker’s psychological distress in terms of reach, adherence, and user evaluation [29]. • Dementelcoach (telephone coaching) and STAR e-learning (online platform to learn about dementia) [25]. • A virtual reality simulation movie and e-learning course: Through the D’mentia Lens (TDL), providing information about dementia care in order to help the informal caregivers understand how dementia patients feel and act [26]. The outcome measures were evaluated by standard assessment methods. The researchers used pre- and post-tests, online questionnaires, semi-structured interviews, and statistical analysis. The research subject samples ranged between 15 and 279 caretakers. The same was also true for the period of assessment, which usually lasted from 3 weeks to 3–4 months, without the follow-up period. Three articles were written by multinational teams from Australia, Denmark, the Netherlands, UK, Spain, or Poland [26–28], one article was of Chinese origin [24], one came from the USA [29], one article was written by Dutch researchers [25]. All the mentioned studies complied with the basic quality criteria [30]. The findings show that the informal caregivers in all identified articles were satisfied with the e-learning platform, program, or the course whose content tried to help them discover more information about the disease, as well as educate them how to cope with the perceived distress and enhance empathy for their patients.

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Limita ons

• reduc on of psychological and emo onal symptoms [2426, • flexibility of e-learning programs [25-26] • easy accessibility [24-28] • personalized approach [24-25, 27] • user-friendliness of elearning programs [24-25, 27-29] • avoidance of s gma zed professional (psychiatric) help [25] • development of social network of informal caregivers [26] • cost-effec veness of elearning programs [25]

• problems with the implementa on of elearning programs in terms of qualified personnel, adequate finances, laws and regula ons, and na onal policies [28] • a rela vely high number of dropouts [27]

Fig. 8.2 Key benefits and limitations of the e-learning tools for informal caretakers of persons with dementia found in the selected studies

Figure 8.2 lists all the main benefits and limitations of the e-learning tools for informal caretakers found in the selected studies.

8.4 Discussion and Conclusion The results of the identified research studies reveal that e-learning programs/courses/portals seem to contribute to the enhancement of caretaker’ s knowledge about dementia and its patients. In addition, they help them understand how these patients feel and act. In this way, e-learning is an asset in reducing caregiver’s stress and depression. These findings were also confirmed by other review studies on this topic [20–23]. For example, Wasilewski et al. [20] state that the family caregivers valued the reduction of depression and their burden while performing the Web-based interventions. This was also true for Jackson et al. [23], who discovered that caretakers had reduced depression, burden, and increased self-efficacy, especially when conducting multicomponent interventions delivered through telephone and the Internet. As the findings also show the caregivers appreciate comfort of easy accessibility of the e-learning programs from anywhere and at any time, it saves them time and expenses on traveling to a healthcare center (cf. [24]).

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However, this review also indicates that the e-learning programs should be tailored to caretaker’s needs in order to be effective and thus prevent dropouts of caregivers from these courses [5, 20, 25]. They should be a caregiver-centered in terms of developing his/her autonomy and responsibility for learning, while providing him/her with needed skills and strategies for learning. Furthermore, Wasilewski et al. [20] report that these technology-based interventions should be interactive. Ho et al. [24] report that the family caretakers need a relevant training because usually only younger caregivers are willing to use the e-learning programs, as well as those with higher educational level. In addition, there is a need of qualified personnel, adequate finances, laws and regulations, and national policies when implementing the e-learning programs [25]. Currently, there is a growing number of other e-learning programs such as [31–33]. The main reason is to ensure the appropriate quality of healthcare services and, in this respect, to provide both formal and informal caretakers with needed knowledge and skills [34]. One of the main limitations of this review is a lack of studies found on the research topic. However, this fact has been confirmed by other research studies, such as Surr et al. [21] or Krick et al. [35], who reveal that technologies evaluated for informal caregivers are rare. Therefore, more research should be done in this area. Other limitations of this review involve different methodologies used in the detected studies, follow-up observation, and assessment period of the included studies. All these shortcomings may lead to the overestimation of the results on the use of e-learning tools for informal caretakers of patients with dementia and thus decrease the reliability and validity of the results. For further limitations and potential drawbacks of the use of e-learning platforms, see the research of Pikhart [36–38]. Acknowledgements This study is supported by the SPEV project 2020, run at the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic. The authors thank Josef Toman for his help with the data collection.

References 1. World Health Organization: Dementia (2019). https://www.who.int/news-room/fact-sheets/ detail/dementia 2. Klimova, B., Maresova, P., Valis, M., Hort, J., Kuca, K.: Alzheimer´s disease and language impairments: social intervention and medical treatment. Clin. Interv. Aging 10, 1401–1408 (2015) 3. Klimova, B.: Learning a foreign language: a review on recent findings about its effect on the enhancement of cognitive functions among healthy older individuals. Front. Hum. Neurosci. 12, 305 (2018) 4. Klimova, B., Maresova, P., Kuca, K.: Non-pharmacological approaches to the prevention and treatment of Alzheimer´s disease with respect to the rising treatment costs. Curr. Alzheimer Res. 13(11), 1249–1258 (2016)

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5. Zwaanswijk, M., Peeters, J.M., van Beek, A.P.A., Meerveld, J.H.C.M., Francke, A.L.: Informal caregivers of people with dementia: problems, needs and support in the initial stage and in subsequent stages of dementia: a questionnaire survey. Open Nurs. J. 7, 6–13 (2013) 6. Brodaty, H., Donkin, M.: Family caregivers of people with dementia. Dialogues Clin Neurosci. 11(2), 217–228 (2009) 7. Romero-Martínez, A., Hidalgo-Moreno, G., Moya-Albiol, L.: Neuropsychological consequences of chronic stress: the case of informal caregivers. Aging Ment. Health. 18, 1–13 (2018) 8. Dementia Statistics Hub: (2019). https://www.dementiastatistics.org/statistics/impact-oncarers/ 9. Eisdorfer, C., Czaja, S., Loewenstein, D., et al.: The effect of a family therapy and technologybased intervention on caregiver depression. Gerontologist 43, 521–531 (2003) 10. Klimova, B., Simonova, I., Poulova, P.: Blended learning in the university English courses: case study. In: Cheung, S., Kwok, L., Ma, W., Lee, L.K., Yang, H. (eds.) Blended Learning. New Challenges and Innovative Practices. ICBL 2017, vol. 10309, pp. 53–64. Lecture Notes in Computer Science (2017) 11. Connan, V., Marcon, M.A., Mahmud, F.H., Assor, E., Martincevic, I., Bandsma, R.H., Vresk, L., Walsh, C.M.: Online education for gluten-free diet teaching: development and usability testing of an e-learning module for children with concurrent celiac disease and type 1 diabetes. Pediatr Diabetes. 20(3), 293–303 (2019) 12. Chong, M.C., Francis, K., Cooper, S., Abdullah, K.L., Thin, N., Hmwe, T., et al.: Access to, interest in and attitude toward e-learning for continuous education among Malaysian nurses. Nurse Educ. Today 36, 370–374 (2016) 13. Moniz-Cook, E., Hart, C., Woods, B., Whitaker, C., James, I., Russell, I., et al.: Challenge demcare: management of challenging behaviour in dementia at home and in care homes— development, evaluation and implementation of an online individualised intervention for care homes; and a cohort study of specialist community mental health care for families. Programme Grants Appl. Res. 5(15) (2017). https://doi.org/10.3310/pgfar05150 14. Kurz, A., Bakker, C., Böhm, M., Diehl-Schmid, J., Dubois, B., Ferreira, C., et al.: RHAPSODY—Internet-based support for caregivers of people with young onset dementia: program design and methods of a pilot study. Int. Psychogeriatr. 28(12), 2091–2099 (2016) 15. Lazzari, C.: Ecological momentary assessments and interventions in Alzheimer’s caregiving. Curr. Alzheimer Res. 15(11), 1027–1031 (2018) 16. Fam, J., Mahendran, R., Kua, E.H.: Dementia care in low and middle-income countries 17. Curr Opin Psychiatry (2019). https://doi.org/10.1097/yco.0000000000000523 18. Billis, A., Mantziari, D., Zilidou, V., Bamidis, P.D.: Co-creation of an innovative vocational training platform to improve autonomy in the context of Alzheimer’s disease. Stud Health Technol Inform. 251, 309–312 (2018) 19. Van Asch, I., Prins, M., Willemse, B.: Development of an e-learning for caregivers to manage challenging behavior of people with dementia. Gerontologist 56(Suppl. 3), 538 (2016) 20. Giguere, A.M.C., Lawani, M.A., Fortier-Brochu, É., Carmichael, P.H., Légaré, F., Kröger, E., et al.: Tailoring and evaluating an intervention to improve shared decision-making among seniors with dementia, their caregivers, and healthcare providers: study protocol for a randomized controlled trial. Trials 19(1), 332 (2018) 21. Wasilewski, M.B., Stinson, J.N., Cameron, J.I.: Web-based health interventions for family caregivers of elderly individuals: a scoping review. Int. J. Med. Inform. 103, 109–138 (2017) 22. Surr, C.A., Gates, C., Irving, D., Oyebode, J., Smith, S.J., Parveen, S., et al.: Effective dementia education and training for the health and social care workforce: a systematic review of the literature. Rev. Educ. Res. 87(5), 966–1002 (2017) 23. Dam, A.E., de Vugt, M.E., Klinkenberg, I.P., Verhey, F.R., van Boxtel, M.P.: A systematic review of social support interventions for caregivers of people with dementia: are they doing what they promise? Maturitas 85, 117–130 (2016)

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24. Hattink, B., Meiland, F., van der Roest, H., Kevern, P., Abiuso, F., Bengtsson, J., et al.: Webbased STAR e-learning course increases empathy and understanding in dementia caregivers: results from a randomized controlled trial in the Netherlands and the United Kingdom. J. Med. Internet Res. 17(10), e241 (2015) 25. Pot, A.M., Blom, M.M., Willemse, B.M.: Acceptability of a guided self-help Internet intervention for family caregivers: mastery over dementia. Int. Psychogeriatr. 27(8), 1343–1354 (2015) 26. van Rijn, A., Meiland, F., Dröes, R.M.: Linking two new E-health caregiver interventions to meeting centres for people with dementia and their carers; a process evaluation. Aging Ment. Health. 23, 1–10 (2019) 27. Jackson, D., Roberts, G., Wu, M.L., Ford, R., Doyle, C.: A systematic review of the effect of telephone, Internet or combined support for carers of people living with Alzheimer’s, vascular or mixed dementia in the community. Arch. Gerontol. Geriatr. 66, 218–236 (2016) 28. Ho, D.W.H., Maka, V., Kwokab, T.C.Y., Au, A., Ho, F.K.Y.: Development of a web-based training program for dementia caregivers in Hong Kong. Clin. Gerontologist. 38, 211–223 (2015) 29. Nunez-Naveira, L., Alonso-Bua, B., de Labra, C., Gregersen, R., Maibom, K., Mojs, E., et al.: UnderstAID, an ICT platform to help informal caregivers of people with dementia: a pilot randomized controlled study. Biomed. Res. Int. 2016, 5726465 (2016) 30. Wijma, E.M., Veerbeek, M.A., Prins, M., Pot, A.M., Willemse, B.M.: A virtual reality intervention to improve the understanding and empathy for people with dementia in informal caregivers: results of a pilot study. Aging Mental Health. 22(9), 1115–1123 (2018) 31. Health Evidence Quality Assessment Tool for review articles (2005). https://www. healthevidence.org/documents/our-appraisal-tools/quality-assessment-tool-dictionary-en.pdf 32. Home Instead Senior Care (2019). https://www.helpforalzheimersfamilies.ca/alzheimersdementia-education/ 33. Free dementia care online course from John Hopkins University and Coursera (2015). https://ec.europa.eu/eip/ageing/news/free-dementia-care-online-course-john-hopkinsuniversity-and-coursera_en 34. MacDonald, C.J., Walton, R.: e-Learning education solutions for caregivers in long-term care (LTC) facilities: new possibilities. Educ. Health (Abingdon). 20(3), 85 (2007) 35. Krick, T., Huter, K., Domhoff, D., Schmidt, A., Rothgang, H., Wolf-Ostermann, K.: Digital technology and nursing care: a scoping review on acceptance, effectiveness and efficiency studies of informal and formal care technologies. BMC Health Serv. Res. 19(1), 400 (2019) 36. Pikhart, M.: Interculturality in blended learning: challenges of electronic communication. In: Smart Innovation, Systems and Technologies. vol. 144, pp. 97–106. Springer Nature Singapore (2019) 37. Pikhart, M.: Aspects of intercultural communication in IT: convergence of communication and computing in the global world of interconnectedness. In Lecture Notes in Elctrical Engineering, vol. 590 (2019) 38. Pikhart, M.: The use of cloud computing in managing companies and business communication: security issues for management. Lect. Notes Elctrical. Eng. 574, 94–98 (2019)

Chapter 9

Automation of e-Workshop Project Control for Knowledge-Intensive Areas Elena A. Boldyreva and Lubov S. Lisitsyna

Abstract This article discusses the issues of systematic processing of electronic workshops content to prepare competitive graduates, of various majors, in knowledge-intensive areas. The research proposes an approach to an automated workshop design for a specific student major considering numerous factors which influence the changes in the regulatory framework, student learning outcomes of the previous workshop passage, employers’ assessment, and the labor market needs. The analysis of expert assessment and the workshop content formation in this approach involves the implementation of the following stages, such as calculation of the confidence-in-experts degree, selection of subject learning tasks, development of a workshop project considering the relevance of each learning task, and the task complexity and evaluation (points) for each major. The result of this study is an information system for managing the project of an electronic workshop. It implements the approach to the formation of the actual practical part content of subjects for each major. The approach is based on the ADDIE model of pedagogical design. Using the information system, we have developed a workshop project of the major “Design of embedded systems” for the subject “Embedded systems.” The workshop was tested by the fourth-year bachelor students. The workshop results were entered into the system, processed, and used to make recommendations for changing the content of the workshop.

E. A. Boldyreva · L. S. Lisitsyna (B) Department of Software Engineering and Computer Science, ITMO University, Saint Petersburg, Russia e-mail: [email protected] E. A. Boldyreva e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_9

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9.1 Introduction A key condition for improving the efficiency of the educational process is timely interaction with the labor market, which is a consumer of university graduates. Currently, universities are trying to increase the degree of employer companies’ participation to form educational trajectories and content of subjects. University education is not only aimed at transferring knowledge to students but creating competitive specialists in the labor market [1–4]. Thus, when developing educational materials, it is necessary to focus on the main stakeholders of the educational process, namely potential employers for students. A characteristic feature of actively developing knowledge-intensive areas is their constant updating of applied technologies, methods, programming languages, etc. In addition, when choosing practical content for subjects related to knowledge-intensive areas, for example, in the IT sphere, it is necessary to consider a wide variety of labor functions and labor tasks the graduate should be prepared for within their major. Thus, the improvement of education quality depends not only on design workshops that reflect the current state of a subject area and contain tasks confirmed by labor market experts but also re-evaluating them annually.

9.2 Project Goal Project goal. The research project’s goal focuses on students’ education in knowledge-intensive areas that have regular technologies updating, and proposes the solution for designing workshops with actual learning content. The approach is based on an information system that considers various factors—changes in the major regulatory documentation, students’ learning outcomes during the previous workshop, employers’ assessments, and the labor market needs.

9.3 Approach to Automated Design and Control of an e-Workshop for Knowledge-Intensive Areas Based on the Employers’ Assessment This article describes an approach to solving this problem, namely the model of automated design and control for the workshop project based on the employers’ assessment in knowledge-intensive areas. Based on the approach, an information system for e-workshop project control has been developed. This system implements the ADDIE model of pedagogical design [5, 6] at the stages of analysis and design. A characteristic feature of the approach is its application in knowledge-intensive subject areas with annual technologies and tools update. The similar approaches [7–9] use the method of initial competencies detailing to plan and select expected

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103 Educational standards

Professional standard

Required skills

Selection of labor tasks Past year's workshop kernel

Base learning tasks

This year's workshop kernel

Analysis of expert assement

Workshop kernel Significance estimate

Ontology modeling

Selection of key skills

Modeling of education process

Labor market needs Ontology modification Content modification Teacher

Recommendations

Workshop project Laborintensity estimate

Results Learning Analysis outcomes

Implementation

Subject N

...

Subject 1 Workshop project l Points estimate

Fig. 9.1 Process of workshop content creating for a specific major for the current year

learning outcomes from the excess educational content. This approach enables organizers to design a workshop project considering different majors based on the expert assessments of potential employers. The approach describes the process of creating and modifying the workshop content for a major. The model of this process is shown in Fig. 9.1. The approach to the e-workshop project for knowledge-intensive areas is based on the employers’ assessments [10] and involves the following steps:

9.3.1 Forming the Workshop Kernel for a Specific Major During the first design, the information system forms the workshop kernel—a list of the main learning tasks for a specific major. The main learning tasks at this stage are the adaptation of labor tasks the student should be ready to solve to be competitive in the labor market. The selection of learning tasks is performed through the analysis of professional standards and documentation and the major corresponding vacancies from online-recruitment systems. Labor tasks are selected from the standards, and required skills and responsibilities of applicants are extracted from the vacancy description. If the workshop project is not the first, then the list of learning tasks can be based on the previous year list.

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9.3.2 Forming a List of the Main Learning Tasks of the Workshop Based on the Analysis of Experts’ Assessment Experts in the field evaluate the list of learning tasks for the workshop kernel. All experts are active specialists in this professional area and can provide information about actual tasks. They face these tasks in their professional activities. Experts are selected exactly for the major based on their professionalism coefficient Ci :   Ci = Nprof , Nteah , Nproject , Nlevel , Npos where Nprof —assessment of the experts’ professional experience; Nteah —assessment of pedagogical experience; Nproject —the number of successfully implemented projects the expert participated in; Nlevel —the position on the labor market (an expert can be an employer or employee); Npos —the position of an expert in the company. A reference expert is selected as a specialist with a huge professional and pedagogical experience within this area. The expert is required to: 1. select technologies and methods of solution for each learning task from the list, which are currently relevant in his opinion; 2. add new tasks to the learning tasks list to maximize the scope of professional activity; 3. evaluate the learning tasks based on their significance, complexity, and basic skill level. Based on the ratings obtained, the confidence coefficient for each expert is calculated (9.1) as follows: m

j=1 (C i j + (ρmax − ρi ))  m  i=1 j=1 C i j + (ρmax − ρi )

K i = n

(9.1)

where Ci j —the score of the i-th expert on factor j; ρi —the distance (Euclidean norm of the difference matrix) of the i-th experts evaluation matrix to the reference expert evaluation matrix (reference matrix); ρmax —the maximum possible distance of the i-th expert evaluation matrix to the reference matrix; n—the number of experts; m—the number of factors. Experts’ scores are adjusted based on the confidence coefficient. The rating of the main learning tasks is compiled. The learning tasks are selected according to the significance rating higher than the threshold the workshop developer set. Each task is also assigned text tags with the technologies, programming languages, and solution methods recommended by experts. According to these tags, the main learning tasks are divided into clusters by topics, technologies, and implementing tools. Such clusters allow to group tasks by subjects when forming an ontological model of the subject area for a major.

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These stages involve annual interaction with experts to update the list of main learning tasks and assess their significance and technologies for solving them.

9.3.3 Formation of the Workshop Project with the Calculation of Labor Intensity Characteristic of Learning Tasks and Division of These Tasks into Auxiliary Ones The complexity of learning tasks in the workshop project is determined in accordance with the weight coefficient of each task. The weight coefficient of the problem is calculated using the formula (9.2) m

j=1 C i m i=1 j=1

Wi = n

∗ Kj Ci ∗ K j

(9.2)

where Wi —the weight coefficient of the i-th learning problem; Ci —expert score of the i-th factor of the learning task in points; K j —confidence level of the j-th expert; n—the number of learning tasks for the workshop; m—the number of evaluation factors. According to the value of the weight coefficient, each main learning task is assigned to a part of the total workshop labor intensity. Each of the main learning tasks can be presented as a block of auxiliary tasks (sub-tasks) as follows: Ai = a1 , a2 , . . . , an , afin ,

(9.3)

where Ai —i-th main learning task; an —the sub-task with the number n in the learning path; afin —the final task confirming the harnessing of the knowledge. Consistently solving sub-tasks, the student acquires and fulfills the skills provided by the educational standard and necessary for solving the main learning task. Subtasks for each main task are developed with the help of subject teachers since the automation of this process cannot fully consider all the features of student training known to teachers due to pedagogical experience.

9.3.4 Formation of Workshops Projects for Subjects of a Major Based on Educational Process Modeling Papers [11–15] emphasize the influence of professional area knowledge on the educational process and describe the features of knowledge control in knowledge-intensive areas using various models.

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The ontological model of the subject area allows to determine the relationship between the main learning tasks and arrange them in the optimal order for studying. The distribution of the main learning tasks by subjects is conducted automatically based on the profile curriculum, data from the ontological model, the complexity of the tasks, and the number of academic hours allocated for the subject practical and independent practical parts. The blocks of sub-tasks (9.3) selected at the previous stage are analyzed for duplication—if the main learning task i contains one or more identical sub-tasks with the previous (within the educational process) task i − 1., then these sub-tasks are excluded from the preparatory set i.. This reduces the “preparatory” trajectory of the main learning task without losing the quality of students learning. Implementation of the main learning tasks and sub-tasks is performed using data obtained from experts at the stage of workshop kernel forming. Technologies and tools recommended by experts are included in the educational process. Since we are talking about training specialists in knowledge-intensive areas, where popular technologies and tools are constantly changing, constant monitoring of the labor market and an annual survey of specialists allow timely updating of the implementation of learning tasks, even if their list remains unchanged.

9.3.5 Processing the Learning Outcomes of the Workshops of Disciplines and Making Recommendations for Modifying the Workshop Content Once the projects and content of the subject’s workshops have been generated, verified by the developer and approved by the workshop teacher, the workshop can be launched for students. e-Workshops allow organizers to collect data, such as scores obtained by students for each task, the time spent on each task, number of attempts, and final workshop score. It is possible to evaluate additional workshop realization. In this work, we propose a method of recommendation for modifying the electronic workshop content based on the results of its last iteration. This method involves obtaining feedback data based on the results of a particular subject of the major—a students’ scores for eh task, the number of attempts, the time of tasks’ completion (tasks were completed on time or with a delay of one or more time periods), and the final workshop assessment of the student. The time period is understood as the time allotted for solving the block tasks of a certain subject. In electronic workshops being developed, a week is usually given for a block. It was decided to evaluate the quality of implementation of the learning tasks by the average percentage of its completion (9.4). n Rj =

=1

Ci · Ki · 100% = n

n i=1

 Ci · 1 − n

k K

 · 100%

(9.4)

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Table 9.1 Recommendations for the workshop content modifications Average pass rate j-th learning task R j

Recommendations to the developer of the workshop

Less than 60%

Task was too difficult for this stream of students. It is recommended to review the implementation of the task, the time allotted for it, the position in the learning path, and possibly simplify or include additional sub-tasks in the workshop

From 60 to 80%

Task implementation does not need to be revised

From 80 to 100%

Task was too simple for this stream of students. It is recommended to review the implementation of the task, the time allotted for it, the position in the training path, and possibly complicate it

where R j —the average percentage of its completion i-th learning tasks; Ci —the task completion percentage of i-th student; K i —the student coefficient of late for the j-th learning task; k—the number of “late” time blocks; K —the total number of time blocks; n—the number of students who completed the workshop to the end. Recommendations for modifying the content of the subject’s workshop are formed depending on the value of R j ; they are presented in Table 9.1. Modification of the ontological model for a major and the model of the educational process can be made only after all the subject’s workshops have been completed. Then, based on the results of their last iteration, we can draw a conclusion about the need to revise the current learning trajectory and the links between the main learning tasks and subjects.

9.4 Structure and Interaction of Users in the e-Workshop Project Control Information System The proposed approach to e-workshop design and control assumes that the workshop developer will aggregate a large amount of learning outcomes and feedbacks, communicate with experts in a timely manner, and analyze and revise the structure and content of the workshop. To support the developer, a workshop control information system has been developed implementing this approach. This system is implemented in the Python programming language using Flask for Web interface creating and database using MySQL technologies. Use-case diagram was used to define and describe the system functional purpose. The diagram is a system conceptual model during its design and development. There are three actors in the system—the workshop developer, the expert, and the subject teacher: 1. The developer can create and edit experts’ profiles, create and edit the workshop project, select experts for this workshop (manually and automatically using the

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Fig. 9.2 Sequence diagram for evaluation stage tasks

system), interact with experts about evaluating learning tasks, upload and view learning outcomes, and analyze them. 2. The expert can evaluate the proposed learning tasks for workshops attached to the expert, add new tasks to this list, create, and edit his/her profile. 3. The teacher can view the workshop project, leave comments to the developer about its structure and content, view experts’ profiles, and learn outcomes. The actor’s roles are defined by a special label in the profile. The role values are stored in the database in the “Users_status” table. Before modeling the designed system behavior, it is necessary to detail the algorithmic and logical implementation of the operations performed by the system. Sequence diagrams can be used for this purpose. For further consideration, a precedent was chosen: the collection of employers’ expert assessment by means of an information system, since it is the most significant point of the proposed approach to designing a workshop for knowledge-intensive areas. Three actors are involved in this case—the developer of the workshop, the information system, and the expert. The sequence diagram of this precedent is shown in Fig. 9.2. In addition to the basic flow, this diagram also shows alternative flows that determine system behavior and actors depending on certain conditions.

9.5 Practical Application of the Proposed Approach Using the proposed approach and the workshop control information system, the workshop for the major “Embedded systems’ design” was designed, and the e-workshop for the subject “Embedded systems” was implemented. The e-workshop is designed

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Fig. 9.3 a Distribution of workshop assessments; b Visualization of the workshop tasks correlation analysis

for 32 academic hours and contains nine main learning tasks. The list of tasks is presented in Table 9.2. Seventeen experts, including two experts from foreign companies, were involved in workshop content forming. The employer with more than 40 years professional and pedagogical experience was selected as a reference expert. For this workshop, relevant learning tasks were selected from the initial list into 42 main learning tasks. Using the proposed approach, the developers selected and included nine main learning tasks in the workshop. The workshop was approved by fourth year bachelor students—73 students passed the workshop. Workshop learning outcomes were obtained and analyzed. Visualization of the workshop results and the correlation analysis of the tasks’ scores are shown in Fig. 9.3a, b. Of the 73 students, two students did not receive a passing grade (60%), 63% of students received grades in the range of 60–80%, and 34% received grades was in the range of 80–100%. The calculated correlation coefficients show that the highest correlation is for learning task № 9 with learning tasks № 2 and № 3 (due to the fact that tasks № 2 and № 3 allow to develop basic skills for solving task № 9), and for tasks № 6 and № 7 (the tasks are not directly related to each other; however, as the average percentage of completion showed, the tasks caused difficulties for this students’ flow). Table 9.2 shows the assessments made by experts and the average pass rate of each task based on the e-workshop iteration outcomes. The table shows that there is a discrepancy between the expert assessment of the task significance and the regression assessment for tasks № 3, № 6, and № 7. The average percentage of task № 3 completion was 91.8%, so the developer should complicate and expand this task. At the same time, tasks № 6 and № 7 with high expert significance received a pass rate below 60%. This may indicate that these tasks were too difficult for implementing that there was not enough time to solve them, or that they do not have enough preparatory tasks. This assumption was confirmed by individual feedback received from students who passed this e-workshop. Based on this, it was decided to review the location of these tasks in the learning trajectory and consider increasing the number of academic hours allocated for their solution. We also consider replacing task № 6 with an alternative one and transferring it to an additional part of the e-workshop.

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Table 9.2 Results of workshop realization Task num.

Task

Expert assessment of significance tasks (max. 10)

Average pass rate (%)

1

Development of the control algorithm for the technical task and system specification

9.18

75.4

2

Development of the software structure

7.34

79.1

3

Programming simple timers

7.31

91.8

4

Development of programs with the interrupts processing from timers

8.81

80.5

5

Development of serial interface management programs

7.33

68.7

6

Development of programs for receiving and transmitting data from the Ethernet controller.

7.27

56.2

7

Development of drivers for the I2C bus

9.01

53.4

8

Development of drivers for the keyboard and display

7.18

73.2

9

Development of drivers for the CAN interface

8.65

64.7

After the realization workshop is re-evaluated and revised, next students’ flow will be offered a revised version to complete. Thus, it will be possible to judge the correctness of the assumptions made earlier and improve the methods of electronic workshop content correction.

9.6 Conclusions. Future Steps Conclusions. As a result of the research, an approach to e-workshop project control was proposed for knowledge-intensive areas that have annual technologies, tool updates, and update of the labor market requirements. A content modifying method for the electronic workshop based on the workshop learning outcomes was proposed. Based on the proposed approach, an information system for e-workshop project control has been developed. The developed system allows timely updates to the

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content and structure of multi-subject workshops in knowledge-intensive areas, using data from the workshop stakeholders—employers, teachers, and students. Developed models of users’ action and interaction in the control system represented by the use-case diagram and sequence diagram. With the help of the control information system, we developed a project of the multi-subject workshop in the major “Embedded systems design” and electronic workshop for subject “Embedded systems.” The workshop was tested by fourth-year bachelor students. The workshop learning outcomes were entered into the system and used to make recommendations for workshop content modifying. The average percentage of main learning task № 3 completion was 91.8%, so the developer should make it more complicated. At the same time, learning tasks № 6 and № 7 with high experts’ significance received a pass rate below 60%. This may indicate that the tasks have too difficult implementation that there was not enough time to solve them or that there had not enough sub-tasks. Currently, work is underway to improve the learning task implementation in accordance with the recommendations received. The proposed software and tools allow organizers to re-evaluate the workshop content relevance and improve the existing model of specialists’ training within the professional area to make them competitive in the labor market. The proposed approach and tools for multi-subject workshops’ design for specific major in knowledgeintensive areas currently have no analogs among the tools for learning system and labor market analyzing. Future steps. Based on obtained research outcomes, the next step of this research is to involve various types of stakeholders (experts, students, teachers) into a testing of various features and functions of the developed information system and further prototyping of general workshop project of major “Computer science and technologies” (specialization—Embedded Systems) to ensure quality of proposed conceptual and software solutions. Also, in the future, it is planned to increase the pool of experts for this workshop, including foreign companies.

References 1. Mann, A., Archer, L.: Understanding employer engagement in education: theories and evidence (2014) 2. Tomlinson, M.: Employers and Universities: conceptual dimensions, research evidence and implications. High. Educ. Policy (2018). https://doi.org/10.1057/s41307-018-0121-9 3. Elias, K.L.: Employer perceptions of co-curricular engagement and the co-curricular record in the hiring process. Available at: https://tspace.library.utoronto.ca/bitstream/1807/67968/1/ Elias_Kimberly_L_201411_MA_thesis.pdf (2014) 4. Balganova, E.V., Bogdan, N.N.: Assessment by employers of competencies of future specialists in the field of personnel management as a basis for improving the educational process. Prof. Educ. Modern World 6(2), 290–296 (2016). https://doi.org/15372/PEMW20160214 5. Chookaew, S. et al.: Computer assisted learning based on ADDIE instructional development model for visual impaired students. In: Proceeding 22nd International Conference on Computers in Education, ICCE 2014 (2014)

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6. Ngussa, B.M.: Application of ADDIE model of instruction in teaching-learning transaction among teachers of mara conference adventist secondary schools., Tanzania. J. Educ. Pract. 5(25), 1–11 (2014) 7. Lisitsyna, L.S., Efimchik, E.A.: An approach to development of practical exercises of MOOCs based on standard design forms and technologies. Lecture Notes of the Institute for Computer Sciences. Soc. Inf. Telecommun. Eng. 180, 28–35 (2017) 8. Lyamin, A.V., Cherepovskaya, E.N., Chezhin, M.S.: An outcome-based framework for developing learning trajectories. Smart Innovation Syst. Technol. 75, 129–142 (2018) 9. Lisitsyna, L.S.: The Pedagogical Design of Electronic Courses. 67p ITMO University, St. Petersburg (2018) 10. Boldyreva, E.A.: Approach to the automation of design processes for a workshop based on the views of employers. Bulletin of the Astrakhan State Technical University. Series: Management, Computing and Informatics. 2020. No. 1. pp. 94–104. (2020). https://doi.org/10.24143/20729502-2020-1-94-104 (in Russian) 11. Bari, M., Djouab, R.: Quality frameworks and standards in e-learning systems. Int. J. Comput. Internet Manage. 22(3), 1–7 (2014) 12. Ward, J., Aurum, A.: Knowledge management in software engineering—describing the process. In: 15th Australian Software Engineering Conference (ASWEC 2004) Melbourne, pp. 137– 146. IEEE Computer Society Press, Australia (2004) 13. Watson, W.R., Watson S.L.: An argument for clarity: what are learning management systems, what are they not, and what should they become? TechTrends 51(2), 28–34 (2007) (Springer Verlag) 14. Hynes, B., Costin, Y., Birdthistle, N.: Practice-based learning in entrepreneurship education: a means of connecting knowledge producers and users. High. Educ. Skills and Work-Based Learn. 1(1), 16–28 (2010). https://doi.org/10.1108/20423891111085366. Access date: 02 Jan 2020 15. Bridgstock, R.: The university and the knowledge network: A new educational model for 21st century learning and employability (2016). https://doi.org/10.1057/978-1-137-57168-7_16

Chapter 10

Assessment of Student Work and the Organization of Individual Learning Paths in Electronic Smart-Learning Systems Leonid L. Khoroshko, Maxim A. Vikulin, and Alexey L. Khoroshko Abstract The popularity of electronic smart-learning systems leads to their implementation in various fields of education, and this requires the systems themselves to have extensive functionality for working with students. One of the features of using smart-learning systems in educational institutions is the need to evaluate student activities online. Another feature is an individual approach to students, the provision of which becomes more difficult both due to the remote learning form and because of the large number of students. This document contains the examples of the implementation of this functionality using the LMS Moodle electronic smart-learning system.

10.1 Introduction The electronic smart-learning systems are being used more often not only in open education, but also in the organization of paid postgraduate courses, as well as in various educational institutions. This aspect of the use of smart-learning requires new functionality, the need for which was absent in open education, namely the assessment of learning results. The assessment system should not only interact with all the many activities used in electronic smart-learning, but also have a flexible configuration that would allow creating a rating system depending on the needs of a particular training course. The gradebook should be accessible both to the tutor—in order to monitor the progress of all students—and to the student himself, who should be able to view only own grades. L. L. Khoroshko (B) · M. A. Vikulin · A. L. Khoroshko Moscow Aviation Institute (National Research University), Moscow, Russia e-mail: [email protected] M. A. Vikulin e-mail: [email protected] A. L. Khoroshko e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_10

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On the other hand, the development and popularization of electronic smartlearning platforms attract more and more students, and it conditionally increases the flow of students for each individual course. Thus, the training course, which was previously attended by ten students, now can have an incoming flow of more than thirty students [1]. Simultaneous training of more students requires more autonomy of the smart-learning system. This is due to the fact that in case of such volumes, the tutors cannot provide work with each student by means of an individual approach. Thus, the electronic smart-learning system should provide an individual approach. This document describes the solution to these problems by means of an example of LMS Moodle electronic smart-learning system.

10.2 The Gradebook In order to view the grades and create a rating system for the course, a special component, the gradebook, is installed in the LMS Moodle smart-learning system. When making settings to the course [2], it is possible to select the option of showing grades to students, which allows to display this gradebook to each student and allows him to see his rating. The gradebook is available through the “Settings” block, the “Course Management” group, the “Ratings” link. If the student opens the gradebook for the course, then only his grades will be displayed to him. Each student can view his grades in expanded form and read reviews on the submitted creative assignments. When viewing a gradebook by the tutor, the full version is being displayed, where the grades of all students enrolled in a training course for each educational activity can be displayed (Fig. 10.1).

Fig. 10.1 Displaying of gradebook for tutor

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The following options are available to the tutor for viewing: grader report— displays a complete list of student grades for all elements of the gradebook and categories, performance report—a separate way of assessment of students based on indicators and scales, an overview report—allows to display all grades of a student in courses to which he has access (it actually allows to see the current student rating for semester courses), and a user report. The overview report includes all courses that the student has access to and his final grades in the format of the course. By default, the system displays ratings in percent format, but this can be reconfigured—for example, some courses use scales to translate grades into a five-point system (in the system, this is defined by letters similar to the American grading system—A, B, C, D, E, F). In order to edit categories and elements, “Simple View” item is used in the “Categories and Elements” tab of the drop-down list. This item allows editing the structure of the gradebook and will be discussed later. The full view of the editing tab contains more settings (Fig. 10.2). If the whole table does not fit on the page, you can use the zoom in the browser page. The system by default has several scales that the students can be evaluated upon, but this grade is set by the tutor, which means it can be subjective. We do not recommend the use of scales for student assessment [3]. We recommend using letters for the purpose of translating grades from percent to a five-point system. The system is configured to transfer from percent to a five-point system by default. Nevertheless, it is possible to change the settings for each course. If you click on the “Edit letter grades” link, the corresponding form will be displayed where you can select the checkbox to override the default site settings and choose the borders of a particular grade yourself. In practice, we recommend using

Fig. 10.2 Full view of the gradebook editing tab

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standard values and focus on them when setting final grades, since all technologies (testing, creative assignments, lectures) are also focused on the corresponding boundaries of rating system grades. Let us proceed to the formation of the rating system of the course. To do this, group the course elements into categories. We will create the “Lectures” gradebook category and the “Testing” gradebook category and then transfer all the lectures of the course to the first category and the testing to the second category. In order to edit the structure of the gradebook in the drop-down list, select “Categories and elements— Simple view.” At the bottom of the page, the two buttons called “Add category” and “Add rating element” are displayed. First of all, we will create a new category using the “Add category” button. The system will open the editing interface for a new category of course grades. We will introduce “Lectures” as the name of the category. It is possible to choose the options for putting the final grade for the category. This parameter determines how the final grade is calculated. Possible options: • Mean value—the sum of all grades is divided by their number; • Median value—a value is selected that is in the middle of an ascending list of grades; • The worst grade and the best grade; • Grades mode—the grade that is most common; • Sum of grades—the sum of grades without taking grades set with scales into account. There are also several additional general settings for calculating the final grade: • Consider only non-empty grades—this parameter determines whether grades from subcategories are used in calculating the total grade (by default, we recommend considering only non-empty grades); • Do not take into account the worst results—this parameter allows not taking into account a certain pre-defined number of the lowest grades when calculating the final grade (usually, this parameter is not used). The “Name of category total” field stores information about the element being rated, which will be displayed in the gradebook (the category name is displayed by default). If you want to reduce the width of the columns of the gradebook, you can enter a short abbreviation here. You can enter additional data in the “Information” field. This information is not displayed anywhere and is used only by the tutor, so this field can be left blank. In the “Identifier” field, you can enter an identification number that allows you to identify the course element when calculating the grade (at this stage, you can leave it blank and fill in later). If the element is not involved in the calculation of the grade, then the identification number field can be left blank. The identification number can also be set in the gradebook, but it can only be changed on the element editing page. You can select four types of grades in the “Type of grades” field: • Not rated—no rating is given (for example, if you do not plan to include any category in the rating);

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• Value—a numerical value with a maximum and a minimum; • Scale—one element from the list (a number of scales are installed in the system, but we recommend using percentages); • Text—a text review only. In order to calculate the final grade, only grades of the “Value” and “Scale” types can be used. The type defining grades associated with course elements is set on the course element settings page. If a scale is used, it can be selected in the corresponding list. The scale of grades associated with course elements is set on the course element settings page. In the following fields, you can select the maximum and minimum grades as well as the passing score (the category will be considered completed). Grades above the passing score are highlighted in green in the gradebook, and those below the passing score are highlighted in red. The presentation format determines how ratings are displayed in the gradebook and reports; the following options are possible: • Value—actual grades; • Percentage; • Letter—letters or words are used to represent the ranges of grades (you can use the translation of percentages into words—“Excellent,” “Good,” etc., which will determine, for example, the rating of the course design as part of the course). In the next field, the number of shown decimal places for each grade is displayed (we recommend using 2 decimal places). This does not affect the calculation of grades that are calculated to the nearest 5 decimal places. The next setting is the “Hide” flag; if checked, the grades are hidden from students. Optionally, a “Hide until” date can be set to provide grades after the assessment is completed. The last field is “Block after”; if this field is set, then the grades for the associated course element will not be automatically updated after reaching the specified date. After filling in all the fields and submitting the form, a new category will be added to the gradebook. In addition to categories, it is possible to add assessment elements to the gradebook—it is a service object used to put grades in manual mode, thereby adding grades for working at residential sessions or activities that are not related to the electronic smart-learning system. In fact, this is a field that can be taken into account in the rating, where grades can be put in manual mode. In order to add a new element, click on the “Add rating element” link. The system displays a settings window similar to the rating category. All the difference lies in the type of this element. For example, you can add an element being rated—“Design work” or other additional activities that will be taken into account when rating the course. If you repeat the steps described above completely, you will notice that you can now select a category that you created earlier in the settings of the rated element. Similarly, you can now select the created category in the settings of all previously created course elements.

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Let us create the “Design work” rated element in the course category. After saving the element, it appears in the gradebook. In order to fill it out (grading of students), you should click the “Edit mode” button. After switching to the edit mode, you can also adjust the grades set by the system. We do not recommend adjusting the grades, such a need in practice can arise only in case of an error in creation of learning elements. In editing mode, it is recommended to fill in only the rated elements created specifically for this purpose. In order to switch to normal mode, click the “Finish editing” button again. In addition, you can transfer them from category to category and thus form a gradebook for your course using actions with elements directly in the gradebook. Let us consider in detail the operations in the LMS Moodle electronic smartlearning system that are related to the formation of a rating system for a training course. To do this, go to the “Course” tab in the assessment journal. Here, you can set course rating parameters. We recommend standard parameters— the location of the results column at the end (i.e., to the right). For a number of languages, this rule is changing. The presentation format of the rating is “Letter (Percentage)”—this allows the student to see the translation of his grades onto a standard scale and the percentage obtained for the course (Fig. 10.3). Next, you can configure the display of the final grade in various reports (it corresponds to the tabs—Overview report, User report). We recommend that you leave these default settings, as they are solely related to the display of ratings and are optimally configured. After saving, go to the “Categories and elements—full view” tab of your course. If you have not been able to transfer lectures to a category earlier, then you can do this as follows—select the checkboxes opposite the lectures in the last column of the table, and then, at the bottom of the page in the drop-down list, select the category

Fig. 10.3 Settings for the final grade of the course

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that you want to transfer “Lectures” to. After updating the gradebook, all lectures will be in the right category. By default, the system will consider the rating as the average of all rated elements of the course [4], which is not always convenient. Therefore, we recommend setting a formula for calculating the rating of a training course. Perform the following to go to the rating calculation formula. In the “Final grade of the course” line, click on the icon in the form of a calculator. You will see a simple interface for entering a rating calculation formula. At the bottom, there will be all the elements of your course—you can create identifiers right here; for example, for the “Lectures” category, you can create “Lectures” identifier, for practical tasks—“Practic,” etc. After pressing the “Add Identifier” button, they will appear opposite the elements in the form of [[Lectures]], etc. After identifiers are generated, enter the formula for determining the rating in the “Calculation” field. For example, if the course contains lectures [[Lectures]], practical tasks [[Practic]], and the final test [[Test]], then the formula for calculating the rating may look like this: “= [[Lectures]] * 0.3 + [[Practic]] * 0.5 + [[Test]] * 0.2.” When entering the formula, the sum of shares should be equal to 1, then the overall rating will be 100%, and the contribution of each component is entered as a share.

10.3 Individual Paths Individual paths in the electronic smart-learning system allow working with students according to an individual approach. Such paths become necessary in case of an increasing flow of students, especially if the training course implies the variability of topics at the discretion of the student. But even in the absence of such functionality, students have a different level of training and starting knowledge, which means they will cope with the materials and tasks of the course at a different pace and with different successes [5]. In order to streamline the study of the course in electronic smart-learning system, individual paths are used to process the deviation of a particular student from the ideal learning path. However, electronic smart-learning systems cannot independently consider topic variability and deviation behavior. Such a system should be preconfigured by an expert tutor in the training course. Thus, the expert tutor sets the logic for mastering the course, which works in autonomous mode after defining the settings. The configuration of individual paths and the definition of the logic of mastering the course become relevant when several lectures are already created in the course. Using the LMS Moodle electronic smart-learning systems, you can determine the learning logic in any order. For these purposes, you can limit access to it depending

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Fig. 10.4 Course studying logic

on various conditions in the settings page for each lecture. We recommend using this tool to formulate the logic of mastering the course. For example, we need to formulate the following logic of study (Fig. 10.4). Logic can be determined by the requirements of the teacher to study the material. For example, less important components of material may have lower requirements for the percentage of correct answers (preferably at least 50%), and more important components of material may have higher requirements (up to 100%). Such a system works within the framework of the permitted repeated studying of lectures (when students can improve their results). Based on the given logic, access to the first lecture is open in any case, but if it is performed by more than 50%, it should open access to lecture 2 and lecture 4. In the settings of the first lecture, you need to set the menu display depending on the rating (with a rating of more than 50%). For the second lecture, it is necessary to configure the “Restrict access” group in edit mode (Fig. 10.2). For the second lecture, it is necessary to select lecture 1 in the “Rating check” field and set the following rating parameters: at least 50%. The settings for lecture 4 will be similar to those of lecture 2. Lecture 6 differs from others. In the settings of this lecture, it is necessary to set the rating check for lecture 3 to be from 60 to 100, and add one more group of check fields by clicking on the “Add rating check” button, that should contain the dependence on lecture 5—from 70 to 100. Similarly, more complex dependencies are set up when it is necessary to allow a student to study a lecture depending on various criteria (for example, completed homework in the form of a course assignment). While thinking over the logic of the student’s movement, it is still important to make a setting of a lecture, which is offering the student to go to the next lecture. For example, after studying lecture 1, we can direct the student to both lecture 2 and lecture 4. This is determined solely by the logic defined by the tutor (which of the parallel branches of study you want to implement first).

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10.4 Conclusions Thus, the LMS Moodle electronic smart-learning system contains tools to solve the tasks set at the beginning of this work, which are caused by the growing popularity of such systems. A correct and competent rating system, together with a well-thoughtout logic of mastering the training course, will help not only reduce the load on tutors, but also increase the involvement of students, as well as increase the effectiveness of the training itself. However, the functionality described in this work is only a toolkit that allows you to implement the necessary rating system and development logic, which requires using a meaningful approach and a well-thought-out algorithm for student interaction with the course, which should be developed by the teaching staff.

References 1. Nurjabova, D.S., Rustamov, A.B.: Improving the quality on line learning process with MOOC. Academy. 6(21) (2017) 2. Khoroshko, L.L., Vikulin, M.A., Kvashnin , V.M.: Technologies for the development of interactive training courses through the example of LMS MOODLE. Springer International Publishing AG: Smart Innovation, Systems and Technologies, pp. 302–309 (2018). ISBN 978-1-4673-6110-1 3. Khoroshko, L.L., Vikulin, M.A., Kvashnin, V.M.: Knowledge control in smart training on the example of LMS MOODLE. In: Smart Education and e-Learning 2018. KES SEEL-18 2018. Smart Innovation, Systems and Technologies, vol. 99. Springer, Cham. ISBN 978-3-319-923628 4. Pastuscha, T.N., Sokolov, S.S., Ryabova, A.A.: Creating e-learning course. Lection in SDL MOODLE: teaching aid. SPb.: SPSUWC, 44p (2012) 5. Khoroshko, L.L., Vikulin, M.A., Kvashnin, V.M., Kostykova, O.S.: Communication with Students in Smart e-Learning System Using LMS Moodle. In: Uskov, V., Howlett, R., Jain, L. (eds.), Smart Education and e-Learning 2019. Smart Innovation, Systems and Technologies, vol 144. Springer, Singapore (2019)

Chapter 11

Implementation of Blended Learning into ESP for Medical Staff Ludmila Faltýnková

Abstract The use of blended design of teaching and learning foreign languages in higher education has increased rapidly in the twenty-first century. For many universities, the tool of blended learning is a part of mainstream education and represents a sort of continuum between traditional and pure online courses. Reflecting the latest results of technical and technological development, smart solutions are applied in numerous fields of human lives, including education. In this article, attention is paid to the education at tertiary schools for medical professionals, particularly to English for specific purposes (ESP) courses. The main objective of the presented research was to discover whether/how the smart blended approach could enhance and support the process of learning ESP in these students. The research was conducted at the Tertiary School for Medical Staff in Olomouc, Czech Republic. The research sample included 86 first-year students divided in the experimental group (N = 46) and control group (N = 40). Traditional pedagogical experiment was conducted defining the form of study materials (electronic or printed) to be the variable. Content of the study materials was the same in both groups. Data were collected via two written credit tests administered in the middle and at the end of semester. It was discovered that there are no significant differences in the test scores between these two groups. However, the students with lower level of English performed slightly better in the experimental group.

11.1 Introduction e-Learning and its modalities are more and more frequently used means of teaching and learning within higher education. There are many reasons why, for instance, better access to computers and other smart technologies together with constant connection to the internet, personalized learning, or flexibility of learning time. Furthermore,

L. Faltýnková (B) Faculty of Education, University of Ostrava, F. Šrámka 3, Ostrava, Czech Republic e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_11

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smart technologies enhance students’ motivation or broaden educational methods, thus being beneficial for both students and teachers. Tertiary schools for medical staff in the Czech Republic can add one more reason on the list. They provide education not only in the specialized fields of medicine, e.g. nursing care, urgent medicine, and dental hygiene, but also focus on educating students in English for specific purposes. Students usually get 3–4 lessons per week during their three years of study and English is a compulsory part of their final exam. However, very few study materials fit such education. There exist numerous particularized study materials for students of medicine focusing on higher level of English. Moreover, many specialized materials published online cover much wider scope of knowledge than is desired in these courses. As a result, teachers have to prepare their own study materials in different forms. Thus, there arose an idea of preparing suitable materials for the ICT-enhanced process of teaching and learning of English for specific purposes by exploiting digital learning materials and the blended learning model. Customized study materials were placed within the LMS Moodle, where students could access them at any time. When this approach was experimentally applied, this study about possible relationship between such learning and students’ performance was conducted to find out whether smart technologies could have also impact on students outcomes.

11.2 Theoretical Part 11.2.1 ESP English for specific purposes (ESP) has become one of the most important areas of English Language Teaching (ELT) today, as the mobility of workforce increases due to globalization and growth of multinational companies. ESP is usually defined as a branch of ELT and a language focusing on specific and immediate needs of students [4]. According to [2], ESP’s role is to meet specific needs of the learners and focus on the language corresponding with the activities of the target discipline using aspects of language such as grammar, lexis, or the four language skills. Furthermore, ESP is likely to be designed for adult learners, for example, at a tertiary level of school or work targeting lower intermediate or more advanced students. Moreover, it assumes some prior knowledge of the language system as well as the targeted field of expertise [2].

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11.2.2 ESP and General English English for specific purposes and general English differ in many ways but on the other hand, there are many aspects of language where they overlap. Rosenberg [6] illustrated the following differences: ESP • • • •

Job-related motivation specialized vocabulary goal-oriented specific writing formats.

General English • • • •

relaxed and communicative atmosphere time for games and songs student-oriented general vocabulary and writing skills.

The overlapping area comprises for instance: • general and travel vocabulary • grammar and language skills • small talk and everyday English. Rosenberg [6] highlights especially the similarities in ESP and general English stating that teachers of English often tend to separate these two courses methodologically as they: “…feel that they need to teach these specialized fields in totally different way.” However, she suggests using the fun, communicative, general English course activities in the “serious” world of ESP and vice versa embedding ESP activities and strategies into general English lessons.

11.2.3 ESP for Medical Staff At the Tertiary School for Medical Staff in Olomouc, CR General English is usually taught at the beginning of the first semester in year 1 to find out about students’ level of English, to get to know the study group and to put students at ease dealing with something familiar to them. Gradually, the course turns into real ESP introducing more and more complex medical topics related to the particular field of expertise. Students are provided with many opportunities to present chunks of the topics themselves as well as to have discussion over the topics. In order to be able to do that, they need to do the background study and get familiar with specific vocabulary. Smart technologies, computers, and blended learning are thus an inevitable part of such ESP course.

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11.2.4 Smart Blended Approach to Education Before describing Smart Blended Approach to Education, the definition of the title words “smart” and blended in needed. The term “smart” was originally used with regard to electronic devices with access to the Internet [7]. Nowadays, the adjective smart involves similar characteristics to the ones attributed to a smart person and is frequently used by educational researchers to form new terminology such as Smart Learning, Smart Education or Smart Classroom [8]. Blended, in the context of learning, is closely related to computers and other smart devices such as a tablet PC or a smartphone. It can be defined as “a way of learning that combines traditional classroom lessons with lessons that use computer technology and may be given over the internet” [1].

11.2.5 Smart Education Smart Education is a learning system that has the ability to enhance learner’s capacity by moving from uniform to individualized education and from standardized to diversified knowledge. It can bring innovation to the education system, influencing its contents, teaching methods, environment, or evaluation [5]. Smart Education can be implemented to schools by establishing wireless networks, allowing students to learn independently as well as providing education information accessible in PCs, laptops, smartphones, or tablet PCs. Such study materials are a great part of Smart Education, transferring the traditional paper textbooks into online digital materials [5]. Digital study materials are directly linked to online or blended learning. Online classes enhance students’ autonomy allowing them to study whenever and wherever they need.

11.2.6 Blended Learning The term blended learning refers to language learning that combines face-to-face approach with computer-mediated instructions [3]. The use of ICT in education nowadays is pervasive and has the ability to change the process of education [10]. The most essential elements offered by ICT are a powerful learning environment, allowing students to deal with knowledge in a self-directed and more constructive way [9]. It is possible to define blended learning as a way of learning focusing on reaching the targeted study aim via the most suitable technologies corresponding with the learning style of an individual [3].

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Blended learning is one of the e-learning modalities replacing the obsolete traditional teaching methods and pure online learning. As previously stated, it defines traditional instructor-led training supplemented with other electronic formats. It is unlikely to create the ideal blended learning format, as there are many possibilities how to combine modern technologies with traditional proceedings. Zounek and Sudický [11] provide the following examples of such combinations: • printed and electronic study materials • offline and online learning (education in the classroom connected with self-study with the help of ICT) • individual and group learning (both ICT-enhanced) • structured and non-structured learning (structured texts in a textbook and nonstructured online documents). Graham [3] pinpoints the possibility of using blended learning as a combination of online and traditional classroom courses within a study program. Combination of various teaching and learning methods is well known but what is quite new is the link between traditional pedagogical models with ICT-enhanced learning unknown to the previous generations of teachers. An example can be digital, free of charge study materials accessible online [11]. The use of blended learning at a particular school depends on teachers and students as direct participants of blended course but also school management and authorities responsible for the contents of study programs.

11.2.6.1

Smart Blended Course Implementation into ESP for Medical Staff

Because of the lack of study materials suitable for the ESP course of medical English, some of the teachers of English decided to prepare and subsequently share the study materials that would meet the requirements of the course with the other colleagues. What is more, these materials could also ensure the unity of the course across the school, which is also a great issue considering the fact that there might be more than one lecturer of ESP within the three-year study course. Some teachers still prefer the traditional printed materials but majority of the staff together with students agreed on materials in digital form. Later, an idea of creating a blended course appeared as both students and teachers could benefit from the use of smart technologies either personal or available at school. Initially, a few medical topics were utilized and placed within the online environment together with supplementary grammar, vocabulary, and listening exercises. First-year students were divided into two groups and the following pedagogical experiment was conducted.

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11.3 Research Part 11.3.1 Research Objective The main objective of the research was to collect data comprising students’ learning outcomes connected with two different types of course delivery (traditional an blended) to discover whether using digital study materials contributes to better learning results of students.

11.3.2 Research Question Based on previous studies proving that smart technologies can enhance the efficacy of the learning process, the research questions were formulated. Q1: Do students reach higher test scores if they use digital study materials on medical topics compared to those who do not use them? Q2: Is there any significant difference between the test results of the experimental group using digital study materials and the control group provided with the traditional course delivery format?

11.3.3 Settings and Participants The author who designed and conducted this research has been teaching at the Tertiary School for Medical Staff in Olomouc, the Czech Republic, for six years and started to use smart technologies in her English for specific purposes courses three years ago. Technologies have already been used in technical subject courses, however not on the regular basis. The study was conducted during the winter semester 2019/2020. The course lasted from 1st September 2019 to 20th December 2019 (16 weeks) and the minimal level of English of all students at the beginning of the study was B1. The participants of this study were 86 first-year students taking the compulsory course in ESP, medical English in particular. The content of the course is identical for all the medical specialties for the first two years (four semesters) of their three-year training. This study included students of certified pharmacology assistants, certified dental hygienists, certified dental technicians, and certified laboratory technicians. All the study groups are subsidized with three lessons of ESP per week and they are usually organized into one double and one single lesson a week. The students were divided into two groups: experimental and control. The former one was using

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the study materials and supplementary exercises in the digital form accessible in LMS Moodle in PDF or doc formats with embedded links to videos, dictionaries, and useful web pages. The latter group was provided with the same materials in the form of hard copies.

11.3.4 Research Method Pedagogical experiment was applied as the main research method. Student participants were divided into two groups (experimental and control) of approximately same number of students. The structure of the research sample is displayed in Table 11.1. Furthermore, the study employed a comparative analysis of students’ performance including two credit tests; one administered in the middle of the target semester and one at its end. Test scores were compared in the form of figures presenting results of experimental and control groups (Figs. 11.1, 11.2) and in Test 1 and Test 2 (Figs. 11.3, 11.4). Table 11.1 Study participants Medical specialization

Type of group

Amount of students (N)

Male students (N)

Certified pharmacy technicians

Experimental

24

4

Certified dental hygienists

Control

24

0

Certified dental technicians

Control

20

1

Certified laboratory technicians

Experimental

22

3

30 25

25 22

20 15 11 8

10 5 0

8 5

5 2 A

B Experimental Group

Fig. 11.1 Test 1 results

C Control Group

F

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28

25

24

20 15 10

0

9 8

6 3

5 A

5 3 B Experimental Group

C

F

Control Group

Fig. 11.2 Test 2 results

120 100 80 60 40 20 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 Test 1

Test 2

Fig. 11.3 Experimental group test results

11.3.5 Results and Interpretation The content of the course was the same for both the groups participating in the study. The face-to-face interaction was accompanied by further studying using either digital material available online or in printed, paper form. Both the forms required students’ autonomous learning necessary for successful fulfilling of the credit tests. The tests consisted of three parts: • grammar • vocabulary • medical topics. and took 60–90 min. The cut-off score for passing the tests was 60% altogether out of all the parts. Tables 11.2 and 11.3 below show the description of the tests contents.

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120 100 80 60 40 20 0

1

3

5

7

9

11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 Test 1

Test 2

Fig. 11.4 Control group

Table 11.2 Test1 content Test 1 Test part

Content

Types of tasks

Number of items

Points per item

Points in total

Grammar

Present and past tenses

Filling in

10

1

10

Vocabulary Medical topics

Translation

5

2

10

Multiple choice

10

1

10

Lexical items and phrases

Translation

15

1

15

Definition

5

2

10

Cell, human body, hospitals

Open questions

15

2

30

The tables above imply that both tests share very similar structure and the ratio among individual parts. However, they differ in the content corresponding with the subject curriculum. What’s more, the final test was also slightly longer and demanding with a higher number of items. The following figures illustrate the tests outcomes in both groups. All tests were assessed and marked according to the following criteria: • • • •

A: 100–90% B: 89–79% C: 78–60% F: 59–0%

According to Figs. 11.1 and 11.2, it might be concluded that within the scope of this study, only slight differences were detected between the test scores of experimental

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Table 11.3 Test 2 content Test 2 Test part

Content

Types of tasks

Grammar

Preposition, giving and asking for instructions

Filling in

10

1

10

Translation

10

2

20

Cloze

10

1

10

Lexical items and phrases

Translation

20

1

20

Definition

5

2

10

My future profession, first aid, disease symptoms

Open questions

15

2

30

Vocabulary Medical topics

Number of items

Points per item

Points in total

and control groups in Test 1 and Test 2. Therefore, it can be stated that students who worked with the digital study materials within the blended model did not demonstrate considerably better performance at the credit tests than those who had worked with traditional materials in the printed form. For making the difference more visible, Fig. 11.3 shows percentage scores of individual students in the experimental group in both tests. It can be noted that the students’ performances are mostly very close to each other. Some difference can be recognized in weaker students, who performed slightly better in Test 2. The same feature can be recognized in Fig. 11.4 illustrating students’ performance in the control group. There are slightly better outcomes in the students with the lower scores in the second test. It may be concluded that the use of materials in the online part of blended learning course of medical English did not significantly reflect at either of the credit tests. However, it is important to state that the factor of students’ motivation is also important when predicting their performance. To sum up, it can be stated that the implementation of blended design into the ESP course is as effective as the use of traditional way of teaching.

11.4 Conclusion Analysis of the students’ outcomes of the ESP course for medical staff in experimental and control groups provided information about the relationship between the types of study materials used in individual groups and students performances. It was found that students in both groups performed approximately the same in both tests regardless the type of study material used. Furthermore, the study showed that in both groups,

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students demonstrated better results in Test 2. However, it is worth mentioning that students in the experimental group at both ends of the result scale showed slightly better scores than students in the control group. Based on the research results, it is possible to assume that there is hardly any relationship between the study material format and study outcomes. The reason might be that the goal of most students is to pass the test regardless the score. However, student motivation could be the subject of another study. Acknowledgements This paper is supported by the project N. SGS03/PdF/2019-2020; ICTEnhanced Teaching English.

References 1. Cambridge: Cambridge Advanced Learner’s Dictionary, 4th edn. Cambridge University Press, Cambridge (2013) 2. Dudley-Evans, T., St. John, M.J.: Developments in English for Specific Purposes: A MultiDisciplinary Approach. Cambridge University Press, Cambridge (1998) 3. Graham, C.R.: Blended learning systems: definitions, current trends and future directions. In: Bonk, C.J., Graham, C.R. (eds.), The Handbook of Blended Learning: Global Perspectives, Localdesigns, pp. 3–21. Pfeiffer, San Francisco (2006) 4. Johns, A., Dudley-Evans, T.: English for specific purposes: international in scope, specific in purpose. TESOL Quarterly, 25, 297–314 (1991) 5. Kim, T., Cho, J.Y., Lee, B.G.: Evolution to smart learning in public education: a case study of Korean Public Education. In: Ley, T., Ruohonen, M., Laanpere M., Tatnall A. (eds.), Open and Social Technologies for Networked Learning. OST 2012. IFIP Advances in Information and Communication Technology, vol 395. Springer, Berlin, Heidelberg (2013) 6. Rosenberg, M.: Never the twain shall meet. Engl. Teach. Profess. 11(35), 36–37 (2004) 7. Spector, J.M., Ren, Y.: History of educational technology. In: Spector, J.M., Ifenthaler, D., Johnson, T.E., Savenye, W.C., Wang, M.M. (eds.) Encyclopedia of Educational Technology. Sage, Thousand Oaks, CA (2015) 8. Uskov, V.L., Bakken, JP., Heinemann, C., Rachakonda, R., Guduru, V.S., Thomas, A.B., Bodduluri, D.P.: In Smart Education and Smart e-Learning. Building Smart Learning Analytics System for Smart University, vol 75, pp. 191–204. Springer International Publishing (2017) 9. Wang, Y., Han, X., Yang, J.: Revisiting the blended learning literature: using a Complex Adaptive Systems Framework. Educat. Technol. Soc. 18(2), 380–393 (2015) 10. Wong, K.T., Goh, P.S.C., Khatijah, Siti: Exploring the affordances and obstacles of blended e-learning pedagogical practices: perspective of Malaysian teachers. Int. J. Instruct. Technol. Dist. Lear. 5(14), 49–57 (2017) 11. Zounek, J., Sudicky, P.: E-learning: uˇcení (se) s online technologiemi. Wolters Kluwer CR, Praha (2012)

Part III

Smart Pedagogy

Chapter 12

Providing an Ethical Framework for Smart Learning: A Study of Students’ Use of Social Media Michele T. Cole and Louis B. Swartz

Abstract This paper presents the results of a series of surveys conducted in 2018– 2019 with Masters in Business Administration (MBA) and Masters in Human Resource Management (MSHR) students and with undergraduate business law students concerning their perceptions of academic integrity in the smart classroom. The surveys also sought to capture students’ assessments of the efficacy of instructors’ use of smart technology, specifically social media, to enhance e-learning. One hundred and eighty-eight graduate and 26 undergraduate students participated in the study. Independent samples t-tests were conducted on select questions based on enrollment status, gender, and age. There were statistically significant differences on responses to two of the three questions on academic integrity based on enrollment status and on age. Results were inconclusive with regard to the second question with respect to the impact that social media has on students’ learning, due to students’ limited exposure to smart technology as an instructional tool. Students’ perceptions of the value of social media for e-learning were similar to findings in researchers’ earlier study of instructors’ use of social media to enhance learning. Where social media had been incorporated in the course, the students, as well as the instructors, chose Google Docs and YouTube as the most effective for learning.

12.1 Introduction In the last decade, there have been a number of studies that have examined the use of smart technology for learning, both in online instruction and in the classroom. In an early study, Baird and Fisher [1] noted that education was entering a new era M. T. Cole (B) Department of Management, Robert Morris University, Moon Township, PA, USA e-mail: [email protected] L. B. Swartz Department of Economics and Legal Studies, Robert Morris University, Moon Township, PA, USA e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_12

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of student-centered learning, one in which teaching as well as learning would be enhanced by smart technology and social interaction. Jackson, Helms, Jackson, and Gum [2] argued that “The influx of technology into education has begun a transformation of the classroom” (p. 294). For Al Hamad and Al Qawasmi [3], e-learning “transforms teaching and learning relationships, opportunities and outcomes” (p. 11). Uskov, Bakken, and Pandey [4], writing about the next-generation smart classroom, observed that the growth of new smart technology offered significant opportunities to enhance self-learning. Along with the considerable opportunities for enhanced learning come concurrent challenges for the instructor and for the student to maintain an ethical learning environment. As Al Hamad and Al Qawasmi [3] noted in their proposal for an ethical framework for learning management systems, learning involves sharing resources and interaction among students and between students and instructors, particularly in the e-learning environment. The argument could be made that the challenges to creating a framework for ethical smart learning are greater in e-learning than they are in the classroom. But are they? This study examined students’ attitudes toward academic integrity and their views of the efficacy of smart technology, specifically social media, in e-learning.

12.1.1 Academic Integrity in Smart Learning Academic integrity (AI), as used in this paper, is as defined by the International Center for Academic Integrity (ICAI) [5], a commitment to six fundamental values on which ethical behavior rests: honesty, trust, fairness, respect, responsibility, and courage. Established in 1992 at Clemson University, ICAI is a consortium of learning institutions dedicated to the belief that integrity is at the heart of the educational enterprise and to promoting the adoption of the precepts of academic integrity worldwide. When asked if the University’s Code of Student Conduct [6] was sufficient to prevent academic dishonesty from occurring in e-learning, it was evident from the responses to the survey question that awareness of the Code was not universal. One student observed, “I believe most don’t read it.” The Code begins with a declaration of integrity which, in part, states: I recognize that technology is a powerful tool and a pivotal part of the learning experience. Therefore: I vow to respect the power of technology and never use it to promote discord or gain an unfair advantage. I promise to use technology as a resource to aid in the creation of original student work and not to use it as a means of plagiarism or other forms of academic dishonesty (p. 2). Unfortunately, students continue to commit acts of academic dishonesty despite institutions’ and instructors’ best efforts to develop, publicize, and enforce academic integrity policies and codes of conduct.

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Selwyn [7] observed that the increasing use of smart technology presents its own set of difficulties for instructors trying to ensure the ethical use of data and ethical integrity while integrating smart technology into the curriculum. Students encounter similar challenges when faced with the wealth of easily accessible data. Examining the use and misuse of the Internet, Hineman [8] also observed the challenges students experience with the intersection of technology and academic integrity. As new technologies evolve, it can be expected that new ways to cheat will emerge as well. Speaking to both the learner and the instructor, Osborne and Connelly [9] argued that developing an awareness of how to use social media ethically could proactively safeguard against the dangers inherent in emerging technology, while allowing students to make the most of the considerable opportunities for learning that smart technology offers.

12.1.2 Social Media and Smart Learning In earlier surveys of students’ use of smart technology for e-learning [10], researchers determined that students were using web-based sources of information and other smart technologies to enhance learning. Similarly, Huang and Nakazawa [11] observed that smart technologies could improve learning by facilitating students’ access to the instructor and to others in the course. Student comments in this study indicate an appreciation for smart technology as a tool for learning. For example, “I think social media is a powerful tool for teaching and sharing. I don’t think the school should focus on the negative aspects of cheating - there will be always be [sic] outliers.” In one study examining the integration of smart technology with learning, Forbes [12] noted that increasing attention is being given to incorporating social media into the curriculum. Building on earlier assessments of the use of social media in higher education, Forbes referred to its widespread acceptance as a “fast trend.” Discussing professional learning networks, the author underscored several issues that learners and their instructors experience when using smart technology for learning, including ensuring the ethical use of data. It can be argued that instructors’ increasing incorporation of Twitter, blogging, and other digital media into the curriculum as the means to enhance instruction is evidence of a “fast trend.” For this study, researchers focused first on Facebook, Twitter, and Snapchat/Instagram as the preferred social media for enhanced learning. Other social media, such as YouTube, Blogs. LinkedIn, Google Docs, Wikis, and Google + were added to determine the extent to which students had experienced the use of social media for learning.

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12.2 The Study This study focused on two critical elements in the ethical smart classroom, academic integrity, and the use of smart technology. Students were surveyed on their perceptions of academic integrity and their views of the use of select smart technology for learning.

12.2.1 Research Questions RQ 1: How do students view academic integrity in smart learning? RQ 2: How effective have students found the use of social media for e-learning?

12.2.2 Methodology Researchers developed a 30-question survey in QuestionPro, the web-based survey instrument supported by the University for this study. Students in the Summer, 2018, Fall, 2018, and Fall, 2019, sections of the University’s Master’s in Business Administration and Master’s in Human Resource Management programs were asked to participate in the survey. Undergraduate students in the Fall, 2019 business law course were also asked to participate. Students were offered extra credit to take the survey. Survey results were transferred from QuestionPro to SPSS for analysis of selected responses. Instrument. The survey included five questions establishing the profile of the sample (academic level, enrollment status, gender, age, and experience with online instruction). Additionally, there were multi-part questions addressing the use of social media for instruction and on students’ perceptions of the value of the social media used for instruction for learning. There were six questions that targeted Facebook, Twitter, and Snapchat/Instagram, asking if students had observed others using any of the three to enhance learning or to gain an unfair advantage in their courses. There were three additional questions concerning the use of other smart technology, such as smartphones, and videos to cheat. The final four questions asked students their views of academic integrity. There were three open-ended questions asking students to elaborate on their responses. Sample. The study sample was composed of 188 graduate students from the University’s Master’s in Business Administration (MBA) and the MS in Human Resource Management (MSHR) programs and 26 undergraduates. Of the graduate students who responded, 137 students were enrolled in MBA courses, and 51 students were enrolled MSHR courses. There were 435 views of the survey recorded by Question

12 Providing an Ethical Framework for Smart Learning … Table 12.1 Participant profile

141

Demographics

# Responses

Percent (%)

Academic level

Graduate—188

87.85

Undergraduate—26

12.15

Part-time—112

52.34

Enrollment status

Full-time—100

46.73

Male—109

50.93

Female—105

49.07

18–24

71

33.18

25–34

99

46.26

35–44

39

18.22

45–54

4

1.87

55+

1

0.47

First

37

17.29

1–3

79

36.92

4–6

47

21.96

7–10

24

11.21

10+

16

7.48

N/A

11

5.14

Gender Age

# Online courses taken

Pro. Two hundred fourteen of the 214 students who began the survey finished it for a completion rate of 100%. Part-time students accounted for 52.34% (112) of the survey sample. Full-time students accounted for 46.73% (100) of the survey sample. There were two respondents who self-identified as “other.” Males made up 50.93% (109) of the sample; females, 49.07% (105). Ninety-nine students (46.26%) were between the ages of 25 and 34. Seventy-one students (33.18%) were between the ages of 18 and 24. Thirty-nine students (18.22%) were between 35 and 44. Four students (1.87%) identified their age range as 45–54. One student (0.47%) was over 55. Seventy-nine students (36.92%) had taken one to three fully or partially online courses. Forty-seven students (21.96%) had taken four to six courses, and 24 students (11.21%) had taken seven to ten fully or partially online courses. Sixteen respondents (7.48%) had taken more than ten fully or partially online courses. For 37 students (17.29%), the course in which the survey was administered was their first experience with e-learning. Eleven respondents (5.14%) answered “N/A.” Table 12.1 displays the participant profile.

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12.2.3 Results RQ 1: How do students view academic integrity in smart learning? To analyze responses to RQ1, researchers examined the responses to survey questions ## 13, 14, and 24. Students, when asked how they felt about cheating or any type of academic dishonesty (Q. 13), replied that it was always wrong and unethical (159 or 74.30%). Thirty-nine students (18.22%) responded that they did not cheat themselves, but did not really care if others did. Eight (3.74%) said that they had no opinion, while two students (0.93%) said it was okay if you did not get caught. Independent samples t-tests were run on this question based on enrollment status, gender, and age. There were statistically significant differences in four instances. There was a statistically significant difference at the 0.01 level (0.005), equal variances not assumed, based on enrollment status. There were statistically significant differences based on age at the 0.01 level (0.001) for the age group 18–24 compared with age group 25– 34, equal variances not assumed; at the 0.05 level (0.035) for the age group 25–34 compared with age group 35–44, equal variances not assumed; and at the 0.01 level (0.000) for the age group 18–24 compared with age group 35–44, equal variances not assumed. There were no statistically significant differences based on gender. Table 12.2 presents the results. In response to question 14, asking what the student would do in the event that he or she observed another cheating, 86 (40.19%) said that they would ignore it. Forty-one students (19.16%) responded that they would let the offender know that he/she disapproved. Seventy-five students (35.05%) would report the incident to the instructor. Another five students (2.34%) said that they would threaten to report the incident, but not carry through. Seven students (3.27%) chose “other.” Independent samples t-tests were run on this question based on enrollment status, gender, and age. There were statistically significant differences at the 0.01 level (.004), equal variances not assumed, based on age (18–24 compared with 25–34), and at the 0.01 level (0.000), equal variances assumed for age group 18–25 compared with age group Table 12.2 Students’ view of academic integrity Q. 13

Enrollment status age

n

M

t

Sig. (2-tailed)

Part-time

112

1.25

2.823

0.005

Full-time

100

1.62

18–24

71

1.83

3.327

0.001

25–34

99

1.28

25–34

99

1.28

1.468

0.035

35–44

39

1.08

18–24

71

1.83

3.917

0.000

35–44

39

1.08

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Table 12.3 Students’ response to academic dishonesty Q. 14

Age

n

M

t

Sig. (2-tailed)

18–24

71

1.93

−2.897

0.004

25–34

99

2.54

18–24

71

1.93

−3.804

0.000

35–44

39

2.92

35–44. There were no statistically significant differences based on enrollment status or gender for this question. Table 12.3 presents the results. When asked if they thought that acts of academic dishonesty were more prevalent in the online environment than in a classroom setting (Q24), 109 students (50.93%) said “no.” Fifty-six students (26.17%) responded “yes.” Thirty-four students (15.89%) had no opinion. Independent samples t-tests were run on this question based on enrollment status, gender, and age. There were no statistically significant differences based on enrollment status, gender, or age for question 24. Fifteen students (7.01%) chose “other,” indicating that the incidents of academic dishonesty were as likely to occur in the online environment as in a classroom setting. Table 12.4 presents the student comments made under “other.” RQ 2: How effective have students found the use of social media for e-learning? Students’ experience with social media as an instructional tool varied. One hundred sixty-four students (39.42%) had used YouTube; 82 (19.71%) had used Google Docs; 47 students (11.30%) had used blogs in their courses; 38 (9.13%) had used LinkedIn. Fewer students reported having been exposed to or having used Twitter (13 or 3.14%); Google + (12 or 2.88%). Six students reported having used Facebook and Wikis (1.44%). Only four students (0.96%) had been exposed to Snapchat or Instagram. Thirty-three students (7.93%) responded that they had not taken any courses in which the instructor had used social media. Eleven students (2.64%) chose “other.” “Other” included features found on YouTube, such as TED Talks, and Yammer. When used by instructors to enhance instruction, on a scale of 1, “very effective” to 3, “not effective,” students evaluated Google Docs as most effective, 1.168 followed by YouTube, 1.266, LinkedIn, 1.615, Blogs, 1.656, and Google + at 1.815. Facebook, Twitter, Snapchat/Instagram, and Wikis were rated closer to “not effective.” Table 12.5 presents the responses. General comments on the efficacy of social media for learning indicated a divergence of opinion on its value. For example, one student said, “Social media, if used in the right way, can be an engaging and different way to learn.” Another stated, “Social media is being over used. It does not provide a quality education.”

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Table 12.4 Students’ comments regarding academic dishonesty online versus in the classroom Student

Responses

27,608,723

I think they are equally prevalent

27,589,951

I think both have equal chances of dishonesty

27,580,993

I think they are prevalent in both. Maybe less in online courses because most of your classmates are strangers and not people you would feel comfortable asking answers, etc. from

27,566,390

Since this is my second class, I’m not sure if I have enough experience to accurately answer this question

27,513,152

I think they are pretty much the same. Dishonest student can find creative ways to cheat in a classroom environment [sic] just as much as online

27,296,374

possible in online/traditional class

25,016,637

Could be

24,967,976

Probably, I wouldn’t be surprised

24,955,994

i [sic]think timed tests help to limit the dishonesty on exams, otherwise in my classes there was little reasons to cheat. You can’t cheat your own opinions and plagiarism software can limit dishonesty in papers etc. In my math classes profs allowed open book. That would aid students with vocab type questions but you can’t google a math question you know how to calculate it or not

24,946,413

Sometimes, it depends on the student

24,786,301

I don’t believe professors structure courses to enable academic dishonesty. For example, no true or false answers given. Online courses I’ve taken are very logical and make you think. Therefore, it’s based on your understanding of the information reviewed. Some students may google additional resources to understand the material but not to be dishonest

24,595,752

Unsure

24,574,643

Not if the course is conducted in a way that does not allow it

24,532,801

Not really relevant

23,995,699

I am not sure, but I would guess it would be

Table 12.5 Students’ view of the effectiveness of social media for learning

Social media

Responses

Effectiveness rating

Facebook

18

2.667

Twitter

27

2.370

Snapchat/Instagram

15

2.600

169

1.266

Blogs

61

1.656

Google docs

95

1.168

LinkedIn

52

1.615

Wikis

22

2.136

YouTube

Google+

27

1.815

Other (e.g., TED Talks)

17

1.824

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12.2.4 Limitations The study was conducted at one private nonprofit university in Southwestern Pennsylvania with a limited number of graduate and undergraduates students in its business school programs. Due to the size of the undergraduate sample, researchers were not able to compare responses based on academic level. The robust completion rate was most likely due to the offer of extra credit for participation.

12.3 Discussion and Conclusion Referring to ethical issues in the digital age, one author [13] noted that change was happening rapidly in all areas of life. Smart technology is evolving rapidly. The student population is also changing as fewer traditional undergraduates are matriculating; institutions are revising and enhancing their offerings to keep pace with a different market. One example is the emphasis on smart learning. Mainly, Leonard, and Riemenschneider [14] found that shifting generational attitudes and widespread adoption of smart technology confounded the challenges instructors face in maintaining academic integrity in the classroom. In pointing to the spread of e-learning systems, Al Hamad and Al Qawasmi [3] added that the ethical issues that have surfaced with the adoption of smart technology had yet to be adequately addressed. While incorporating an ethical framework for smart learning could enhance the learning process, they stated, failing to do so could minimize the effectiveness of e-learning and decrease its value proposition. In this study, researchers found that students’ understanding of academic integrity, that is, what is required to ensure ethical behavior in the classroom, was uneven. While almost 75% of those responding said that cheating or any type of academic dishonesty was always wrong and unethical, only 35% said that they would report the incident. Forty percent said that if he or she observed anyone else cheating, they would ignore it. Responses to the third survey question in RQ 1, asking if students felt that cheating was more prevalent in the online environment than in the classroom, were consistent with those in researchers’ 2013 study [15] of students’ perceptions of academic integrity in online business courses. In that study, almost 65% of students’ responding thought that the precepts of academic integrity—honesty, fairness, respect, responsibility, and trust—were the same in both the online and classroom environments when writing a paper, developing a project, or taking an exam. Just over 35% of those responding in the 2013 study, disagreed. In this study, almost 51% of the students responding did not believe that cheating was more likely to occur in the elearning environment than cheating in the classroom. Twenty-six percent disagreed. Several respondents indicated that it was not a question of cheating being more likely to happen in one learning environment over another, but rather it was a question of intent. When asked to expand, students said “If people want to cheat, they’ll figure

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out how to do it online or in class. It is irrelevant.” “Dishonesty and unethical behavior will occur regardless of online or traditional classes. That is more of an individual decision no matter setting.” Because of the difference between the student profiles in each of the graduate programs, it would have been interesting to determine if there were statistically significant differences in the response to the first research question asking their views on academic integrity in smart learning. Unfortunately, the survey design did not allow for the identification of students by field of study. The survey was administered to students enrolled in MBA and MSHR courses. Both programs have cross over elective privileges. For the most part, the MBA program is composed of mid-level corporate employees and students from the University’s accelerated program. The accelerated program admits undergraduate students in their senior year to begin the MBA program. Students in the MSHR program are mostly HR professionals from a variety of organizations, for profit and nonprofit. This study examined students’ perceptions of the effectiveness of select social media on learning, but not on performance. With regard to how well students felt that social media had helped them to learn course material, results would not support a positive or a negative relationship between the use of social media and learning. Students’ exposure to social media as a means to enhance their learning experience was limited. There is no reason to imagine that the growth of smart technology will wane. As a “fast trend,” the use of social media as an instructional tool will continue to pose challenges for the development of an ethical learning environment. That being the case, additional studies of best practices, that is, how do institutions and instructors enhance the learning experience using the best of new technology within a clearly understood and accepted ethical framework for learning, can be expected.

References 1. Baird, D.E., Fisher, M.: Neomillennial user experience design strategies: Utilizing social networking media to support “always on” learning styles. J. Edu. Technol. Syst. 34(1), 5–32 (2005–2006) 2. Jackson, M.J., Helms, M.M., Jackson, W.T., Gum, J.R.: Student expectations of technologyenhanced pedagogy: a ten-year comparison. J. Edu. Bus. 86(5), 294–301 (2011) 3. Al Hamad, A.Q., Al Qawasmi, A.Q.: Building an ethical framework for e-learning management system at a university level. J. Eng. Econ. Dev. 1(1), 11–16 (2014) 4. Uskov, V., Bakken, J.P., Pandey, A.: The Ontology of Next Generation Smart Classrooms. Springer International Publishing (2017). https://doi.org/10.1007/978-3-319-19875-0 5. International Center for Academic Integrity: The Fundamental Values of Academic Integrity. http://www.academicintegrity.org/icai/resources-2014 6. RMU Code of Student Conduct Studentlife.rmu.edu/student-conduct/ 7. Selwyn, N.: Social media in higher education. The Europa World of Learning (2011). www. worldoflearning.com 8. Hinman, L.M.: Academic integrity and the world wide web. Comput. Soc. 33–42 (2002) 9. Osborne, N., Connelly, L.: Managing your digital footprint: Possible implications for teaching and learning. In: Mesquita, A., Peres, P. (eds.), Proceedings of the 2nd European Conference on

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Social Media ECSM 2015, pp. 354–361. Academic Conferences and Publishing International Limited, Porto, Portugal (2015) Cole, M.T., Swartz, L.B., Shelley, D.J.: Enhancing online learning with technology a survey of graduate and undergraduate business students on their use of social media in course work. In: Proceedings of the 11th Hawaiian International Conference on Education (HICE 2013), pp.3745–3809 (2013) Huang, W.D., Nakazawa, K.: An empirical analysis of how learners interact in wiki in a graduate level online course. Interact. Learn. Environ. 18(3), 233–244 (2010) Forbes, D.: Professional online presence and learning networks: Educating for ethical use of social media. Int. Rev. Res. Open. Distrib. Learn. 18(7), 175–190 (2017) Jurkiewicz, C.: Big data, big concerns: Ethics in the digital age. Public Integrity 20, S46–S59 (2018). https://doi.org/10.1080/10999922.2018.1448218 Manly, T.S., Leonard, L.N., Riemenschneider, C.K.: Academic integrity in the information age: virtues of respect and responsibility. J. Bus. Ethics. 127, 579–590 (2015) Swartz, L.B., Cole, M.T.: Students’ perception of academic integrity in online business education courses. J. Bus. Edu. Leadersh (JBEL) 4(1), 102–112 (2013)

Chapter 13

“Product-Based” Master Program at ASCREEN Interactive Center Slavyana Bakhareva, Natalya Minkova, Irina Semyonkina, and Denis Yarygin

Abstract Innovative training technologies, based on comprehensive modeling of professional activities in the educational setting, are of great interest in the context of the integration of professional standards in higher professional education. The competency-based approach and contextual training provide the methodological basis for the process implementation while professional and educational tasks, along with collaborative learning and new interactive formats of work in various training centers, provide powerful tools of putting it into practice. Since 2017, a model of a “product-based” master program has been tested as a pilot project at the Sevastopol State University on the basis of the Institute of National Technological Initiative. The program’s key principle is the market readiness of students’ qualification work as the product of the master’s course and its evaluation by the professional community. The methodology and technology for implementation of the “product-based” master program in the ASCREEN Interactive Center are presented in this paper using the example of the “Innovative Shipbuilding” master program of the Sevastopol State University.

13.1 Introduction The Bologna Process has brought a new type of professional standards in Russia, which are essentially a reflection of the national qualification’s frameworks, developed on the basis of covering European qualification frameworks. The professional standard has become a normative document that defines the requirements for the profession on the basis of qualification levels and competencies, taking into account the quality and productivity of the work performed. It serves as a valuable link between industry and academia because it enables employers to interact with educational S. Bakhareva (B) Moscow State Pedagogical University, Moscow, Russia e-mail: [email protected] N. Minkova · I. Semyonkina · D. Yarygin Sevastopol State University, Sevastopol, Russia © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_13

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organizations at all levels in terms of competencies and skills of master students. Both—a set of professional standards, developed by consolidated employers, and educational standards, created on a basis of professional standards—are granting their continuous mutually beneficial harmonization and make contribution to the high quality of professional training as required by industry [1].

13.2 Literature Review The qualification framework of higher education is directly related to academic standards—federal state educational standards, which are based on the competency-based approach. The competency-based approach harmonizes the needs of the labor market and professional education. At the same time, not the amount of learned information, but the person’s ability to professionally act in various challenging real-world situations, or, in other words, mastering competencies in the professional area, present the result of training [2]. The creation of the specialist model—the one that meets the above-mentioned requirements—can be achieved using the following guidelines: (1) a description of the types of professional activities; (2) the scope, main components and structure of a professional activity, applications of professional activity in various real-world situations, and how to cope with them, including typical professional tasks and functions; and (3) description of the specialist’s personality model, along with the employees’ essential personality characteristics and qualities [3].

13.3 Project Environment The described approach was originally implemented in the design of the “productbased” master program [4]. This paper will discuss an implementation of this approach for the professional training of specialists in shipbuilding design and construction area, whose professional activities involve creating projects for competitive vessels, floating structures and their components using (1) modern analysis and design methods, (2) innovative computer-aided design systems and design solutions, (3) conceptual constructional design, (4) mathematical, computer-based and physical modeling in shipbuilding area, and (5) various types of computer-based simulations and modeling [5].

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13.3.1 Competency Components The description of the specialist model for shipbuilding design and construction area in terms of academic standards for the above-mentioned master’s degree includes the following competencies: • ability to identify and analyze relevant scientific and technical problems, formulate design goals and objectives, justify the feasibility of creating new marine and river technology, and draw up the necessary set of technical documentation; • ability to develop functional and structural diagrams of marine and river technical systems, define their physical principles of operation, morphology, and setting technical requirements for individual subsystems and elements; • ability to develop various types of marine (river) equipment, its subsystems, and elements using automation systems in the design and manufacturing; and • ability to apply various methods and options of analysis, development, and identification of trade-off solutions. Talyzina [6] proposed that the first step in the transition from the specialist model to the model of their training is the selection and full description of professional tasks that they will have to solve in their future professional activity. The above-mentioned analysis enables us to conclude that the solution of a professional problem can be considered as a process of the subject’s activity for an application of obtained knowledge, skills, and experiences to achieve a goal aimed at development of a specific professional solution in the given conditions of professional activities or situations [7, 8]. Thus, by the professional tasks of a specialist in the shipbuilding design and construction, we mean conscious professional situations in the activities of a construction engineer or design engineer related to the creation of ship designs, floating structures and their components, their support at all stages of the life cycle, and project management.

13.3.2 Professional Task: The Main Components and Implementation Stages A professional task includes the following components: • content-related components: they determine what material, facts, attitudes, and judgments are used to formulate the problem; • procedural components: they include actions, behavioral elements, operations, assessments, and situations of choice and dialogue that will arise in the course of solving the problem; • contextual components: they provide a connection of the specific problem formulation to the general problem context, for example, personal, social, educational, informational, communicative, professional, etc.

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A solution for a professional task involves the implementation of several stages: (a) analytical (i.e., customer requirements’ analysis), (b) planning, (c) performing, and (d) evaluating. Analytical stage. At this stage, the initial data for a professional task is analyzed. For this purpose, it is necessary to understand and get data to answer the following main questions: What is given? What should be obtained? Therefore, it is advisable to make rough estimates in problem analysis by answering the following specific questions: (1) Can a requirement (or, condition) be satisfied? (2) Is it sufficient to carry out a quality product activity? Or insufficient? Or excessive? Or contradictory? The meaning of unfamiliar words in a professional thesaurus should be clarified. If this situation involves an interaction with the customer, then a set of clarifying specific questions should be formulated to the customer. As a result, at this stage, the student converts the available information into a more acceptable form; in other words, students translate the customer requirements into the formal language of professionals [7]. Planning stage. At this stage, a plan should be outlined, providing steps required to perform a formal professional task, specifically (1) the conditions for implementing the problem solution should be determined; (2) the corresponding systems and tools should be selected in accordance with the expected outcomes; and (3) the sequences of operational components, necessary for solving the problem, should be identified and arranged. At the end of this stage, students present an algorithm for performing a professional task, taking into account, the relationship between the available sources and unknown data. Performing stage. This stage involves an implementation of the proposed solution according to the developed algorithm of operational actions. The process is closely controlled by the operator; each step of the plan implementation should be carefully tested and checked. As a result, the accuracy of a proposed solution is controlled at the last step of the algorithm’s execution by matching the obtained results to the conditions of the task. If the result does not meet the conditions of the task, then all previous steps are repeated. This iterative cycle can be repeated several times until the operator makes sure the assurance that the task is completed correctly. At the end of this stage, students submit a report on the work performed. Evaluating stage. At this stage, the student’s performance is assessed in terms of solving a professional problem from two perspectives: (a) evaluating the product of their work and (b) self-assessment of their activities as a prospective construction engineer. Structure, content, and sequence of professional tasks in the aggregate should cover all the main labor activities included in professional activities [8]. Professional tasks, that a prospective specialist in shipbuilding design and construction area should be able to execute and complete, include the following ones:

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1. conduct theoretical analysis and experimental studies in the area of creating new models of ships, floating structures, and their components in accordance with the technical design specifications; 2. design, develop, and implement new models of ships, floating structures, and their components, 3. run computer-based simulations based on theoretical methods as well as the analysis of domestic and foreign achievements, best practices and experiences in the development of ships, floating structures and their components; 4. design development and coordination of documentation relevant to preliminary design, conceptual design, and technical design; and 5. development of working construction documentation and operational documentation for various departments of the organization, customer representatives, and third parties.

13.3.3 Professional Training-Oriented Tasks In order to enable a student to demonstrate their professional competencies to solve a professional problem, it is good practice to assign a set of tasks, which help reveal knowledge of the methods and conditions of professional activities, as well as knowledge of objects and work equipment. Professional training-oriented tasks, developed on the basis of professional tasks, in the context of the content of engineering training, have a specific structure: • a generalized task statement is presented in the form of a description of the contradiction, challenges, or problem statement from real practice; • a key task, which determines what should be presented as the result of solving the problem—the final product and document; • the context of the task performance—the existing conditions, characteristics of people, resources, and a specific situation; • a specific task type, which results in the performance and allows to set the level of shown competencies; • assessment criteria, which helps the student to focus on a high-quality achievement of the final result. Based on ideas of Talyzina [6] regarding the development of curriculum for master students in shipbuilding design and construction major, the goals of training and education were formulated as a system of professionally oriented tasks, which the students have to perform in a course of their future professional activities. The key professionally orientated task (based on a simulation of a real professional activity) in the process of training of a prospective specialist in shipbuilding design and construction is a technical task to produce a design of a vessel, a floating structure and its components. Such a complex professional task can be successfully completed using a teamworking approach.

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13.3.4 Collaborative Learning Collaborative learning is a key pedagogical technology in the practical training of students for professional activities in the area of shipbuilding design and construction [9]. Collaborative learning 1. is focused on the joint development of a common goal and content for the organization of collaborative educational activities of students and faculty, their joint creative work, and 2. involves mutual trust and friendly relations and mutual assistance in solving educational problems [10–13]. Students should be involved in business practice-oriented communication and get corresponding experience using one of several professional roles in a product design team—a leader (or manager), organizer, expert (or subject matter expert), system analysts, speaker, operator, and researcher.

13.4 Development Outcomes and Research Findings 13.4.1 Project Office For the purpose of professional training of future specialists in shipbuilding design and construction, the Project Office was created at the Sevastopol State University within the framework of the “product-based” master program named “Innovative Shipbuilding”. The functional structure of the Project Office is based on the principle of a linear organizational management structure. The roles of the head of the Project Office and chief designer are played by teachers or invited employers. Students perform the roles of managers and employees of specialized units—hull production, systems and power generation, or electrical equipment. The main functional responsibilities of the heads of specialized units are: (1) to get the target plan for the specialized unit from the chief designer, (2) to distribute required work between the employees—students, (3) to monitor the timing and quality of ongoing work, (4) to maintain the general technical control of the work performed, and (5) take technical and design decisions related to their specialization. The duty of employees of specialized units is to perform engineering and design tasks on the basis of the technical requirements within the given timeframe and at high-quality standards.

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13.4.2 ASCREEN Interactive Center The proposed Project Office is organized on the basis of the ASCREEN Interactive Center (AI Center, co-learning center), which is a high-tech professional environment designed to provide teamwork, presentations, and video conferencing sessions. AI Center provides opportunities of an individual or collaborative solution of problems, in particular, tasks of distributed design and vessel lifecycle management provides video and graphic information on screen(s), including the assembly of a ready-made solution on the team screen and the final assembly on the screen of the Project Office head. At the same time, it allows project participants to use all the most common data storage media and provides sound accompaniment of the displayed video materials, sound amplification of speakers’ speech, and quick connection of participants’ portable computers to the co-learning center devices with the ability to output video information from them to the screens of a collective display system. The spatial organization of AI Center is shown in Fig. 13.1.

Fig. 13.1 Spatial organization of the ASCREEN Information Center: a scheme of the spatial organization of the work of three groups with the output of the individual work of the group on the screen, b visualization of the spatial organization, c visualization of the group work, and d view of the teacher’s screen

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It effectively simulates the professional environment of a shipbuilding product design bureau, which serves as a basis for developing professional competencies of students and implementing project training.

13.4.3 Student Teamworking The joint student project team can be divided into sectors responsible for various subsystems of the designed vessel in accordance with the professional-orientation task—the technical design assignment. Nevertheless, each student of the master’s program is responsible for the entire project as a whole, and the teamworking mode in the Project Office implies the mandatory “immersion” of all students in the team in the design process of all ship subsystems [4]. At the first stage of the project training, students—Project Office employees— should study: (1) the computer-aided design system (CAD) interface and (2) mechanisms and capabilities of product lifecycle management (PLM) of the T-Flex system—innovative full-scale solution in the field of product lifecycle management and organization of enterprise activities. The T-Flex system integrates various components, including CAD systems, computer-aided engineering (CAE) systems, computer-aided process planning (CAPP) systems, product data management (PDM) systems, and customer relationship management (CRM) systems. This innovative and integrative solution enables specialists to (1) effectively organize work at all stages of the ship’s life cycle and (2) expand the standard boundaries of PLM solutions with additional capabilities for managing all processes involved in the production [14]. Professionally orientated tasks at this stage are connected with mastering of CAD and PLM systems’ interfaces, creating electronic documentation in CAD, and then coordinating it in PLM. An example of outcomes of team professional design activities using T-Flex system is presented in PLM (Fig. 13.2). Students—employees of the Project Office—work in teams of 3–5 people (Fig. 13.1). Each team performs a training task to produce the design of a vessel element or a floating structure, which is carried out in collaboration with the supervisor.

13.4.4 Development of Design Specifications The development of design specifications for the construction is carried out in several consecutive stages. Conceptual design. The preliminary, technical specifications, basic economic calculations and estimates, and technical documentation are developed on this stage.

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Fig. 13.2 An example of “Nomenclature and Products” tools in T-Flex DOCs system

This gives the product design team an idea of the vessel general layout and possible design options. On the basis of these initial design solutions and calculations, preliminary vessel characteristics are selected by the product design team, including general layout drawings and basic architectural elements. On the basis of update, suggested by the customer and optimal economic options, the main elements and dimensions of the vessel are identified later, specifically (a) a line drawing, (b) sea-keeping qualities, and (c) strength characteristics of the hull. Additionally, the characteristics of machines, mechanisms, and equipment are identified, and a potential main supply of contractors is determined on this stage of product design. Later, the design team should (1) elaborate on economic qualities of the vessel and its optimal elements and (2) create the final design documentation, including general layout drawings, lines drawing, and elements of the lines drawing. At the very end of this stage, the conceptual design is submitted for approval by the observing organizations and by the customer. Technical design. All the elements of the vessel and its characteristics are finalized during this stage as well as the basic design and technological issues regarding the hull, equipment, power generation system, location, and equipment for identified premises. The outcome of this stage—the technical project—is the final document; it is authorized and approved by the supervisory bodies. Upon completion of the documentation in accordance with the unified system of design documentation, it is signed with a personal electronic signature and sent for approval as required by the established procedure of the business process. The participants in the business process (for example, sector managers and chief designer) coordinate electronic documentation in the PLM system at all stages of

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design. The results of all iterations are displayed on all screens of the ASCREEN Interactive Center for general approval. The technical design of the vessel, that (1) was developed in full compliance with the requirements of the supervisory authorities, and (2) is ready to be presented to the manufacturer in the modern digital format, is the final outcome of the student project team—final product. The technical design, approved by the team’s supervisor, is a final market product [4]. Thus, the implementation of the “product-based” master program at ASCREEN Interactive Center enables us to simulate the work of a real-life design bureau using student teamworking. Students play various roles in accordance with their future professional activities. As a result, upon completion of the designated master course, a coordinated team of designers is created—a project design team that is capable of fulfilling the customer requirements for the ship design. The use of collaborative learning enables the participants to (1) implement research, analysis and problem-solving methods, (2) apply the knowledge gained in joint or individual activities, (3) develop critical thinking, communication culture, and (4) develop the ability to perform various social roles in joint team-based activities. Students, in accordance with their abilities, get great opportunities to (1) achieve specific results in various fields of learning, (2) comprehend the obtained knowledge, (3) gain valuable experience in quasi-professional and educational-professional activities, and (4) learn various important aspects and details of project team-based work.

13.5 End-User Feedback and Obtained Outcomes A conducted survey of students, enrolled in the “product-based” master program at ASCREEN Interactive Center, took into account various components of the training process, including (1) gained knowledge of ship design technologies, (2) obtained skills in using software products, and (3) opinion of employers. A total of 87 students and 8 faculty took part in the initial survey. The main obtained outcomes of completed surveys are as follows: 1. 78% of students demonstrated a higher level of knowledge of ship design technologies as well as the ability to use various software systems aimed at quality ship design; 2. 89% of students did not experience any problems (personal, communication, etc.) while working as a member of the studentship design team; 3. 26% increase in quality of training of control group (which has been trained in the ASCREEN Center) as compared to a traditional group of students who have been trained using traditional approach to learning/training—the one that is based on the design of individual parts of ships rather than working on the project as a whole;

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4. 18% increase in students’ learning motivation (as recognized by faculty); 5. 68% of employers do not experience difficulties in adapting student design teams at the real-world workplace; the employers also noted that there is no need for additional professional training for students who have been trained at the ASCREEN Center.

13.6 Conclusions. Future Steps Conclusions. The obtained outcomes of this research, design, and development project enabled us to make the following conclusions: 1. The innovative methodology of designing a master program with the goals to (a) obtain a market product and (b) students’ work in project design teams, implies that corresponding professional tasks and activities are cohesively integrated with (a) organizational structure of educational process, and (b) structural and logical relations with various educational modules, methods, technologies, and teaching aids used in that master program (Sect. 13.3). 2. Collaborative learning has been chosen as the key teaching/learning technology for the practical preparation of students for the real-world professional activities. This learning technology is aimed at facilitating students’ teamwork, active cognitive process, and work with various sources of information (Sect. 13.3.4). 3. The innovative Project Office was created at the Sevastopol State University within the framework of the “product-based” master program named “Innovative Shipbuilding.” The roles of the head of the Project Office and chief designer are played by teachers or invited employers. Students perform the roles of managers and employees of specialized units—hull production, systems’ design, power generation, electrical equipment, etc. (Sects. 13.4.1 and 13.4.2). 4. Student teams are working as a crucial component of professional training of future specialists in shipbuilding design and construction. Teamwork provides students with unique opportunities to get experience and practice in various roles in a product design team—a leader (or manager), organizer, expert (or subject matter expert), system analysts, speaker, operator, and researcher (Sect. 13.4.3). 5. We conducted the formative and summative surveys of students, faculty, and potential employers on the outcomes of proposed and implemented professional training in the developed Project Office at the ASCREEN Center. The outcomes of surveys and a summary of the end-user feedback strongly support the main concepts, methods, technologies and approaches developed and implemented into the “product-based” master program at ASCREEN Interactive Center (Sect. 13.5). Next steps. Due to successful outcomes of the described research, design, and development project, we plan to extend the application of the “product-based” master program’s concepts, methods, and activities to other programs of study (or majors), for example, to professional training of school teachers.

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References 1. Chuchalin, A.I.: American and Bologna engineer models: comparative analysis of competencies. https://cyberleninka.ru/article/n/amerikanskaya-i-bolonskaya-modeli-inzhenerasravnitelnyy-analiz-kompetentsiy (2007) 2. Gromova, N.V.: Competency-based approach as the basis for the formation of professional standards in Russia. Adv. Modern Nat. Sci. 9–3, 543–546 (2015) 3. Markova, A.K.: The Psychology of the Teacher’s Work: A Book for a Teacher, 193p. Prosveshcheniye, Moscow (1993) 4. Nechayev, V.D., et al.: The model of the “product-based” master’s program for training engineers. High Edu. in Russia. 28(3), 57–66 (2019) 5. Professional standard. https://classinform.ru/profstandarty/30.001-spetcialist-poproektirovaniiu-i-konstruirovaniiu-v-sudostroenii.html 6. Talyzina, N.F., Pechenyuk, N.G., Khikhlovsky, L.B.: Ways of Creating a Specialist Profile, p. 176. Publishing house of Saratov University, Saratov (1987) 7. Bukharova, G.D., Arefyev O.N. (eds.): Professional Pedagogy: Categories, Concepts, Definitions: Collection of Scientific Papers, Issue 4, 571p. Yekaterinburg (2006) 8. Verbitsky, A.A.: Personal and Competency-Based Approaches in Education. Integration issues, 336p. Logos, Moscow (2009) 9. Valisheeva A.G.: Methodical system for preparing future engineers in the field of welding production to solving typical professional problems. https://cyberleninka.ru/ article/n/metodicheskaya-sistema-podgotovki-buduschih-inzhenerov-v-oblasti-svarochnogoproizvodstva-k-resheniyu-tipovyh-professionalnyh-zadach (2014) 10. Slavin, R.E.: Cooperative Learning: Theory, Research, and Practice, 2nd edn. Allyn & Bacon, Boston (1994) 11. Johnson, D.W., Johnson, R.: Learning Together and Alone: Cooperative, Competitive, and Individualistic Learning, 5th edn. Allyn & Baco, Boston (1990) 12. Elliot Aronson, S.P.: Cooperation in the Classroom: The Jigsaw Method. Longman, New York (1997) 13. Verbitsky, A.A.: Theory and Technology of Contextual Education, 268p. Publishing House of Moscow State Pedagogical University, Moscow 2017 14. Top systems. http://www.tflex.com

Chapter 14

Developing a Conceptual Framework for Smart Teaching: Using VR to Teach Kids How to Save Lives Tone Lise Dahl, Siw Olsen Fjørtoft, and Andreas D. Landmark

Abstract We describe a conceptual framework that teachers can utilize when embedding immersive virtual reality with HMDs in their classroom for educational purposes. The concept is based on an ongoing research project where an immersive virtual reality application is being produced and will be used in K-12 education for learning first aid. By using virtual reality, we redefine the way students learn first aid by exposing them to real-life scenarios in an immersive virtual environment where they feel a sense of presence, that otherwise would not have been possible due to resources, risks and ethical reasons. The conceptual framework we have developed is an attempt to close a gap between the focus on the benefits of this promising learning technology and the practical considerations and guidelines from a teacher’s perspective in an educational context. We believe that there is immense value in providing teachers with a framework they can use when embedding immersive virtual reality in the dynamic environment of classrooms.

14.1 Introduction Virtual reality (VR) is a computer technology that allows a user to interact and participate in an entirely virtual world, realistic or fictional. This allows users to “travel” to remote places, or to interact with objects in realistic or completely unrealistic manners (flying, looking inside, etc.). Recent development in relatively cheap, but powerful head-mounted equipment has opened up for the use of VR in many settings, including education. The combination of cheaper consumer-grade equipment and more appropriate content has led to VR being used for educational purposes, from K-12 to post-tertiary education. The potential for VR to revolutionize education has been discussed for decades, but the technology has not been on a level where it could be applied in education and training at large until the first developer versions of virtual reality head-mounted displays (HMDs) at consumer price was introduced T. L. Dahl (B) · S. O. Fjørtoft · A. D. Landmark SINTEF Digital, S.P. Andersens veg 3, 7031 Trondheim, Norway e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_14

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in 2013 by Oculus Rift [6]. HMDs may present the same image to both eyes (monoscopic), or two separate images (stereoscopic) making depth perception possible and offers a very realistic and lifelike experience by allowing the user to be completely surrounded by the virtual environment [10]. This paper is structured with a description of the theoretical background for smart teaching and immersive virtual reality, followed by a chapter about the ongoing research project this paper is based on and its methodology. We use this to describe a conceptual framework for the inclusion of VR in the classroom as an augmentation to more traditional methods in both the classroom and first aid education. This is presented in order to discuss the ongoing research project using VR in first aid education in Norway.

14.2 Theoretical Background 14.2.1 Smart Education in a Digital Era Smart education describes learning in a digital era with an objective to improve learner’s quality of lifelong learning. Smart environments, smart pedagogy and smart learning are three key elements within the concept of smart education [14]. Smart learning is a new concept of technology-enhanced learning for developing more powerful and helpful learning environments by incorporating new technologies and new criteria for learning [5]. Smart learning is described as a new learning paradigm that offers personalized content, easy and convenient communication environments, and rich resources [7]. Aligned with the rapid development of technology, learners in modern societies use smart devices to access digital resources. This allows learners to immerse in both personalized and seamless learning. Smart pedagogy with flexible and efficient learning methods for students are also increasingly being developed. Contextual, personalized and seamless learning to promote learners’ intelligence and ability to solve problems in smart environments are key elements of smart education. Smart education will confront many challenges, such as pedagogical theory, educational technology leadership, teachers’ learning leadership, educational structures and educational ideology [14].

14.2.2 The Technological Pedagogical Content Knowledge Framework The technological pedagogical content knowledge framework (TPACK) aims to describe the kinds of knowledge needed by a teacher for effective technology integration. The framework emphasizes the connections between the understanding of content, pedagogy and technology and how these three knowledge areas interact with

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Context Pedagogical Content Knowledge (PC) Fig. 14.1 Technological pedagogical content knowledge framework (TPACK). Adapted from Koehler et al. [8]

one another to produce effective teaching. A key aspect of the framework has to do with the teachers’ autonomy and seeing teachers as designers. One of its limitations is that it does not focus on what kind of content that needs to be covered and how it should be taught [8]. Koehler et al. argue that it is necessary to develop new techniques and approaches that recognize the pragmatic, applied and creative goals of teaching with technology that go beyond existing methodologies [8] (Fig. 14.1).

14.2.3 Immersive VR—An Emerging Learning Technology in An Educational Context Many studies have been conducted on the use of VR in education and training, and research has shown its value and its many advantages for learning and training. The attention surrounding VR and the investment by large companies like Facebook, Apple, Google, Samsung and Microsoft indicate that VR will be used for many applications including learning an emerging trend which emphasizes the importance of gaining a better understanding of the utility of VR when it is applied in an educational context [10]. There seems, however, to be a lack of research on immersive virtual reality delivered via head-mounted displays (HMDs) in K-12 education regarding the impact it has on the teacher’s role and best practices from a practical point of view.

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Immersive VR with HMDs seems to be preferred by students compared to desktop VR because of the feeling of presence, motivation, enjoyment and the intrinsic motivation for students using immersive VR has been identified as higher compared to desktop VR [10]. A motivation for using VR HMDs in education is that it can expose learners to challenging or educational situations and allow students to repeatedly practice new skills in an environment that enables correction and nondangerous failure. The use of virtual reality HMDs seems to be useful for skills acquisition, including cognitive skills related to remembering and understanding spatial and visual information and knowledge; psychomotor skills related to headmovement, such as visual scanning or observational skills; and affective skills related to controlling your emotional response to stressful or difficult situations [6]. While it seems to be many affordances and learning benefits of using VR in education and training based on learning theories like constructivism and situated learning [2], there are some challenges with embedding immersive VR into classrooms, for example, ethical and safety issues. Negotiating the organizational context of a school system and problem-solving within the context of institutional restrictions on internet access, educational reflections on collaborative learning and gender dynamics are other challenges [11]. The optimistic predictions of using HMDs in the classrooms have been based on better and much cheaper hardware while barriers related to the lack of content and to the hardware seems to have been overlooked [6]. For HMDs to become a relevant tool for teachers, one may claim that it is important that they can produce and edit their own content. Furthermore, most educational VR simulations have not been designed as tools adjusted to different educational levels and pedagogical approaches. Current HMDs were not designed for classroom use and require a level of technical skills that is a challenge to many instructors, including software updates and issues with streaming, preloading materials and managing user profiles make it difficult for teachers to manage more than just a couple of HMDs [6].

14.2.4 First Aid in K-12 Education Mandatory training of school children has shown to be one of the most efficient ways of increasing the rate of bystanders providing first aid [3]. For instances, bystander CPR increases the chance of survival manyfold. The effect of inclusion of first aid training in K-12 in Scandinavia for decades has been demonstrated [13]. Kids from the age of 12 and earlier can render efficient first aid. In the European Resuscitation Council’s ten principles for increasing survival [3], we find these two recommendations: Training must involve hands-on practice which may be augmented with theoretical – including virtual – learning. Training has also been performed without sophisticated equipment or specific resuscitation manikins. With “Kids Save Lives”, children will also learn relevant social responsibility and social skills.

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While the tenet of first aid training has been manikins and hand-on training in resuscitation, there is also a need for augmented theoretical approaches to teaching the willingness to help, developing fundamental social responsibilities and general preparedness (recognizing a need for help, risk assessment and personal safety, etc.). The new Norwegian curricula from August 2020 have four interdisciplinary themes, one of which is public health and life management. Teaching first aid and risk assessment may be made relevant in almost all subjects and at all levels. In addition to this, some subjects have specific goals related to first aid . For example, in physical education (PE), 7th grade students (age 11/12) should know how to assess safety in outdoor activities, while at 10th grade (age 15/16) they should learn how to understand and carry out lifesaving first aid. Situated learning, constructivism, HMDs that give valuable first-hand experiences, audiovisual learning and kinesthetic learning are some affordances mentioned for using immersive VR for first aid training. The fact that you can train on making decisions without worrying about hurting anyone is especially considered as a valuable affordance when it comes to first aid training [2].

14.3 Research Methodology This paper is based on an ongoing research project where a Norwegian municipality wants to explore the potential of using immersive VR as a digital learning tool in K-12 education. The VR application will be used for teaching within the interdisciplinary theme of public health and life management. The overall goal of the research project is to develop a user-centred VR application that will be used in the classrooms to teach and stimulate in-depth learning within first aid. The VR application is thought to improve the children’s ability to make good decisions in critical real-life situations by enabling them to recognize and act appropriately in emergency situations, as well as to enhance the teaching within the theme of first aid in K-12 education. The overall project goals include: • Use participatory design and involve both teachers and students in the design process. • Conduct an iterative design process with user tests where the feedback will be used for further improvements. • Evaluate the co-design process and the effects of utilizing and embedding the final VR application in authentic educational situations. • Establish guidelines and recommendations for how to use the VR application for teaching within the theme of public health and life management for teachers and school owners. The methodical approach of the project is participatory design involving both students and teachers in the design process to create an immersive experience where the students can feel a high sense of presence and that the teachers will find useful for teaching within the theme of public health and life management. This is mainly done

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through workshops and meetings. Teachers are participants in both the project group and the group working with the manuscript for the VR application. Through discussions with teachers, we have identified several factors which should be considered when using novel technologies in education. Based on these findings, we see the need for developing a conceptual framework that can be useful for research and academic purposes, but also amongst teachers in their professional learning networks.

14.4 Developing a Conceptual Framework for Smart Teaching The teachers play an important role in any learning environment. We will discuss several considerations which are crucial when teaching with technology. Teacher’s use of technology is highly related to their pedagogical beliefs [12]. Therefore, it is important to take pedagogical views and practices into account when doing research about educational technology. Furthermore, teachers with a constructivist view (studentcentred) on learning seem more likely to experiment with new technology, than those with a behaviouristic view (teacher-centred) [12]. Teachers do not necessarily see teaching as one way or the other, some could have a multi-dimensional approach. When introducing new technology in class, it might be necessary for the teacher to be both an instructor and a facilitator [1]. For example, as in our case we use VR, which is an unknown technology for many students. The teacher needs to give instructions on how to use it. Also, the teacher needs to facilitate the learning process and give guidance during the VR session. The TPACK-model illustrates the interaction between the technological, pedagogical content knowledge that teachers should possess in order to succeed in a modern classroom. But in order to do so, the teachers must be aligned to participate in a professional learning network with their colleagues. The school context also seems to play an important role in understanding pedagogical views and the use of technology [12]. Time is another important factor for teachers. Furthermore, teachers are to some extent designers of their own lessons, and therefore by addressing didactical considerations, we emphasize the importance of content and methods. In a Norwegian survey on digital practices in K-12 education, (“Monitor 2019”), more than 8 out of 10 teachers respond that didactics are the most crucial factor for the use of digital resources in teaching [4]. In our extended framework, we take into consideration several factors, both within and outside the classroom, that will affect the teacher’s knowledge and experience with VR and other smart technologies.

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14.4.1 A Conceptual Framework for Smart Teaching The SMADICT framework consists of three competences necessary for teachers: content knowledge, basic skills in the specific technology and didactical considerations teachers make before, during and after their lessons. All of this is affected by time, and by external factors like technical support, personal learning network and support from school management (Fig. 14.2). Content knowledge is a critical prerequisite for good teaching [9]. In our case, this will be knowledge about first aid. Second, the teacher needs some basic skills in the specific technology. For immersive and more advanced technologies, this probably means that it is necessary to attend a formal or informal course. The teacher also needs time to explore the technology and adapt it with the subject-specific content, preferably by using a “trying and failing” approach. The next steps will be designing a session suitable for the students and test it in the classroom. After a few sessions, the teacher will have enough experience to adjust the teaching practice, based on both pedagogical and technological matters. In the framework, we illustrate how external factors also might affect both the input and outcome of smart teaching. Technical support is defined as somebody that

Fig. 14.2 SMADICT—a conceptual framework for smart teaching

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can help the teacher with both software and hardware issues. For instance, many Norwegian schools have an ICT supervisor that supports teachers with such matters. Furthermore, educational research emphasizes the importance of a good personal learning network (PLN) or professional development (PD) [12]. This could be sharing practices and ideas with your colleagues and or with other teachers and professionals around the world. For example, the Flipped Learning Network and other online worldwide hubs for educators. The third factor we consider as important is support from school management. The school leader should allow teachers to explore and develop novel practices. In order to do so, they must provide both time and resources.

14.5 Discussion Smart education, smart learning and smart pedagogies are some emerging paradigms within technology-enhanced learning that confronts traditional pedagogical theories and educational structures and ideology [14]. Technology-enhanced learning and smart education require interdisciplinary knowledge for effective technology integration in classrooms [8]. There are many learning benefits and affordances with using immersive VR with HMDs in classrooms, as well as challenges related to embedding it into classrooms. Based on the TPACK framework, successful use of VR as a learning technology in education requires a combination of technological knowledge, content knowledge and pedagogical knowledge. Virtual reality with HMDs is a distinct type of technology, relatively new to both teachers and students that might create a challenging integration. For teachers to use virtual reality HMDs in classrooms, they need to master some basic technical skills. They need for instance to know how to update the software, manage issues with streaming, know how to preload materials and how to manage user profiles on the HMDs. These kinds of issues might make it difficult for teachers to manage the use of many HMDs at once. Another technical skill that might come in handy is knowing how to produce content considering the teachers’ autonomy and the value of enabling them to design both what kind of content that needs to be covered and how it should be taught. It is also important that teachers are aware of ethical and safety issues. We believe that this is especially important when the content might involve exposing students to “tough” real-life situations. This is relevant in our case where the objective is for children to learn about first aid so they can be able to save lives in critical situations. The content knowledge in a subject is a critical prerequisite for good teaching [9]. An example to illustrate this is when the immersive experiences in the classrooms have ended and the students remove their HMDs. The role of the teacher will then change from being a technical instructor to facilitator of reflection and deeper learning related to the virtual experience. The teacher will need to know the content not only to prepare the class and design an efficient learning path, but also to be able to facilitate good discussions with the students afterwards. The teacher needs to both

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give instructions on how to use the technology, give guidance during the VR session and to facilitate the students learning processes. Tondeour et al. argue that the teachers use of technology is highly related to their pedagogical beliefs [12], and according to the Norwegian survey Monitor [4], didactics seems to be the most crucial factor for the use of digital resources in teaching. A key aspect of TPACK has to do with the teachers’ autonomy and seeing teachers as designers, but one of its limitations is that it does not focus on what kind of content that needs to be covered and how it should be taught. Our framework—SMADICT— smart teaching when using smart technologies in classrooms—is based on TPACK and consists of three competences needed by the teacher: content knowledge, basic skills in the specific technology and the didactical considerations teachers make before, during and after their session. An element of external factors that includes technical support, personal learning network and support from school management is added to illustrate factors that might affect both the input and outcome of smart teaching.

14.6 Conclusions and Future Steps Conclusions. This paper is based on an ongoing research project where an immersive virtual reality application will be used in K-12 education for learning first aid. The methodical approach of the project is participatory design involving both students and teachers in the design process. Current findings point to a need for a framework that teachers can use when adapting immersive virtual reality in the dynamic environment of classrooms. The SMADICT framework is based on the TPACK framework and further developed by adding an element of external factors and focusing on didactical considerations when adapting immersive VR or other novel technologies into the classrooms. The limited number of studies within the use of immersive virtual reality in K-12 education point to a need for further and more rigorous research that examines the use of HMDs in authentic educational situations. The technology itself is relatively new, and the application in education likewise. The effects are relatively undescribed, and hence there are few results that can be applied for formative pedagogical frameworks. We are now in the co-design phase of the ongoing research project where the manuscript is being written and the production of the VR application will be finished within a few months. Future steps. Future steps include testing a prototype, collecting data from students and teachers for further development, and to evaluate the effects of using the final VR application in authentic educational situations. Acknowledgements The ongoing research project (#296188) is funded by the Regional Research Fund Hovedstaden.

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References 1. Blau, I., Shamir-Inbal, T.: Re-designed flipped learning model in an academic course: The role of co-creation and co-regulation. Comput. Edu. 114, 69–81 (2017). https://doi.org/10.1016/j. compedu.2017.07.014 2. Bucher, K., Blome, T., Rudolph, S., von Mammen, S.: VReanimate II: training first aid and reanimation in virtual reality. J. Comput. Edu (2018). https://doi.org/10.1007/s40692-0180121-1 3. Böttiger, B.W., Bossaert, L.L., Castrén, M., Cimpoesu, D., Georgiou, M., Greif, R., Grünfeld, M., Lockey, A., Lott, C., Maconochie, I., Melieste, R., Monsieurs, K.G., Nolan, J.P., Perkins, G.D., Raffay, V., Schlieber, J., Semeraro, F., Soar, J., Truhláˇr, A., Van de Voorde, P., Wyllie, J., Wingen, S., et al.: Kids save lives—ERC position statement on school children education in CPR. Resuscitation, 105, A1–A3 4. Fjørtoft, S.O., Thun, S., Buvik, M.P.: Monitor 2019—En deskriptiv kartlegging av digital tilstand i norske skoler og barnehager. SINTEF Digital, Trondheim (2019) 5. Hwang, G.: Definition, framework and research issues of smart learning environments—a context-aware ubiquitous learning perspective. Smart Learn. Environ. 1, 4 (2014). https://doi. org/10.1186/s40561-014-0004-5 6. Jensen, L., Konradsen, F.: A review of the use of virtual reality head-mounted displays in education and training. Educ. Inf. Technol. 23, 1515–1529 (2018). https://doi.org/10.1007/ s10639-017-9676-0 7. Kim, S., Song, S.M., Yoon, Y.I.: Smart learning services based on smart cloud computing. Sensors 11(8), 7835–7850 (2011) 8. Koehler, M.J., Mishra, P., Kereluik, K., Shin, T.S., Graham, C.R.: The Technological Pedagogical Content Knowledge Framework, In: J.M. Spector, et al. (eds.) Handbook of Research on Educational Communications and Technology (2014). https://doi.org/10.1007/978-1-46143185-5_9 9. Loewenberg Ball, D., Thames, M.H., Phelps, G.: Content knowledge for teaching: what makes it special? J. Teach. Edu. 59(5), 389–407 (2008). https://doi.org/10.1177/0022487108324554 10. Makransky, G., Lilleholt, L.: A structural equation modeling investigation of the emotional value of immersive virtual reality in education. Edu. Tech. Res. Dev. 66, 1141–1164 (2018). https://doi.org/10.1007/s11423-018-9581-2 11. Southgate, E., Smith, S.P., Cividino, C., Saxby, S., Kilham, J., Eather, G., Scevak, J., Summerville, D., Buchanan, R., Bergin, C.: Embedding immersive virtual reality in classrooms: ethical, organisational and educational lessons in bridging research and practice, Int. J. ChildComput. Interact. 19, 19–29 (2019). ISSN 2212-8689. https://doi.org/10.1016/j.ijcci.2018. 10.002 12. Tondeur, J., van Braak, J., Ertmer, P.A., Ottenbreit-Leftwich, A.: Understanding the relationship between teachers’ pedagogical beliefs and technology use in education: a systematic review of qualitative evidence. In: Educational Technology Research and Development. Springer, September 2016. https://doi.org/10.1007/s11423-016-9481-2 13. Wissenberg, M., Lippert, F.K., Folke, F., et al.: Association of national initiatives to improve cardiac arrest management with rates of bystander intervention and patient survival after outof-hospital cardiac arrest. JAMA 310, 1377–1384 (2013) 14. Zhu, Z., Yu, M., Riezebos, P.: A research framework of smart education. Smart Learn. Environ. 3, 4 (2016). https://doi.org/10.1186/s40561-016-0026-2

Chapter 15

Blended Learning Technology Realization Using a Basic Online Course Lubov S. Lisitsyna, Marina S. Senchilo, and Evgenii A. Efimchik

Abstract This paper analyzes an approach to the realization of the blended learning technology with the use of a basic online course to integrate modern smart pedagogy in higher schools. A basic online course on discrete mathematics has been designed for bachelors of the intramural form of study at ITMO University. The Remote Laboratory Control Protocol (RLCP)-compatible virtual laboratory technology for MOOC’s “Methods and algorithms of graph theory” has been modified and improved. It allows using virtual stands for practical tasks in the training mode. The article outlines the results of experimental research that proves the effectiveness of the blended learning technology with the use of a basic online course. The average number of attempts to solve problems has reduced by 45% in comparison with that of the students who completed the MOOC; the progress assessment results have increased by 18.8% in comparison with the traditional learning format. Tolls among students show their satisfaction with the blended learning technology using a basic online course (almost 80%).

15.1 Introduction Today, high-school smart pedagogy is studied in different aspects [1], and approaches to its realization presuppose the use of the information, educational environment with a developed infrastructure that integrates various platform solutions and e-learning services. With the advent of open education platforms and massive open online courses (MOOC) [2] on their basis, MOOC was thought to completely substitute some university subjects. However, that appeared to be far from the reality. Students’ personality characteristics are an inherent part of the competencies formed [3]; they can only be formed and assessed through face-to-face contact between students and professors. It should be noted that in the process of education, one has to satisfy the L. S. Lisitsyna · M. S. Senchilo (B) · E. A. Efimchik ITMO University, Saint Petersburg, Russia e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_15

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growing expectations of students. Students tend to wish to learn more than traditional lectures in class allow as they are limited by students’ workload. The experience of developing video lectures and online courses shows that the time of the theoretical part presentation in an electronic format is three times shorter compared to the traditional one [4]. This fact highlights the utility of video lectures as a means of concentrated theory presentation. The important advantage of online courses is their practical tasks and exercises that are used to form and assess skills to solve typical problems. During online classes, virtual stands realize practical tasks through automatic generation of different variants with tasks of equal complexity, interactivity of step-by-step solution input, and differentiation in the assessment of problem-solving [5–7]. Virtual stands also solve the problem of individualization and the assessment of student learning in practical sessions. However, some problems, which a student faces while studying video lectures and solving practical tasks on virtual stands, may and should be timely resolved removed to face-to-face communication with the professor. At the same time, the professor should have access to services that allow the fastest solution for students’ problems and encourage them to study further. In this context, blended learning that keeps a balance between electronic and traditional learning is a compromise [8, 9]. Therefore, online courses may be used as supplemental content as well as a basic course for the subject. Therefore, online courses may be used as supplemental content as well as a basic course for the subject. According to Jonker et al. [10], to ensure curriculum flexibility, teacher training institutions must develop and implement a mixed curriculum with full-time and online components. In addition, Means et al. [11] note in their research that mixed learning technologies can contribute to positive results in student performance. They also noted that further research and development is needed on various technologies of mixed learning, and experimental research is needed to test the principles of mixing online and face-to-face learning for different types of students. All this indicates the relevance of the study. This paper is devoted to the results of designing and realization of the blended learning technology among university students within an online course on discrete mathematics, which is studied by first year students of various training bachelor programs at ITMO University. The research results are likely to interest higher school professors who already have a ready MOOC to use for blended learning.

15.2 The Structure and Content of Discrete Mathematics Basic Online Course Table 15.1 shows the structure and content of a basic online course on discrete mathematics. Topics 1 and 2 are studied in the first semester. Their content was copied from MOOC’s “Methods and algorithms of graph theory” on the National Platform of Open Education of the Russian Federation [4–6]. Topics 3 and 4 are studied in the second semester; new content has been designed for them. Figure 15.1

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Table 15.1 Structure and content of course Number

Topic

1

Theory of fuzzy sets

35

3

2

Graph theory (Part 1)

30

9

3

Graph theory (Part 2)

18

6

4

Network theory

25

6

108

24

TOTAL

Amount of video lectures with quizzes

Amount of task

Fig. 15.1 Example of virtual stand of practical exercise of the course

illustrates an example of a virtual stand for problems solutions. At the end of each semester, an e-exam is held within the online course. Table 15.2 shows the structure and content of the first semester e-exam.

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Table 15.2 Structure and content of online exam Part

Characteristic of part of online exam

Numbers of topics

Number of tasks

Max score

A

Checking the ability to apply knowledge in practice

1, 2

10

8

B

Checking skills of solving typical problems applying studied algorithms

1

1

6

C

Checking skills of solving typical problems applying studied algorithms

2

1

6

Total

12

20

15.3 The Approach to Realize the Blended Learning Technology Within a Basic Online Course A basic online course only guarantees the formation of expected learning outcomes and other learning outcomes are achieved during contact classes. Such an approach aims to maintain a balance between e-learning and traditional learning, creating new services and technologies for intellectualizing and personalizing the learning and teaching processes. Moreover, it allows activate students to independently search for new knowledge. The basic online course presents a theoretical part in video lectures which are structured according to the training schedule (16 weeks per semester). In-class lectures and practical training are given in the format of group consultations. Each session involves answering students’ questions, studying theory relevant problems according to the training program of students, running workshops on working with virtual stands with practical tasks and exercises, and assessing tasks with personal identification in class. An example of a virtual stand is shown in Fig. 15.1. After completion of every topic, computer tests and progress check are assessed with a written test. An online exam is held at the end of each semester. Table 15.3 Table 15.3 Types and forms of control Number

Types of control

Forms of control (amount)

1

Current knowledge control

computer test (2)

Max score 20

2

Current Skill Control

task (12)

30

3

Milestone control of knowledge and skills

written test (2)

20

4

Student personality control

delivery of tests and tasks on time (2)

10

5

Exam

online exam (1)

Total

108

20 100

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shows the types, forms, and maximum scores of progress control in each semester. Current knowledge control is realized online, while progress assessment is always held face-to-face by the professor. The personal qualities of students are evaluated two times per semester after the completion of each topic. In this case, some grades, such as computer tests and tasks, are transferred into an electronic journal automatically and other grades, such as performance in topic discussion and online course completion, are given by the professor. Only students who have achieved at least 60% of maximum scores in lines 1–4 (Table 15.3) are permitted to take an online exam. Otherwise, students take a traditional exam faceto-face with the professor. The main problem to integrate an online course into the educational process is the absence of training before an online assessment. For instance, while solving practical tasks in an online course, students are given five attempts which significantly decrease the efficiency of education and requires the improvement of the RLCP-compatible virtual laboratory technology [5–7]. It should provide two modes of virtual stand usage (Fig. 15.1)—education (training) and assessment of learning outcomes. The education mode provides a student with limitless instances of a virtual stand usage and a report with a diagnosis of their mistakes. The control mode provides a limited number of attempts and requires additional personal identification. In this case in the information educational environment, electronic reports are only available to the professor, who can use them to help students. Further, the results of the RLCP-compatible virtual laboratory technology modernization are analyzed for the usage of the stands in both modes.

15.4 The RLCP-Compatible Virtual Laboratory Technology for Blended Learning The sequence diagram, which represents control of the session of task solving, is given in Fig. 15.2. Each RLCP-compatible virtual laboratory consists of a virtual stand and a RLCP-server. The virtual stand is a tool, which is used by a student to get details of the task given and to solve it including all intermediate results. That solution is checked by RLCP-server. RLCP-server is a special TCP-server, which provides the interface for RLCP. RLCP-server is also responsible for composing of task variants and performing special intermediate calculations that are to be held in safe environment. These two modules are independent and interact through a special control environment (hereinafter—the environment), which automatically manages each session of task solving assigned to a student. Such a structure provides protection against an unauthorized access. The environment assigns methods of RLCP-server to provide a student with an individual task variant, initializes a special page for the virtual stand, which is called a task frame. If performing a task needs safely held intermediate calculations, the environment passes corresponding request to RLCP-server and provides the virtual

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Fig. 15.2 Sequence diagram of session of solving practical exercise using RLCP-compatible virtual laboratory

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stand with results. When a student finishes performing, the environment sends his/her solution it to RLCP-server for evaluation. When the evaluation is finished, a student gets evaluation results and comments on it, if needed. The virtual laboratories of the course aim to form and evaluate the skill to solve a typical task using known algorithms, which requires rigid action sequences and logical methods. This skill development requires the variability of tasks and strict control of their complexity. To solve this problem, each virtual laboratory uses a special algorithm to compose variants of a specified class of complexity. On the other hand, this type of tasks allows it to check all intermediate results of the solution comparing it to the solution provided by the reference algorithm. This enables it to detect an exact stage of the solution at which student made a mistake and provides him a useful comment on it. In addition, this evaluation method automatically determines and assesses the proportion of correct results in a task solution which is more beneficial than the binary “right/wrong” grading rule that leaves no room for mistakes.

15.5 The Efficiency of Blended Learning Technology Using a Basic Online Course. Results Table 15.4 shows the experimental research results which prove the effectiveness of the education mode for the solution of practical tasks. Data is given about the average number of attempts made to solve a problem in the assessment mode among the students of MOOC’s “Methods and algorithms of graph theory” [4] and students who solved similar tasks after discrete mathematics basic online course (data within last two years of education, 490 persons in general). It should be noted that the data Table 15.4 Average number of attempts of problem-solving in the assessment mode Number

Related algorithm

MOOC

Blended learning

1

Lee algorithm

2.55

1.15

2

Bellman–Ford algorithm

2.33

1.15

3

Roberts–Flores algorithm

3

1.42

4

Prim algorithm

1.56

1

5

Kruskal algorithm

1.51

1.15

6

Magu-Weismann algorithm

3.23

2.1

7

Method based on Magu-Weisman algorithm

2.41

1.86

8

Greedy heuristic algorithm

3.1

1.34

9

Hungarian algorithm

3.38

1.8

10

Algorithm based on ISD method

2.23

1.62

11

Gamma algorithm

3.41

1.15

2.61

1.43

Average

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Table 15.5 Milestone control results Number

Topic

Traditional learning

Blended learning

1

Theory of fuzzy sets

6.46

8.34

2

Graph theory (Part 1)

–

8.76

3

Graph theory (Part 2)

–

–

4

Network theory

7.2

–

features only the attempts of the students who successfully completed the MOOC. The table shows a proportional decrease in the number of attempts among students by 45% on average in comparison with successful MOOC students. This proves the importance of training within blended learning based on online courses. An experimental research was performed comparing the progress assessment results of written assignment submissions in discrete mathematics by ITMO University students (Table 15.5). The maximum score is 10. Students of 2018–2019 academic year (169 persons) studied topics 1 and 4 traditionally and topics 2 and 3 using blended technology within the basic online course “Methods and algorithms of graph theory” [3]. Table 15.4 shows average scores for written exam assignments of progress assessment on topics 1 and 4 studied traditionally. Students of 2019–2020 academic year (328 persons) study this subject using only the blended learning technology, and Table 15.4 shows average scores for written exam assignments of milestone control on topics 1 and 2. Comparing the results of topic 1 “Theory of fuzzy sets,” one can see that in the case with the blended learning technology, student performance is 18.8% higher, which proves its efficiency compared to the traditional technology. At the course completion in the 2018–2019 academic year, a poll was held among students. Almost 80% of students claimed that the basic online course on the topic significantly facilitated studying, while more than 6% of students expressed the opposing opinion. They claimed to like traditional lectures more than online lectures of the course.

15.6 Conclusions Modern smart pedagogy in higher school requires the availability of the information educational environment with a developed infrastructure that integrates various platform solutions and e-learning services. However, this is a necessary but not sufficient condition for the effective realization of the blended learning technology. The approach proposed realizes the blended learning technology by means of a basic online course establishing a balance between electronic and traditional learning. It is also aimed at the creation of new services and technologies of the intellectualization and personalization of education and teaching processes. Moreover, such an approach encourages students to individually search for new knowledge and skills.

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The study features the results of a discrete mathematics basic online course development for blended learning. This online course includes the resources of MOOC’s “Methods and algorithms of graph theory” on the National Platform of Open Education of Russian Federation as well as similar resources on other topics of discrete mathematics (Theory of fuzzy sets, Network theory). The absence of the training mode (“solve any number of problems at a convenient time from any computer”) in virtual laboratories is balanced by the improvement of the RLCP-compatible virtual laboratory technology. It allows the use of virtual stands in the learning mode, automatically generating equally complex tasks and diagnosing all the possible mistakes made during their solution. The experimental research conducted within this study has proven the importance of such training for students’ independent studies (the number of their attempts to solve problems reduced, on average, by 45% in comparison with that of the students who successfully completed the MOOC). The approach has been tested in the 2019–2020 academic year among students of the intramural form of study at ITMO University (328 persons). Experimental research has shown the effectiveness of blended learning. The students’ progress assessment results improved by 18.8% in comparison with the students who studied this subject last year in the traditional form. A poll among students proved their preference for blended learning technology: almost 80% of the students (262 persons) think that the basic online course significantly facilitated their studies.

References 1. Uskov, V.L., Bakken, J.P., Aluri L.: Crowdsourcing-based learning: the effective smart pedagogy for STEM education. In: IEEE Global Engineering Education Conference, EDUCON (2019), pp. 1552–1558 2. Vasiliev, V., Stafeev, S., Lisitsyna, L., Ol’shevskaya, A.: From traditional distance learning to mass online open courses. Sci. Tech. J. Inf. Technol. Mech. Opt. 89, 199–205 (2014). (in Russian) 3. Lisitsyna, L.S., Pershin, A.A., Kazakov, M.A.: Game mechanics used for achieving better results of massive online. In: Smart Education and Smart e-Learning, pp. 183–193 (2015) 4. Lisitsyna, L.S., Efimchik„ E.A.: Designing and application of MOOC «Methods and algorithms of graph theory» on National Platform of Open Education of Russian Federation. In: Smart Education and e-Learning, vol. 59, pp. 145–154 (2016) 5. Lisitsyna, L.S., Efimchik, E.A.: An approach to development of practical exercises of MOOCs based on standard design forms and technologies. In: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 180, pp. 28–35 (2017) 6. Lisitsyna, L.S., Efimchik, E.A.: Making MOOCs more effective and adaptive on the basis of SAT and game mechanics. In: Smart Education and e-Learning, vol. 75, pp. 56–66 (2018) 7. Chezhin, M.S., Efimchik, E.A., Lyamin, A.V.: Automation of variant Preparation and Solving estimation of algorithmic Tasks for Virtual Laboratories Based on Automata model. In: ELearning, E-Education, and Online Training, pp. 35–43 (2015) 8. Li, K.: Visualization of learning activities in classroom blended with e-learning system. In: Smart Education and e-Learning, vol. 144, pp. 139–148 (2019)

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9. Rutkauskiene, D., Gudoniene, D., Bartkute, R., Volodzkaite, G.: Smart Learning objects for online and blended Learning approach. In: Smart Education and e-Learning, vol. 144, pp. 169– 199 (2019) 10. Jonker, H., März, V., Voogt, J.: Curriculum flexibility in a blended curriculum. In: Aust. J. Educ. Technol. 36(1) (2020) 11. Means, B.M., Toyama, Y., Murphy, R.: The effectiveness of online and blended learning: a meta-analysis of the empirical literature. Teach. Coll. Rec. 115(3) (2013)

Part IV

Smart Education: Systems and Technology

Chapter 16

Data Cleaning and Data Visualization Systems for Learning Analytics Vladimir L. Uskov, Jeffrey P. Bakken, Keerthi Sree Ganapathi, Kaustubh Gayke, Brandon Galloway, and Juveriya Fatima

Abstract Learning analytics focuses on collecting, cleaning, processing, visualization, and analyzing teaching and learning-related data from a great variety of academic sources. This paper presents the up-to-date findings and outcomes of the research, design, and development projects at the InterLabs Research Institute at Bradley University (USA) that are focused on the analysis and testing of effective systems to clean and visualize student academic performance data for learning analytics.

16.1 Introduction 16.1.1 Data Sources for Learning Analytics (LA) In accordance with the 2020 EDUCAUSE Horizon Report [1], “Over the past decade, institutions of higher education have focused their mission, vision, and strategic planning on student outcomes and high impact practices that promote student success. The availability of tools that measure, collect, analyze, and report data about students’ progress has given rise to the field of LA for student success.” In accordance with the 2016 Hanover Research (USA) report [2], the data for LA may arrive from various sources, including analysis of “social networks, automated advising and coaching, personalized student curriculum, student performance assessment, degree audit, early warning/predictive modeling, and various systems such as predictive performance, enrollment profiling, lifetime value/booster effectiveness, admissions and retention/attrition systems.” V. L. Uskov (B) · K. S. Ganapathi · K. Gayke · B. Galloway · J. Fatima Department of Computer Science and Information Systems and InterLabs Research Institute, Bradley University, Peoria, IL, USA e-mail: [email protected] J. P. Bakken The Graduate School, Bradley University, Peoria, IL, USA e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_16

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In our past work [3], we identified the main sources of student academic data; they include but are not limited to: (1) student profile data such as lists of students’ current courses and courses taken so far and remained courses in the program of study, majors, minors, concentrations, and grade point average (GPA) score; (2) student academic performance (SAP) data such as scores obtained for various learning assignments, tests, quizzes, laboratories, and exams in academic course(s); (3) student learning-related activities’ data such as frequency of logs from learning management systems (LMS) and/or Web sites of online courses, time spent to watch video lectures or participate in online discussions, and the number and quality of posted questions in discussions forums; (4) course syllabi, curriculums, and programs of study; (5) academic department-related data such as admission criteria, offered academic programs, requirements to graduation, laboratory or technological fees; (6) college and/or university-related data such as constraints on credit hours per semester—min and max—to be taken by a student in one semester, constraints on the number of courses to be taken in summer sessions, student-to-faculty ratios, max enrollment, and other inputs.

16.1.2 Data Cleaning Data cleaning or data cleansing (DC) is the process of improving the quality of initial data sets (IDS) by (1) detecting and modifying “dirty” data and (2) replacing or deleting incomplete, incorrect, improperly formatted, duplicated, or irrelevant records in IDS. The DC process is focused on providing high-quality, “cleaned” data in terms of data validity, accuracy, completeness, consistency, uniformity, and integrity. DC process may take up to about 60% of the entire data analytics cycle. Broeck et al. [4] proposed a framework for detecting, diagnosing, and editing of data abnormalities. It includes several stages such as (a) screening of data, i.e., identification of lack and/or excess of data, outliers and/or inconsistencies, strange data patterns, suspect data analysis results; (b) diagnosing of data, i.e., identification of incorrect data, true errors, missing data, true extreme, true normal data, etc., and (c) editing of data, including data correction, deletion, etc. The authors also reviewed the main “usual” sources of data outliers and inconsistencies such as (1) correct data filled in a wrong cell in the spreadsheet, (2) duplicated/repeated data and/or not readable data, (3) writing error, (4) data provided is out of expected (conditional range) range, (5) outliers and inconsistencies carried over from questionnaires or surveys, (6) value incorrectly entered, (7) incorrect data due to data transformation (programming) errors, (8) data extraction errors, (9) sorting errors, (10) data cleaning (programming) process errors, and (11) value incorrectly changed during previous data cleaning. The recent publications by Osborne [5], Whitaker et al. [6], Ganti [7], and other noticeable scholars in the DC area provide a solid framework for the DC process in general. However, the performed literature review of these and multiple other available publications in the DC area clearly show that those publications do not

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provide (a) a thorough discussion of specific characteristics and constraints of SAP data, (b) systematic analysis and comparison of the effectiveness of existing DC systems with applications to SAP data, and (c) potential integration of DC systems with LA systems.

16.1.3 Data Visualization Data visualization (DV) is a graphical representation of information and data. By using visual elements like graphs, diagrams, charts, and maps, data visualization tools provide the end users with an accessible way to see and understand trends, outliers, and patterns in data. Various patterns, trends, dependencies, frequencies, risks, and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization software systems. The most common ways to use DV systems include but are not limited to (1) changing of data over the time; (2) determining data or event frequency; (3) determining relationships (correlations) in data sets; (4) understanding a network, including identification of clusters, bridges between the clusters, influencers within clusters, and outliers; (5) data or events timeline or schedule; and (6) analyzing values and risks to show which opportunities are valuable and which are risky. Ayad [8] described “… a Student Success System (S3) that provides a holistic, analytical view of student academic progress. S3 also provides a set of advanced data visualizations for reaching diagnostic insights and a case management tool for managing interventions.” Ryan and Snow in [9] wrote: “For data visualization to offer the most tangible value as student success assets in an educational setting, visualizations need to be made as functionally and cognitively accessible to students and faculty as possible, allowing both to actively share in data prudent for performance improvement and success.” Raji et al. [10] developed and described “… a visual knowledge discovery system called eCamp that pulls together a variety of population-scale data products, including student grades, major descriptions, and graduation records.” The performed literature review of these and multiple other available publications in DV areas clearly show that, unfortunately, they do not provide a systematic analysis and comparison of existing DV systems with applications to SAP data visualization and their potential integration with LA systems.

16.2 Project Goal and Objectives Project goal. The overall goal of the ongoing, multi-aspect research, design, and development project at the InterLabs Research Institute at Bradley University (USA) is focused on (a) creative analysis, testing and comparison of existing DC and DV

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systems with applications for specific SAP data and (b) integration of selected or developed DC and DV tools with the existing InterLabs Learning Analytics system. Project objectives. The project team identified the following objectives: (1) systematic analysis of the functionality of available DV systems; (2) identification of specific inconsistencies in SAP data; (3) identification of desired automatic functions of the “ideal” SAP DC system; (4) systematic analysis of the functionality of available DC systems against the identified desired functions of ideal SAP DC system; (5) selection of the best DV system to be integrated with the LA system; and (6) integration of DV systems with LA system for effective SAP data visualization. A summary of up-to-date project findings and outcomes is presented below.

16.3 Research Outcomes: Data Cleaning Systems 16.3.1 DC Systems Analyzed The research team systematically analyzed various available open-source and commercial DC systems, including: (a) a group of well-known DC systems that are available for several years such as (1) Open Refine (labeled as OR in Tables 16.1 and 16.2), (2) Talend Data Quality (TDQ), (3) Trifacta Wrangler (TW), (4) TIBCO Clarity (TC), (5) Data Ladder— Data Match Enterprise (DL), (6) WinPure (WP), (7) Datamartist (DM), (8) Ataccama (AT), (9) Microsoft Excel—part of Office 365 (XLS), (10) Tableau (TAB), (11) Alteryx (ALT), and (12) Pandas (PAN); (b) a group of recent (2019–2020) DC systems, including (1) DemandTools, (2) Informatica Data Quality, (3) Dataloader.io, (4) Cloudingo, (5) V12 Data, (6) BriteVerify, (7) RingLead, and (8) SAS. A summary of obtained analysis outcomes for just one analyzed DC system— OpenRefine open-source system—is available in Table 16.1. The similar outcomes of our analysis of other DC systems are available upon written request. The obtained analysis outcomes enabled us to arrive with a comparative analysis table in terms of the main functions of general-purpose DC systems. The outcomes of our analysis are presented in Table 16.2 (where “+” means availability of the designated function in the DC system and an empty cell means an absence of the corresponding function).

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Table 16.1 A summary of obtained analysis outcomes for OpenRefine DC system Features/functions

Details

Name

OpenRefine

Web site(s) to download

• http://openrefine.org/download.html

Web site(s) to learn main functions

• http://openrefine.org • https://www.youtube.com/watch?v= wGVtycv3SS

Free or commercial

Free (open source)

Local or Web-based

• It may work in both modes—on a local computer and over the Internet

Required technical platform and/or operating system

• Supported OS: Windows, Linux, Mac OS • On Windows it does not support Cygwin, MSYS2, or Git Bash for running OpenRefine; instead just use Windows Terminal • If the user is running a proxy or gets a BindException, then change the IP configuration with -i and -p • Java JRE installed (if the user is running a 64-bit operating system, then it’s recommended to install 64 bit Java)

Data files supported to import data

• TSV, CSV, or values separated by a custom separator you specify • Line-based text files, Fixed-width field text files, PC-Axis text files, and MARC files • Excel (.xls, .xlsx) • Open Document Format spreadsheets (.ods) • XML, RDF as XML • JSON • Google spreadsheets • RDF N3 triples

Edit data

• • • • •

Facet data

Each column has a facet function that allows the user to quickly identify data inconsistencies by counting the number of unique occurrences for each piece of data in that column

Transform data

A new column can be made from existing columns

Export data

Provides data export in multiple formats

Trim leading and trailing whitespace Collapse consecutive whitespace Un-escape HTML identities Use title case, uppercase, or lowercase Use number, date, or text data types

(continued)

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Table 16.1 (continued) Features/functions

Details

Cluster data

Clustering will look for patterns of variation without the need for you to (1) sleuth your way through the data set looking for small variations and (2) using facets or filters to eliminate them one at a time

Filter data

Each column contains a “text filter” function. The text filter is useful for identifying pieces of data that may have many variants

Custom text facet

Customers editing a cell (e.g., extracting only student first name)

Numeric facet

Numeric facets to sort numbers which put numbers in numeric range bins

Data history export

Data modification history export (if the user wants to maintain detailed documentation of his/her data history, then this is a great option)

Additional useful functions

If the user uploads an archive file (with extension .zip, .tar.gz, .tgz, .tar.bz2, .gz, or .bz2), then OpenRefine automatically detects the most common file extension in it and loads all files with that extension into a single project

16.3.2 SAP Data Forms, Types, Formats, and Inconsistencies Identified Although a list of identified functions in existing DC systems (Table 16.2) look impressive, most of DC systems can perform automatically (i.e., without manual intervention by the user) about 52–67% of designated functions; the Microsoft Excel application can perform about 86% of those functions under the condition that the user will perform some function semi-automatically (by writing corresponding functions and/or macros). Additionally, our research team decided to tighten the evaluation criteria for existing DC systems. We performed systematic research to answer one of the most important questions: “How good are the general-purpose existing DC systems in terms of effective cleaning of SAP original (or, “dirty”) data in the LA system?” To answer this question, we analyzed the specifics of the original SAP IDS. SAP data specifics—forms, formats, and types. SAP data may be collected in different: • forms (e.g., absolute values of numeric scores for every learning assignment in a course, relative data in %, detailed letter grades, regular letter grades); • formats (e.g., integer, real, char, string, etc.); and

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• types of data (e.g., scores obtained for various learning assignments in one or several courses, scores obtained for learning assignments groups related to student analytical, technical, communication and management skills, scores obtained for optional and/or extra assignments or activities in a course, etc.). A fragment of SAP non-cleaned, original IDS—scores for course learning assignments—in absolute values using integer data format is presented in Fig. 16.1. SAP data specifics—types of inconsistencies identified. Based on our gained experience of cleaning multiple SAP original IDS for the same academic course taught by the same faculty but for different semesters, the usual SAP data inconsistencies may include but are not limited to the following ones: 1.

inconsistent total scores for the same learning assignment but in different semesters; for example, a total of 22 points were available for Test #1 in one semester, and 25 points in the other semester;

Table 16.2 Main functions of analyzed DC systems (as given in the description): a comparison table DC systems analyzed and tested System main functions

OR

TDQ

TW

TC

DL

WP

DM

AT

XLS

Filter data

+

+

+

+

+

+

+

+

+

Add new column

+

+

+

+

+

+

+

+

+

Edit data (to upper, lower)

+

+

+

+

+

+

+

+

+

Export to various formats

+

+

+

+

+

Import in various formats

+

+

+

+

+

+

+

+

Data visualization

+

+

Address validation

+

Predictive interaction

+

+

+

+

+

+

+

Normalization

+

+

Sampling

+

+

+

Profiling

+

+

+

Faceting

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

Checking dependency

+

+

Batch

+

+

Text cleaner

+

+

+

+

+

+

+

+

+

Data type flexibility

+

+

+

+

+

+

+

+

+

Virtualization support

+

+

+

Select/treat blank cells

+

+

+

+

+

+

+

+

+

Highlight errors Fixing dates and times

+ +

+

+ (continued)

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Table 16.2 (continued) DC systems analyzed and tested System main functions

OR

TDQ

TW

TC

DL

14 67%

13 62%

13 62%

14 67%

12 57%

E-mail cleaner Total number of features provided (out of 21 listed above features) and % ratio

2.

3.

4.

5.

WP

DM

AT

14 67%

12 57%

+ 11 52%

XLS + 18 86%

inconsistent total number of points available for students in the same course but in different semesters (e.g., 500 points in one semester and 507 points in the other semester) or by different instructors (e.g., one instructor uses 500 points as maximum and the other instructor 700 or 900 points as a maximum); inconsistent sequence of columns’ names for different learning assignment; for example, for some learning assignments the instructor has used Max, Relative in %, and Actual numeric values, and for other course learning assignments—just Actual values (see an example for Midterm or Test_02 in Fig. 16.1); inconsistent number of columns for every learning assignment or missing columns; for example, for some learning assignments the column labeled as “Relative in %” is missing (see an example for Final Exam in Fig. 16.1); inconsistent numeric values in IDS cells; for example, some cells contain blank values (see examples in red color cells or dark cells, for IN_01, IN_02 in Fig. 16.1);

Fig. 16.1 SAP data—an example of non-cleaned original IDS

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6.

inconsistent total number of columns (or, learning assignments) with SAP numeric data for the same course but for different semesters; for example, in one semester a course may include 14 learning assignments, and in the other semester—15; 7. inconsistent naming convention used for names of columns that belong to the same type of learning assignments; for example, it could be Test_1, Test1, Test01, etc.; 8. an existence of blank columns in IDS; it could be done for the convenience of the instructor; 9. inconsistent focus of columns in IDS; for example, there are columns with student class IDs in between columns with actual SAP data in IDS—it could be done by the instructor for his/her convenience in data structuring, visualization, and understanding; 10. inconsistent categorization (or, grouping) of learning assignments; 11. relevance to student analytical, technical, communication, and management skills; and 12. inconsistent formatting of column names (upper case, lower case, sentence case, etc.). (A note: SAP data inconsistencies identified and listed above go beyond the “usual” data inconsistencies such as writing a wrong value in a cell or similar mistakes).

16.3.3 Data Cleaning Functions of “Ideal” DC System as Required by the LA System Based on our analysis of (1) available original (or “dirty”) IDS of SAP data, (2) multiple conducted experiments relevant to cleaning SAP data, and (3) quality of data required by LA systems, we were able to arrive with a list of requirements— desired main functions (in random order)—for the “ideal” DC system suitable for LA. We arrived at three groups of functions of the “ideal” DC system for cleaning original SAP IDS for highly effective SAP data processing and visualization in the LA system. Group 1—DC functions that can be completed automatically by existing DC systems. A list of these functions include but are not limited to (1) import/export data in different data formats; (2) sort/filter data; (3) merge data and/or merge columns; (4) standardization of columns’ headers, including capitalize/decapitalize columns’ names; (5) identify blank spaces in cells; if needed, removal of blank spaces in IDS or putting assigned values into those cells; (6) find specific data and (in needed) replace of specific data; (7) undo/redo functions; and (8) add/delete columns, and other functions.

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Group 2—DC functions that can be completed by users manually within existing DC systems. These functions include, for example, (1) detect outliers and/or inliers (in most cases); (2) remove duplicated data; (3) normalization and standardization of numeric data; (4) detect and fix missing data; (5) fuzzy matching; (6) check data formats (integer, real, string, char, etc.) and data types (first name, last name, passing grade, regular letter grade, detailed letter grade, etc.), and other functions. Group 3—DC functions that cannot be completed by existing DC systems to satisfy the requirements of LA systems. These functions include, for example, (1) column name standardization in IDS (e.g., existing DC cannot recognize that Test1, Test01, and Test 1 refer to the same learning assignment); (2) aggregation of columns (e.g., aggregate all columns in the IDS that reflect student analytical, technical, or communications skills); (3) filling the cells of newly introduced columns with assigned values (e.g., the “median” numeric values); this is a different function rather than just filling the empty cells with zero numeric values; (4) smart (i.e., taking into consideration specifics of SAP data) detection of outliers/inliers; (5) automatic normalization of master SAP data (based on an “old,” cleaned IDS) after adding a new IDS with an additional column with data about recently introduced course learning assignment (e.g., one more test or quiz in a course); (6) automatic update of master data set (MDS) of “cleaned” SAP data; and (7) handling and keeping various SAP data forms—absolute, relative letter grades, etc.

16.3.4 Our Recommendations About Effective DC System for SAP Data Cleaning in LA Our research team systematically analyzed about 20 existing general-purpose DC systems (see Sect. 3.1 above) against the designated DC functions from Group 3 (most important evaluation criteria for us) and Group 2 (important criteria). Unfortunately, we cannot recommend any existing DC system for effective cleaning of SAP data— IDS that have the above-mentioned specifics. This is the reason that our research and development team designed, developed, and tested a highly effective InterLabs Smart DC system; it is aimed at providing various engines of the LA system with clean SAP data. Based on numerous performed tests and experiments, we recommend the following DC systems for SAP cleaning in LA: • rank #1 (the best): the developed specialized InterLabs Smart DC system [11]; • rank #2: Microsoft Excel system (this is recommended if the total number of records in original SAP IDS is less than 300–500 or, in other words, the number of students in a course is less than 300–500); • rank #3: open-source OpenRefine DC system.

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16.4 Research Outcomes: Data Visualization Systems 16.4.1 DV Systems Analyzed Our research team systematically analyzed various open-source and commercial DV systems, including (1) ZOHO (we will use ZN abbreviation for this system below), (2) QlikView desktop version (QL), (3) Infogram-Basic (IB), (4) Microsoft Power BI desktop version (PBI), (5) Fine report—student (FR), (6) Charito (CH), (7) Tableau (TAB), (8) Sisense (SI), (9) DOMO (DOM), and (10) Google Analytics (GA). A summary of obtained analysis outcomes for just one analyzed DV system—Microsoft Power BI—is available in Table 16.3. We have similar summary tables for each DV system analyzed; those tables are available upon written request. The obtained analysis outcomes enabled us to arrive with a comparative analysis table in terms of general-purpose DV systems’ main functions which are presented in Table 16.4 (where “+” means availability of designated function in DV system, and empty cell means an absence of the corresponding function).

16.4.2 Our Recommendations About Systems for SAP Data Visualization in the LA System Although many DV systems provide users with a good set of important functions, we recommend the following DV systems for SAP visualization in LA: • rank #1 (the best): Microsoft Power BI DV system; • rank #2: Qlik DV system; and • rank #3: Tableau DV system. These recommendations are based on numerous tests and experiments conducted by our research team. The example of the Microsoft Power BI DV system integrated with the InterLabs LA system (developed by our research team) [12] is presented in Fig. 16.2.

16.5 Conclusions. Next Steps Conclusions. The performed research, analysis, and testing of various DC and DV systems, as well as obtained findings and outcomes, enabled us to make the following conclusions: 1. Data cleaning processes play a crucial role in LA systems. It helps to (a) avoid costly errors, (b) improve the decision-making process, (c) increase student

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Table 16.3 A summary of obtained analysis outcomes for Microsoft Power BI DV system Features/functions

Details

DV system name

Microsoft Power BI

Web site to download

• https://powerbi.microsoft.com/en-us/ downloads/

Web site(s) to learn main functions

• https://docs.microsoft.com/en-us/power-bi/ guided-learning/

Free or commercial

Desktop version is free to any single user

Local or Web-based

It works on a local computer

Required technical platform and/or operating system

• CPU: 1 gigahertz (GHz) or faster ×86- or × 64-bit processor • Windows 7/Windows Server 2008 R2, or later • .NET 4.5 • Memory (RAM): 1.5 GB or more is recommended • Display: At least 1440 × 900 or 1600 × 900 (16:9) recommended. Lower resolutions such as 1024 × 768 or 1280 × 800 are not recommended, as certain controls (such as closing the startup screen) display beyond those resolutions • Internet Explorer 9 or later

Can make educated predictions

Built-in machine learning features can analyze data and help users spot valuable trends and make educated predictions

Data visualization using templates

Information can be visualized using powerful templates to allow businesses to better make sense of their data

Cloud-based option available

Power BI is cloud-based, so users get cutting edge intelligence capabilities and powerful algorithms that are updated regularly

Personal dashboards

Powerful personalization capabilities allow users to create dashboards so they can access the data they need quickly

Alerts and KPI

Alerts can be set up on KPIs to keep users up-to-date on important metrics and measurements.

Intuitive GUI

Power BI has an intuitive interface that makes it far more user-friendly and easy to navigate than complex spreadsheets (continued)

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Table 16.3 (continued) Features/functions

Details

Connectivity with multiple other systems

The platform integrates with other popular business management tools like SharePoint, Office 365, and Dynamics 365, as well as other non-Microsoft products like Spark, Hadoop, Google Analytics, SAP, Salesforce, and MailChimp

Data security

With data security being a massive talking point for modern businesses, Power BI ensures that the data is safe, offering granular controls on accessibility both internally and externally

Print dashboards

Power BI provides a unique feature for printing dashboards, which can be handy inboard meetings and discussions

Table 16.4 The main functions of analyzed DV systems (as given in the description): a comparison table DV systems analyzed and tested DV system main functions

ZH

QL

IB

PBI

TAB

SI

DOM

GA

Automated visualizations

+

+

+

+

+

+

Visualization option/user palette

+

+

+

+

+

+

+

+

Guidance for visualizations

+

+

+

+

+

+

+

Customizable dashboards

+

+

+

+

+

+

+

+

Sharing/publish tool

+

+

+

+

+

+

+

+

Community marketplace/gallery

+

+

+

+

+

+

+

Predictive analytics

+

+

+

+

+

+

+

+

Ad hoc analysis

+

+

+

+

+

+

+

+

Application security

+

+

+

+

+

+

+

Database integration

+

+

+

+

+

+

+

+

Drag and drop interface

+

+

+

+

+

+

+

+

Data import/export

+

+

+

+

+

+

+

+

Trend analysis

+

+

+

+

+

+

+

+

Interactive visualizations

+

+

+

+

+

+

+

+

Custom metrics

+

+

+

+

+

+

+

+

Intelligence and anomaly detection

+

+

+

+

+

+

+

+

awareness, (d) increase the efficiency and quality of the entire LA system, and (e) overall student academic success. 2. Multiple general-purpose DC systems are available for users these days (Sect. 3.1); our research team systematically analyzed features and tested the functionality of about 12 open-source and 13 commercial DC systems in terms

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Fig. 16.2 InterLabs Learning Analytics system integrated with the Microsoft Power BI visualization system: Faculty Dashboard mode [12] (A note: The outcome data are shown for Descriptive Analytics and Predictive Analytics outcomes, including (a) scores of “average” past student after midterm and (b) predicted final scores of a selected current student in a course)

of their effectiveness for SAP data cleaning in LA (Table 16.2). We recommend the following DC tools to be used in integrated LA systems: (a) rank #1 (the best): the developed in-house specialized InterLabs Smart DC system [11], (b) rank #2: Microsoft Excel system, and (c) rank #3: open-source OpenRefine system (Sect. 3.4). 3. Multiple open-source and commercial DV systems are available for users these days (Sect. 4.1); our research team methodically analyzed and tested about 10 open-source and commercial DV systems in terms of their effectiveness for SAP data visualization in LA (Table 16.4). We recommend the following DV systems to be used in integrated learning analytics: (a) rank #1 (the best): Microsoft Power BI, (b) rank #2: Qlik, and (c) rank #3: Tableau DV system. 4. The developed DC system [11] and Microsoft Power BI DV system have been integrated with the InterLabs Smart Learning Analytics (SLA) system [12]; the outcomes of integration are presented in Fig. 16.2. The pilot testing of the InterLabs SLA system demonstrated its high efficiency in terms of SAP data cleaning, processing, and visualization. Next Steps. Based on the obtained research/design/development findings and outcomes, the next step in this research project is to involve various types of stakeholders into the testing of having DC and DV systems integrated into academic (institutional), analytics systems.

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References 1. 2020 EDUCAUSE Horizon Report: Teaching and learning edition. https://library.educause. edu/resources/2020/3/2020-educause-horizon-report-teaching-and-learning-edition 2. Learning Analytics For Tracking Student Progress. Hanover Research, 2016, https://www. imperial.edu/research-planning/7932-learning-analytics-for-tracking-studentprogress/file 3. Uskov, V.L., et al.: Smart learning analytics: conceptual modelling and agile engineering. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning 2018, pp. 3–16. Springer (2018). ISBN: 978-3-319-92362-8 4. Broeck, J.V., et al.: Data cleaning: detecting, diagnosing, and editing data abnormalities (2005). https://journals.plos.org/ 5. Osborne, J.W.: Best Practices in Data Cleaning. Sage (2013). ISBN: 978-1500594343 6. Whitaker, L.R.: A Data Scientist’s Guide to Acquiring, Cleaning, and Managing Data in R. Wiley (2017). ISBN: 9781119080022 7. Ganti, V., et al.: Data Cleaning: A Practical Perspective. Morgan & Claypool (2013). ISBN: 9781608456789 8. Essa, A., Ayad, H.: Improving student success using predictive models and data visualisations. ALT J (2012). https://journal.alt.ac.uk/index.php/rlt/article/view/1359 9. Ryan, L., Snow, N.: Supporting student success with intuitive, approachable data visualization (2016). http://repository.cityu.edu/handle/20.500.11803/614 10. Raji, M., et al.: Visual progression analysis of student records data. In: 2017 IEEE Visualization in Data Science (VDS), Phoenix, AZ, 2017, pp. 31–38 (2017) 11. Uskov, V.L., Bakken, J.P., Galloway, B., Gayke, K., Ganapathi, K.S., Jose, D., Fatima, J.: Student academic performance data cleaning system for smart learning analytics. 2020 IEEE Frontiers in Education FIE-2020 international conference, Uppsala, Sweden, October 2020 (under review) (2020) 12. Uskov, V.L., Bakken, J., Shah, A., Hancher, N., McPartlin, C., Gayke, K.: Innovative InterLabs system for smart learning analytics in engineering education. In: Proceedings of the 2019 IEEE Global Engineering Education Conference (EDUCON). IEEE, Dubai, UAE, pp. 1363–1369 (2019). https://ieeexplore.ieee.org/Xplore/home.jsp, https://doi.org/10.1109/ educon.2019.8725145

Chapter 17

Computational Linguistics and Mobile Devices for ESL: The Utilization of Linguistics in Intelligent Learning Marcel Pikhart, Blanka Klimova, and Ales Berger

Abstract The presented paper focuses on the potential use of computational linguistics and machine learning in blended learning and m-learning. It suggests that the utilization of these new phenomena, also coined by the term artificial intelligence, could bring many advantages which are still not present in the traditional use of e-learning, blended learning, and m-learning platforms. The modern approach to e-learning should utilize these new phenomena so that it creates a useful platform for educational process. The paper stresses the importance of such an approach as it brings new challenges for the creators and the users which, however, are extremely useful for the modern utilization of smart devices in the e-learning process.

17.1 Literature Review The mobile phones, smartphones, smart devices, and the use of various apps have been ubiquitous in the past few years, and the width and breadth of the user’s utilization have seen unprecedented rise. More than 90% of the people in the so-called developed countries use a mobile phone or a smartphone, while the use of the traditional desktop has seen a significant drop, as low as 40% as the research shows. Moreover, the use of smartphones is increasing dramatically as well, and more than 77% of Americans use one, which—compared to the year 2012—has doubled [25]. The Czech Republic sees very similar development as per the number of users of smart devices which currently accounts for 63% [33]. M. Pikhart (B) · B. Klimova · A. Berger Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic e-mail: [email protected] B. Klimova e-mail: [email protected] A. Berger e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_17

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For educational purposes, it is crucial to bear this trend in mind because it provides educators with an excellent opportunity to enhance the learning process through these mobile devices. Younger adults (18–29 years) use their mobile phones every day, and 92% of them use one of these devices. The time which is spent on these devices has naturally increased as well and 52% of the time is spent on these devices. The younger age group, i.e., 18–24 years, spend the majority of their free time on mobile devices using various apps. Further research data show that the time spent on these devices is spread into these activities: 43% gaming, 26% social media, 10% entertainment, 10% utilities, 2% on productivity, 1% health and fitness and 5% remaining other activities. To sum it up, the younger generation spends significant time using their smart and mobile devices, and this will prove useful for further educational process, i.e., to utilize the opportunity the younger people have while using their mobile devices. The presence of Internet connectivity in developed countries is crucial for the success of the learning process, and nowadays, this is not an issue, as the connectivity is almost everywhere [24].

17.2 English as a Second Language (ESL) and Mobile Devices ESL is a vast topic both for linguists and educators because English has become a lingua franca of this business, cultural, political, etc. world, and mobile devices can easily use the opportunity of this ubiquity when developing and using various kinds of e-learning and blended learning. The fact that mobile technologies are portable leads to various benefits which are not present in traditional learning approaches. Furthermore, they can be personalized, i.e., the learning environment respects the needs of the users and their learning styles or even progress. Availability is another important aspect, as the users can access them almost anywhere, anytime and can communicate with their peers and teachers, thus creating ideal environment for the learning process. Their adaptability will help enable them to be tailored according to the needs of the users which is very difficult in traditional approaches (for further data see Mehdipour and Zerehkafi [21]). Smart devices are, therefore, very useful in the language acquisition process and bring new opportunities we have to explore. Computational, linguistics and machine learning have sped up the process of transferring theoretical linguistics into practical utilization of the theory. New terms have been coined recently such as mobile-assisted language learning (MALL) [7] and also computer-assisted language learning (CALL) [42]. For the difference between CALL and MALL, see Kukulska-Hulme and Shield [15]. Oz [24] summarizes that the mobile learning (m-learning) process is a useful tool and has these parameters: portability, possibility to study anytime and anywhere, and collaborative learning, which means the possibility to communicate mutually

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throughout the network of users, both the students and the instructors. M-learning also enhances context-learning and can be used by the students who have special needs in the studying process such as dyslexia [38]. The research proves significant improvement in the students suffering from dyslexia using the m-learning process and procedures (cf. Alghabban et al. [1]).

17.3 Mobile Apps and Their Benefits in ESL Acquisition Currently, we have a variety of applications which can be used for learning foreign languages or particularly ESL [36]. Regarding their efficiency on the learning process, we still lack enough data as they have been used for a relatively small time and the data is not available [23, 32, 35]. However, there are certain studies which indicate their benefits and importance in the modern learning process [6, 14, 12]. From an applied linguistics viewpoint, it is possible to conclude that mobile apps have a very important impact on all core language competencies (e.g., [3, 39, 40]), and that their performance in vocabulary development is better than traditional approaches [14]. The vocabulary acquisition has always been the core of any language education [18]. Traditional research shows that the retention level of a single exposure to new words ranges is 5–14%, and the majority of new words are forgotten as early as during the first hours after the learning process. This fact naturally leads us to the retention process which is crucial for the successful learning process. Due to the described features regarding the presence and use of mobile devices, they easily become the tool which can be used so that the new words are transferred into a long-term memory [9] and can be repeated more often so that it will facilitate their retention [31]. Mobile apps can aptly shorten the time for revisions, and the learning performance has improved in almost all users [39, 40]. The apps are so flexible that they can accommodate to the needs of the students/users. Further utilization of artificial intelligence will speed up and improve the process even more, and any m-learning will be even more successful [36]. Some research, however, shows rather different results regarding student’s satisfaction with m-learning and it must be taken for consideration [17]. According to this research, the majority of the respondents showed their preference of traditional printed textbooks over m-learning. Klimova and Poulova [13] confirm the same findings and conclude that it was caused by the need of the stable Internet connection which is not always available everywhere. This issue changes rapidly with the introduction of fast connectivity in our countries and will no longer be an issue. In ESL, we can observe a modern trend of including various kinds of m-learning, e-learning and blended learning tools into traditional textbooks published by prestigious publishing houses as recommended by numerous research [23, 35, 41, 39, 40].

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17.4 Research 17.4.1 Research Question How much is artificial intelligence used in mobile apps for learning a foreign language?

17.4.2 Research Design One of the most widely used apps (with more than 10 million of users) has been tested on the utilization of AI and deep learning. The app has one of the highest rating among other apps in the same category. It is easily accessible both for Android and iOS platforms. It is free in its basic version; however, for a monthly or yearly fee, it is possible to have access to all functions. The full version of the app was tested.

17.4.3 Research Findings The aim of the app is to improve language skills when learning a foreign language. It offers not only English but also 23 other languages. The methodology of the language learning is based on memorizing while the texts and vocabulary are prepared with the help of native speakers. Despite the fact that this app is considered to be the benchmark for other apps used for learning a new language, the research shows that there is no use of artificial intelligence, deep learning or machine learning. All the functions of both the basic or premium version are based on predefined algorithms, which are quite simple and do not provide any enhanced learning experience compared to traditional textbooks with audio recording. The artificial intelligence functions would enable the users to connect their newly acquired vocabulary with current texts on the Internet containing the newly acquired words. The students could be tested in more dynamic ways on their acquisition, etc. To sum it up, the analyzed app, despite its popularity, does not use any current tools which are available in the realm of artificial intelligence, deep learning and machine learning.

17.4.4 Research Limitations Only one app was tested and analyzed. However, we can expect, based on this preliminary research, there will be a very similar situation in other apps used for learning

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a foreign language. Further research is necessary into the use of artificial intelligence in these learning apps so that we can evaluate the situation and new ideas on how to implement artificial intelligence into these apps can be created.

17.5 Discussion Evaluation and assessment are probably the most important improvement in the learning process while using m-learning. When the m-learning platform environment is purposefully designed, it provides the users with various evaluation tools. These tools will even be improved by the further use (in the near future) of machine learning and computational linguistics so that the students will be tested based on their previous performance. This tailor-made approach created the most important factor for m-learning utilization. The potential of computational linguistics used in any kind of e-learning process is vast [32–29]. All these factors are very important and the designers of these m-learning courses should not forget that all the tools should be carefully planned and prepared with the knowledge of psychology, typography, linguistics and technological aspects of communication [10, 30]. The big data analysis is in compliance with Gideon [11] who adds that the m-learning process can be only useful and beneficial if it is adequately controlled. Computational linguistics can provide us with big data which can be implemented in the learning process and mostly in the evaluation of the learning process, and the design of test and testing environment which will be thus personalized according the needs of the particular user [2, 5, 8]. By using m-learning, the users will be motivated because they will be able to see their progress more clearly and their performance will be improved [4] stimulating long-term retention and improving testing methods [16]. From a psychological point of view, m-learning has a significant impact on students’ achievement results as Tingir et al. shows [37]. Crucial findings were published by Lopuch [19] who concludes that the students using m-learning apps combined with traditional approaches such as classroom education reached their learning expectations and learning achievements at 165%. Big data, machine learning and computational linguistics must, therefore, be used in creating these m-learning courses so that they will utilize all their potential and will create beneficial environments for the users, thus, bringing something more than traditional approaches. Bidaki et al. [4] bring the reasons which enhance students’ achievement results while using m-learning, and they can be useful for all creators and designers of mobile apps. They are as follows: delivery contents in small pieces, rehearsing and repeating with time intervals, timely feedback, employing multiple inclusive senses, availability of this device without restrictions of time and place, multimedia capabilities of new mobile cell phones, employing multiple inclusive sense, providing easier communication between the learner and the learning facilitator in comparison with traditional environment, better use of time, connecting formal

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or informal learning environment, flexibility of this approach compared to traditional learning, more comfortable and finally stress-free relationship with the teacher.

17.6 Conclusions There is also counter research bringing the opposite results, i.e., that m-learning has no or negative impact on the learning process and they have to be taken in for account very seriously [20, 22, 34]. However, there is more research which proves the use of the m-learning as very helpful and beneficial for the learning process. This paper claims that machine learning and artificial intelligence applied in the m-learning platforms will dramatically improve the learning process for the user of such an app. If following traditional pedagogical approaches, we can conclude that the learning process using the m-learning will be more motivating for the students and the users will feel engaged. Utilization of machine learning, artificial intelligence and computational linguistics, moreover, will bring possibilities we have never had in the learning process, such as big data utilization and comparing vast amount of information for the benefits if the users. E-learning, blended learning, m-learning, etc., are current approaches to educational process, and it is not only ESL which can use these beneficial tools but they can be implemented throughout the learning process not only in our universities but also in secondary education level so that the students are motivated and their performance is enhanced throughout the curricula. Acknowledgements The paper was created with the support of SPEV 2020 at the Faculty of Informatics and Management of the University of Hradec Kralove, Czech Republic. The authors would like to thank Ales Berger for his cooperation when preparing this manuscript.

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8. Clark, A., et al.: The Handbook of Computational Linguistics and Natural Language Processing. Blackwell, Chichester (2010) 9. Dupalova, P.: Hodnocení aplikací na mobilní zaˇrízení zamˇerˇující se na samostatné studium slovní zásoby v anglickém jazyce (2014). Retrieved 16 Feb 2019, from https://otik.uk.zcu.cz/ bitstream/11025/24285/1/Graduate%20Thesis_Petra%20Dupalova.pdf 10. Elfeky, A.I.M., Masadeh, T.S.Y.: The effect of mobile learning on students’ achievement and conversational skills. Int. J. High. Educ. 5(3), 20–31 (2016) 11. Gideon, A.: Influence of time-on-phone on undergraduates academic achievement in Nigerian universities. Am. J. Educ. Res. 5(5), 564–567 (2017) 12. Klimova, B., Poulova, P.: Mobile learning in higher education. Adv. Sci. Lett. 22(5/6), 1111– 1114 (2016) 13. Klimova, B., Poulova, P.: Mobile learning and its potential for engineering education. In: Proceedings of 2015 I.E. Global Engineering Education Conference (EDUCON 2015), pp. 47– 51. Tallinn University of Technology, Estonia, Tallinn (2015) 14. Klímová, B., Berger, A.: Evaluation of the use of mobile application in learning English vocabulary and phrases—a case study. In: Hao, T., Chen, W., Xie, H., Nadee, W., Lau, R. (eds.) Emerging Technologies for Education. SETE 2018. LNCS, vol. 11284, pp. 3–11 (2018) 15. Kukulska-Hulme, A., Shield, L.: An overview of mobile assisted language learning: from content delivery to supported collaboration and interaction. ReCALL 20(3), 271–289 (2008) 16. Lameris, A.L., Hoenderop, J.G.J., Bindels, R.J.M., Eijsvogels, T.M.H.: The impact of formative testing on study behaviour and study performance of (bio)medical students: a smartphone application intervention study. BMC Med. Educ. 15, 72 (2015) 17. Lee, P.: Are mobile device more useful than conventional means as tools for learning vocabulary? In: Proceedings of the 8th International Symposium on Embedded Multicore/Mangcore SoCs, pp. 109–115. IEEE (2014) 18. Lewis, B.: The lexical approach. LTP, London (1993) 19. Lopuch, M.: The effects of educational apps on student achievement and engagement (2013). Retrieved 2 March 2018, from http://www.doe.virginia.gov/support/technology/technology_ initiatives/e-learning_backpack/institute/2013/Educational_Apps_White_Paper_eSpark_v2. pdf 20. Males, S., Bate, F., Macnish, J.: The impact of mobile learning on student performance as gauged by standardised test (NAPLAN) scores. Issues Educ. Res. 27(1), 99–114 (2017) 21. Mehdipour, Y., Zerehkafi, H.: Mobile learning for education: Benefits and challenges. Int. J. Comput. Eng. Res. 3(6), 93–101 (2013) 22. Miller, H.B., Cuevas, J.A.: Mobile learning and its effects on academic achievement and student motivation in middle grades students. Int. J. Sch. Technol. Enhanced Learn. 1(2), 91–110 (2017) 23. Muhammed, A.A.: The impact of mobiles on language learning on the part of English foreign language (EFL) university students. Procedia—Soc. Behav. Sci. 136, 104–108 (2014) 24. Oz, H.: Prospective English teachers’ ownership and usage of mobile device as m-learning tools. Procedia-Soc. Behav. Sci. 141, 1031–1041 (2013) 25. Pew Research Center: Mobile fact sheet (2018). Retrieved 16 Feb 2019, from http://www. pewinternet.org/fact-sheet/mobile/ 26. Pikhart, M.: Sustainable communication strategies for business communication. In: Soliman, K.S. (ed.) Proceedings of the 32nd International Business Information Management Association Conference (IBIMA), pp. 528–553, 15–16 November 2018, Seville, Spain. International Business Information Management Association (2018a). ISBN: 978-0-9998551-1-9 27. Pikhart, M.: Intercultural business communication courses in European universities as a way to enhance competitiveness. In: Soliman, K.S. (ed.) Proceedings of the 32nd International Business Information Management Association Conference (IBIMA), pp. 524–527, 15–16 November 2018, Seville, Spain. International Business Information Management Association (2018b). ISBN: 978-0-9998551-1-9 28. Pikhart, M.: Multilingual and intercultural competence for ICT: accessing and assessing electronic information in the global world (MISSI 2018). In: Advances in Intelligent Systems and Computing, vol. 833, 2019, pp. 273–278. 11th International Conference on Multimedia and

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Chapter 18

Personal Generative Libraries for Smart Computer Science Education Vytautas Štuikys, Renata Burbait˙e, Ramunas ¯ Kubiliunas, ¯ and K˛estutis Valinˇcius

Abstract The difficulties of retrieving the educational content from conventional digital libraries for personalised learning (PL) are well known. In this paper, to overcome those issues and enforce learning performance, we propose the concept personal generative library (PGL). We will discuss an experimental system that integrates conventional repositories, the teacher’s PGL, the students’ PGLs, their individual repositories along with the personalised learning processes using the developed framework. The teacher’s individual repository stores the personalised content for all students along with assessment tasks for each type of the content. The teacher’s and students’ PGLs have the identical structure. The student’s content is a direct product of PL obtained during the classroom activities by modifying the teacher’s content due to the needs of personalisation or is a by-product created or searched during outside learner’s activities. We have approved this approach in one high school. We will present experimental results of the PGLs usage and the quality evaluation. Our approach enables enforcing the PL significantly in terms of higher flexibility, efficient search and more efficient procedures to form the personalised learning paths for smart CS education.

V. Štuikys (B) · R. Burbait˙e · R. Kubili¯unas · K. Valinˇcius Informatics Faculty, Department of Software Engineering, Kaunas University of Technology, Student˛u 50, 51368 Kaunas, Lithuania e-mail: [email protected] R. Burbait˙e e-mail: [email protected] R. Kubili¯unas e-mail: [email protected] K. Valinˇcius e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_18

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18.1 Introduction In the era of the Internet and omnipresent computing, digital libraries (DLs) play a significant role in multiple fields. In education, DLs are either local or distributed subsystems to support the functionality of smart educational environments [1]. Trends in the development of DLs are towards transforming their static complex structures to systems with a dynamic federation of functional units [2]. The role of DLs is to provide the teaching content along with the management procedures. Nowadays, education encounters with multiple challenges in the content search and management. Firstly, the amount and types of the teaching content tend to increase significantly leading to the advent of novel approaches in education, such as big data and data-centric learning [3] and educational ecosystems [4]. That extends the role of conventional DLs largely and, on the other hand, exacerbates their own problems and issues such as incompleteness of metadata standards [5] or quality of the content per se [6]. Secondly, there is a great need for gaining the interdisciplinary knowledge as effectively and efficiently as possible, e.g. through adequate facilities for creating, retrieving and managing the relevant content [7]. Teaching and learning based on the STEM paradigm [8] are just well-established ways for introducing the interdisciplinary knowledge and content. To overcome the challenges, there are extensive research efforts including personalisation and PL that place learner’s needs at the centre of education as defined in [9–11]. In the paper [12], we have introduced the concept of the personalised content and a framework to support the PL. The aim of this paper is to extend PL by considering this problem at the sub-system, i.e. the library level. We introduce the concept personal generative library (PGL) here. By PGL, we mean either the teacher’s personal DL or student’s personal DL. The contribution of this paper is a distributed structure or architecture that integrates the personal generative library with the external and individual repositories where the content itself resides. In Sect. 18.2, we discuss the related work.

18.2 Related Work Basham et al. [13] indicate five research areas to focus on advancing personalised learning (PL): (i) How educators use data; (ii) how technology is designed to support pedagogical practice; (iii) how to educate personnel to work in personalised settings; (iv) how content is designed; (v) how the curriculum is designed. The volume and complexity of educational content grow extremely rapidly now. As many conventional digital libraries (DLs) aggregate contents from different providers, the challenges with metadata management, metadata integration and serious search problems may occur [14]. Challenges include irrelevant clustering of learning objects (LOs) regarding learner’s profile, knowledge level and learning style [15, 16], the quality

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of DLs [17] and LOs themselves [6, 18]. Automation [19] and personalisation of the content [12] can help handling with these issues. Deng and Ruan [20] introduce the concept of personal digital library for e-learning, consider its functionality and outline constructing and managing aspects. Brusilovsky et al. [14] discuss the problem of social navigation in the context of Ensemble, the computing portal in the US National Science Digital Library to provide access to learning materials and resources for education in the STEM disciplines at all ages and education levels. Brisebois et al. [21] propose a semantic software ecosystem for metadata enrichment to support multi-platform metadata-driven approaches for integrating distributed content management applications. Yoshinov et al. [22] aim at bridging the interoperability gap between DLs and e-learning applications in order to enable the construction of e-learning applications that easily exploit DL contents. Chen and Fox [23] apply machine learning methods to add thousands of new, relevant records to Ensemble, an educational DL for computing education. Park and Brenza [24] examine semi-automatic metadata generation tools, provide their features, functions and techniques, and indicate challenges and barriers in this field. The most semi-automatic generation tools only address part of the metadata generation, providing solutions to one or a few metadata elements but not the full-range elements. This indicates the need to integrate the various tools into a coherent set of working tools. Miller et al. [25] present a framework, the Intelligent Learning Object Guide (iLOG), to investigate the problem of automating metadata generation for SCORM-compliant LOs. In summary, the research in educational DLs varies from the content search improvement, search engine and interface enforcement to personal libraries, specialised portals for computing educators and recommendation systems. However, we still know little on how to combine PL and personal DL into a coherent system with extended capabilities for automated design and use.

18.3 The PGL Concept Educational DLs stand for tools to provide the content. The teacher or learner can find some part of the needed content of the common use within existing conventional repositories. For specific courses, however, there might be specific requirements for DLs functionality and the content itself. In STEM-driven CS personalised learning, we need to design and prepare anew in advance a large portion of the personalised content because: (1) It is difficult or even impossible to retrieve this content from the existing resources, especially in case of using specific courses. (2) The quality and the level of personalisation of the found content are not always enough. (3) We use the generative content to ensure the effectiveness of use through automation. In addition, in PL, we need to ensure the easiness and flexibility in creating and using the content management tools by the users themselves. Therefore, we introduce the concept personal generative library (PGL) and apply it to creating the teacher’s PGL and students’ PGLs.

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The content within the PGLs is highly personalised [12]. The library is generative because of its two distinguishing attributes: (a) the structure of PGL contains within constituents implemented as generators (e.g. MDG—metadata generator, QG—query generator, LOs LG—LOs list generator); (b) a large body of the content we represent as a generative learning object (GLO). Therefore, the content types for PGL include component-based (CB) LOs, GLOs and smart learning objects (SLOs) [12]. The specification of this content has two parts, a descriptive part and the content itself. We separate the descriptive part from the content explicitly. Those parts are stored in different places. The descriptive part resides within the PGL and forms the so-called database (DB) using the tools (MDG, QG and LOs LG). The content items are within the external repositories. Those are of two kinds, public and personally created to support personalised learning. The descriptive part contains metadata of a given content item and link to the repositories where the content itself resides. Typically, the content of CB LOs resides within public repositories, though the user may resend this content to his/her repository, making the needed corrections in the metadata of his/her PGL. Typically, the teacher’s PGL and his/her repository contain all types of LOs pre-designed or searched in advance to cover the topics of the curriculum of a given course. Therefore, when we use the term PGL, we mean both parts, i.e. descriptive part of PGL along with the adequate tools within it and the personal/individual repository of learning resources. Regarding the student’s PGL, there are two possibilities. The first possibility is to make clones of the teacher’s PGL and his/her personal repository if needed. The concept of the student’s PGL has a far more importance than the momentum learning process of the given course. First, a student may want during learning to create own content quite different from the one given by the teacher. Second, a student may accumulate the description of additional resources derived from external repositories. Third, as the learning needs of refining the previous knowledge, it is much easier to find the needed resources in own library. Finally, the student’s PGL may stand for the individual portfolio for long-life learning. The second possibility is that a student can create his/her PGL and his/her personal repository from scratch, of course, with the teacher’s help using the metadata model and adequate tools. Therefore, we have a system of PGLs. It opens a way for considering various scenarios for personalised learning.

18.4 Structure and Functionality of PGL In Fig. 18.1, we present the structural model of the PGL, containing two basic parts. The first part specifies the designer’s actions, while the second—the user’s actions. The CS teacher may act in two roles, i.e. designer and user. The designer’s responsibility is to create the content, assessment modules for each kind of content (CB LO, GLO and SLO) and constituents of the PGL. The constituents include MDG, QG, LOs, LG and database (DB) for storing the descriptive part of

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Content & PGL design phase Designer actions in creating the content & assessment

Designer

Assessment Model

Designer actions in PGL creating

General part

SLOs/ GLOs/ CB LOs

Special part

Metadata Model

Link to LOs in repositories

Metadata Generator 1

LOs

3

2

Query Generator

LOs List Generator

2'

User

3' 1'

1 4' SLOs/GLOs/ CB LOs Assessment tasks

LOs list

Personal Generative Library (PGL)

PGL filling in (

) & PGL using (

) phases

Link to LOs in repository

PGL usage

Fig. 18.1 Structural model of PGL along with external repositories

the content itself. The central role in digital library design plays the metadata model. In our case, we use the feature-based metadata model ([26], see Fig. 10.5, p. 247). Note that the designer needs to concretise this abstract model by specifying concrete values for each abstract terminal node. Finally, having a concrete feature model, the designer can develop the adequate meta-programs to implement adequate generators. We omit presenting details on the assessment model here because that can be found in [12]. The teacher’s responsibility is to fill in DB with descriptive data of the content items. We outline those actions by arrows 1-1-2-3 (see Fig. 18.1). The user, i.e. teacher or student, starts working with the PGL when the content items are already within the individual repository and the DB already contains the descriptive data of those items. We outline user’s actions by arrows 1 -2 -3 -4 to form a list of LOs. With those resources, it is possible to start personalised learning activities. We describe that later. Now we extend the learner’s possibilities to contribute to his/her personalised learning at the library level. We argue that a student can act not only as the user of the teacher’s PGL but also as a designer of own PGL. Structure and functionality of PGL constituents. The reader needs to return to Fig. 18.1 for seeing PGL constituents. We represent them by generators. There are three generators, i.e. metadata generator, query generator and LO list generator. We have implemented all generators as meta-programs. Typically, a heterogeneous

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meta-program requires parameter values supplied by the user through the metainterface. However, there might be cases of generating those values by other generators automatically. For example, the query generator generates parameter values for the meta-program that implements LO list generator (see Fig. 18.1).

18.5 Integration of PGLs into the Framework of PL In Fig. 18.2, we outline a process-based extended framework of personalised learning. We have developed this framework by integrating the teacher’s and students’ PGLs into the framework proposed in [12]. Therefore, the extended framework includes two kinds of processes, i.e. (a) gaining the needed resources from PGLs/repositories and then (b) using those resources for PL activities. In this paper, we focus on the resource gaining process because the PL activities were described intensively in [12]. The resource gaining starts with creating the personalised lesson plan to solve the given task. This lesson plan includes the list of LOs. Typically, for each lesson, the teacher creates this list automatically using two tools residing within PGL, i.e. query generator and LOs list generator. This list is a resource part of the teacher’s plan to provide personalised teaching and learning. Some students, typically beginners or intermediates, use the resource list residing within the teacher’s plan to start forming own personalised learning paths. However, those students who already achieved the status of the advanced learner during learning can either create own

Fig. 18.2 A content management scheme to support PL using PGLs (adapted from [12])

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personalised lesson plan with the list of LOs or, at least, modify the one given by the teacher. This plan, whoever developed, includes the list of LOs (in fact, links to the teacher’s PGL/repository). Advanced students may also use own PGLs, during the classroom time; typically, all students use the resources taken from the teacher’s PGL. The student’s PGL serves for storing the personalised content created or retrieved by the student himself/herself. Of course, a student may provide cloning of some content from the teacher’s repository and send this material to own library/repository. Therefore, the PGLs/repositories stand for providing learning resources of any given type (CB LO, GLO and SLO) to support personalised learning. Now, we describe the teacher’s and students’ actions in more detail. In Fig. 18.2, we present learning activities and processes by shadowed rectangles and doubleline arrows with the text inscription inside. The white rectangles (without shadow) represent the needed resources obtained from the PGLs/repositories. We specify the teacher’s and student’s actions through adequate paths indicated by the numbers as follows (note that numbers specify either actions or the outcome of the adequate action). In our scheme, we define activities by the numbers as follows: 1—task formulation; 2, 2 and 2 —the queries to PGLs adequately: teacher (T) to teacher’s PGL (TPGL), student (S) to TPGL, student to student’s PGL (SPGL); 3, 3 and 3 —the list of LOs derived from PGL adequately: by T from TPGL, by S from TPGL, by S from SPGL; 4 and 4 —lesson plan created by T and S adequately; 5 and 5 —the use of the task solution plan created by T and S adequately; and 6, 6 and 6 —the process of resource retrieving, using T scenario from TPGL, using S scenario from TPGL and using S scenario from SPGL adequately. The teacher’s activities include the sequence 1→2→3→4 (see Fig. 18.3). That is, the teacher applies this scheme in the formal learning during classroom lessons. The teacher develops the lesson plan/scenario for all students for solving a given task. Actions for that are the query to the teacher’s PGL (TPGL) to obtain the list of LOs for forming the scenario. The student’s activities are more diverse. A student has three possibilities: 1. To use the teacher’s lesson plan and TPGL entirely. The student’s path includes 1→5→4→6 in this case. Note that at the point 6 student has the links to all needed resources and he/she can extract the content (i.e. CB LO, GLO or SLO) from the adequate repositories according to the given scenario as outlined that in Fig. 18.1. 2. To create his/her own scenario using resources from TPGL or/and SPGL. There are three possibilities: (1) student uses TPGL resources only, and the activities include the sequence 1→2 →3 →4 →5 →4 →6 (see Fig. 18.2); (2) student uses SPGL resources only and the scenario includes the sequence of activities such as: 1→2 →3 →4 →5 →4 →6 (see Fig. 18.2); (3) student uses resources from TPGL and SPGL. In this case, the learning activities are arranged in this order: 1→2 & 2 →3 & 3 →4 →5 →4 →6 & 6 . 3. To use the teacher’s plan as a template to modify it by selecting other resources from either the TPGL or/and SPGL (the full paths are not marked in the Fig. 18.2).

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Fig. 18.3 Distribution of learning object items in students’ PGLs created by students themselves

The choice/creating/modifying of the learning scenario depend on the student’s previous knowledge, preferences, abilities, motivation, etc. We treat all these activities (task analysis, creating of the scenario with resources) as an initial phase of the personalisation. Therefore, this gives the possibility to form personalised learning paths dynamically. Next, when the scenario/resources are identified, the personalised learning activities start. They include the resource analysis, working with the LO from the list created at the initial phase. This list may contain entities of any type (CB LO, GLO, SLO). Then the task solving procedures defined by the scenario start. In addition, for the solitary GLO or the GLO within SLO, there is the generation process that follows the retrieval. During the generation process (it is not shown in Fig. 18.2), the user selects parameter values related to his/her profile to produce a personalised instance for further examination. The learning activities using obtained resources, including multiple knowledge assessments with multiple feedbacks, follow (see Fig. 18.2 and [12]). Note that during the learning activities the learner can retrieve/create a new content, e.g. through modifications and changes or even from scratch, and to store it into his/her PGL/repository for future learning. In this case, a student becomes a new content and knowledge creator.

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18.6 Case Study and Results This section includes (i) the characteristics of student PGLs and (ii) results of how students evaluate the personalised content taken from the teacher’s PGL. In Fig. 18.3, we outline the distribution of students’ PGL items created by students in the course of personalised learning. Data were retrieved from PGLs of the 63 students (26 girls and 37 boys of the 10th grade). Content items were created during one school year (30 academic hours in the classroom) for the course “Programming Basics”. Students stored the programming task solutions (computer programs and robot control programs) with comments in their PGLs. On average, during one academic hour a student was able to create the 3–6 library items. Students used items from their PGLs as previous knowledge in studying new topics and as a support material for assessment tasks or project-based activities. The distribution trend is practically the same for girls and boys. From these data, we can know about the learning pace. We do not examine the similarity of items from different libraries. We suppose knowing that it would be possible to extract more information on how learners learn. In Tables 18.1 and 18.2, we provide a survey for evaluating the personalised content by students. The respondents were 37 students (13 girls and 24 boys), most beginners for “Programming Basics” with the dominating knowledge marked by nine in the ten-point system. The survey consists of ten questions related to the use of LOs and LOs sequences taken from the teachers PGL. Table 18.1 A survey of the quality of teacher’s PGL (answers in percentage) Question

All

Male

Female

• Beginner A1

62

54

62

• Beginner A2

30

38

30

• Intermediate B1

5

4

5

• Intermediate B2

3

4

3

1. Your level of experience?

2. Your knowledge assessment value in the module “Programming Basics”? • 7

16

13

23

• 8

22

21

23

• 9

57

75

23

• 10

41

46

31

3. To which extent did the topic fit your needs and constraints? • Completely fit

22

29

8

• Fit

76

67

92

3

4

0

• No opinion

30 32

The duration of getting the topic is appropriate

The content is adapted to the student’s profile

Note (* and **) those were not selected

32

The size (number of LOs) of the topic is appropriate 38

38

38

Female

23

15

23

23

65

59

68

68

All

38

Male

All 32

Agree

Strongly agree

The sequence of LOs is consistent with the objectives of the topic

Statement Male

58

54

58

58

Female

77

69

77

77

3

11

3

3

All

4

8

4

4

Male

0

15

0

0

Female

Neither agree nor disagree

Table 18.2 Content quality-related statements (possible answers in percentage according to Likert scale: strongly agree, agree, neither agree nor disagree, disagree*, strongly disagree**)

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Would you recommend this approach to other students? Ninety-two percentage of boys and 54% of the girls recommend this approach to the others; however, 8% of boys and 46% of the girls answered: “I don’t know”. Would you suggest some changes in the topic structure? Respondents of the intermediate level suggested extending the resource search possibilities by adding keyword-based search. Those results were obtained after the only one-year learning practice using this approach. Therefore, it is difficult to reason about the impact of the approach on learning outcomes. However, the current teacher’s observation is that students use the learning time more efficiently and effectively.

18.7 Summary and Conclusion With the ever-growing types and amount of the educational content, its retrieval from conventional digital libraries to ensure the specific learner’s needs, e.g. for personalised learning or course specificity, encounters with many issues such as time, quality or even no search result at all. In this paper, we have introduced the concept personal generative library (PGL) describing its automated design and automated content management for personalised learning. We have built an experimental system that integrates conventional repositories, the teacher’s PGL, the students’ PGLs, their individual repositories along with the personalised learning processes using the previously developed framework. The main contribution of this research is the distributed architecture of the proposed system and generative capabilities of its constituents that ensure a great deal of flexibility and effectivity to manage the delivery of the personalised content as well as its renewal and extension for the needs of personalised learning. We have presented a survey provided by students to evaluate the personalised content of the teacher’s PGL/repository. This survey constructed on the well-known methodology gave a good evaluation. We have also presented some quantitative characteristics of the students’ PGLs/repositories. We have obtained that a student is able to create 3–5 entities for his/her repository during classroom activities only. We have tested this approach for STEM-driven CS education in one high school. This approach, however, in terms of the concept itself and PGL tools proposed, is independent neither of the teaching course, nor of the teaching environment. The possibility to combine the personalised content with management procedures of this content using the developed tools enables enforcing and enhancing the personalised learning significantly in terms of higher flexibility, more efficient search and more efficient procedures to form personalised learning paths. One can treat the student’s PGL/repository as his/her individual portfolio, as the evidence of the progress, achievements and competences.

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References 1. Zhu, Z.T., Yu, M.H., Riezebos, P.: A research framework of smart education. Smart Learn. Environ. 3(1), 1–17 (2014) 2. Paneva-Marinova, D., Pavlov, R.: Improving learner experience within educational nooks in digital libraries. In: Learner Experience and Usability in Online Education, pp. 174–193 (2018) 3. Halibas, A.S., Sathyaseelan, B., Shahzad, M.: Learning analytics: developing a data-centric teaching-research skill. In: Smart Technologies and Innovation for a Sustainable Future, pp. 213–219 (2019) 4. Fournier, H., Kop, R., Molyneaux, H.: New personal learning ecosystems: a decade of research in review. In: Emerging Technologies in Virtual Learning Environments, pp. 1–19 (2019) 5. Hendrix, M., Protopsaltis, A., Dunwell, I., de Freitas, S., Arnab, S., Petridis, P., Rolland, C., Lanas, J.L.: Defining a metadata schema for serious games as learning objects. In: 4th International Conference on Mobile, Hybrid, and On-Line Learning, pp. 14–19 (2012) 6. Cechinel, C., Ochoa, X.: A brief overview of quality inside learning object repositories. In: Proceedings of the XV International Conference on Human Computer Interaction, p. 83 (2014) 7. Self, J.A., Evans, M., Jun, T., Southee, D.: Interdisciplinary: challenges and opportunities for design education. Int. J. Technol. Des. Educ., 1–34 (2018) 8. McDonald, K.S., Waite, A.M.: Future directions: Challenges and solutions facing career readiness and development in STEM fields. Adv. Dev. Hum. Resour. 21(1), 133–138 (2019) 9. Groff, J.S.: Personalised Learning: The State of the Field & Future Directions, Center for Curriculum Redesign (2017) 10. Dockterman, D.: Insights from 200+ years of personalised learning. NPJ Sci. Learn. 3(1), 15 (2018) 11. Alur, R., Baraniuk, R., Bodik, R., Drobnis, A., Gulwani, S., Hartmann, B., Kafai, Y., Karpicke, J., Libeskind-Hadas, R., Richardson, D., Solar-Lezama, A., Thille, C., Vardi, M.: Computeraided personalized education (2016). https://www.cis.upenn.edu/~alur/cape16.pdf 12. Štuikys, V., Burbait˙e, R., Dr˛asut˙e, V., Ziberkas, G., Dr˛asutis, S.: A framework for introducing personalisation into STEM-driven computer science education. Int. J. Eng. Educ. 35(4), 1–18 (2019) 13. Basham, J.D., Hall, T.E., Carter Jr., R.A., Stahl, W.M.: An operationalized understanding of personalized learning. J. Special Educ. Technol. 31(3), 126–136 (2016) 14. Brusilovsky, P., Cassel, L.N., Delcambre, L.M., Fox, E.A., Furuta, R., Garcia, D.D., Shipman, F.M., Yudelson, M.: Social navigation for educational digital libraries. Procedia Comput. Sci. 1(2), 2889–2897 (2010) 15. Domazet D., Veljkovi´c D., Nikoli´c B., Jovev, L.: Clustering of learning objects for different knowledge levels as an approach to adaptive e-learning based on SCORM AND DITA. In: The Third International Conference on E-learning, pp. 27–28 (2012) 16. Sabitha, A.S., Mehrotra, D.: User centric retrieval of learning objects in LMS. In: Third International Conference on Computer and Communication Technology, pp. 14–19 (2012) 17. Chen, Y.: A High-quality digital library supporting computing education: the ensemble approach. Doctoral dissertation, Virginia Tech (2017) 18. Ochoa, X.: Learnometrics: Metrics for learning objects. In: Proceedings of the 1st International Conference on Learning Analytics and Knowledge, pp. 1–8 (2011) 19. Fox, E.A.: Introduction to digital libraries. In: Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, pp. 283–284 (2016) 20. Deng, X., Ruan, J.: The Personal Digital Library (PDL)-based e-learning: using the PDL as an e-learning support tool. Integr. Innov. Orient E-Soc. 2, 549–555 (2007) 21. Brisebois, R., Abran, A., Nadembega, A.: A semantic metadata enrichment software ecosystem (SMESE) based on a multi-platform metadata model for digital libraries. J. Softw. Eng. Appl. 10(04), 370–405 (2017) 22. Yoshinov, R., Arapi, P., Christodoulakis, Kotseva, M.: Supporting personalized learning experiences on top of multimedia digital libraries. Int. J. Educ. Inf. Technol. 10, 152–158, (2016)

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23. Chen, Y., Fox, E.A.: Extending ensemble: an education digital library for computer science education. J. Comput. Sci. Coll. 31(2), 201–207 (2015) 24. Park, J.R., Brenza, A.: Evaluation of semi-automatic metadata generation tools: a survey of the current state of the art. Inf. Technol. Lib. 34(3), 22–42 (2015) 25. Miller, L.D., Soh, L.K., Samal, A., Nugent, G.: iLOG: a framework for automatic annotation of learning objects with empirical usage metadata. Int. J. Artif. Intell. Educ. 21(3), 215–236 (2012) 26. Štuikys, V., Burbait˙e, R.: Smart STEM-Driven Computer Science Education: Theory, Methodology and Robot-based Practices. Springer (2018)

Chapter 19

The Virtual Machine Learning Laboratory with Visualization of Algorithms Execution Process Vadim D. Kholoshnia and Elena A. Boldyreva

Abstract This paper describes the development of a virtual laboratory for teaching various machine learning algorithms with a visualization of the execution process. The novelty of the results lies in the fact that, in contrast to existing virtual machine learning laboratories, the presented one provides the visualization system for machine learning algorithms execution process which instantly shows changes by the parameters and changes in the software implementation code. Also, the open-source structure of an algorithm provides an ability for third-party interested developers to add their own lessons that have passed the validation. Visualization of the machine learning algorithms execution process is demonstrated by the example of solving the task of finding the shortest path. The student can independently build a map of the area in 2D and 3D, dynamically change it and trying different algorithms to find the shortest path. This allows for a comparative analysis of various machine learning algorithms when it comes to spatial orientation. The developed laboratory currently has no analogues among widely available software tools for training specialists in the field of data science and machine learning.

19.1 Introduction Currently, computer science and data science are developing rapidly, which increases the need for specialists in this field. This means that it is necessary to increase the availability of training materials related to this field of knowledge. One of the directions is machine learning algorithms. These algorithms are based on complex mathematical apparatuses and require a lot of time to fully understand the principles of their work. However, the available materials on this topic are mostly theoretical. The practical part is the various software implementations in which the algorithms V. D. Kholoshnia · E. A. Boldyreva (B) ITMO University, Kronverkskiy Pr. 49, Saint Petersburg 197101, Russia e-mail: [email protected] V. D. Kholoshnia e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_19

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are presented in a form of a “black box.” We know what data is fed to the input of the algorithm; we know what should be obtained at the output. You can also define basic parameters such as the accuracy of the prediction or the number of epochs. However, the problem of the “black box” remains unresolved. And in order to understand exactly how, step by step, machine learning occurs, it is necessary to create additional software solutions for visualization. One way to solve this problem is to create a virtual machine learning laboratory for various algorithms with visualization of the learning process. Such a laboratory will provide a lot of advantages, such as the availability of educational materials for everyone from everywhere. Visualization of the machine learning process with the ability to change the code of the software implementation presented in C++, Python, and C# programming languages allows to see the result instantly that facilitates the student’s understanding of the principles of machine learning algorithms. The ability to change the parameters that affect the algorithms execution process is implemented by changing CSV data files. The lesson, written in simple words, allows the student to fully learn all the details of each algorithm. Also, the proposed service has an open-source structure, which allows third-party developers to add their own lessons that have passed validation. To get started, it was decided to take some of the most popular machine learning algorithms—the NEAT genetic algorithm and the Q-learning reinforcement learning as an example. These algorithms are universal and can be easily transformed for any user needs. Such a choice is also supported by the fact that these algorithms are easy to understand with visualization of the machine learning process. For this work, it was decided to solve the problem of finding the shortest path using machine learning algorithms. Reinforcement learning is a machine learning method during which the test system (agent) learns by interacting with a certain environment. The genetic algorithm is a heuristic search algorithm used to solve optimization and modeling problems by randomly selecting, combining, and varying the desired parameters using mechanisms like natural selection. These algorithms allow building routes in space, having information only about the environment. The novelty of the results is that, in contrast to existing virtual machine learning laboratories presented, one provides the visualization system for machine learning algorithms execution process. Such system in accordance with parameters and software implementation shows changes instantly. Also, the open-source structure of an algorithm provides ability to third-party interested developers to add their own lessons that have passed the validation.

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19.2 Statement of the Problem: Literature Review 19.2.1 Virtual Laboratories in Education A virtual laboratory is a software package that allows to conduct research and experiments without access to real software and hardware equipment. The main task of the virtual laboratory is to help in simulating computational process as well as visualizing ongoing experiments and in experiment-based science learning. Virtual laboratories can be used at different educational stages: for school education [1], for high education in science, technology, and engineering [2, 3]. These researches confirm the effectiveness of virtual laboratories usage in the educational process [4, 5]. As computer science and data science become more popular, education must adapt to these knowledge-intensive fields. Virtual laboratories simplify the process of exploring these areas and make learning accessible to a wide audience [6–8].

19.2.2 Existing Virtual Laboratories for Machine Learning Nowadays, there is a small number of virtual machine learning laboratories, since the area, as this research work [2] shows, is only developing. Among these, the following can be distinguished: Machine Learning Lab from Indian Institute of Technology Bombay [9], AI Experiments from Google [10], AI Lab from Microsoft [11], TensorFlow Machine Learning on the Amazon Deep Learning AMI from Cloud Academy [12]. As these research works [7, 8] show, machine-trained visualization simplifies the process of understanding how algorithms work. The presented approach to solving this problem consists in creating a virtual laboratory with an open-source structure that allows third-party interested developers to add their own lessons in order to increase the number of machine learning algorithms available in the virtual laboratory. The presented virtual laboratory, unlike the existing ones, provides the student with the opportunity to directly interact with software implementation code of machine learning algorithms and instantly see the results of changes due to the execution process visualization system that simplifies and accelerates learning. Also, at present, the number of research papers on the topic of machine learning is growing. In this regard, the amount of knowledge necessary to understand the latest articles is constantly increasing. Student needs to have a lot of knowledge in order to study existing papers (e.g., one of the latest OpenAI works—Emergent Tool Use From Multi-Agent Autocurricula [13]) and work on their own. For the most part, articles on the topic of machine learning are based on existing algorithms, the study of which is provided by the presented virtual laboratory. The proposed approach to training specialists in this area decided to demonstrate the example of solving the problem of finding the shortest path. This task is well visualized from the stage of building the map to the process of machine learning.

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19.3 Project Goal Currently, the field of machine learning algorithms application is rapidly expanding, which, in turn, creates a need for specialists in this field of computer science to solve problems requiring this knowledge. With the help of machine learning, one can solve a large number of tasks related to the following areas: computer vision, natural language processing, machine translation, speech recognition and synthesis, etc. The research project goal is to develop a virtual machine learning laboratory for training specialists in the fields of computer science and data analysis.

19.4 Virtual Machine Learning Laboratory with Visualization of Algorithms Execution Process To develop and demonstrate the effectiveness and value of virtual machine learning laboratory with visualization of the algorithm’s execution process concerning the solution of the task of finding the shortest path, the following steps must be completed: 1. To develop a functional structure (implementation) of the virtual laboratory for the shortest path finding task with a choice of machine learning algorithm; 2. To develop a software implementation of the algorithms execution visualization system; 3. To develop a website for educational content of a virtual laboratory; 4. To develop, adapt, and refine lessons for each presented algorithm for the virtual laboratory.

19.5 Development of a Virtual Laboratory Functional Structure (Implementation) for the Shortest Path Finding Task At this stage, the structure of the virtual machine learning laboratory with the shortest path finding system was formed based on the presented machine learning algorithms. The virtual laboratory diagram is shown in Fig. 19.1. After entering the virtual laboratory website, it becomes necessary to choose one of the machine learning algorithms. In the context of this study, it was decided to consider the NEAT genetic algorithm and Q-learning reinforcement learning as the main learning algorithms, since each of these algorithms optimally functions only under certain conditions, which together cover the maximum number of possible conditions of student needs in case of solving shortest path finding task.

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Fig. 19.1 Software implementation diagram

19.6 Software Implementation of the System for Algorithms Execution Visualization The proposed shortest path finding algorithm on a two-dimensional map is described in the C++ and Python programming languages. To visualize the execution process of the algorithm, the SFML graphical interface library was used (Fig. 19.2). To visualize a three-dimensional map, the Unity engine was used. The algorithm is described in the C# programming language (Fig. 19.3).

Fig. 19.2 Visualization of the learning algorithm on a two-dimensional map

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Fig. 19.3 Visualization of the learning algorithm on a three-dimensional map

Also, an algorithm for creating a two-dimensional and three-dimensional map is applied to the software implementation, which allows creating the necessary environmental conditions, load a map from a graphic format file (supported formats: BMP, PNG, TGA, JPG, GIF, PSD, HDR, PIC) and load 3D objects to use them while training (supported formats: FBX, DAE, 3DS, DXF, OBJ, SKP) (Fig. 19.4). The CSV extension data files, if necessary, simplify editing with table editors. To represent dynamic arrays, pointers, and multithreading, the standard libraries were used, the use of which avoids possible errors and memory leaks. Unity.Jobs library was used to distribute processor power, as this library not only monitors all the little things and errors when working with threads, but also provides

Fig. 19.4 Algorithm for creating map from any 3D objects

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maximum performance when using multi-threaded programming technology in the Unity engine. For the genetic algorithm, the student can choose the speed of training and, accordingly, obtain the desired result. The speed and accuracy of training can be regulated by changing the number of agents, the more the agents, the higher the learning speed, as long as the number of agents does not require more computational power. A used multithreading programming technology allows to separate processor power to different streams and use each of them for each layer so as long as the number of layers is less than supported streams by processor there will not be any performance issues. For reinforcement learning, the student can select the learning speed by adjusting the parameter γ. The larger the parameter, the higher the learning speed, but also the higher the probability of missing a useful learning outcome. To implement the ability to use the spatial grid as a map, an algorithm for representing the map in the form of a two-dimensional array of possible states and actions was added.

19.7 An Example of the Experiment with Machine Learning Algorithms and the Practical Part of a Virtual Laboratory In the course presented by the virtual laboratory of machine learning algorithms, not only theoretical lessons are presented, but also there is the possibility of applying the acquired skills by performing practical experiments. In this experiment, the student was asked to analyze and compare the selected machine learning algorithms to solve the problem of finding the shortest path. This work requires knowing almost all details of chosen algorithms presented at the course, so this is a good way to check gained knowledge while attending course. The example of student’s work. The results are based on the software implementation results; a comparative analysis of two training algorithms was carried out. The analysis data is presented in Table 19.1. From Table 19.1, it is seen that the complexity of the map affects the runtime of the Q-learning reinforcement learning algorithm: If number of the obstacles on the map increases, the execution time decreases, since it decreases as the number of possible initial positions of the agent. Due to the structure of the NEAT genetic algorithm, the complexity of the map increases the learning time, but with the correct settings, the increase in the running time of the algorithm can be almost completely avoided. Thus, the decision on the appropriateness of sharing various training algorithms within the same shortest path finding system can be considered justified, since the user can choose a training algorithm for certain environmental parameters (map). Based on the results of the comparative analysis, the following recommendations were formed:

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Table 19.1 Comparative analysis of various approaches Algorithm/parameter

Q-learning (map resolution: 25 × 25)

NeuroEvolution of augmenting topologies (map resolution: 80 × 80)

The average time to calculate the desired result in the simple environment (min)

3.17

5.56

The average time to calculate the desired result in the complex environment (min)

3.02

7.89

The average dependence of training time on the complexity of the environment (%)

95.27

141.91

Ability to choose an arbitrary starting point after training

+

−

Adjustable parameters

Obstacles on the map, number of map fields (map resolution), discount coefficient, number of iterations

Obstacles on the map, mutation rate parameter, map resolution, number of layers, number of agents, size of directions array, required number of generations passed after reaching the goal (autocompletion parameter)

If it is necessary to obtain an exact path for a specific environment, it is proposed to choose the genetic algorithm NEAT, the result of which will be an array of object movements for a specific starting point. But when using the NEAT training method with the presented “fitness” formula, it is necessary to take into account that the initial position should not be beyond the obstacle, for which the agent would need to go a greater distance than the possible deviation, regulated by the mutation rate parameter (degree of variability). To solve this problem, it is necessary to increase the mutation rate parameter, which can lead to a loss of accuracy for the same period of training time or use a system of additional rewards placed on the map and indicating to the agent how to get around the barrier. Also using this logic, the student can adjust the trajectory, if necessary. When using training with Q-learning reinforcement learning as a result, the one receives a two-dimensional array of possible states and a certain reward for actions that allows selecting any available starting point on the map after training. However, to obtain a more accurate result, more resources will be required (it is necessary to increase the number of map fields, which entails an increase in the amount of memory used).

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19.8 Conclusions. Future Steps Conclusions. In the course of the research work, a functional structure (implementation) of the virtual laboratory for the shortest path finding task with a choice of machine learning algorithm was developed, and a software implementation of the algorithms execution visualization system was developed. The effectiveness of software implementation is achieved through the use of multi-threaded programming technology and processor power distribution using the built-in thread control libraries, as well as the use of modern methods and standard libraries. The Web implementation for educational content of the virtual laboratory is developed and tested for convenient user interaction while attending the courses. Lessons for the machine learning courses for the virtual laboratory have been designed in terms of availability to gaining knowledge. An example of a student’s work comparing machine learning algorithms with NEAT and Q-learning while attending a laboratory course in a virtual machine learning laboratory was added. The system for adding third-party developers’ lessons has been developed. Future steps. In the future, it is planned to improve the system for adding lessons for third-party developers, develop a complete course for the sequential study of all available machine learning algorithms, and test the developed virtual laboratory in a real educational process. In addition, it is planned to add an analysis system of virtual laboratory based on machine learning to improve students’ experience, using their answers and key actions to train models of neural networks.

References 1. Pramono, S.E., Prajanti, S.D.W., Wibawanto, W.: Virtual laboratory for elementary students. J. Phys. Conf. Ser. 1387, 012113 (2019). https://doi.org/10.1088/1742-6596/1387/1/012113 2. Potkonjak, V., Gardner, M., Callaghan, V., Mattila, P., Guetl, C., Petrovi´c, V.M., Jovanovi´c, K.: Virtual laboratories for education in science, technology, and engineering: a review. Comput. Educ. 95, 309–327 (2016). ISSN 0360-1315. https://doi.org/10.1016/j.compedu.2016.02.002 3. Son, J., Irrechukwu, C., Fitzgibbons, P.: Virtual lab for online cyber security education. Commun. IIMA 12 (2012) 4. Rajendran, L., Veilumuthu, R.: A study on the effectiveness of virtual lab in E-learning. Int. J. Comput. Sci. Eng. 2 (2010) 5. Hwang, W.-Y., Kongcharoen, C., Ghinea, G.: To enhance collaborative learning and practice network knowledge with a virtualization laboratory and online synchronous discussion. Int. Rev. Res. Open Distrib. Learn. 15(4) (2014). https://doi.org/10.19173/irrodl.v15i4.1805 6. Yosinski, J., Clune, J., Nguyen, A.M., Fuchs, T.J., Lipson, H.: Understanding Neural Networks Through Deep Visualization (2015). http://arxiv.org/abs/1506.06579 7. Vega, O., Londoño-Hincapié, S., Toro-Villa, S. Virtual Labs for Science Teaching. Ventana Informatica (2016) 8. Russell, I., Markov, Z., Neller, T.: Teaching AI through machine learning projects. In: Proceedings of the 11th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (ITICSE ’06). Association for Computing Machinery, New York, NY, USA, p. 323 (2006). https://doi.org/10.1145/1140124.1140230 9. Indian Institute of Technology Bombay, Machine Learning Lab: http://vlabs.iitb.ac.in/vlabsdev/labs/machine_learning/labs/explist.php. Last accessed: 9 Mar 2020

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10. Google: AI experiments. https://experiments.withgoogle.com/collection/ai. Last accessed: 9 Mar 2020 11. Microsoft, AI Lab, https://www.microsoft.com/en-us/ai/ai-lab. Last accessed: 9 Mar 2020 12. Cloud Academy, TensorFlow Machine Learning on the Amazon Deep Learning AMI, https://cloudacademy.com/lab/tensorflow-machine-learning-amazon-deep-learning-ami. Last accessed: 9 Mar 2020 13. Baker, B., Kanitscheider, I., Markov, T., Wu, Y., Powell, G., McGrew, B., Mordatch, I.: Emergent tool use from multi-agent autocurricula (2019). ArXiv, abs/1909.07528

Part V

Smart Education: Case Studies and Research

Chapter 20

The Use of Students’ Digital Portraits in Creating Smart Higher Education: A Case Study of the AI Benefits in Analyzing Educational and Social Media Data Svyatoslav A. Oreshin, Andrey A. Filchenkov, Daria K. Kozlova, Polina G. Petrusha, Lubov S. Lisitsyna, Alexander N. Panfilov, Igor A. Glukhov, Egor I. Krasheninnikov, and Ksenia I. Buraya Abstract The problem of a smooth and synchronous integration of smart education into the traditional educational system of higher education institutions (HEIs) and transforming it into a person-centered, blended, systematic adaptive educational environment is becoming a new paradigm of higher education institutions (HEIs). But there are a few studies that discuss the role of artificial intelligence (AI) in providing comprehensive and predictive analysis not limited to educational outcomes but focused on students’ individual portraits and aimed at improving the quality of education and provision of an integrated educational environment. This paper investigates role, application, and challenges of applying AI to targets and features of smart education concept: proactive, predictive analysis based on instruments aimed at building up a system that not only records students’ performance but also predicts and influences it. This study offers the concept of the comprehensive system of the university targeting to provide predictive analysis of students’ performance based on quantitative and qualitative information from multiple sources concerning students’ education, research performance, and social network information joined into unified, comprehensive dataset of each student (digital portraits) that allows to design individual tracks and use advising system to correct this track according to students’ needs and personal goals. As a result, we can predict students’ dropout with almost 90% accuracy. The main research problem of the study is to observe the implementation of AI instruments in higher education according to the needs and goals of universities and personal goals of each student and investigate the impact of this in blending different types of educational activity and creation of the integrated educational environment. Our findings are based on the analysis of the information about more than 20,000 students in 2013–2019. S. A. Oreshin (B) · A. A. Filchenkov · D. K. Kozlova · P. G. Petrusha · L. S. Lisitsyna · A. N. Panfilov · I. A. Glukhov · E. I. Krasheninnikov · K. I. Buraya ITMO University, Saint Petersburg, Russia e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_20

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20.1 Introduction Challenges, which higher education institutions (HEIs) face at the beginning of the twenty-first century, require fundamental innovations in both (a) the process of delivering education to students according to their needs, competences, and career plans and (b) administrative, technological, and organizational processes supporting the educational process. These innovations are intended to create new educational paradigm also known as smart education contributing implementation individualized educational tracks created by the following blocks of data: (1) in-depth and comprehensive analysis of students’ preferences, individual educational outcomes, social background, psychometric details, (2) their career plans, (3) competences required by various actors of digital economy, and (4) prerequisites and planned outcomes of courses and programs taught in HEIs. The early stage of our analysis is creating a comprehensive model of students’ behavior, primarily intended to investigate the correlation between students’ performance and dropout and their activity and psychometric parameters in social networks. This model allows creating data providing background knowledge for the next steps of the digital, data-driven smart universities: proactive advising system for helping at-risk students, development of individual tracks that, finally, result in creating optimal career guidelines for each student. The successful implementation of these future stages is strongly based on the accuracy and reliability of student’s digital portrait. Our research is focused on collecting, analyzing data for this portrait and demonstrating how we can use this portrait to predict different targets and analyze the results of such predictions. The paper is arranged in the following way. In Sect. 20.1, we analyze related work. Section 20.2 is dedicated to the description of the research targets and goals. In Sect. 20.3, the proposed methodology and related approaches are presented together with the background, target, and goals. In Sect. 20.4, the experimental results of the case study by ITMO University are described. Also, an approach of predicting the probability of dropout using our methodology is presented and analyzed. Finally, conclusions and future work are proposed.

20.2 Related Work Higher education institutions in the twenty-first century face numerous challenges: competition with new forms of education including various types of e-learning: MOOCs, SPOCs, education through mobile applications, increasing student numbers and diversity, alongside a global competitive education market and significant reductions in government funding [1]. These challenges make HEIs rethink the way in which education is delivered, supported and implement changes in the educational process the best, which is the most conservative from all university processes. Among these changes, we can name the following: (1) integrating online learning

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programs and massive open online courses (MOOCs) in traditional classroom learning, (2) changing education models, (3) implementing personalized student support services (e.g., curriculum support, learning support, and career counseling) [2], (4) analyzing student enrollment trends, study outcomes, dropout rates that intended to allocate learning support resources effectively, and (5) creating educational environment intended to make higher quality learning experience based on personalized educational tracks and designed according to trends in demand to educational and human capital. These days, information that was historically collected by learning management systems (LMS) is sufficient for creating analytical and information background for implementing innovations in educational environment creation. To reach this target, HEIs develop learning analytics, educational data mining, and educational big data systems aimed to better understanding and supporting students’ learning [3], to predict students’ successful behavior and shape the educational process according to the courses demand from students and employers. Finally, analytics provides a new model for universities that improves their teaching, learning, organizing efficiency, and decision making. Basing on LAK and UNESCO Institute for Information Technologies in Education definitions, we made a conclusion that academic analytics should be considered as an “umbrella” term joining all types of systems (LA, EDM, and EBD) that provide intelligent analysis of data generated by various information systems in the learning process and accumulated in different digital repositories. “Academic analytics” marries large datasets, statistical techniques, and predictive modeling. It could be thought of as the practice of mining institutional data to produce “actionable intelligence” [4]. To make the differentiation and taxonomy of different types of AA more systematic and clearer, in our studies, we tried to find and reveal conceptual and process distinguishing features of three core strands of academic analytics without linking differentiation according to a technological platform. Data mining techniques, as a process of extracting meaningful and relevant information from data, applied as groundwork and instrument to find out solutions in collecting, processing, reporting, and visualization of data generated, and applied in educational process, are often referred to as educational data mining [5]. Definitions of EDM and LA vary in their focus: EDM is mostly concentrated on the automated discovery, while LA focuses on leveraging human judgment. Besides, EDM system models refer to analysis of relationships between individual components of the process (classification, clustering, tracking, visualization, and predicting individual or group (cluster) students’ educational outcomes, successful and dropout patterns and models) [6, 7] and other performance indicators of HEIs activity: scientometric indicators, resources, and KPIs of administrative and other processes. Although both LA and EDM research fields are still in the infantry stage, they have even a “younger” successor, educational big data (EBD) that focuses on analysis of students’ behavior, not metrics and multiple external non-academic unstructured data sources (social networks, forums, volunteering, hobbies) without using foregone

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assumptions and statistical models, moreover, making conclusions and prognosis that stand out from the obtained data [8]. Thus, EBD analysis mostly uses data from educational data mining and learning analytics systems to predict students’ academic and career performance and could be considered as an upgrade or add-on for both systems. We suppose that the choice of one or several analytical instruments should be based on the complex strategic way that these technological solutions can be used according to actual targets and desired benefits of a particular university.

20.3 Target and Goals of the Study The target of this study is to collect both quantitative and qualitative information from multiple sources concerning students’ education, research performance, and social network information to create a unified, comprehensive dataset of each student (digital portraits) that allows to predict students’ success or failure in their studies in university and design individual tracks and use advising system to correct this track according to students’ needs by means of AI approaches. This target is expected to be completed by achieving the following goals: Goal 1. Collect all the available information concerning each student from multiple sources applying the data mining methodology. Create a unified model of each student’s features reflecting comprehensively the emotional portrait, interests, and educational background. Goal 2. Design machine learning models that can predict students’ behavior and integrate them in the decision-making process to make the educational process personalized and relevant for the needs of each student, personal goals, competencies, skills, and features. Goal 3. Create a unified database of educational programs that completely reflects topics, complexity, prerequisites, and outcomes in a “competencies—skills— knowledge” model, which also should be adjusted to the knowledge about the student collected after achieving Goal 2. Goal 4. Design individual educational tracks according to features, needs, “competencies—skills—knowledge” performance of the students reflected in the digital portrait and their correlation with the unified database of educational programs from Goal 3. After reaching the goals listed above, the system should be adjusted and improved according to the needs and expectations of all stakeholders (faculty, students, administration, and others) to avoid resistance and to provide the maximum support of all these groups. According to the concept of smart education, this system should be flexible and currently adjusted according to changing environment, new trends in delivering knowledge, interests and goals of a new generation of students and global educational trends. In this paper, we indicate this global goal and overview the first step in reaching this global problem. We propose a methodology of doing each step and overview the results of ITMO University case.

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20.4 Methodology The methodology of developing students’ digital portrait is dedicated to describing students’ behavior and interests using analysis of socio-demographic, psychometric, and educational data obtained from University LMS and external sources such as social networks. Since this task has a data-driven nature, the first step is always to define the data that we can collect. In this section, we describe common steps of the data mining process for this type of problem that are applicable in most scenarios when processing students’ data.

20.4.1 Educational and Socio-Demographic Data Processing The main problem in processing educational and socio-demographic data is to obtain such features that can fully describe a certain student using data that a university has. Features as hometown, gender, age, citizenship are common for almost all the universities databases. More informative features describe students’ backgrounds. These features can be generated from the entry examinations score, previous achievements in sport, subject prizes, conferences, and other activities. Moreover, in some cases, we can identify students’ interests and use them for personalized invitations in universities’ activities. The information about already received grades might be especially useful when we combine it with data about students’ backgrounds. We can process that data interactively during each semester and refresh the students’ portrait each semester. Using that combined data, we can calculate such statistics as a mean grade, standard deviation, mean grade in different subjects to get information about students’ preferences and the subjects they are most and least successful in.

20.4.2 Social Media Data Processing Social media contain a large amount of multimodal data representing its users’ personalities. We want to use available public information on social networks to extract features that somehow describe a student’s psychological portrait and his or her interests. For the first task, we can extract features as the mean number of likes on posts, sentiment of comments and posts, the number of friends, preferred types of music and groups. Also, we can create a social graph of friends from a university and find out how close a student to a center of clusters in this graph. This information can describe how a student is involved in interactions with other people in the university. The second type of data that can be extracted from social media is students’ interests and involving. Based on the data we gathered from students’ pages, we calculated statistics such as the mean number of likes on each page, the total number

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of posts on a particular page, the mean number of media attached to student’s posts, and the number of friends. Thus, we collected 14 different attributes. We can use pre-trained classifiers to extract sentiment of opinions in students’ posts and already known classes of interests or we can apply unsupervised topic modeling algorithms as latent Dirichlet allocation (LDA) [9]. For this purpose, we used a pre-trained model for sentiment analysis to generate topics out of overall data from students’ pages.

20.5 Case Study: ITMO University In this section, we describe a case study illustrating how one can process data from university and social networks to make a digital portrait of a student. In this case, we focus on University of Information Technologies, Mechanics and Optics (ITMO University). A national research university founded in 1900, ITMO University, is one of the leading higher education institutions in Russia, providing training and research in advanced science, humanities, engineering, and technology. Offering a wide range of undergraduate, graduate, and postgraduate (Ph.D.) programs, the university is home to over 12,000 students, 19% of whom are international students from 87 different countries. ITMO University is continuously holding positions in international academic rankings: In 2019, the university has been ranked 74 worldwide in Times Higher Education (THE) subject rankings in Computer Science, and 76–100 in Automation and Control within Shanghai Ranking by subject. We used data from the university’s internal IT and LMS services ISU (Informational System of University), integrating the greater part of information concerning students educational performance at traditional classes and CDE (Center of Distance Education) that concentrates information about students’ online education to obtain educational and socio-demographic information about students. Also, we mapped some students to their accounts in the largest social network in Russia, VK.

20.5.1 ISU and CDE Data Mining As a first step, we used data from the ISU and CDE services. Fifty-six separate data chunks from ITMO University databases were collected and aggregated. All the general sources that we used to get data from are represented in Table 20.1. We needed to develop a unified student description system based on the available data from various sources. First, we have split students according to their study levels (bachelors, masters) using ISU data. Also, at the first step, we obtained basic information such as unique student id, name, birth date, selected curriculum, faculty, and group number. After the first step of students’ identification, we added information provided by students when they have applied to the university (state examinations results, subject prizes) and then merged our data with enrollment details for each student (whether or not a student pays for education, additional funding) and entrance

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Table 20.1 Main data sources from ITMO University Data source

Extracted features

Applicants information

Entry or state examinations, subject prizes, hometown, etc.

ISU data

Internal documents, groups and curricula, university’s scholarships and other academic performance data obtained from the traditional educational process

CDE data

Grades and other academic performance data obtained from online courses on ITMO platforms, etc.

Security data

Time of entering and leaving the university campus

First-year student surveys

Students’ interests, sports achievements, etc.

survey (sports interests, spoken languages, special skills, preferred student clubs). During the next step, we took data that were the most important for further prediction models, namely information about administrative orders and displacements for each student including the number of gap years, the number of times the student was dropped out of ITMO University before. Also, we used data about grades and attendance. For each student, we extracted grade point average (GPA) for every passed semester in a given range for further prediction of GPA in next semester depending on the current student year. We can make the same calculations with attendance data—predict student’s attendance during the next week or month or even making a prediction of the average student group attendance. These dynamic features should be re-calculated for each student in equal time stamps. These features can represent student’s activity as time series which can additionally describe their successes and problems. We used such features as GPA, grades’ standard deviation, number of failed examinations, and attendance per each week to describe.

20.5.2 Social Networks The first problem in matching students to their accounts in social networks is choosing the correct known data from the university for further mapping. We used data as the first name, last name, surname, and birth date to find the account of a certain student in social network using open data. As a result, we found pages for 40% of students. The reasons for accuracy not being very astonishing are: • not every student has a page on a social network; • not every student publishes his or her real birth date and name; • not every student has an open profile. After we matched accounts of students in social networks, we were able to use all the open information to generate useful features that may represent students’ social behavior. Even the absence of some information can be a piece of information itself.

240 Table 20.2 Correlation between extracted features from social networks with the target

S. A. Oreshin et al. Feature Mean number of comments on the student profile Number of friends

Correlation 0.07 0.03

Mean number of likes on the student profile

−0.03

Mean number of posts

−0.05

Mean number of reposts

−0.06

Also, we generated statistics such as the mean number of likes on each page, the number of friends, frequency of posts, and the total number of posts. All these features differently describe the psychological profile of a student. We get student’s interests and we used topic modeling algorithm LDA [9] on indicated interests, groups, and posts. Also, we used pre-trained models for sentiment analysis of overall comments and posts on a page [10]. Table 20.2 presents correlation between some of the extracted features and the dropout target. We used point-biserial correlation coefficient to measure the strength of association between a continuous variable and a binary variable. We can conclude that there is no strong linear relationship between features from social networks and the selected target. These features can be useful for further machine learning models especially in predicting students’ interests. According to the correlation shown in Table 20.2, the currently extracted features seemed to be not linearly dependent on the fact of whether students graduated successfully.

20.5.3 Implementing the Digital Portrait We have extracted integral and dynamic features from the ITMO databases where integral features represent initial student portrait, at the moment of student’s application and dynamic features that characterize the student during studying at the university and change throughout the learning process. We showed the process of extracting psychometric features from social networks using the largest social network in Russia, VK (vk.com). We used our resulted profile to predict the dropout of a certain student. Only integral features were used for this task. We took a fact of dropout in the whole future process of studying for each student as a binary target. Generally, about 37% of students drop out of the university for some reason. Based on digital portrait data, we trained four types of classifiers: logistic regression, random forest, gradient boosting on decision trees (XGBoost implementation), and gradient boosting on decision trees (CatBoost implementation) [11–13]. We used three scores to compare the mean results of cross-validation: accuracy, ROC-AUC, and recall [14]. This decision was motivated by the imbalance of target classes and the necessity of obtaining a degree

20 The Use of Students’ Digital Portraits in Creating Smart … Table 20.3 Models’ results in student dropout prediction

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Model

Mean accuracy

Mean ROC-AUC

Mean recall

Logistic regression

0.84 ± 0.02

0.93 ± 0.01

0.82 ± 0.05

Random forest

0.88 ± 0.01

0.95 ± 0.01

0.84 ± 0.03

XGBoost

0.88 ± 0.02

0.96 ± 0.01

0.81 ± 0.04

CatBoost

0.91 ± 0.02

0.97 ± 0.01

0.83 ± 0.02

of prediction confidence, which later would be used for supporting decisions of facilitating selected students. Model performance was tested on nested time-series split cross-validation which helps to recreate the inference process of these models. The results of the models’ performance are presented in Table 20.3. CatBoost implementation has greater values for each score. The main reason for this leading performance might be in built-in mean encodings of categorical features [13] as chosen faculty, hometown, and others. As a result, we can identify almost 90% of all the students who dropped out of ITMO University. This high performance of models means that we successfully created a digital portrait of a student that can be used to describe his or her future behavior in studying. Our next research was dedicated to estimating the importance of used portrait features. The most important features according to the CatBoost model in dropout prediction are: • • • • • • • • • •

Type of certificate of secondary education (importance is 41%), Hometown (12%), Faculty (8%), Tuition fee funding (7%), Special enrollment condition (6%), Applicant’s age (6%), Russian language state examination (3%), Mathematics entry examination (2%), Benefit recipient’s enrollment (2%), Citizenship (2%).

20.6 Conclusion In this paper, we report the results related to creating data background, as a part of the general global task of making a personalized learning track for each student, which is an essential step for achievement smart university educational paradigm. We propose a solution for the first step of this global problem which is creating a digital portrait

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of a student in university. We indicated and overviewed all the main approaches and sources of information as university’s data and social networks to solve this datadriven task. Also, we demonstrated how our methodology works using data from ITMO University applying and selecting the most powerful algorithms for analyzing and visualization. In the last part, we demonstrate how resulted digital portrait can be used to predict future student’s behavior as dropout. The obtained results showed that it is possible to predict student’s behavior with 87.5% accuracy using only unified features about student’s background and social networks profile. This result indicates that digital portrait allows to predict more targets as students missed assignment and shows the probability of dropout in a certain semester, GPA, scientific interests, and others. After reaching this goal, it is possible to make personalized impact on a certain group of students (especially at-risk students) and propose individual educational tracks that is a very important step in creating comprehensive career guidelines according to students’ features and smoothly integrate smart education with the best practices of traditional education in HEIs. Our studies show promising methods to create data-driven smart ecosystem of higher education in which data, algorithms, practices, teachers, administration, and students all interact effectively using data richness and algorithmic efficiency with human intelligence to implement tangible innovations in higher education.

References 1. Srivastava, R., Gendy, M., Narayanan, M., Arun, Y., Singh, J.: University of the future—A Thousand Year Old Industry on the Cusp of Profound Change. Ernst & Young, Melbourne, Australia (2012). Retrieved from http://www.ey.com/Publication/vwLUAssets/University_of_ the_future/$FILE/University_of_the_future_2012.pdf 2. Ognjanovic, Ivana, Gasevic, Dragan, Dawson, Shane: Using institutional data to predict student course selections in higher education. Internet High. Educ. 29, 49–62 (2016). https://doi.org/ 10.1016/j.iheduc.2015.12.002 3. Schumacher, C., Ifenthaler, D.: Features students really expect from learning analytics. Comput. Hum. Behav. 7, 397–407 (2018) 4. Campbell, J.P., Oblinger, D.G.: Academic analytics. EDUCAUSE White Paper (2007). http:// www.educause.edu/ir/library/pdf/PUB6101.pdf 5. Baker, R.S.J.d.: Data mining for education. In: McGaw, B., Peterson, P., Baker, E. (eds.) International Encyclopedia of Education, vol. 7, 3rd ed., pp. 112–118. Elsevier, Oxford, UK (2010) 6. D’Mello, S., Olney, A., Person, N.: Mining collaborative patterns in tutorial dialogues. J. Educ. Data Min. 2(1), 1–37 (2010) 7. Cambruzzi, W.L., et al.: Dropout prediction and reduction in distance education courses with the learning analytics multitrail approach. J. UCS 21(1), 23–47 (2015) 8. Rajeswari, S., Lawrance, R.: Classification model to predict the learners’ academic performance using big data, pp. 1–6 (2016). https://doi.org/10.1109/icctide.2016.7725338 9. Blei, D.M., Ng, A.Y.; Jordan, M.I., Lafferty, J. (ed.).: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(4–5), 993–1022 (2003)

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10. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of Twitter data. In: Proceedings of the Workshop on Languages in Social Media, pp. 30–38. Association for Computational Linguistics (2011) 11. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011) 12. Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785– 794. ACM (2016) 13. Prokhorenkova, et al.: CatBoost: unbiased boosting with categorical features. NeurIPS 2018 (2018) 14. Kelleher, J.D., et al.: Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies, p. 2015. MIT Press, Cambridge, MA (2015)

Chapter 21

Using Smart Education Together with Design Thinking: A Case of IT Product Prototyping by Students Studying Management Elvira Strakhovich Abstract This article discusses examples and results of using smart education and Design Thinking methods in teaching students enrolled in a university business school. Using these methods together in the classroom involves teamwork and collective participation in creative processes and decision-making. The article considers the case of the IT project management discipline of the bachelor’s program in business school. Typically, undergraduate students have no experience in creating IT products. Using the considered approach allows them to create a prototype of an IT product using a problem-based learning approach in training and rapid prototyping using modern software tools.

21.1 Introduction In recent years, the digitalization of the economy and the use of information technology have significantly influenced the growth of interest in the development and implementation of IT products. Along with this process, interest in project management in the field of information technology and the work of project managers has increased. Business schools are introducing IT project management courses. When observing business school course programs, it can be noted that they either study project management methods irrespective of the area of application, are intended for specialists working in business (such as Vlerick Business School), master or MBA students (such as Rome Business School or Heriot-Watt University Business School in Edinburgh), IT professionals or computer science students (such as the University of Washington course on the edX online platform or the course at Bauman Moscow State Technical University). When examining an IT project management course in a bachelor program at a business school, it should be considered that as a rule, the bachelor students are E. Strakhovich (B) Graduate School of Management, Saint-Petersburg State University, Saint-Petersburg, Russia e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_21

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yesterday’s schoolchildren. Most of them have only basic knowledge in information technology and do not have software development experience, but they are usually active and experienced users of computers, smartphones, and other digital devices. This allows for actively using software products in education in order to improve learning outcomes. The purpose of this article is to consider a practical example of organizing a learning environment that combines elements of smart and traditional education in a course on IT project management for students studying management.

21.2 Research Background Modern universities are implementing and actively using the smart education approach formulated by IBM [1]. It is difficult to imagine a university that does not use, for example, a learning management system to manage the educational process and create an environment that supports this process. Not only do information and communication technologies comprise smart education, educational outcomes, and organizational dimensions are also important in reflecting it [2]. Traditionally, business schools use case studies, teamwork, and group discussions in education [3]. When using smart education approaches, “educational institutions must tailor learning experiences to their students” [1]. Some approaches to education offered in smart education and in business school training are the same. These include collaboration, teamwork, and problem-oriented learning [1, 3, 4]. As a rule, students like to work in groups, learn from each other, and share their knowledge with each other. Such work in groups can be directed depending on the learning context, and the context-based method can be applied [4]. Process-based learning is not as widespread in business schools as the case study method [5], although the future work of managers largely consists of managing processes. Speaking about the future work of managers and the required competencies, we must refer to the skills that are in demand in the twenty-first century [6]. These skills include creative thinking, listening to each other, teamwork, and empathy. According to researchers in the education field, these skills are trained well using the Design Thinking method. Students are usually interested in executing tasks that involve learning-by-doing. One such task in the IT project management course is the task of developing a business case of a project. This task reflects the IT specifics (the area under study) and the sequence of steps for its implementation (steps of the Design Thinking method). Design Thinking involves problem-solving and prototyping. The online services for website construction are well suited for presenting a prototype of a specific class of IT tasks (e.g., creating a Web service). These include, for example, Google Sites, Tilda, or Wix.com. Using such services allows a person to create a site without knowledge of programming language, which allows management students to use them. On the other hand, students acquire certain knowledge and skills by working with IT products.

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21.3 Design Thinking Method in Education Design Thinking was originally widely used in technical fields and then was successfully applied in the humanities, including education [7]. Researchers in the field of education have proposed designing curricula on the basis of Design Thinking for all levels of education, such as secondary school and college. The Design Thinking method is actively promoted by universities and schools teaching this method, including Stanford d.school and Aalto University. When researchers in the field of education became interested in the opportunities of the method, they recommended it for use primarily for studying project management disciplines in business schools. Using Design Thinking in business schools was also recommended based on the fact that students often study business cases in terms of their results; Design Thinking, on the other hand, is a process-oriented method that is also very important in management [5]. This paper considers the Design Thinking method as outlined by Stanford University’s design school [8], which defines the five steps in this method: empathize, define, ideate, prototype, and test. This method was adapted to the learning course goals as described in the section “Case description.” Each step, except the first, includes the possibility of a return to previous steps to clarify additional conditions for the design or to offer and evaluate new ideas for the solution. The method allows for as many iterations as necessary to make a decision that satisfies the target audience. This method focuses on studying and understanding the needs of the target audience and ensuring their satisfaction. Therefore, one of the main characteristics of this method is human-centered design. Comparing the method of Design Thinking with other approaches to learning, we can highlight its proximity to problem-based learning. Problem-based learning assumes that there is a problem for which there are no ready-made solutions, and students must offer, implement, and evaluate a solution. Design Thinking, as a part of knowledge management, uses tools such as visualization, brainstorming, polling, interviews, and customer journey mapping. The role of visualization is particularly important in finding new solutions through the association of visual images. At the same time, in the first steps of the Design Thinking method, students actively use analysis to determine the user’s needs; then, at the stages of idea creation and prototype development, they actively use the synthesis method. Thus, the Design Thinking method combines elements of contemporary learning approaches. The Design Thinking combines two parts: problem-solving and rapid prototyping. The problem-solving part is relevant to the problem-based learning approach. Rapid prototyping includes not only a description of the product prototype but also some demonstration of it. Among modern software development tools, we can distinguish design systems that allow the user to create a prototype of a product to give an idea of its design and some functionality. For example, such systems include website

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builders like Tilda and Wix.com. They do not require the user to have programming skills and can be used by students without knowledge of programming languages to prototype IT services.

21.4 Case Description In developing the curriculum of the IT project management discipline, the content was based on an analysis of the subject area and the project ontology we created [9]. Some topics of the course, such as risk management, finance, and personnel management, are widely studied in other disciplines of the business school, and during this course, students study only the aspects related to project management. At the same time, managing the scope and results of a project is a new topic to explore. In our business school, the bachelor’s students studying information management learn IT project management as well. The requirements collection is an important part of every project, especially an IT project, because the results of this procedure impact the project results. The first step before beginning a project is to define a business case as the basis for determining the project’s business value. As mentioned, students in the bachelor’s program have little development experience in IT and need some additional clarification of the product specifics and project requirements for this area. At the same time, these students have studied various IT business systems and learned their functionalities. As mentioned above and according to project management processes defined by the Project Management Institute, every project should start with a business case determination. The business case serves to confirm the necessity of the project, and the main functions and functionalities of the product are clarified based on the project business case. Therefore, it is important to define the scope of the work of the project. The considered learning case was organized using several IT systems (such as the Blackboard LMS to control the task execution, website builders for the prototype development, and mentimenter.com as an audience engagement tool to vote for the best business case proposed by the students) and the Design Thinking method to support the business case development process. Figure 21.1 shows an example of how this training case was organized. The students worked in groups of five to seven people. They were faced with the task of describing a business case related to an IT service for the students of the business school. Thus, the audience for which the business case was under development was the students themselves, and they well understood the audience’s needs. Therefore, they easily managed to complete the first step, empathizing. All steps executed for the business case development are described in Table 21.1. Each step description includes the definition of objectives and the method or tools to execute this step. The IT tools are added to follow the specifics of the learning course.

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Fig. 21.1 Use case of the training case structure

21.5 Research Outcome and Discussion Visualization plays an important role in every step of the Design Thinking method application. The final result of each training case was visualized for prototyping purposes using one of the website building tools. For example, one of these prototypes is shown in Fig. 21.2. The students in the group had different levels of IT knowledge, and from time to time, while discussing ideas and prototypes, they exchanged information and learned new things from each other. They had enough knowledge and information to form ideas, develop a prototype, and test it, involving other students of the same school in the evaluation of the prototype. A better understanding of the business case would be based on the previous practice and experience of the students [10], but the collective work and information exchange compensated for the lack of experience. Some researchers propose that it is better not to implement the entire Design Thinking cycle into the learning process but to include just some of the steps, arguing that this also benefits the development of soft skills [11]. According to our experience in the case, when the first step, empathy, was skipped and students were given instructions with the description of the business case and scenario of its usage and they were tasked with determining the necessary functionality, the proposed solution turned out to be limited in comparison with other possible solutions. Based on this observation, it can be inferred that the inclusion of the empathy step opens

– Seek to understand

Visualization

Tasks

Tools

– Journey map – User’s portrait – Interview

Learn about the audience for whom a design will be performed

Empathize

Steps

Objective

Description

– – – –

User scenario analysis Empathy map Value chain analysis Mind map

– Role objectives – Decisions – Challenges

Construct a point of view that is based on a design user’s needs and insights.

Define

Table 21.1 Adapted steps of the Design Thinking method

Share ideas All ideas worthy “Yes, and…” thinking Prioritize

– Brainstorming – Concept development based on organizing ideas into connected groups

– – – –

Brainstorm creative solutions

Ideate

– PowerPoint – Website building tool – Wiki page

– Mockup – Storyboards – Rapid prototyping

Build a representation of the design idea

Prototype

– Voting

– Understand impediments – What works – Role play

Get feedback

Test

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Fig. 21.2 Visualization of the idea of IT service developed with the Wix.com tool

up opportunities for a creative approach to problem-solving and allows for the most interesting cases. From the teacher’s point of view, the use of smart education approaches together with the Design Thinking method helps to involve students in the learning process [12], organize the exchange of information and knowledge in groups, apply analysis to study, evaluate the project environment, and synthesize the solution. By applying critical thinking and teamwork in the group, a symbiosis of knowledge and soft skills is formed. The practical application and development of the method in educational projects gives a result that will be useful in future management work. Student feedback on the training case used for the IT project management course was collected. Thirteen students took part in the survey. The survey results are collected in Table 21.2. We can summarize the advantages of using the smart education and Design Thinking methods together in the training project as follows: • Student involvement in the learning process increases their interest in the results of the work grows through participation in the process of creating this result, and they take an active approach to learning. • Students working on the business case understood the essence of the planned system and formulated and understood the functional requirements of the system well. • Collaboration on the task execution and human-centered orientation following the Design Thinking method allowed students to develop soft skills. • Given the different levels of the students’ familiarity with information systems, the joint work required students to exchange knowledge and learn from each other. • A clear understanding of the problem area and the business case allowed students to better execute subsequent tasks for the training project.

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Table 21.2 Results of the student survey on the training case Strongly agree (%)

Agree (%)

The task is effective to collaboratively work on ideas and concepts

31

69

The task is effective to promote my project participation

23

The task is effective for developing the ability to listen to each other

Neutral (%)

Disagree (%)

Strongly disagree (%)

0

0

0

38

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21.6 Conclusion Based on our experience, we can state the positive results of the usage of the smart education together with the Design Thinking method in teaching the business students about IT project management. The involvement of students in the educational process, the creation of new common knowledge in the course of work, the development of integrative and critical thinking skills, and the development of soft skills will create a positive impact on the future professional work of modern students. A positive assessment by students of the process and the results confirms the usefulness of the suggested usage of smart education together with the Design Thinking method in the classroom.

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References 1. Smarter Education. Building the foundations of economic success. IBM Corporation (2012). ftp://software.ibm.com/la/documents/gb/mx/Smarter_Education.pdf 2. Tikhomirov, V., Dneprovskaya, N., Yankovskaya, E.: Three dimensions of smart education. In: Uskov, V.L., et al. (eds.) Smart Education and Smart e-Learning, Smart Innovation, Systems and Technologies, pp. 47–56. Springer International Publishing Switzerland (2015) 3. Martensson, P., Bild, M., Nilsson, K. (eds.): Teaching and learning at business schools: transforming business education. Gower, 331 (2008) 4. Uskov, V.L., Bakken, J.P., Penumatsa, A., Heinemann, C., Rachakonda, R.: Smart Pedagogy for Smart Universities. In: Uskov, V.L., et al. (eds.) Smart Education and e-Learning 2017, Smart Innovation, Systems and Technologies, pp. 2–16. Springer International Publishing AG (2018) 5. Matthews, J., Wrigley, C.: Design and design thinking in business and management. High. Educ. J. Learn. Des. (Special Issue: Business Management) 10(1), 41–54 (2017) 6. Transforming our world: the 2030 agenda for sustainable development. In: Proceedings of the Resolution Adopted by the General Assembly, Paris, France, 25 September 2015. https:// sustainabledevelopment.un.org/post2015/transformingourworld 7. Melles, G., Anderson, N., Barrett, T., Thompson-Whiteside, S.: Problem finding through design thinking in education. In: Blessinger, P., Carfora, J.M. (eds.) Inquiry-Based Learning for Multidisciplinary Programs: A Conceptual and Practical Resource for Educators (Innovations in Higher Education Teaching and Learning, Volume 3), pp. 191–209. Emerald Group Publishing Limited (2015) 8. Stanford d.school Homepage. The design thinking process. http://web.stanford.edu/group/ cilab/cgi-bin/redesigningtheater/the-design-thinking-process. Last accessed 26 Jan 2020 9. Gavrilova, T., Leshcheva, I., Strakhovich, E.: Gestalt principles of creating learning business ontologies for knowledge codification. Knowl. Manag. Res. Pract. 13(4), 418–428 (2015) 10. Wang, S., Wang, H.: A Design thinking approach to Teaching knowledge management. J. Inf. Syst. Educ. 19(2), 137–139 (2008) 11. Ewin, N., Luck, J., Chugh, R., Jarvis, J.: Rethinking project Management education: a humanistic approach based on design thinking. Procedia Comput. Sci. 121, 503–510 (2017) 12. Ching, H.Y.: Developing a curriculum framework for a business undergrad program. Eur. J. Bus. Manag. Res. 4(3), 1–8 (2019)

Chapter 22

Application of Smart Education Technologies on the Disciplines of the Music-Theoretical Cycle in Musical College and University Svetlana A. Konovalova, Nataliya I. Kashina, Nataliya G. Tagiltseva, Lada V. Matveeva, and Denis N. Pavlov Abstract The article discusses the possibility of introducing musical-theoretical disciplines into the educational process, such as “Listening to Music,” “Solfeggio,” “Elementary Theory of Music,” and “Harmony” in music colleges and universities of new instruments related to the smart education technology segment. This toolkit (computer programs for recording compositions or fragments, musical editing, digital musical instruments—electronic pianos, synthesizers, samplers, workstations, multimedia computers, computer programs, audio-MIDI sequencers, VST-instruments, audio editing/editing programs, etc.) is used in musical improvisation and composing music by students. For teachers who use this toolkit, this allows to overcome the performing orientation of training in music colleges and universities that is characteristic of Russian music education today, since students in the process of composing music find their own methods of self-expression, carry out creative self-realization; optimize and intensify the process of musical creativity. In a related move, students successfully master the means of musical expressiveness and comprehend the nature of music, and their musical thinking is activated. The effectiveness of introducing smart education technologies in the educational process of such disciplines as musical-theoretical course in music colleges and universities is confirmed by a

S. A. Konovalova (B) · N. I. Kashina · N. G. Tagiltseva · L. V. Matveeva Ural State Pedagogical University, Yekaterinburg, Russia e-mail: [email protected] N. I. Kashina e-mail: [email protected] N. G. Tagiltseva e-mail: [email protected] L. V. Matveeva e-mail: [email protected] D. N. Pavlov Surgut College of Russian Culture named after A.S. Znamensky, Surgut, Russia e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_22

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qualitative and quantitative analysis of experimental work data using the capabilities of the statistical analysis apparatus (Pearson criterion).

22.1 Introduction At the modern stage of education development all over the world, a new toolkit with extensive potential is being introduced. Smart education technologies, which are based on the achievements of modern information and communication technologies, allow users to achieve new results. The concept of “smart education technology” is meaningfully connected with the concept of “smart education,” which is understood as an educational system that provides Internet-based interaction with the environment and the process of training and education for citizens to acquire the necessary knowledge, skills, abilities, and competencies [1]. Today, smart education technologies are actively being introduced into the practice of music education, significantly enriching both the sphere of musical creativity and the field of musical pedagogical theory and practice, as evidenced by numerous studies in this area [2–4]. In accordance with the Federal State Educational Standard of Secondary Professional Education, students enrolled in the “Vocal Art,” “Instrumental Performance,” “Solo and Choral Folk Singing,” “Choral Conducting,” “Musical Sound-Mastery Skills” programs of study must become proficient in (1) the ability to organize own activity, defining methods and ways to accomplish professional tasks, use information and communication technologies to improve professionally oriented activities in a frequent change of technology in professional activity (general cultural competence); ability to demonstrate skills in recording, mixing, and editing phonograms, reproducing an artistic image in recordings based on knowledge of the specifics of the musical language, arranging musical works using a computer, using computer arrangement for recording (professional competence). In accordance with the Federal State Educational Standard for Higher Education 44.03. 01 “Pedagogical education” (including the “Music education” profile), students must become proficient in the method of using scientific and mathematical knowledge to guide people in the modern information space (general cultural competence). Thus, the mastery by students of music colleges and universities of these competencies is due to the requirements of modern regulatory documents for learning outcomes. The educational content for students studying at music colleges and universities suggests mastering such disciplines of the musical and theoretical course as Musical Choice, Elementary Theory of Music, Solfeggio, and Harmony. The content of these

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disciplines includes activities such as musical improvisation (the process of composing new musical material spontaneously) and composing music (the teacher’s educational process to create a musical composition organized by a teacher), which contributes to students’ successful mastery of the musical language, their understanding of the nature of music, etc. [5, 6]. The use of smart education technologies in the process of musical improvisation and composing music will optimize and intensify the creative process of creating musical texts and presenting them in their final form. In this regard, the development of ways and means of introducing smart education technologies into the educational process of music-theoretical disciplines in music colleges and universities is extremely relevant. In addition, these technologies will make it possible to implement the tasks set in the “National Doctrine of Education in the Russian Federation,” “Concepts of the Federal Target Program for the Development of Education for 2016–2020”—creating favorable conditions for self-realization of a person, free development of his creative abilities, incentive mechanisms professional and personal development of youth.

22.2 The Theoretical Foundations of Smart Education Technologies Introduction into the Educational Process of Music-Theoretical Disciplines in Music Colleges and Universities Modern researchers reveal the didactic aspects of the introduction of smart education technologies in the system of domestic music education. Gorbunova [7], Krasilnikov [8] and others are considering the possibilities of developing musical creativity of students on the basis of electronic tools. There are studies addressed to people involved in creating music on a computer, which reveal the process of composing and arranging music on a computer (R.Yu. Petelin, Yu.V. Petelin), technology for using musical typesetting programs (GR Azatyan, SP Polozov, SI Sirotin), and the process of recording and editing sound using musical effects (T. Brown, AP Zagumennov). The authors conclude that today smart education technologies are an important way to store and broadcast musical works, element compatible and organically in interaction of the traditional methods of teaching music. They do not replace the teacher, but contribute to increasing the effectiveness of the educational, creative, and culturaltranslational process that takes place in institutions of the musical education system [2, 7, 8]. According to the studies of musicologists Aranovsky [9] and Gotsdiner [10], in the process of composing music, it is necessary to distinguish several stages. At the first stage, the concept of musical improvisation or musical composition is born through its selection and performance on musical and digital instruments (electronic pianos, synthesizers, etc.). At the second stage, in some cases, conscious, in other cases, subconscious thinking about the concept of the composition of the

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musical composition, the choice of its genre and the corresponding musical and thematic material during vocal or instrumental rehearsal work on the execution of the text of the future musical product using digital tools and elements computer music studio (music markup programs, a mixing console, a voice microphone system, etc. are used here). In the third stage, the synthesis of the details of the musical composition being compiled takes place, the idea develops into a holistic form, being realized as a musical improvisation or composition during an audio recording using elements of a computer music studio (audio editing, editing programs, VST tools are used here). At the fourth stage, the incubation phase is completed, and fragments of musical improvisation and musical composition and their instrumental and vocal parts are edited, as well as mastering of the music product using audio editing/editing programs. Thus, smart education technologies are used in end-to-end of students’ work on creating a musical work or musical improvisation. They optimize and intensify the process of musical creativity; students find self-expression techniques and comprehend creative self-realization.

22.3 An Experiment to Introduce Smart Education Technologies into the Educational Process of Music-Theoretical Disciplines in Music Colleges and Universities 22.3.1 Organizational Basis of the Experiment The main goal of the pedagogical experiment was to assess the effectiveness of the introduction of smart education technologies in the educational process of musictheoretical disciplines in music colleges and universities for the development of creative self-realization among students. Smart education technologies were included in the educational process of Surgut College of Russian Culture named after A.S. Znamensky, Surgut College of Music and the Institute of Music and Art Education of the Ural State Pedagogical University. 96 people participated in the experiment, of which control and experimental groups were formed, as well as five teachers of musical and theoretical disciplines. The experimental study included three stages: ascertaining, search, and control. The goal of the ascertaining stage of the experiment was to identify smart education technologies that contribute to the optimization and intensification of the process of musical improvisation and composing music and, as a result, the development of creative self-realization of students. The criteria and indicators of the development of creative self-realization were: motivational and value (the nature of the personality’s orientation toward self-realization; awareness of the subjective significance of this type of creative activity for self-development), practical activity (originality of the

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created art products, the ability to self-organize and self-correct musical and compositional activities), and reflectively emotional (the nature of the emotional response, the nature of self-esteem, introspection of the results of creativeness). The results of this stage made it possible to determine the research problem and confirm its relevance.

22.3.2 The Search Stage of the Experiment The purpose of the search stage was the introduction of smart education technologies in the educational process in the educational process of music-theoretical disciplines in music colleges and universities for the development of creative self-realization among students. The process of musical creativity was carried out in four stages. At the first two stages of the incubation phase, students’ musical and compositional work was carried out in support of two types of intuition—sensual, operating with visual and auditory images, and intellectual, revealing compositional details when operating with emotions, compressed/expanded sound complexes. Students formed plans for their own future musical products, including: improvisation on folklore samples (Russian Tune, Uzbek Dance) and on a given artistic image (Ancient Photography, In the Metro); vocal processing of folk songs (“In the nasty forest path” and “I’m sitting on a pebble”); auteur miniatures. At this stage, students used the keyboard workstations Korg PA1000, Roland Fantom G, Yamaha Motif XF to form the ideas of improvisation/compositions and for rehearsal work on the execution of the text of the future music product: Korg Kross 2-88 MB, RolandGaiaSH-01, YamahaPSR-S670 synthesizers; digital pianos Casio Privia PX-S3000, KorgC1-BK, YamahaYDP-164. We also used the musical layout software Finale, MuseScore, Sibelius to translate the most promising musical and sound ideas into musical text. When playing music and listening to synthesized tones, which are related to acoustic in sound, students developed the maximum number of promising musical and sound ideas. The use of Yamaha MG10XU, Behringer Q802 mixing consoles, as well as ShureSM58, BehringerXM1800S vocal microphone systems in rehearsal work made it possible to bring students’ performance feelings closer to those that needed to be reproduced in the future—in studio audio recordings. At this stage, the actions of students were associated with checking auditory and visual images, musical expressive complexes to establish their significance, with clarifying the form of future improvisations/compositions and their musical and artistic content. The third stage was the highest point of the whole process of intuitive actions related to students comprehending the artistic content of the future musical product, the upsurge of their creative forces. Musical and thematic material, not previously connected in a harmonious form, acquired internal orderliness at the time of such a vision, as a result of which all superfluous was excluded in order to preserve the most striking themes. At this stage, the main role was given to the use of such elements of

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a computer music studio as: VST-instruments Edirol HQ Orchestral, Edirol Hyper Canvas, LA Scoring Strings, Native Instruments Kontakt, Studio Drummer; audio editing/editing programs AdobeAudition, SoundForge, WaveLab. The completed texts of musical compositions were saved in MIDI format for sounding them with synthesized timbres in CubaseSX, Sonar, FL Studio audio-MIDI sequencers by using VST-instruments. Musical improvisations, voiced on musical digital instruments, as well as vocal parts of folk song processing performed by students, were recorded on multimedia computers using audio editing/editing programs (AdobeAudition, SoundForge, WaveLab). Recording of musical text plans was carried out in various ways—under the control of students (by pressing the Record/Stop virtual keys) or automatically when certain flags were selected in the program interfaces: Auto-startifsounddetected, which means recording starts when a signal is input to a linear input above an acceptable dynamic level; Auto-startatgiventime, indicating the start of the recording at the specified time; Auto-stopifsilence, which means recording stops when a signal is received at a linear input below a permissible dynamic level; Auto-stopaftergivenduration, which means stop recording after a specified period of time. The audio files of recorded plans of musical texts were stored on multimedia computers/digital storage media for the purpose of further implementation of their audio editing and editing. An important role for the artistic expression and processing of the sound of musical product plans was played by the students’ tuning of the effect modules, including: Chorus, which creates the effect of the sound separation and is characterized by a delay time in milliseconds, the frequency, and depth of modulations (distortions); Echo, creating an echo effect, characterized both by the sound delay time and the output dynamic level on the left and right channels of the sound panorama; Reverb, which creates the effect of sound reflection, characterized by a pre-delay time in milliseconds, the frequency width of the reflected sound signal, etc. Audio editing was carried out by students in several ways: by copying audio fragments and pasting them through the clipboard at the right time in the editing session; by compiling a list of skipped fragments; using a professional multi-channel nondestructive audio editing module. Mastering of sound recordings of improvisations and compositions was aimed at preparing and transferring a specific soundtrack to a digital information medium. The students applied stereo work methods aimed at noise reduction, dynamic sound compression, and expansion of the stereo sound field, while receiving the final sound options for music products. The students also used computer programs for creating PowerPoint presentations and video editing/editing Windows Movie Maker, Sony Vegas to create improvisations and compositions by students for creating video sequences/videos using students’ music and associated graphic images.

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22.3.3 The Control Stage of the Experiment The aim of the control stage of the experiment was to verify the effectiveness of the introduction of smart education technologies in the educational process of musictheoretical disciplines in music colleges and universities for the development of creative self-realization among students. A comparative analysis of the initial and final sections showed significant dynamics in the development of creative self-realization among students. Quantitative analysis of experimental data was carried out using the capabilities of the statistical analysis apparatus. The tested hypothesis is that the data differences in the initial and final slice are significant. Pearson’s criterion was used here. The theoretical value of the Pearson criterion X 2 t = 9.21. The experimental value of the criterion X 2  SG = 21.36. The experimental value of the X 2 e criterion of the CG group = 3.92. The result is (X 2 e SG = 21.36) > (X 2 e CG = 3.92). The hypothesis is confirmed. An analysis of the theoretical and experimental value of the Pearson criterion for group A showed that (X 2 e EG = 21.36) > (X 2 t = 9.21), which means that there is a relationship between the levels of development of students’ creative self-realization and the introduction of smart education technologies in this group. A similar analysis of data on the control group showed that between the levels of development of creative self-realization of students and the implemented communication program there is no (X 2 e CG = 3.92) < (X 2 t = 9.21.). An analysis of the data obtained for the SG and CG groups allowed us to confirm the presence of significance of the difference between these groups by the studied indicators.

22.4 Discussion Smart education technologies have great pedagogical potential. Previously, a musician could perform one of three functions—a composer, performer, or listener/sound engineer. Today, relying on smart education technologies, each student, within the framework of his own creativity, is able to combine these types of activities into a consecutive triad. A triad that is multifaceted and productive. Smart education technologies making it possible to overcome the performing orientation of traditional music education contribute to the activation of musical thinking, musical intelligence, and the development of more creative self-realization of students of music colleges and universities.

22.5 Key Findings Results of the introduction of smart education technologies in the educational process of music-theoretical disciplines in music colleges and universities have shown that

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these technologies influence the development of students’ creative self-realization: the manifestation of their focus on self-realization in creative activity, awareness of its subjective significance for self-development; the ability to carry out self-organization and self-correction in creative activity, to create artistic products that are distinguished by originality and the presence of a figurative sphere, melody, and harmony of form; the manifestation of emotional responsiveness of students; the implementation of adequate self-esteem of the results of their activities; the ability to self-analysis.

22.6 Research Prospects This study opens up prospects for the further implementation of smart education technologies in the educational process of secondary and higher vocational education: The efforts of teachers can be aimed at developing students’ skills of independent activity, the skills of creating timbre arrangements (instrumentation) of musical works in homework, etc.

References 1. Dneprovskaya, N.V., Yankovskaya, E.A., Shevtsova, I.V.: The conceptual framework of the concept of smart education. Open Educ. 6, 43–51 (2015) 2. Konovalova, S.A., Kashina, N.I., Tagiltseva, N.G., Ward, S.V., Valeeva, E.M., Mokrousov, S.I.: Application of smart-education technologies in the institutions of the russian system of additional education of children. Smart Innov. Syst. Technol. 99, 204–213 (2019) 3. Konovalova. S.A., Aksarina, O.O.: Modern computer programs and sites to help the teacher of pop vocal. Music Time 12, 46–49 (2019) 4. Apasov, A.A.: Music and computer technology as a basis for introducing students of a pedagogical university to composition and arrangement: dis. … cand. of ped. sciences, p. 160 (2015) 5. Kashina, N.I., Pavlov, D.N.: The problem of the development of creative self-realization of students of the college of culture and arts in the process of musical compositional activity. Innov. Projects Programs Educ. 3, 11–15 (2016) 6. Iophis, B.R.: Formation of the skills of improvisation and composing music by future teachers in the process of professional university training: dis. … cand. of ped. sciences, p 164 (2006) 7. Gorbunova, I.B.: The concept of music-computer teacher education in Russia. World Sci. Culture Educ. 4(77), 267–275 (2019) 8. Krasilnikov, I.M.: Electronic musical creativity in the system of art education: Diss…. EdD M., p. 494 (2007) 9. Gotsdiner, A.L.: Musical psychology: textbook. International Academy of Pedagogical Sciences; Moscow Humanitarian Lyceum, p 190 (1993) 10. Aranovsky, M.G.: The experience of building a model of the creative process of the composer. Methodological problems of modern art history. L.: Music, Pub. 1, pp. 127–141 (1975)

Chapter 23

Research on ‘Diteracy’ Measurement as a Smart Literacy Element Seyeoung Chun, Jeonghun Oh, and Seongeun Lee

Abstract This preliminary study focuses on investigating whether digital literacy, which is termed ‘diteracy,’ can be measured and how it relates to the conventional concepts and terms of literacy and numeracy by proxies of math and language achievement. First, the results of this study indicated relatively higher diteracy scores of the sixth-grade elementary students and the third-grade secondary ones. While no significant relations were found in terms of smart device using experiences of elementary students, the diteracy scores of the secondary ones indicated higher level of diteracy in some sub-components. Second, math scores are found to have more relations with diteracy than language scores.

23.1 Introduction Smart education is becoming more and more important with the changes in literacy practices and technological advancement. According to smart education policy announcement in 2011 by Education Ministry in South Korea, smart education is defined as ‘the power for innovating ecology of education including technological environment, content, pedagogy and assessment’ [1]. In a practical and strategic view, smart education can be restated as ‘21st century educational paradigm, which incorporates information technology and network resources into pedagogy to help students become global leaders.’ Smart education is a paradigm shift in education and preparing for the future and expanding the boundaries of time and space, content, S. Chun (B) Department of Education, Chungnam National University, Daejeon, South Korea e-mail: [email protected] J. Oh Modong Middle School, Busan, South Korea e-mail: [email protected] S. Lee Korean Council for University Education, Seoul, South Korea e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_23

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competency, and pedagogy in current educational systems to new areas. As a result, the focal point of the smart education is to embrace key competencies such as critical thinking, problem solving, creativity, innovation, collaboration, leadership, cultural awareness, communication, information technology, as well as career and life skills by extending traditional literacies such as reading, writing, and arithmetic (3Rs). As attention to the future of education is raised, key life-long competencies needed for future society are being discussed. One of the examples is Definition and Selection of Competencies (DeSeCo) project which had been done for 7 years from 1997 to 2003 initiated by OECD. The project sets a conceptual and theoretical basis for the research on key competencies. In addition to EU members, advanced countries, and global corporations such as Finland, Australia, Cisco, Microsoft, and Intel are investing the research on competencies for the future. As a result, the Assessment and Teaching of Twenty-First-Century Skills (ATC21S) project began in 2009. Reflecting the discussions and research studies on competencies for future education, smart education can be understood by expanding the areas of the traditional 3R’s of education, in other words, smart education that is based on traditional education without being separated from it. ATC21S project also includes the 3R’s as components of future competencies as well as ten other competencies categorized under ‘Ways of Thinking,’ ‘Ways of Working,’ ‘Tools for Working,’ and ‘Living in the World.’ Youngsook Park [2] also argues that in spite of the needs for future education, rote learning will continue to exists because it can facilitate brain activity that builds the basis for reflective thinking needed for meaningful learning. She goes further to explain that students can benefit from learning basic subjects with the help of pencils, books, newspapers, magazines, and calculators in non-digitalized environments. Even in a hyper-digital era, learning basic subjects in traditional ways will be one of the key objectives in education. Therefore, the goal of smart education lies in enhancing both the 3R’s and digital competency. This leads to the need for assessing them in a balanced way: the changes in literacy education should be addressed in the development of smart education. In this study, the concept of smart literacy and preliminary development of a smart literacy assessment tool will be discussed and suggested. Smart literacy is categorized into literacy, numeracy, and digital literacy. Literacy includes reading and writing in the 3R’s. Arithmetic, counting, and calculations in the 3R’s are re-categorized into numeracy. Finally, the new term of competency, ‘diteracy’ is created by combining ‘digital’ and ‘literacy.’ The assessment tool for diteracy was preliminarily developed by applying it to assessing diteracy of primary and secondary school students. The analytic results of the assessment will be used to construct the taxonomy of smart literacy in the future.

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23.2 Conceptual Journey to ‘Diteracy’ by Measurement Theory Review 23.2.1 Twenty-First-Century Skills The doubt that schools help students to enhance the abilities needed for future society led to research studies on the skills or competencies required for the twenty-first century. In addition, the innovation to teach them was brought up as one of the major agendas (Partnership for 21st Century Skills, 2009). The interest in twentyfirst-century skills has resulted from the awareness of how education can facilitate students to improve competencies needed in accordance with rapid development in science and technology. Definition and Selection of Competencies (DeSeCo) project by OECD [3] divides key competencies needed for future society into three categories: using tools interactively, interacting in heterogeneous groups, and acting autonomously. First, using tools interactively is associated with using language, symbols, texts, knowledge, information, and technology interactively. Second, interacting in heterogeneous groups means relating well to others, working in teams, and managing conflicts. Third, acting autonomously refers to acting within the big picture, conducting life plans and personal projects, and asserting rights, interests, limits and needs. Assessment and Teaching of Twenty-First-Century Skills(ATC21S) project further developed the concept of competencies conceived by OECD. ATC21S suggested ten skills under four categories: ‘Ways of Thinking,’ ‘Ways of Working,’ ‘Tools for Working’ and ‘Living in the World’ [3]. As shown in Table 23.1, ten skills are listed under the four categories of twenty-first-century skills.

23.2.2 Measurement and Assessment of Twenty-First-Century Skills The ATC21S project has had a significant impact on the trends of change in teaching, learning and evaluation by developing a way of assessing key competencies online. For example, OECD Program for International Student Assessment (PISA) planned to include test problems to assess collaborative problem-solving skills in the categories—ways of thinking and ways of working—starting in 2015. In addition, International Association for the Evaluation of Educational Achievement (IAEEA) tried to assess ‘learning skills in digital networks’ using tools for working and ways of living in the world. These trends of change indicate the importance of collaborative problem-solving skills and learning skills in networks. ATC21S project divides collaborative problem-solving skills into social and cognitive skills. Socials skills are divided into participation, perspective taking, and social regulation. Cognitive skills are divided into task regulation and knowledge

Ways of Thinking

– Creativity, innovation – Critical thinking, Problem solving – Learning to learn, metacognition

Category

Skills

Table 23.1 Twenty-first-century skills [4] – Communication – Collaboration (Teamwork)

Ways of Working – Information literacy – ICT literacy

Tools for Working

– Citizenship (local/global) – Life and career – Personal, social responsibility

Ways of living in the world

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Table 23.2 Participation skills indicators in collaborative problem solving [4] Level

Evaluation indicators

Low level

None or very little activity Acknowledging communication directly or indirectly Maintaining presence only

Middle level

Activity in familiar contexts Responding to cues in communication Identifying and attempting the task

High level

Activity in familiar and unfamiliar contexts Initiating and promoting interaction or activity

building. Each skill is assessed by evaluation indicators including several qualitative behavioral factors with three levels. For example, Table 23.2 shows evaluation indicators to assess participation skills.

23.3 Conceptual Journey to ‘Diteracy’ Literacy is the concept traditionally confined in reading and writing skills. However, as literacy environments and languages as communication tools change, the meaning of literacy is expanded to include the ability to understand, interpret, and use multimodal media such as video languages and digital languages [5]. As a result, new frameworks of literacy such as computer literacy, information literacy, technology literacy, visual literacy, media literacy, new literacy, and digital literacy have been suggested so that they can cover all types of communication tools using multimedia. One of the most meaningful efforts in this trend was the 2009 DRA, digital reading ability and test under the framework of OECD PISA which is distinctively different from the conventional reading ability test. DRA focused on the core and future competencies for reading Internet-mediated texts characterized as nonlinear and complex, and thus require another capability toward the plurality of the texts which is constantly connected with digital environments. About 38,000 secondary school students participated in the DRA test from countries around the world, and Korean students ranked first with 568 points, 69 points higher than the OECD average (499). However, DRA was a very limited approach only to access and comprehend the major information type of the Internet and digital world. Many other efforts and trials have been emerging created by various scholars and global research groups. ETS [6] defined digital literacy as the competency to use digital technology, communication equipment, and network as well as access, manage, integrate, and evaluate information in order to function adequately in the knowledge-based society while OECD [3] focused on the ability to create a digital tool and produce information as well as to access, integrate, and evaluate. ACER [7] emphasized the ability to accelerate the cognitive development at the individual dimension and then to enhance communication with others.

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Chun et al. [8] comprehensively re-conceptualized the ICT literacy concept based on the previous study review, and proposed a smart literacy which may be rather widely used in the smart era. He proposed the ability to observe and solve various problems in real life through informational thinking in harmony with ethics. Therefore, cultivating smart literacy means facilitating the development of the skills to collect, analyze, create, and manage with the understanding of ICT technology and smart technology and thus develop the ability to share information and communicate effectively with others. In this study, the concepts discussed at various levels and dimensions such as the existing DRA, ICT literacy, media literacy, smart literacy, and 21C capability can be generally accepted as shown in Table 23.3. In Table 23.3, ‘Applying SMART’ refers to five elements of SMART education, which were presented in the 2011 Smart Education Promotion Strategy by the MEST of South Korea [1], S (Self-directed), M (Motivated), A (Adapted), R (Resourceenriched), and T (Technology-embedded). They are conceptually matrixed with the elements proposed by 7C framework which was proposed by the ACT21C, the international twenty-first-century learning capacity measurement project conducted by OECD.

23.4 Measurement Tool Development and Research Method 23.4.1 Developing Measurement Tools of ‘Diteracy’ For this study, the smart literacy measurement tool developed by Chun Se-yeoung et al. (2013), was modified with some supplements. In addition to the existing smart literacy tool, we have added and complemented ten items in the area of the networking and wireless and video editing in addition to the existing 30 items. Now, the tool is composed of 40 questions as shown in Table 23.4 based on the content and ability factors.

23.4.2 Sample The fourth-grade students from three elementary schools and the third-grade students from three middle schools in Daejeon City and Chungnam Province in South Korea participated in the study (Table 23.5).

Smart media Interests

Understanding the type and functionality of Resource-Enriched the application (Google Goggles, Art Project, Technology Embedded Naver Museum, etc.) Understanding the type and function of the authoring tool (i Tunes U, Fdesk, your iBook Author, etc.) Documents kind understanding and capabilities (Google Docs, Prezi, OpenOffice, etc.) Understanding the type and function of the educational program (Open AR, Open Course Ware, etc.)

Understanding the software

SMART application Understanding smart technology, Technology Embedded understanding of smart devices (electronic blackboard, Tablet PC, personal pads, smart phones, etc.) Wi-Fi connection, understanding of the connection between the devices utilizing such devices using Bluetooth

Understanding of smart devices

Content Elements of smart competence

Table 23.3 Smart competency/literacy matrix

(continued)

Computing Critical Thinking Collaboration Creativity

Computing Critical Thinking

7C application

23 Research on ‘Diteracy’ Measurement as a Smart … 269

Smart media utilization

SMART application

Communication, creation of knowledge, Self-Directed utilizing a shared, digital textbook knowledge Motivative, Adapted

Use of information

Self-Directed Resource-Enriched Technology Embedded

Multimedia type (audio, graphics, video, etc.), materials production, utilizing the application materials produced, using the authoring tool materials produced, using the Docs document production, utilizing the educational program materials production, knowledge fusion-type materials production

Content Creation

Adapted

Reconstructing the retrieved information, information for use in the production of new information

Understanding the network, understanding of Technology Embedded computer communication, understanding of the cloud

Analysis of the information

Understanding Cloud Computing

Content Elements of smart competence

Table 23.3 (continued)

(continued)

Citizenship Collaboration

Creativity Collaboration

Creativity Critical Thinking

Computing

7C application

270 S. Chun et al.

Understanding the SNS community to Adapted, communicate with, Aesthetic (sensitivity) Resource-Enriched with a liberal arts and cultural qualities Smart Citizenship Protection of privacy and intellectual Motivative, Adapted property rights, the right attitude and critical consciousness netizens, the Internet and gaming addiction prevention, implementation of social accountability

Smart ethics

Self-Directed Motivative, Adapted

Smart Culture

SMART application Smart social characteristics and future, understanding the smart use of technology, new jobs and prospects for the smart society

Smart media world

Smart Society

Content Elements of smart competence

Table 23.3 (continued)

Critical Thinking

Communication Collaboration

Citizenship

7C application

23 Research on ‘Diteracy’ Measurement as a Smart … 271

Sum

Smart media world

Smart media utilization

Smart media interests

3

7

7

1

Smart ethics

3

1

2

Smart Culture

1

2

Analysis/Assessment

Smart Society

Use of information

Content Creation

Analysis of the information

1

Understanding Cloud Computing

Navigation

4

2

Awareness

Capacity factor

Understanding the software

Understanding of smart devices

Content Elements

Table 23.4 40 item composition for smart diteracy measurement

4

4

Organization/Creation

12

3

1

4

1

1

1

1

Utilization/management

7

4

1

2

Communication

40

4

1

3

6

9

5

4

5

3

Total

272 S. Chun et al.

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273

Table 23.5 Study sample for diteracy assessment Division Sex

Grade

Elementary School

Middle School

Frequency

(%)

Frequency

(%)

Male student

78

(54.9)

46

(29: 1)

Female student

64

(45: 1)

112

(70.9)

Sum

142

(100: 0)

158

(100: 0)

4th (elem)/1st (mid)

24

(16.7)

71

(44.7)

5th (elem)/2nd (mid)

85

(59: 0)

37

(23.3)

6th (elem)/3rd (mid)

35

(24.3)

51

(32: 1)

Sum

144

(100: 0)

159

(100: 0)

23.4.3 Analytical Methods As Table 23.6 displays, the research variables for this study are the demographic background variables of the study subjects such as school grade, grade, gender, and smart phone usage period. For collecting data related to the research variables, first, the diteracy scores of students were collected by using the diteracy measurement tool developed in this study. Second, in order to find out the relationship between the diteracy variable and the literacy variable, the reading ability corresponding to the existing 3R’s, the numeracy, the Korean language, and mathematics scores were collected and used as the proxy variables. Table 23.6 Data source and computing formula for variables used Variable name

Material

Variable calculation method

Schoolmates/Year

Elementary school 4–6 grade

Grade 1 = 1/2 grade = 2/3 = 3 grade/fourth grade = 4/5th Grade = 5/Grade 6 = 6

1–3 year junior high school Sex

Male Female

M = 1, F = 2

Smartphones period

Periodic intervals

1, no use/2, less than six months/3, 6 months to 1 year/3, 6 months to 1 year/4, 1 to 2 years = 4/5, more than 2 years

Diteracy

40 multiple-choice questions

Content elements and skills elements Average score

Literacy

Language subject achievement score

T score conversion (10Z + 50)

Numeracy

Math subject achievement score

274

S. Chun et al.

23.5 Results and Discussions 23.5.1 Overall Descriptions About Digital Literacy Level of the Sampled Students As shown in Table 23.7, although there are some minor differences according to the populations, T score can be confirmed with the average (50) and the standard deviation (10). A significant difference between the scores of the elementary and middle school students was found as shown in Table 23.8; the latter was higher than the former in all content areas except for the smart society variable. As shown in Table 23.9, by sex, female students from the elementary schools showed higher level of digital literacy than their counterpart, while the score of male students from the elementary schools was higher than that of male students from the secondary schools. Regarding the experiences of using smart devices, no significant differences in the scores of diteracy were found except for utilizing smart media as shown in Tables 23.10 and 23.11. This indicates the necessity for further study regarding the reliability and validity of the diteracy measuring tools.

23.5.2 Relations Between Diteracy and Language/Math Scores In this study, students’ language score as a proxy was set to literacy, and math score to numeracy. Table 23.12 indicates a significant positive correlation at p < .0.05 level between the diteracy scores and language/math achievement in the elementary and middle schools. The average scores by factors indicated a significant positive correlation except for three factors: understanding of smart devices, understanding of software, and smart society. Korean language scores of the elementary school students were not significantly correlated with diteracy (p < 0.05). Math scores were positively related to diteracy scores in general, and the math scores showed significant positive correlation with the scores of the female and fifth-grade students. In short, the results indicated that language and mathematics scores have significant correlations with digital literacy. However, this significant correlation does not have the effect size of influence between them but correlation exists between them even though it is a very small. This indicates that diteracy needs to be cautiously studied and investigated with the better framework of assessment.

Math

Language

Division

24 85 34

4th (elem)/1st (mid)

5th (elem)/2nd (mid)

6th (elem)/3rd (mid)

65

Female student

Grade

76

Male student

Sex

34

6th (elem)/3rd (mid) 143

85

5th (elem)/2nd (mid)

All

24

65

Female student

4th (elem)/1st (mid)

76

Male student

Sex

Grade

SD

50.00

50.00

50.00

49.73

50.04

50.00

50.00

50.00

50.00

49.66

50.00

10.15

10.06

10.22

10.96

9.31

10.04

10.15

10.06

10.22

10.65

9.49

10.04

16.62

14.14

30.52

14.14

16.55

14.14

55.76

60.66

59.92

60.66

60.66

60.66

57.32

64.90

−2.75 11.69

62.66

64.90

−2.75 28.90

64.90

11.69

Max. 64.90

Min. −2.75

52

36

69

110

47

157

52

36

69

110

47

157

Frequency

50.00

M

Frequency 143

All

Middle School

Elementary School

Table 23.7 Korean and math scores computed in Z-score of sampled students M

50.00

50.00

50.00

49.75

50.58

50.00

50.00

50.00

50.00

50.25

49.42

50.00

SD

10.10

10.14

10.07

10.18

9.75

10.03

10.10

10.14

10.07

10.07

10.04

10.03

Min.

31.09

26.46

21.86

21.86

23.72

21.86

24.69

24.40

19.22

19.22

24.69

19.22

23 Research on ‘Diteracy’ Measurement as a Smart … 275

276

S. Chun et al.

Table 23.8 Diteracy scores by school level Division

Elementary School

Middle School

M

M

SD

t

SD

Score

0.47

0.17

0.52

0.19

−2.34*

Smart Media Understanding

0.49

0.20

0.57

0.19

3.61 ***

0.52

0.26

0.65

0.24

− 4.41 ***

Understanding of smart devices Understanding the software

0.49

0.24

0.52

0.22

− 0.91

Understanding Cloud Computing

0.47

0.30

0.59

0.29

3.35 ***

0.50

0.22

0.54

0.25

−1.22

Analysis of the information

0.55

0.31

0.57

0.31

− 0.4

Content Creation

0.48

0.23

0.50

0.26

−0.77

Use of information

0.49

0.28

0.56

0.31

−1.89

Utilizing smart media

0.35

0.19

0.39

0.21

−1.95

Smart Society

0.31

0.21

0.28

0.24

1.21

Smart Culture

0.26

0.44

0.30

0.46

−0.87

Smart ethics

0.40

0.31

0.50

0.30

−2.97 **

SmartMedia world

23.6 Conclusions and Suggestions This study was designed to investigate the level of digital literacy in elementary and middle school students. Although the measurement tools and evaluation methods still have limitations on paper-based evaluations, it is useful for school sites to reconsider their literacy practices in regard to smart education and realize the future education vision by measuring the level of diteracy of our students. Digital literacy, termed into ‘diteracy,’ will be the critical research topic in smart education as the new paradigm of education. This study showed the meaningful step about its connection with the conventional concept of literacy, that is, the 3R’s. Further investigation needs to be focused above all on development of valid and reliable measurement tool.

23 Research on ‘Diteracy’ Measurement as a Smart …

277

Table 23.9 Diteracy by sex Division

Elementary School

t

Middle School

t

Male student

Female student

Male student

Female student

M

M

M

M

SD

SD

SD

SD

Score

0.43 0.17 0.51 0.17 −2.72 **

Smart Media Understanding

0.46 0.20 0.53 0.18 −2.03* 0.55 0.20 0.59 0.18 −1.33

Understanding of smart devices

0.51 0.27 0.53 0.26 −0.42

0.67 0.26 0.64 0.23 0.57

Understanding the software

0.46 0.23 0.53 0.24 −1.91

0.45 0.22 0.55 0.21 −2.49*

Understanding Cloud 0.43 0.31 0.52 0.27 −1.82 Computing

0.42 0.18 0.56 0.18 −4.48 ***

0.57 0.29 0.60 0.28 −0.55

Utilizing smart media

0.46 0.21 0.55 0.23 −2.47* 0.40 0.23 0.60 0.23 −4.86 ***

Analysis of the information

0.51 0.32 0.60 0.30 −1.77

0.40 0.31 0.64 0.28 −4.65 ***

Content Creation

0.45 0.22 0.52 0.24 −1.66

0.39 0.24 0.55 0.25 3.77***

Use of information

0.44 0.28 0.56 0.27 −2.71 **

0.40 0.30 0.62 0.30 −4.17 ***

0.32 0.19 0.39 0.19 −2.2 *

0.29 0.20 0.44 0.21 −4.2***

Smart Society

0.29 0.20 0.34 0.22 −1.49

0.22 0.22 0.30 0.24 −1.9

Smart Culture

0.23 0.42 0.30 0.46 −0.89

0.13 0.34 0.37 0.48 3.47 ***

Smart ethics

0.36 0.32 0.45 0.30 −1.65

0.38 0.29 0.56 0.28 3.66 ***

Smart Media world

0.54

0.53

0.52

0.64

0.49

0.47

0.42

0.35

0.24

0.52

Understanding Cloud Computing

Utilizing smart media

Analysis of the information

Content Creation

Use of information

Smart Media world

Smart Society

Smart Culture

Smart ethics

0.55

Understanding of smart devices

Understanding the software

0.51

0.54

Smart Media Understanding

0.18

0.30

0.43

0.20

0.17

0.28

0.21

0.28

0.21

0.33

0.27

0.29

0.24

0.43

0.34

0.13

0.25

0.28

0.42

0.42

0.53

0.44

0.41

0.65

0.46

0.52

0.38

0.35

0.15

0.20

0.24

0.14

0.41

0.19

0.13

0.26

0.25

0.15

0.14

SD

M

M

SD

Less than 6 months

not used

Elementary School

Score

Division

0.45

0.29

0.25

0.36

0.48

0.51

0.50

0.50

0.49

0.39

0.60

0.48

0.46

M

0.37

0.46

0.26

0.23

0.29

0.26

0.38

0.26

0.31

0.21

0.27

0.21

0.21

SD

6 months–less than 1 year

Table 23.10 Diteracy scores of elementary students by smart device experiences

0.35

0.32

0.28

0.32

0.56

0.50

0.53

0.53

0.47

0.50

0.48

0.49

0.47

M

0.30

0.47

0.22

0.20

0.27

0.23

0.31

0.21

0.30

0.22

0.25

0.16

0.17

SD

Year–less than 2 years

0.33

0.24

0.33

0.32

0.48

0.46

0.54

0.49

0.43

0.47

0.49

0.47

0.45

M

0.28

0.43

0.20

0.18

0.29

0.25

0.28

0.22

0.29

0.22

0.23

0.18

0.16

SD

More than 2 years

0.40

0.26

0.31

0.35

0.49

0.48

0.55

0.50

0.47

0.49

0.52

0.49

0.47

M

Sum

0.31

0.44

0.21

0.19

0.28

0.23

0.31

0.22

0.30

0.24

0.26

0.20

0.17

SD

2.43

0.43

0.15

2.00

0.76

0.37

0.84

0.34

0.61

2.29

.09

0.74

0.63

F

278 S. Chun et al.

0.36

0.35

0.49

0.48

0.42

0.60

0.48

0.33

0.40

0.60

Understanding Cloud Computing

Utilizing smart media

Analysis of the information

Content Creation

Use of information

SmartMedia world

Smart Society

Smart Culture

Smart ethics

0.53

Understanding of smart devices

Understanding the software

0.46

0.40

Smart Media Understanding

0.19

0.22

0.55

0.33

0.21

0.25

0.30

0.36

0.27

0.42

0.0.26

0.18

0.22

0.45

0.50

0.33

0.33

0.42

0.56

0.39

0.50

0.47

0.46

0.37

0.56

0.44

0.32

0.52

0.30

0.26

0.36

0.24

0.28

0.23

0.25

0.15

0.27

0.16

0.17

SD

M

M

SD

Less than 6 months

Not used

Middle School

Score

Division

0.53

0.33

0.22

0.39

0.54

0.49

0.64

0.54

0.58

0.51

0.67

0.57

0.52

M

0.36

0.50

0.24

0.25

0.35

0.25

0.31

0.25

0.25

0.20

0.24

0.16

0.19

SD

6 months–less than 1 year

Table 23.11 Diteracy scores of secondary students by smart device experiences

0.58

0.27

0.24

0.41

0.65

0.54

0.65

0.60

0.64

0.52

0.71

0.61

0.57

M

0.30

0.46

0.15

0.18

0.27

0.27

0.29

0.24

0.21

0.26

0.26

0.19

0.18

SD

Year–less than 2 years

0.48

0.30

0.29

0.39

0.54

0.51

0.56

0.53

0.60

0.53

0.65

0.58

0.52

M

0.29

0.46

0.25

0.22

0.32

0.26

0.31

0.25

0.29

0.22

0.23

0.18

0.20

SD

More than 2 years

0.50

0.30

0.28

0.39

0.56

0.51

0.57

0.54

0.59

0.52

0.65

0.57

0.52

M

Sum

0.30

0.46

0.0.24

0.22

0.31

0.26

0.31

0.25

0.29

0.22

0.24

0.19

0.19

SD

0.64

0.09

0.42

0.27

0.59

0.56

0.78

0.56

.37

.42

0.93

2.09

0.64

F

23 Research on ‘Diteracy’ Measurement as a Smart … 279

Secondary

Elementary

Total

Elementary

Math Total

Lang

Sub total

0.224

Girl 0.620*** 0.166

0.608*** 0.072

6th grd

0.000 −0.130

0.145 −0.052

−0.130

0.591*** 0.381**

3rd grd

0.386**

0.227 0.266

0.088

0.188

0.274*

Girl 1

0.213

0.165

0.179* 0.182

0.014

0.092

0.271*** 0.234*** 0.144*

0.220**

Boy 1

1

1

0.613*** 0.234

2nd grd

0.194

0.133

0.152

0.126*

0.139

0.205

0.137

0.141

0.502*** 0.304*

1st grd

0.280*

0.167

Girl 0.578*** 0.382*** 0.322*** 0.156

0.152 0.103

0.273

−0.003

0.151

−0.053

0.301*

0.207

Boy 0.513*** 0.181

0.557*** 0.314*** 0.307*** 0.171*

0.601*** 0.159

5th grd

−0.032

0.506*

4th grd

−0.095

−0.160

−0.095

Boy 0.540*** 0.049 0.066

0.069

−0.056 −0.139

0.111

0.055

0.586*** 0.098

0.086

0.141

0.164

0.136

0.122

0.121

0.008

0.121

0.022

0.133

0.022

0.078

0.115

0.253

0.184

0.251*

0.304*

0.105

0.284*

0.296*

0.099

0.211

0.285**

0.027

0.214**

0.263

0.058

0.192

0.070

0.183

0.117

0.170**

content creation

0.357**

0.228

0.186

0.326**

0.053

0.245**

−0.055

0.164

0.136

0.163

0.065

0.095

0.177**

using inform’

0.083

0.193

0.142

0.248*

0.201

0.217**

0.240

0.130

0.182*

0.212

0.290*

0.242**

0.184

0.029

0.095

0.249*** 0.257*** 0.226*** 0.241*** 0.180**

0.509*** 0.362**

0.192

0.286*

0.354*** 0.345*** 0.261**

0.331*

0.348*** 0.271*** 0.255**

−0.008

0.128

−0.049

0.068

0.042

0.060

0.205*** 0.204*** 0.170**

inform’ analysis

Utilizing smart media

software cloud comput’

Sub total

smart devices

Understanding smart media

0.046

Total

0.571*** 0.214*** 0.176**

Math

Table 23.12 Correlation between diteracy scores and math and language subject scores

0.113

0.180

0.024

0.196

0.079

0.095

0.195

0.046

0.119

0.167

0.045

0.095

0.076

−0.049

0.082

0.063

(continued)

0.168

0.029 0.106

0.095

0.141*

0.045

0.143

0.253*

−0.016 0.098

0.123*

0.295*

0.083

0.220

0.273** 0.188*

0.025

0.215** 0.161*

0.221

0.157

−0.227

−0.089

0.002

−0.042

0.072

0.049

0.158** 0.044

0.199

0.173

0.264*

0.279** 0.156

0.074

ethics

0.159** 0.061

culture

−0.170 −0.309

0.108

0.082

0.100

0.113

society

0.220** 0.124

0.165

0.091

−0.369

0.025

0.048

0.035

0.136*

Sub total

Smart media world

280 S. Chun et al.

1

6th grd

0.096

0.284**

0.053

0.053

0.163 0.122

0.186

1

3rd grd

0.361**

0.269

1

2nd grd 0.439**

0.093

0.002

0.058

0.152

0.018

0.385** 0.213

0.012

0.162

0.312**

1

1st grd

0.276*

0.388*** 0.337*** 0.201*

0.216

Girl 1

0.241

0.268

0.273

0.235*

0.152

0.159

0.198

0.187

0.355*

0.269*

0.118

content creation

0.227

0.309*

0.317

0.279*

0.478*** 0.307*

0.181

0.341**

0.328*

0.097

0.294*

0.369*** 0.368*** 0.296**

0.340*

0.240

0.255

0.241*

0.287**

0.207

0.357*** 0.290*** 0.266*** 0.241**

0.033

0.257*

−0.032

0.127

−0.014

Sub total

inform’ analysis

Utilizing smart media

software cloud comput’

Sub total

smart devices

Understanding smart media

0.317*** 0.297*** 0.205** 0.101

0.237

0.280**

0.101

Total

Boy 1

1

1

5th grd

*p < 0.05, **p < 0.01, ***p < 0.001

Secondary

1

Math

4th grd

Table 23.12 (continued)

0.196

0.191*

0.188

0.156

−0.089

Sub total

0.257

0.357*

0.218

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References 1. Chun, S.: Birth and major strategies of smart education initiative in South Korea and challenges. In: Uskov, V.L., et al. (eds.). Smart Education and e-Learning 2017, Smart Innovation, Systems and Technologies, 75 (2018). https://doi.org/10.1007/978-3-319-59451-4_44 2. Park Young Sook: 2020 Future Education report. Trend media, Seoul (2010) 3. Organization for Economic Co-operation and Development: Definition and Selection of Key Competencies: Executive Summary, OECD, Paris, viewed 04 Feb 2020 (2005), http://www. oecd.org/dataoecd/47/61/35070367.pdf 4. Griffin, P., et al.: Summarized in ‘Assessment and Teaching of 21st Century Skills’. Springer, Berlin (2012) 5. Gardiner, J.M.: Functional aspects of recollective experience. Memory Cognition 16, 309–313 (1988). https://doi.org/10.3758/BF03197041 6. ETS (Educational Testing Service): Digital Transformation: A Framework for ICT Literacy. A Report of the International ICT Literacy Panel (2002) 7. Australian Council for Educational Research (ACER) & Ministerial Council on Education, Employment, Training and Youth Affairs (Australia) (MCEETYA), John Ainley, Julian Fraillon, Chris Freeman (2007). National Assessment Program: ICT Literacy: Years 6 and 10 Report 2005 8. Chun, S., et al.: Study on the Indicator Development for Smart Education Policy Evaluation. KERIS (2013)

Chapter 24

Internet Resource as a Means of Diagnostics and Support of Artistically Gifted University Students Nataliya I. Kashina, Svetlana A. Konovalova, Anastasiya I. Suetina, Sergey I. Mokrousov, Elvira M. Valeeva, and Anastasia A. Gizatulina Abstract The article reveals the pedagogical potential of the Art-edu72.ru Internet resource, Department of Arts of the Institute of Psychology and Pedagogy, Tyumen State University developed and used from 2017 to the present at the of This e-learning tool takes advantage of the global information society to provide educational services of a new quality. This Internet resource, firstly, is aimed at conducting remote diagnostics of student’s talents and interested in teaching an Internet audience in the field of artistic and aesthetic activity (painting, graphics). Secondly, it allows you to differentiate students by the degree and orientation of talents and build individual trajectories of learning, to carry out their support before and during study at the university. Thirdly, this e-learning tool enables distance learning—the conduct of webinars by teachers, monitoring of special courses’ disciplines, providing the ability to view the archive of webinars, video files on various topics, followed by registration for on-line communication with the teacher, and participation in group discussions. Fourth, it helps to minimize technical and technological barriers to the creative self-realization of students and other users of the Internet audience: designing creative works for presentation at virtual exhibitions (including personal ones), N. I. Kashina (B) · S. A. Konovalova · A. I. Suetina Ural State Pedagogical University, Yekaterinburg, Russia e-mail: [email protected] S. A. Konovalova e-mail: [email protected] A. I. Suetina e-mail: [email protected] S. I. Mokrousov Tyumen State University, Tyumen, Russia e-mail: [email protected] E. M. Valeeva Southern Ural State University, Chelyabinsk, Russia e-mail: [email protected] A. A. Gizatulina Chelyabinsk State University, Chelyabinsk, Russia e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_24

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posting photographs of works at the exhibition, receiving an electronic version of a diploma, certificate of participation exhibition or competition, providing the opportunity to unite the jury members remotely on one platform, and career guidance for all users who have visited this in Internet resource.

24.1 Introduction The rapid development and dissemination of information technologies, which is happening all over the world, have led to their active implementation in educational practice. According to the decree of the President of the Russian Federation “On National Goals and Strategic Tasks of the Development of the Russian Federation until 2024,” one of the guidelines of the Russian education system is to create modern and safe digital educational environment that provides high quality and accessibility of education of all types and levels. At the same time, a number of regulatory documents related to the content of education in Russia (for example, in “The Concept of Long-Term Socio-Economic Development of The Russian Federation for the Time Period Until 2020”, “The Concept of the Nation-Wide System for Identifying and Developing Young Talents”, and other documents), strongly emphasize the need to create various advanced systems to identify, support and develop talented children and talented youth, and realize their potential. For this, it is necessary to use valid methods for identifying talent and accompanying them at all stages of formation and development. One of the steps toward the implementation of this idea is the launch in Russia in 2016 of the information resource “The resource on talented children” (https://talantypoccii.pf), the tasks of which are: recording information about talented children, their individual achievements, educational organizations in which they study, taking into account information about olympiads and competitive events, competitions, and generating analytical reports. Here is also formed a register of summary electronic portfolio of talented children for their further individual accompaniment. The information of this resource is the basis for state support of talented youth (for receiving grants from the President of the Russian Federation). Internet resources and remote technologies allow today to identify and diagnose talented children. For this, special sites and portals are created that allow on-line diagnostics. So, for example, according to Malkova et al. [1] reveals the contents of the educational portal, focused on the development of talented children and adolescents, tested in the Siberian Federal District. The authors conclude that the use of information technology allows us to solve the problem of the maximum individualization of the educational process, which is a condition for the full and adequate implementation of talented children and youth, since talented children “outgrow” quickly the traditional school curriculum and require the construction of an individual educational route. But, with all the variety of forms of informatization of education, the Russian Internet space is quite acutely aware of the lack of educational Internet resources,

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the content of which would offer diagnostic tools for identifying and accompanying children and youth with special educational needs in narrowly specific types of activities, which are artistically creative activity (including choreographic, stage, literary and poetic, visual, and musical). This article will generalize the experience of the practical use of such an e-learning tool as an Internet resource for diagnostic work and support of talented youth in the field of artistic and creative activities. This information resource, on the one hand, combines traditional talent diagnostics for modern psychologists and educators (expert visual assessment of art products, questionnaires, psychometry). On the other hand, this e-learning toolkit has been presented to recipients (university students and interested Internet audiences) in a new format—the format of the Internet resource. This allows you to combine and synchronize in a single process the work of experts, teachers and psychologists who analyze the artistic talent, and then, based on the results of diagnostics, build individual student learning, development and self-realization routes, and accompany him. This format implements the principles of smart education (formulated by Dneprovskaya et al. [2]): the principle of organizing independent cognitive, research and project activities of students; the principle of the implementation of the educational process in a distributed learning environment; the principle of interaction between students and the professional community; the principle of implementing flexible educational paths, individualization of learning. It also takes into account the predisposition of modern youth to visual perception of culture and more optimal work with visual sources of information. At the same time, providing access to diagnostic tools for a wide range of users in the Internet resource described in this article makes the diagnostic process more transparent, technological, optimal, and creates the conditions for repeating the experience gained by the authors of this article at other universities in Russia.

24.2 Theoretical Basis What kind of person can we call artistically talented? In modern scientific literature, talent is understood as a systemic, developing during the life quality of the psyche, which determines the ability of a person to achieve higher, extraordinary results in one or more types of activity compared to other people [3]. The high-quality originality of a person’s talent is due to heredity, the influence of the environment, vigorous activity, psychological mechanisms of self-development of a person, etc. Endowment in the field of visual activity refers to the artistic and aesthetic appearance [3]. It can manifest itself in a number of aspects: practical (motor skills in the visual arts), cognitive (interpretation of works of art), communicative (communication with colleagues and spectators), spiritual, and value (embodiment of a certain value content in an artistic image). Endowment in the field of visual activity involves the simultaneous inclusion of all levels of mental organization with a dominant significant level for a particular type of activity. In this case, either sensorimotor qualities

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(then it comes to mastery) or emotionally expressive (visual expressiveness) may come to the fore. To solve the problem of accompanying talented students and school children in this field successfully, modern researchers suggest introducing new approaches to organizing educational activities: • “smart e-Learning”—the integrated use of information technologies (audiovisual materials, 3D worlds, Internet sites, etc.) in the development of musical and artistic abilities of students [4]; • tutoring at a university with differentiation of role positions (teacher-tutor, tutorteacher) [5]; • remote interaction between university teachers and gifted students [6], the use of information and communication technologies based on Internet resources when working with talented children outside school hours [7]; • the use of the information environment to develop individual learning routes for students [8, 9]. Experts in the field of psychology and pedagogy note that talent is a systemic quality of the psyche that develops throughout life and determines the ability of a person to achieve higher, extraordinary results in one or more types of activity compared to others [3]. They recommend that research be carried out in the form of monitoring—long-term measures using various diagnostic tools, since this approach allows more reliable results [6, 10], which was carried out in the work of the authors of this study.

24.3 Experimental Part 24.3.1 Organizational Basis of the Experiment The main goal of the pedagogical experiment was to evaluate the effectiveness of introducing the e-learning tool—the Internet resource Art-edu72.ru for diagnosing and accompanying artistically talented students at the university. The experimental work was carried out at the Department of Arts of the Institute of Psychology and Pedagogy of Tyumen State University in December 2017 and January 2018. The experiment involved 19 people—students of the Tyumen State University, of which control and experimental groups were formed, as well as six teachers and experts. To conduct diagnostic work, relying on the research of Epiphany and Mokrousov et al. [1, 11] identified the following criteria and indicators of artistic talent: • motivational (expressed student interest in one or more areas of visual activity, readiness for in-depth development of one or several areas of visual activity); • cognitive (the degree of completeness of knowledge in one or several areas of visual activity, the presence of a formed cognitive interest, breadth of mind, the

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need to expand and deepen theoretical knowledge and practical skills in one or more areas of visual activity); • operational-technological (degree of ownership of the toolkit in one or several areas of visual activity, the presence of an individual style, a specific strategy of activity, including speed of development, variability, generation of new goals); • personal-value (high demands on the results of their activities, rejection of standard and template decisions, and the presence of their own value ideas).

24.3.2 Search Stage Experiment The diagnostic work was divided into two stages: preliminary, at which persons from the total number of students were selected with signs of talent; stating, during which the preliminary results of the study were verified, problem areas in talent were determined for each researched, then an individual map and a program for the talent development in the field of visual activity were compiled. The following research methods were used: pedagogical observation, expert assessment, analysis of activity products, tests, and a survey. For successful activity in the field of visual activity, the dominant of figurative thinking, creativity, and to a lesser extent objective, symbolic, and symbolic thinking are necessary. Experimentally confirmed studies of IQ intelligence have convinced us of the need to introduce this indicator to identify the contingent [12, 13]. To identify the IQ level of students with signs of giftedness, we used the test methods of J. Bruner and J. Raven, which were available in the “Personal Account” of the user after completing the registration procedure on the Art-edu72.ru Internet resource. Based on above-mentioned criteria, six students remained after the preliminary stage. The main reasons for exclusion from this list were low motivation, participation in one exhibition of one work, the presence of one positive expert review, and low ratings for viewing creative works. At the ascertaining stage of the experiment, verification of the results of preliminary selection and identification of students with signs of talent in the field of visual activity was carried out: the specifics of motivation, the presence and degree of development of the cognitive and cognitive sphere were identified (comprehension of basic knowledge, the presence of conceptual thinking, breadth of mind, etc.), specifics individual strategy of activity (work in series, individual style), the presence of personal-value representations (adoption of templates, the identity of the author’s position, stereotypes). Further, a questionnaire was conducted, the purpose of which was to verify the specifics of motivation, to identify the degree of formation of the cognitive sphere, the degree of understanding of the individual strategy of activity, and the presence of personality-value ideas. Based on the processed materials of the questionnaire, the results of the primary diagnosis, a summary diagnostic map of the results of the ascertaining stage was compiled.

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The results of this stage made it possible to draw up an individualized diagnostic map for each student, which made it possible to identify problem areas and draw up an individualized program for each student for the development of mild signs of talent. Based on the data obtained and the completed diagnostic cards, individualized programs for each student were developed. Their implementation suggested: replacing the teacher’s position with the position of tutor or tutor-teacher, choosing one of four types of talented work programs: acceleration, deepening, generalization, problematization. The educational Internet resource www.art-edu72.ru allowed solving the following tasks at this stage: • providing teachers with the opportunity to conduct, record, and demonstrate video webinars in real time with various groups of students (micro-groups with up to 6 persons), differentiation of groups of students based on the level of training, and the results of preliminary testing. • providing the opportunity for all interested users from among the Internet audience (teachers, students, parents, applicants, etc.) to participate in the discussion of relevant topics at the forum, to conduct monitoring (on-line voting) on implemented disciplines of special courses. • providing the ability to view the archive of webinars, video files offline on various topics and subsequent registration on the “liked” webinar for on-line communication with the teacher. • removal of technical and technological barriers for the manifestation of motivated students’ activity in the field of artistic and aesthetic activity (registration of works for presentation at virtual exhibitions, moving to places of exposure, posting photographs of works at the exhibition, obtaining an electronic version of a diploma, certificate of participation in an exhibition or competition, etc.). • providing the opportunity to unite a competent jury remotely on one platform (various regions, universities, etc.). • career guidance for all users who have visited this Internet resource (which contains information about the activities of the Department of Arts of the Institute of Psychology and Pedagogy of Tyumen State University, its areas of work, history, traditions, news, curricula, etc.). The talent in the field of artistic and aesthetic activity with the help of such an e-learning tool as the Internet resource Art-edu72.ru is developed due to: identifying and self-actualizing the creative potential of the student’s personality; comparing personal artistic experience with similar experience of representatives of various groups of students; stimulating students’ motivation and activity (through participation in contests, exhibitions, webinars, etc.); distance learning with educational material in the “Webinar” section (videos of lectures by teachers, students, testing in the educational sections “Drawing,” etc.); students completing methodological developments and posting on the site; participation in remote on-line classes in micro-groups in the webinar mode.

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24.3.3 The Control Stage of the Experiment. Experimental Results The purpose of the control phase of the experiment was to check the effectiveness of using the Internet resource Art-edu72.ru to diagnose and accompany artistically talented students at a university. This stage was carried out in December 2018. A comparative analysis of the initial and final sections showed significant dynamics in the development of all the indicators of students participating in the experiment (motivational, cognitive, operational-technological, personal-value).

24.4 Discussion The e-learning tool, Internet resource Art-edu72.ru, implements the principles of smart education and allowed: to remote diagnostics of talented students in the field of artistic and aesthetic activity; to differentiate students by the degree and direction of talent and build individual trajectories of learning and development, to accompany them during training at the university; to carry out distance learning, communication with the professional community; minimize technical and technological barriers to students’ creative fulfillment; and to provide vocational guidance for students.

24.5 Key Findings Thus, the use of such an e-learning tool as the Internet resource Art-edu72.ru allows you to increase the effectiveness of teaching artistically talented university students through the timely identification, differentiation, and targeted individualized work. The results of the experiment led to the conclusion about the effectiveness of this e-learning tool. Its application demonstrates that the mobility of domestic education is a response of educational structures to the social order of society to ensure high quality and accessibility of education of all types and levels.

24.6 Research Prospects The study opens up prospects for the further implementation of smart education technologies in the educational process of higher professional education: expanding the content of this Internet resource Art-edu72.ru for talented students in other areas of artistic and creative activity, for example, stage, literary, poetic, and musical.

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References 1. MalkovaI, Yu., Matsuta, V.V., Podoynitsyna, M.A.: The educational portal as a condition for the formation of a professional community focused on the development of the giftedness of children and adolescents: substantiation of the content and results of testing in the Siberian Federal District. Siberian Psychol. Mag. 49, 50–58 (2013) 2. Dneprovskaya, N.V., Yankovskaya, E.A., Shevtsova, I.V.: The conceptual framework of the concept of smart education. Open Educ. 6, 43–51 (2015) 3. Epiphany, D.B., Shadrikov, V.D., Babaeva, Yu.B., Brushlinsky, A.V., Druzhinin, V.N., et al.: The Working Concept of Talent, 2nd edn, Expanded and revised (2003) 4. Tagiltseva, N.G., Konovalova, S.A., Kashina, N.I., Valeeva, E.M., Ovsyannikova, O.A., Mokrousov, S.I.: Information technologies in musical and art education of children. Smart Innov. Syst. Technol. 75, 112–119 (2017) 5. Mokrousov, S.I.: Tutoring of gifted students studying in artistic and pedagogical specialties (Electronic resource). Pedagogy of art, no. 1, pp. 43–48 (2017). Access mode:http://www.arteducation.ru/sites/default/files/journal_pdf/mokrousov_43-48.pdf 6. Filippov, S.A., Komelina, E.V.: The system of training teachers for working with gifted children based on modern information technologies. Bull. RUDN Univ. Ser. Inf. Educ. 1, 28–34 (2013) 7. Larionova, T.V., Sologubova, N.B.: The use of information and communication technologies when working with gifted children outside school hours. Gaudeamus 15(1), 94–97 (2016) 8. Lapenok, M.V., Tagiltseva, N.G., Matveyeva, L.V., Patrusheva, O.M., Gerova, N.V, Makeeva, V.V.: Formation of the individual learning pathin the information and educational school environment. Smart Innov. Syst. Technol. 59, 553–562 (2016) 9. Tagiltseva, N.G., Matveeva, L.V., Byzova, M.A.: Personally oriented development models of musically gifted children. Educ. Sci. 21(3), 106–124 (2019) 10. Cold, M.A.: Psychological Mechanisms of Intellectual Giftedness: From Traditional Ideas to a New Understanding of the Nature of the Phenomenon, Webinar, 20 Feb 2013. Access mode:https://www.youtube.com/watch?v=S3eVFgiC4nQ 11. Mokrousov, S.I.: The Development of Professional Competence of Teachers of Fine Art in the Field of Computer Modeling (in the Continuing Education System) [Electronic resource]. Pedagogy of art, no. 3, pp. 1–8 (2010). Access mode: http://www.art-education.ru/sites/default/ files/journal_pdf/11_09_mokrousov.pdf 12. Herrnstein, R.J., Murrey, Ch.: The Bell Curve: Intelligence and Class Structure in American Life. FreePress, N.Y. (1994) 13. Schneider, W.: Acquiring expertise: determinants of exceptional performance. In: Heller, K.A. (ed.). International Handbook of Research and Development of Giftedness and Talent, p. 311– 324. Pergamon, Oxford (1993)

Part VI

Smart University Development: Organizational and Managerial Issues

Chapter 25

Strategic Management of Smart University Development Leyla F. Berdnikova, Irina G. Sergeeva, Sergey A. Safronov, Anastasia Yu. Smagina, and Aleksandr I. Ianitckii

Abstract In modern market conditions, each organization needs an effective management system. Such a management system is needed that meets the laws of the economic system, scientific, and technological progress and is associated with the achievement of strategic goals, ensuring the interest of employees in the highest final results. Managing a smart university requires multilateral knowledge, a research approach, strategic, analytical thinking, organizational skills, communication skills, responsibility, innovation, and enterprise. Management Theory is constantly evolving. In practice, there are no two organizations with absolutely identical management models since management methods are constantly being adjusted to reflect changes in the external environment. Success in the activities of a smart university depends on the effectiveness of strategic management of its development. The main goal of the article is to improve the strategic management of the development of the smart university. As a result of the study, a strategic university model of smart university is proposed. The study made it possible to single out the project-planning and program approaches to the strategic management of the development of the smart university, which takes into account the features of its activities. The result of the study is the recommended main stages of strategic management of the development of the smart university. The results obtained were tested by the strategic development department of the smart division of the university on the example of the department.

L. F. Berdnikova (B) · A. Yu. Smagina · A. I. Ianitckii Togliatti State University, Togliatti, Russia e-mail: [email protected] I. G. Sergeeva St. Petersburg National Research University Information Technologies, Mechanics and Optics, ITMO University—Saint-Petersburg, Saint-Petersburg, Russia S. A. Safronov Samara State Transport University, Samara, Russia © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_25

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25.1 Introduction At present, the importance of a strategy aimed at long-term development in competition has increased significantly. Rapidly changing environmental conditions, the emergence of new requests and changes in the consumer’s position, the opening of business opportunities, the wide availability of modern technologies, and other reasons have led to an increase in the importance of developing a smart university development strategy. It should be noted that in our understanding, smart university is the “university of the future,” focused on modern technologies, and focused on intellectual and innovative development [1]. Many scientists, for example, Serdyukova [2], Serdyukov et al. [1, 3, 4], Coombs [5], Bhattacharya et al. [6, 7], Glukhova et al. [8] developed and published significant research outcomes in the areas of smart universities, smart education, and smart e-learning. In the economic literature, quite a lot of works are dedicated to the study of strategic management, for example, Kavenkin [9], Nikiforova et al. [10], Chichkina and Toymentseva [11], Kaplan and Norton [12], and publications by other researchers. Currently, significant attention is paid to the study of strategic management of the University’s development, for example, Khokhlov et al. [13, 14], Ekkel et al. [15]. Currently, the development of science and technology, the intellectualization of labor, and the emergence of new technologies contribute to the transformation of the education system, which requires improving the tools for managing the development of smart universities. Practice shows that there is no single strategy for all organizations. Each organization is unique by its nature, so the strategy development process must be individual. This is also justified by the position of smart university in the market, the dynamics of its development, the behavior of competitors, the quality of services provided, work performed, the state of the social sphere, economy, and science. In our opinion, the strategy is considered as a long-term, well-defined direction of smart university development, which concerns all areas and directions of its activities, the system of its internal relationships, as well as the university’s attitude to the environment, contributing to the achievement of its goals. The strategy is a set of rules that guide management personnel in making management decisions aimed at achieving the mission and strategic goals of the smart university.

25.2 Statement of the Problem in General Form and Its Connection with Important Scientific and Practical Tasks Currently, the concept of smart university development strategy is reaching a new level and requires increased attention for the purposes of effective management. In

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this regard, the smart university’s development strategy is to develop measures aimed at its long-term successful operation. As practice shows, many organizations tend to adhere to a certain strategic orientation for 10–15 years before they make significant changes in the direction of development. The main factors driving strategic change include: changes in the economic situation; the change of the political system; changing the organization’s management bodies; change of business owner; threat of takeover of the firm; the need for a “breakthrough” in the company’s activities if it does not reach the expected results. Management activities aimed at developing, implementing, and adjusting the strategy are considered as strategic management. We believe that the essence of strategic management of smart university development consists in a well-organized comprehensive strategic planning that ensures the development of a long-term strategy to achieve the university’s goals and the creation of management mechanisms for implementing this strategy through a system of plans. Strategic management is an integral part of smart university system management and is based on procedures and methods of analysis, selection of strategic goals and ways to achieve them. If a smart university is a complete mechanism, then its strategy should be comprehensive, taking into account the relationships between individual subsystems of the enterprise and the influence of the external environment on them. This article is devoted to solve the problem of improving the strategic management of the development of the smart university, including modeling, approaches and stages of strategic management of the smart university. The proposed strategic management model will allow us to achieve the main goal of the smart university, long-term effective development, taking into account the needs of the external environment. The strategic management stages recommended in the article will make it possible to evaluate the effectiveness of the strategic management of the smart university according to such criteria as: growth in the performance of the main subsystems of the organization, implementation of the development program, increase in the indicators of socio-economic, scientific and research development of the smart university. This article pays significant attention to the expansion of the conceptual apparatus regarding the terms “smart university strategy,” “strategic management of smart university development.” Thus, the effectiveness of smart university activities can be achieved by building and implementing a clear strategy and an effective system of strategic management of smart university development. In this regard, special approaches to manage the strategic management of smart university development are needed.

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25.3 Presentation of the Main Research Material with Full Justification of the Obtained Scientific Results 25.3.1 Modeling of Smart University Strategic Management In the process of functioning, smart university should focus on successful development in the future, strive to achieve the intended strategic goals. Development should be implemented in such a way that all changes in the organization, its structure, management, educational process, investment, innovation, and research activities allow to preserve and improve the financial results in rapidly changing market conditions, as well as other characteristics that correspond to the interests of owners and the interests of employees agreed with them. In general, the process of strategic management of a smart university is presented in Fig. 25.1. Environment analysis is the initial process of strategic management, as it provides the basis for developing the mission and goals of smart university. The analysis of the environment involves the study of three parts: (1) the macroenvironment; (2) the microenvironment of the environment; and (3) the internal environment. Macroenvironment analysis involves studying the influence of such elements of the environment as: economy; legal regulation; political processes; natural environment; socio-cultural components of society; scientific and technological progress, and so on. Microenvironment analysis involves the study of: the activities of potential customers, suppliers, competitors, the labor market, etc. The analysis of the internal environment is aimed at identifying the internal potential of the smart university, which is necessary for achieving its goals and functioning in a competitive environment. Defining a mission and goals as a separate process includes sub-processes. The first of them is to define the mission of the smart university. The next sub-process is aimed at defining long-term goals. The third sub-process is the definition of shortterm goals. The strategy selection process is the center of strategic management. Through special techniques and methods, smart university determines how it intends to achieve its goals and implement its mission. This stage is the development of a generalizing model of actions necessary to achieve the set goals by coordinating and allocating resources. Implementation of the strategy is a management activity to achieve the selected strategy, monitoring its implementation. Performance assessment helps to make adjustments to the strategic planning methods for implementing strategic management due to new circumstances. At the same time, adjustments can be made to the mission, goals, strategy, and methods of its implementation. This task allows you to close the loop of strategic management.

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Analysis of the external environment

Establishing a mission and goals

Choice of strategy

Smart university

Defining methods and ways to achieve the strategy

Strategy realization

Evaluating and monitoring the implementation of the strategy Fig. 25.1 Modeling of smart university strategic management

This confirms that strategic management is a continuous process that allows you to track changes in the situation both within the smart university and outside of it. However, despite the many advantages, strategic management has a number of disadvantages and limitations on its use, which indicate that this type of management, as well as all others, does not have the universality of application in all situations to solve any problems. Strategic management cannot provide an accurate and detailed picture of the future. Modeling in strategic management of the desired future smart university is a set of quality wishes for what state the university should be in the future, what position it should occupy in the market, what kind of organizational culture it should belong to, and which business groups to enter. However, all this together should be what

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determining whether smart university will survive in the future in the competition or not. Thus, as a result of the study, a strategic management model of the smart university is proposed.

25.3.2 Approaches to Strategic Management of Smart University Development Strategic management of smart university development is a process that is constantly implemented. As it is implemented, new long-term and short-term goals can be set. At the same time, managers must solve tactical tasks at each stage, moving toward a more complete and better solution. In this regard, there are two different approaches to strategic management of smart university development (Fig. 25.2). In our opinion, the strategic management of a smart university can be implemented using project-planning or program approaches. The project-planning approach is based on the fact that the goal is set as a project for the desired development of a smart university. The goal involves: increasing competitiveness; increasing the number of students; introducing innovative educational technologies; expanding the market share of educational services; increasing the scale of research activities; qualitative changes in the activities of smart university, which will provide performance indicators and sustainable development.

Approaches to strategic management of smart university development

Project-planning approach

In the project-planning approach, the goal is defined as the project of the desired state of the enterprise for the long term

Programmatic approach

The program approach is based not on the final goal, but on the problems and opportunities for solving them that exist at the initial moment of development

Fig. 25.2 Approaches to strategic management of smart university development

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Using the project-planning approach, it is assumed that the factors of the external and internal environment of smart university can be predicted for the entire longterm period. In accordance with this forecast, the goal of strategic development is determined. The strategy in the project-planning approach is understood as a comprehensive long-term plan that ensures the achievement of the set goal. However, the project-planning approach can be effective if the overall economic stability is maintained. Otherwise, it can lead to difficult-to-implement plans. This is because in the process of implementation of the plan, circumstances may change that will make the goal irrelevant. The program approach does not originate from the final goal, but from the problems and opportunities for solving them that exist in the current development. In the process of its implementation, the set goals, methods of achieving them, and program activities are determined in stages. The goal of each stage is determined before a specific stage is completed, taking into account the assessment of the current situation and the results of the previous stage. We believe that using a software approach in the strategic management of smart university development allows us to improve performance and eliminate unattainable goals. The program approach allows you to adjust management actions at each stage based on changes in the environment and the actual results achieved. The program approach helps to integrate economic, social, legal, and other changes into a single process, forming a single mechanism for this process, which is difficult to achieve with a project-planning approach. In the process of strategic management of smart university development, the mission can be maintained or changed. When changing a mission, the concept must specify both the old and new missions to be implemented as a result of development. In addition to the mission, the concept of the strategic development program includes other ideas that allow you to define the development process and manage it. The concept sets out which areas should be preserved and which ones should be changed in the activities of the smart university or the existing system of relations. The strategy determines how this can be achieved. We believe that it is necessary to group conceptual ideas and strategic principles according to the types of activities or relationships, specifying the key ideas for the development of the educational complex, research activities, marketing, financial activities, administration relations with staff, and others. As a result of the study, the design-planning and program approaches to the strategic management of the development of the smart university are highlighted, taking into account the features of its activities.

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25.3.3 Stages of Strategic Management of Smart University Development Strategic management of smart university development should be carried out in stages, taking into account the peculiarities of its activities. We believe that the strategic management of smart university development should focus not only on its own potential but also on changes and needs of the external environment. In Fig. 25.3, we present the recommended main stages of strategic management of the development of the smart university. We believe that the strategic management of smart university development should begin with the analysis of the external environment, including the assessment of the macroenvironment and microenvironment. The second stage involves the analysis of management goals and criteria for smart university subsystems, namely educational, methodological, research, management, economic, marketing, economic, and innovation activities. The third stage is aimed at strategic planning and development of the smart university development program. At this stage, it is important to set goals and criteria for managing a smart university, as well as strategic ideas for its development by subsystems. This stage involves the development of a development program and the forecast of indicators of socio-economic, scientific, and research development of the smart university. The fourth stage is dedicated to managing the implementation of the smart university development strategy. It involves the implementation of targeted programs for the development of smart university subsystems, as well as monitoring the implementation of its development program. The fifth stage includes evaluating the implementation of the smart university development strategy. At this stage, the results of the strategic management of smart university development are evaluated, as well as the socio-economic, scientific, and research development of smart university are evaluated. Based on our research outcomes, we recommend the designated main stages of strategic management of the development of the smart university.

25.4 Conclusions and Next Steps Conclusions. The study showed that for the effective development of the smart university, it is necessary to improve the strategic management system. 1. The study revealed the need to develop and implement the right smart university development strategy, which defines long-term goals and allows you to increase competitive advantages, expand markets, and increase business results. In this regard, the strategic management model of the smart university is formed in the article.

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Stage 1. Analysis of the smart university's external environment

Stage 2. Analysis of management goals and criteria for smart university subsystems

Stage 3. Strategic planning and development of the smart university development program

Macro environment analysis Analysis of microenvironment educational activity; methodological activities; research activities; management activity; economic activity; marketing activity; economic activity; innovative activity setting goals and criteria for managing a smart university; setting strategic development ideas for smart university subsystems; development of a development program; forecast of indicators of socioeconomic, scientific and research development of smart university

Stage 4. Managing the implementation of the smart university development strategy

implementation of targeted programs for the development of smart university subsystems; monitoring the implementation of the smart university development program

Stage 5. Evaluation of the smart university development strategy implementation

evaluation of the results of strategic management of smart university development; assessment of socio-economic and research development of smart university

Fig. 25.3 Recommended main stages of strategic management of smart university development

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2. The article outlines the project-planning and program approaches to the strategic management of the development of the smart university, which take into account the features of its activities. 3. As a result of the study, the main stages of strategic management of the development of the smart university are recommended. Stages include analysis of the external environment, analysis of goals and management criteria for smart university subsystems, strategic planning, and development of a program for its development. The stages also include managing the implementation of the smart university development strategy and assessing its achievement. The model and stages of strategic management of the development of the smart university proposed in the article take into account the needs of the external environment and the internal capabilities of the organization. These tools will improve the management process and bring smart university to a new level of development. Currently, the proposed strategic management stages are used in the development of the smart university. Next Steps. The next stages of the study will be the development of methods and a system of indicators to assess the implementation of the development strategy of the smart university and its individual departments.

References 1. Serdyukova, N.A., Serdyukov, V.I., Slepov, V.A, Uskov V.L., Ilyin V.V.: A formal algebraic approach to modelling smart university as an efficient and innovative system. In: SEEL2016, Smart Education and Smart e-Learning, Smart Innovation, Systems and Technologies, vol. 59, pp. 83–96. Springer, Cham (2016) 2. Serdyukova, N.: Algebraic formalization of smart systems theory and practice, chapter 6. Algorithm for a Comprehensive Assessment of the Effectiveness of a Smart System, 6.2.1 The Algorithm of a Complex Estimation of Efficiency of Functioning of the Innovation System, page101 3. Uskov, V.L., Bakken, J.P., Howlett, R.J., Jain, L.C. (eds.): Smart Universities: Concepts, Systems, and Technologies, 421p. Springer, Cham (2018). ISBN 978-3-319-59453-8. https://doi. org/10.1007/978-3-319-59454-5 4. Uskov, V.L., et al.: Smart university taxonomy: features, components, systems. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.). Smart Education and e-Learning 2016, pp. 3–14. Springer, Cham (2016). ISBN 9783319396897. https://doi.org/10.1007/978-3-319-39690-3 5. Coombs, S.: The psychology of user-friendliness: the use of information technology as a reflective learning medium. Korean J. Think. Probl. Solving 10(2), 19–31 (2000) (Korea: Keimyung University) 6. Bhattacharya, M., Chatterjee, R.: Collaborative innovation as a process for cognitive development. J. Interact. Learn. Res. 11(3/4), 295–312 (2000). Special Issue on Intelligent Systems/Tools in Training and Life-long Learning. https://www.learntechlib.org/p/8381/. Accessed 15 Mar 2018 7. Bhattacharya, M., Narita, S.: Design of a computer based constructivist tool for collaborative learning. In: Crawford, C., Davis, N., Price, J., Weber, R., Willis, D. (eds.). Proceedings of SITE 2003–Society for Information Technology and Teacher Education International Conference, pp. 3251–3254. Association for the Advancement of Computing in Education (AACE), Albuquerque (2003). https://www.learntechlib.org/p/18686/. Accessed 15 Mar 2018

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8. Glukhova, L.V., Syrotyuk, S.D.,Sherstobitova, A.A., Pavlova, S.V.: Smart University Development Evaluation Models—Smart Education and Smart e-Learning, Smart Innovation, Systems and Technologies, vol. 144 pp. 539–551. Springer, Cham (2019) 9. Kalinkin, A. A.: Strategic Management of Enterprises. Actual Problems of Aviation and Cosmonautics. 2016. No. 12. URL: https://cyberleninka.ru/article/n/strategicheskoe-upravleniepredpriyatiem-2 10. Nikiforova, E.V., Berdnikova, L.F., Ivanova, V.A.: Content and sources of information for strategic analysis of the external and internal environment of the organization. In: Nikiforova, E.V., Berdnikova, L.F., Avinova, V.A. (eds.). Vestnik Samgups, no. 1, pp. 126–130 (2011) 11. Chichkina, V.D., Toymentseva I.A.: Strategic management of the processes of functioning, improvement and development of the enterprise. Bull. Eurasian Sci. 5(18) (2013). https://cyberleninka.ru/article/n/strategicheskoe-upravlenie-protsessami-funktsionirovaniyasovershenstvovaniya-i-razvitiya-predpriyatiy 12. Kaplan, R.S., Norton, D.P.: Stratgey Maps: Converting Intangible Assets into Tangible Outcomes, p. 7. Harvard Business School Press, Boston, Massachusetts, USA (2004). ISBN 1-59139-134-2 13. Hokhlov, A.F., Strongin,R.G., Grudzinsky, A.O.: Design-Oriented University. Higher Education in Russia, no. 2, pp. 3–11 (2002) 14. Grudzinsky, A.O.: The Concept of a Project-Oriented University. University Management: Practice and Analysis, Yekaterinburg 3(26), 24–37 (2003) 15. Eckel, P., Hill, B., Green, M.: On the Way to Transformation. University Management: Practice and Analysis, no. 1, pp. 30–37 (1999)

Chapter 26

Concepts of Educational Collaborations and Innovative Directions for University Development: Knowledge Export Educational Programs Svetlana A. Gudkova, Tatiana S. Yakusheva, Elena A. Vasilieva, Tatiana A. Rachenko, and Ekaterina A. Korotenkova Abstract Nowadays, the necessity for development of export educational programs in higher educational institutions is known as a priority task of the country’s development. It is considered as being urgent after the national project introduction, where the route maps for the requirements for knowledge export in Russian higher education institutions are specified. The aim of the article is to study the peculiarities of educational collaborations and the export educational programs’ implementation in various activities. Export educational programs represent resources which are relevant and required at the international educational market. The methods of research are represented by system analysis and synthesis, statistical methods of information processing, and expert methods. The practical value of the study and educational collaboration is proved by the fact that the integration of performers’ efforts leads to a synergetic effect which positively affects the smart university’s development, the designing and development of its export opportunities, and further increasing its competitiveness at the international educational market.

26.1 Introduction Current economic and social development of the country promoted new forms of educational activities. That has been considerably facilitated by the introduction of the passport of the priority project “Development of export potential of the Russian S. A. Gudkova (B) · T. S. Yakusheva · E. A. Vasilieva Togliatti State University, Togliatti, Russia e-mail: [email protected] T. A. Rachenko Volga Region State University of Service, Togliatti, Russia e-mail: [email protected] E. A. Korotenkova Russian University of Cooperation, Moscow, Russia e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_26

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educational system” in 2017 [1]. The conditions for the introduction of this guidance document were the urgent need to find management tools for the strategic development of the country and to increase its competitive advantages in various fields of the international market. It is common knowledge that implementation of intelligent technologies in production and in business directly depends on professional and meta-professional competencies [2] of a specialist as educated by the educational system—competencies which allow them to master innovations more promptly. Lately, Russian authors have produced a number of studies which emphasize the necessity of export opportunities development at higher education and necessity of the educational programs’ export for foreign specialists’ training process [3, 4]. The process addresses the need to increase the University’s funding in terms of providing educational services including language courses, post-graduate studies, traineeships, and additional professional training. The research made by Melikyan [5], who contemplates the university’s internal resources for the purposes of organization of exported educational services, can be considered as interesting and important. The conditions of current activities of universities have been reflected in the theory of neoliberalism that regards universities as autonomous organizations capable of promoting their educational services in market competition conditions striving to increase the efficiency of their activities and their competitive position for maximizing the revenue obtained [6]. Implementation of ideas related to knowledge export transfer brought a lot of higher institutions to the establishing of various integration associations, educational collaborations whose development is aimed at implementation of innovative ideas in science, business, and industry by increasing the joint efforts of many participants in their project work [7].

26.2 Literature Review: Knowledge Export Activity and Modern Economy According to the neoliberalism theory postulates, universities are regarded as participants of the educational market enjoying a certain degree of autonomy, having an opportunity to independently strengthen and expand their positions in the international university education market. It is assumed that development of the respective spheres of a university’s activity, changing the properties of the educational services offered as well as of the conditions for their implementation, may even in a short-term prospect, favors the growth of its export activity performance indicators. For universities’ export activity performance evaluation, quantitative indices of its profitability and scope have been used. For the level of commercialization and diversification of universities’ export activity performance, the competitive advantages and the cost of the educational services are regarded as the internal factors of universities’ export activity.

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Expanding the range of the educational services offered by a university increases the probability of increasing the number of foreign students and extending the time period during which they retain their status as university services consumers. Russian universities offer foreign nationals to undergo preliminary training for enrolling to and to further educate under university education programs—bachelor, master, post-graduate (i.e., to follow “an uninterrupted educational track”). The HerfindahlHirschman index (IndexHH) was used for evaluation of the diversification level of a university’s educational programs [8]. The Herfindahl-Hirschman Index shows the place and share of higher education institutions with small shares of export educational programs at the educational market.

26.3 Theoretical Basis of Export Educational Activity Today, the task of educational services export development is defined as one of the priority areas of the state policy for higher education. University services export is understood as providing knowledge transfer to foreigners both in the country where the university is located and in the territories of other countries via transnational educational or e-learning educational training programs. Among such innovations, there may be noted collaborations as the process of joint efforts of several project activity participants for achievement of the targeted tasks for the university’s strategic development [9, 10]. An export educational program (EEP) is understood as an integrated set of educational properties allowing to broadcast or export the studied knowledge in various communications. For example, in implementation of interaction within “teacher– student,” “student–educational Internet environment,” “mentor at an enterprise–university graduate,” “university academic teaching staff (ATS)–enterprise specialists” patterns, etc. [11–13]. Since 2019, the Togliatti State University has been implementing export educational programs based on the university’s collaboration with different business sectors of economy.

26.3.1 Innovations in Education via Collaboration of University’s Academic Teaching Staff The collaboration with one of the country’s leading universities became possible due to the competition announced by the Ministry of Science and Higher Education of the Russian Federation this autumn. The competition’s key objective is development and implementation of advanced university educational programs jointly with those Russian universities that are included in the top 200 thematic global ratings.

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According to the competition terms, development and realization of any advanced educational program should be effected with involvement of industrial partners. In the Togliatti State University, the basic industrial partners are all chemical and machine-building industry enterprises of the city. The work is performed within the framework of achievement of the results according to the Federal Project “Young Professionals (Increasing Professional Education Competency)” with the aim of increasing the quality of the educational product as well as increasing the academic and teaching personnel’s incentive for development of new inter-disciplinary export-oriented educational programs. Establishment of new programs related to machine-building technologies is of top priority; their implementation is aimed to form new competencies for realizing new advanced industrial technologies.

26.3.2 Content and Language Integrated Learning (CLIL) Technologies as Innovative Activity for Export Educational Services Implementation of CLIL technologies into higher educational institutions is conditioned by the need for forward-looking preparation of teaching staff whose particular professional hard and soft skills include commitment to teach the principal subject in a foreign language. For the purposes of evaluation of the CLIL methodology level for the university’s staff, the authors suggest a qualimetric competency’s training which is understood as the totality of knowledge, skills, and capabilities necessary for successful professional and teaching activity related to evaluation and quality management of students’ training. CLIL technology is represented on the example of teaching a linguistics subjects course for students of nonlinguistic training programs. The index and expert qualimetry methods are described as the research methods. In particular, an example of the group expert evaluations method is given and the results of the qualimetric competency’s training are described. According to the ordinance of the President of the Russian Federation (Ordinance no. 204 of 7 May 2018 “On National Objectives and Strategic Goals of the Russian Federation Development for the Period to 2024”), the increase in the number of foreign students studying at higher educational institutions and scientific organizations is at least twice, as well as implementation of a set of measures for employment of the best of them in the Russian Federation is considered as being the key task for higher schools. Thus, in order to attract foreign students, it is necessary to prepare a number of programs that may be offered for teaching in a foreign language. In this aspect, CLIL methodology is known as the range of tools that allows to comply with the identified demands from the modern business international environment.

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26.3.3 Statistical Analysis of Knowledge Export at University Figure 26.1 shows the results of the university’s educational services exports analysis for the period of 2014–2019. Figure 26.2 reflects the dynamics of growth of foreign students’ involvement in export educational activities for the analyzed period. As it can be seen from the data presented, admission of students taking part in export educational activities increased by 4.6 times. Most students are students from Tajikistan, Kazakhstan, and Uzbekistan but there are also representatives of such countries as Germany, Israel, Congo, Korea, Serbia, Croatia, and other countries (Fig. 26.3). 25 23 20 18 15

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Export educational activities reach out to 23 countries. The forecast includes development of the scope of the educational services increasing it by 11% of the existing one, and attraction of students from other countries (China, Iran, Turkey).

26.4 Results 26.4.1 Hard and Soft Skills’ Modeling for Targeted Training of University Graduates The employer-targeted training is affected on the collaboration basis of JSC AVTOVAZ, the machine-building department of the Togliatti State University (TSU) and lectures from the supporting departments of theory and practice of translation and theory and practice of teaching foreign languages. The above-mentioned program is based on the CATIA and English-integrated programs, developed by the engineers, professors, and managers intended for effective work at motor vehicles industry. The program targeted for hard and soft skills development includes the following subjects: English, CAD system, designing cars on CATIA platform in English, car servicing, industrial engineering, industrial Internet of things (starting since 2019), project management, and risk management (starting since 2019). Every year, four groups, totaling 50 people, are trained according to the above-mentioned employertargeted training program. The fourth year students receiving additional professional training, which is in demand at JSC AVTOVAZ are usually included in the groups. Later on, all of them are successfully working in real industry conditions.

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26.4.2 CLIL Training for Export Educational Activities An educational extended professional training program was developed for advanced professional training of the university teachers. It is aimed at training practical skills of command of a foreign language within the process of teaching special subjects to students of various technical professions. Figure 26.4 shows a part of the program. The advanced professional training process was preceded by the process of preliminary testing of those teachers who were non-linguists in order to assess their level of English proficiency. In order that CLIL could perform in an effective manner at the initial stage, a teacher should have at least B1 level, in compliance with the Common European Framework of Reference (CEFR) standard requirements. This standard reflects the requirements for the level of knowledge and language skills in English. The testing was conducted by certified specialists. At the next stage, accumulation of the language skills into the teacher’s subject is affected. For example, such subjects as machine-building, chemical industry, and information technology. The result of this stage is development of intellectual maps for organizing a CLIL class in one’s own subject. After that, discussion of the developed intellectual maps, finalizing of comments and correction of any made errors of style, etc., took place. Afterwards, a cyclic process aimed at step-by-step improvement of the practical type of activities was performed. A website was offered as an aid to the trainees (http://www.macmillaninspiration.com/new/resources/web-projects). The link leads to a working interactive desktop where it is possible to form one’s own CLIL-classes using a suggested template (Fig. 26.5) The first process flow diagrams for studying the subject were obtained as the result of the training. Figure 26.6 shows a fragment of a process flow diagram from “Information Technologies” subject. The collaboration consisted of professors from

Fig. 26.4 CLIL technologies for teachers’ training (fragment)

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Fig. 26.7 Example of practical task performance at a CLIL class

the Institute of Mathematics and Informatics, the Humanitarian Institute and an expert (IT specialist from the free economic zone “Zhigulevskaya Dolina.” Figure 26.7 shows a part of a CLIL worksheet for “Machine-Building Technology.” The collaboration included professors from the Machine-Building Institute, the Institute of Informatics and the Humanitarian Institute from Togliatti State University, as well as experts representing JSC AVTOVAZ.

26.4.3 Conceptual Model of Educational Collaborations for Scientific Activities Figure 26.8 shows the model of designing and training the scientific cross-cultural competency based on the pedagogical communications tools. To implement this model, a special course, aimed to the university’s staff training the international, scientific competence including several stages, has been designed. It contains six educational modules and control tools. The successful performance of the training and its graduates is proved by the increased ranks of the tested higher school at the University World Ranking and Webometrics Transparent ranking.

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S. A. Gudkova et al. Goal: forming of scientific cross-cultural competence from various perspectives Tasks: mastering the scientific cross-cultural communication knowledge, skills and practice Training contents: mastering the structure and contents of scientific cross-cultural competence through the system of teachers’ special course training and self-assessment Interaction methods: imitative, reproductive, productive, problem, project, Case-Study…

Fig. 26.8 Model of smart infrastructure designing and training for the scientific cross-cultural communication competence (the picture segment)

26.5 Conclusions and Future Trends The described multi-aspect research is aimed at consideration of innovative teaching strategies and methodologies targeted at the university’s competitiveness at the international educational market and the world ranking. The obtained results enabled us to make the following conclusions: 1. It is necessary to continue the development of export educational programs providing international educational and scientific activity of the university. 2. Innovative ways of development allow top managers of smart universities to design and create innovative opportunities for their employees, as well as to develop the smart university competitiveness at the educational market. 3. The designed courses of employer-targeted education and the university’s staff training based on the integration of knowledge and methodology of CLIL have raised the rating of the tested university in the international educational market. 4. Knowledge transfer and export program development are based on the integration and collaboration of the staff from different departments of the smart university. The following future trends can be suggested: 1. The relevant knowledge export program’s base for the smart university is to be created. 2. The programs for CLIL teachers training are to be designed and tested. 3. The targeted training programs for the university graduates are to be designed and implemented on the basis of educational and scientific collaboration of the university staff and employees from businesses and enterprises at the region. The buddy and apprenticeship systems are to be designed, developed, and assessed.

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References 1. Development of Export Potential of the Russian Education System: Priority Project Passport. http://static.government.ru/media/files/DkOXerfvAnLv0vFKJ59ZeqTC7ycla5HV.pdf 2. Gudkova, S.A., Yakusheva, T.S., Sherstobitova, A.A., Burenina, I.: Modeling, Selection, and Teaching Staff Training at Higher School (2019). https://doi.org/10.1007/978-981-13-82604_54. Retrieved from www.scopus.com 3. Gudkova, S.A., Yakusheva, T.S., Sherstobitova, A.A., Burenina, V.I.: Modeling of Scientific Intercultural Communication of the Teaching Staff at Smart University (2019). https://doi.org/ 10.1007/978-981-13-8260-4_48. Retrieved from www.scopus.com 4. Chirikov, I.: How global competition is changing universities: three theoretical perspectives. Research and Occasional Paper Series: Center for Studies in Higher Education, no. 5.16. pp. 1–7 (2016). https://cshe.berkeley.edu/publications/how-global-competition-changing-universitiesthree-theoretical-perspectives-igor 5. Melikyan, A.B.: Main characteristics of international university networks. Education Issues. Educational Studies Moscow, № 3, pp. 100–117 (2014). https://doi.org/10.17323/1814-95452014-3-100-117 6. Aleksandrov, AYu., Barabanova, S.V., Vereschak, S.B., Ivanova, O.A., Aleksandrova, Z.A.: Educational services export as an obligatory condition for increasing quality and competitiveness of Russian education. Modern J Language Teach Methods 7(5), 29–39 (2017) 7. OECD: Innovating Education and Educating for Innovation: The Power of Digital Technologies and Skills. OECD Publishing, Paris (2016) 8. Hirschman, A.O.: The paternity of an index. Am. Econ. Rev. 54(5), 761–762 (1964) 9. Uskov, V.L., Bakken, J.P., Howlett, R.J., Jain, L.C. (eds.): Smart Universities: Concepts, Systems, and Technologies, 421p. Springer, Cham (2018). ISBN 978-3-319-59453-8. https://doi. org/10.1007/978-3-319-59454 10. Aleksandrov, A.Yu., Ivanova, O.A., Vereshchak, S.B., Getskina, I.B.: SMART university in digital learning space. In: Proceedings of the 34th International Business Information Management Association Conference, IBIMA (2019) 11. Sergeeva, M.G., Bedenko, N.N., Tsibizova, T.Y., Mohammad Anwar, M.S., Stanchuliak, T.G.: Organisational economic mechanism of managing the growth of higher education services quality. Espacios 39(21) (2018). Retrieved from scopus.com 12. Sergeeva, M.G., Bedenko, N.N., Karavanova, L.Z., Tsibizova, T.Y., Samokhin, I.S., Mohammad Anwar, M.S.: « Educational company » (technology): peculiarities of its implementation in the system of professional education. Espacios 39(2) (2018). Retrieved from scopus.com 13. Karpov, A.O.: Education in the knowledge society: genesis of concept and reality. Int. J. Environ. Sci. Educ. 11(17), 9949–9958 (2016). Retrieved from scopus.com

Chapter 27

Project Management as a Tool for Smart University Creation and Development Yana S. Mitrofanova, Valentina I. Burenina, Anna V. Tukshumskaya, and Tatiana N. Popova

Abstract The paper suggests using project management as a tool for smart university for creation and development. The methodological and information infrastructure of smart university project management is considered; it will simplify and speed up the process of management decision making and it more visible to the project team members, the project office, and the university management. Mathematical models of project management support are proposed. They can be applied in the project offices’ activities for managing groups of projects for the development of smart university. It is proposed to use business process management system (BPMS) as a core of the information support infrastructure of smart university and smart technology integration center. The use of an application programming interface (API) in the knowledge management system is also considered. The project management experience for smart university creation and development is studied on the example of Togliatti State University.

27.1 Introduction In the modern world, the main indicators of any project success are the high speed of management decision making and the speed of project implementation. Considering the project as a set of time-separated activities aimed at connecting material, financial, labor, and non-material resources in order to create and sell products and services, the project management process is a complex activity that requires careful preparation, organization and constant monitoring of the progress of its implementation. Y. S. Mitrofanova (B) · T. N. Popova Togliatti State University, Togliatti, Russia e-mail: [email protected] V. I. Burenina Bauman Moscow State Technical University, Moscow, Russia A. V. Tukshumskaya Moscow Pedagogical State University, Moscow, Russia © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_27

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The modern smart university development is impossible to imagine without the use of the latest management techniques, where project management is one of them. Project management is recognized as a high-class, organizational, and thinking culture for the projects of various types of realizations. The use of flexible, interactive, adaptive and hybrid environments and smart technologies for project implementation are the project approach adaptation as a management innovation to digital economy conditions. Project activities have the goal of getting the planned results through certain changes. If we consider this issue in the appendix to the university management, the project activity involves the qualitative implementation of the management’s general functions, namely planning, regulation, coordination, organization, motivation, control, research through the management connecting processes such as decision making and communication. Project activities are limited in time and space and have a certain cost. Project management is a process, or to be precise; these are series of processes that occur consistently or in parallel. There are the following project management processes: project initiation, development, planning, the work execution on the plan, control, and the project completion. They accompany the entire life cycle of the project and lead to its results. Project management processes are carried out not only through general and connecting management functions, but also through specific project management functions, such as project security and others. Thus, it can be concluded that project activities are complex and multifunctional, consisting of different types of activities [1, 2]. Nowadays, universities are faced with the task of finding technological solutions for smart university creating and developing the transition to a new scientific and educational process model. In the nearest future, the competitive universities will be those that can use the ideas embedded in the concept of Industry 4.0, including individualization of learning paths and lifelong learning [3, 4]. Project management is the tool that will accelerate the classical university transformation into smart university, and its further development will ensure the rapid and high-quality implementation of smart technologies [5, 6]. In such circumstances, it is necessary to have a methodological and information system to support the university’s project management which will simplify and speed up the process of making management decisions. This will in turn make it more visible to members of the project team and the project office and the university management. By methodological infrastructure, the authors understand a set of various methods, recommendations, normative documents, and regulations that affect the learning processes. The received decisions are directed on the development of processes of training.

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27.2 Project Management of Smart University Development Based on Conceptual Models Applying the project approach and setting the task of modeling smart university development management system, it is necessary to consider the structure and technologies of this process. The technological process of project management in smart university can be characterized by the following properties: 1. Disjointedness of the project management process into consistent actions. This property takes into consideration the modern requirements of standards and smart technologies of process management which are based on the structural analysis methodology. The process becomes statistically manageable, adding the ability to quantify the completion of each of the sub-processes. Management processes are considered from four sides, where the structural analysis method is used to assess the quality of functioning in each of the processes. Project management of smart university is complicated by a block-hierarchical structure (hierarchical structure of works) which has not only vertical, but also horizontal links and different control actions. In terms of the theory of sets used in control automation, binary mathematics is used to process discrete information [7, 8]. Then, the theoretical model of project management of one stage can be presented in the following way: • the set of input signals is represented by Descartes product: (i+1/i)

L i{m1+m2+m3} = L {m1}

(i/i)

⊗ L (i) {m2} ⊗ L {m3}

(1)

• the set of control signals is a Descartes product: (i/i)

(i/i)

i M{n+n2} = M{n1} ⊗ M{n2}

(2)

• the set of output signals is represented by Descartes product: (i+1/i)

i E {k1+k2+k3} = E {k1}

(i/i)

(i) ⊗ E {k2} ⊗ E {k3}

(3)

where • L (i+1/i) is a set of input signals from the previous stage (information obtained in {m1} the previous stage); • L (i) {m2} a set of input signals (information received at this stage); • L (i/i) {m3} a set of feedback signals at this stage;

(i/i) • M{n1} a set of control information of this stage; (i/i) • M{n2} a set of control information from higher levels; (i) • E {k2} information received at the output at this stage; (i/i) • E {k3} feedback;

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Fig. 27.1 Structural circuit of a single-stage control model (i+1/i • E {k1} information required by the higher level.

The structural circuit of a single-stage control model is presented in Fig. 27.1. 2. The results dependence between stages. The results of each previous stage of smart university development management process directly or indirectly affect the next stage results or smart education quality. 3. The relative lack of consequences. If there is a probability that the results of each subsequent stage of smart university development management depend on the previous one, then the whole process will have no aftereffect and can be represented as a Markov chain. At any stage of smart university development management, the university management can control the process by influencing the state of the system and transition probabilities. In the most general form, a complex model for controlling the development of smart university can be formulated as follows: to find the optimal control actions (strategies) for a complex dynamic process that ensures the satisfaction of the requirements of the final quality vector (standard) with limited material and time resources. To optimize the organizational structure of the project management system for the development of smart university, it is recommended to use a methodology for structuring goals and functions based on the concept of a system that takes into consideration the environment and goal setting [5]. The process of making management decisions in project controlling should be expressed in certain quantitative indicators (performance indicators) which will make it possible to synthesize an adaptive control system. The definition of indicators is based on the use of mathematical models. The point model is the simplest mathematical model used in some organizations. This model can be applied to the system of project management of smart university development. The main point of this model is as follows. Let I1 j , I2 j , . . . , In j be a set of quantitative indicators that characterize the state (potential) of the object (project) with the number j = 1, 2, …, N at this time t and the results of its project activities for the previous control period τ .

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Then the general rating R j of the object with the number “j” at time t is calculated by Formula (4): Rj =

n 

bi Ii j , j = 1, 2, . . . , N

(4)

i=1

where n is the number of all indicators; N is the number of all objects included in the control system; bi which means weight factors are assigned by the higher management experts heuristically. The advantages of the point model are its simplicity and low labor intensity, and the disadvantages of the point model include the following: 1. Weight multipliers selection by expert method. 2. Lack of consideration of actual dependencies between a number of indicators Ii j that cannot be established without deep mathematical processing of statistical data. 3. Summing up the quantities having different dimensions. 4. Lack of division among indicators into potential and effective groups. It does not allow obtaining objective ratings since the effectiveness of the implementation of the potential in relation to the achieved result is not taken into account. Some score mathematical model development is the standard classification and reference models for calculating ratings. The essence of the normative classification model [6] is that the entire set of initial indicators Ii j of the object state and activity with the number “j” is divided into two sets of indicators: n • I jk (t)—indicators that characterize the status and potential performance of various projects (k = 1, 2, …, n) an object with the number “j” at time t by K; m • I js (t, τ )—indicators that characterize the results of the object operation with the number “j” for the previous planning period duration τ at time t by the results of the project completion s (s = 1, 2, …, m). n Then the indicators I jk (t) are divided into a number of groups, each of them m (t, τ ) are divided into a number of has specific characteristics, and the indicators I js projects. Further, the project indicators and potential possibilities of each group are normalized relative to some values that make economic sense. For each group of projects and potential possibilities, the expert method determines the weight coefficients and calculates potential ratings for different types of potential possibilities and activity ratings (effectiveness) for different projects. Next, in the same way as in the score system on the values of the general ranking, all the objects of the project control system (projects, events) are subdivided into several groups, and, depending on the belonging to one group or another, decisions about the allocation of material and financial resources for projects in a certain period of time.

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These models can be applied in the of project office activities to manage a group of projects for smart university development.

27.3 The Organizational Infrastructure of Project Management and Knowledge Management in Smart University The knowledge-based economy turns the intellectual capital of smart university into the main source of its competitiveness. Analysis of different points of view on the problem of knowledge management allows us to define this phenomenon as a combination of strategy and tactics which allows creating and distributing knowledge and competencies systematically. Knowledge management is an integral part of quality project management in smart university, and the process of closing the project and making the most of the work done is also a knowledge management process [9, 10]. Knowledge management allows you to activate the project participants’ knowledge exchange which will help to make the right conclusions from the successes and failures (ups and downs) of individual projects. This makes a contribution to the project management effectiveness. In this regard, the project office can be safely called as the organizational infrastructure element of smart university, where project management and knowledge management become interrelated processes [11, 12]. Also, the project office is a repository of knowledge that provides knowledge transfer and effective interaction of the project team with all co-executors. Knowledge management within project offices allows more efficient use of intellectual capital which is carried by employees’ smart university. In its turn, it has a positive impact on project management processes which allows organizations to move to a higher level of project management maturity [13]. The project office is one of the project management methodology tools which allow us to improve the used model of organizational project management maturity to therefore increase smart university’s efficiency of the activity and its development. The knowledge repository of the project office can become an infrastructure element of smart university that covers the entire educational, scientific, and intellectual potential of the university, including smart learning analytics data [14–16]. The use of the API will ensure the data collection in a consistent form about all the activities of smart university from many supporting information technologies. At the same time, different information systems can safely build communications and collect and share the flow of actions using a simple xAPI dictionary. For example, the learning record store (LRS) collects and stores all students’ actions records. LRS can be built in the learning management system (LMS) or located outside. Figure 27.2 shows one record of the user’s activity in the system “Rosdistant” of Togliatti State University. The student (“actor”) looked at (or “experienced-” so “verb” is specified“experienced”) the presentation slide (“object”). All actions are recorded in the form: “subject-verb-object” or “someone-did something—with some object.”

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"verb":{

{ "_id":ObjectId("5b99773548177e9f44cf9cab"),

"id":"http://adlnet.gov/expapi/verbs/experienced", "display":{

"lrs":{

"en-US":"experienced" }

"_id":ObjectId("561f834948177e2e3bf8c4de") },

},

"lrs_id":ObjectId("561f834948177e2e3bf8c4de

"object":{ "objectType":"Activity",

"),

"id":"ispring://presentations/2913d469-a719-

"client_id":"561f839548177ecd67f8c4e0", "statement":{

4cb1-95bc-c5ce23e6b380/slides/8", "definition":{

"version":"1.0.0",

"name":{

"actor":{ "objectType":"Agent",

…

"name":"student12234673", "mbox":"mailto:[email protected]" },

"id":"f0334f19-3a5e-4c4a-a2b6-54a32084f5a0", "timestamp":"2018-09-12T20:19:53.0Z", "authority":{

Fig. 27.2 A fragment of a user action record in the system (xAPI)

Business process management system (BPMS) technology, which structure is shown in Fig. 27.3, can become the core of smart university information support infrastructure and the center of smart technology integration.

27.4 Experience in Project Management for Smart University Creating and Developing We will consider the experience of project management in smart university creation and development on the example of Togliatti State University (TSU) and its digital transformation. Since 2005, the university has been implementing project management. Project management training was based on the study of project management experience, PMI PMBOK, and agile methodologies and standards. In 2015, a project was launched as online education “Rosdistant”. Due to smart technologies’ introduction and development and the project “Rosdistant” development, the number of university students at the end of 2019 was more than 17 thousand people. Nowadays, students from 82 regions of Russia and 19 countries of the world get higher education in “Rosdistant” system. If we compare the annual students’ growth who wants to study online in 2019 compared to 2018, it was 20%. About 7.5 thousand students of the university are enrolled in “Rosdistant” system. Project management made it possible to create a smart model of the university from separate technological smart elements to build a single technological chain of

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Enterprise Architecture

Low-code

API Process Mining Robotic Process Automation Chatbots & Natural Language Processing Customer Journey Mapping

INTELLIGENT AUTOMATION PLATFORM

iBPMS

Internet of Things Machine Learning & Artificial Intelligence

Fig. 27.3 Structure business process management system

the university’s basic processes. They allowed achieving a new quality of either the processes themselves or the university’s work results. The university’s development management system includes a strategic planning group. A project office was created on the base of it. The information infrastructure of the project office support is based on the automated information management system “The Program of development,” built on a combination of such systems as ERP Galaxy, CMS Bitrix24, CRM Bitrix24, and LMS Moodle. The core of this integration is the business process management system (BPMS). The project portfolio basis of Togliatti State University is: • development program; • transformation program; and • 3 strategic projects (119 activities).

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These projects’ implementation within the project office framework is helped to transform the university into a smart university. Today, further development of smart university of TSU is planned. 28 internal projects (1398 activities) were directed to the implementation of these plans. We include the following: • • • • • • • • •

updating the content of smart education and students’ support; the collection and analysis of data; work with the student’s digital footprint; typology of learning behavior and building an adaptive smart learning system; knowledge management; big data management; full digitalization of university management system; intellectualization of the educational process control system; and smart campus creating.

27.5 Conclusion and Next Steps Conclusions. The digital economy and the concept development of Industry 4.0 have an impact on all areas of human activity, and, first of all, on education. Nowadays, the most competitive universities will be those that can transform into smart universities based on smart technologies. This will create a basis for the development of smart education, smart economy, and smart society concepts in Russia. There are some necessary aspects for smart university creation and development. These are: 1. changing the university’s organizational structure and transition to project and process management; 2. a flexible system creating for the formation of individualized educational trajectories using smart technologies; and 3. creating a smart university infrastructure, which provides the implementation of Industry 4.0 ideas in relation to the educational system. Next Steps. We plan to continue this project in the following directions: 1. study of BPMS technology elements; 2. introduction of BPMS into smart university management processes; and 3. evaluation of the effectiveness of BPMS implementation in smart university activities.

References 1. Aleksandrov, A.Y., Ivanova, O.A., Vereshchak, S.B., Getskina, I.B.: Modern concepts of the quality management system in higher education: Russian practice and International experience. In: Proceedings of the 33rd International Business Information Management Association

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Y. S. Mitrofanova et al. Conference, IBIMA 2019: Education Excellence and Innovation Management through Vision (2020) Sergeeva, M.G., Bedenko, N.N., Tsibizova, T.Y., Mohammad Anwar, M.S., Stanchuliak, T.G.: Organisational economic mechanism of managing the growth of higher education services quality. Espacios 39(21) (2018) Uskov, V.L., Bakken, J.P., Howlett, R.J., Jain, L.C. (eds.): Smart Universities: Concepts, Systems, and Technologies, 421 p. Springer, Cham (2018) Uskov, V.L., et al.: Smart university taxonomy: features, components, systems. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning 2016. Springer, Cham (2016) Glukhova, L.V., Mitrofanova, Y.S.: Digitalization of economy and the particularities of its application in an integrated facility’s activity. Bull. Volga Region State Univ. Serv. 4 (2017) Mitrofanova, Y.S.: Modeling the assessment of definition of a smart university infrastructure development level. In: Sherstobitova, A.A., Filippova, O.A. (eds.) Smart Innovation, Systems and Technologies, vol. 144, pp. 573–582 (2019) Kanovei, V.G., Lyubetskiy, V.A.: The modern theory of sets: Borel and design sets, 320 p. MCCNMO, Mexico (2010) Mitrofanova, Y.S.: Modeling smart learning processes based on educational data mining tools. In: Mitrofanova, Y.S., Sherstobitova, A.A., Filippova, O.A. (eds.) Smart Innovation, Systems and Technologies, vol. 144, pp. 561–571 (2019) Gudkova, S.A., Yakusheva, T.S., Sherstobitova, A.A., Burenina, V.I.: Modeling, selection, and teaching staff training at higher school Smart. In: Innovation, Systems and Technologies, vol. 144 (2019). https://doi.org/10.1007/978-981-13-8260-4_54 Karpov, A.O.: Education in the knowledge society: genesis of concept and reality. Int. J. Environ. Sci. Educ. 11(17), 9949–9958 (2016) Karpov, A.O.: Socialization for the knowledge society. Int. J. Environ. Sci. Educ. 11(10), 3487–3496 (2016) Aleksandrov, A.Y., Ivanova, O.A., Vereshchak, S.B., Getskina, I.B.: SMART university in digital learning space. In: Proceedings of the 34th International Business Information Management Association Conference, IBIMA (2019) Aleksandrov, A.Y., Ivanova, O.A., Vereshchak, S.B.: SMART-university: new opportunities for individuals with disabilities. In: Proceedings of the 34th International Business Information Management Association Conference, IBIMA (2019) Gudkova, S.A., Yakusheva, T.S., Sherstobitova, A.A., Burenina, V.I.: Modeling of scientific intercultural communication of the teaching staff at smart university. In: Innovation, Systems and Technologies, vol. 144 (2019). https://doi.org/10.1007/978-981-13-8260-4_48 Sergeeva, M.G., Bedenko, N.N., Karavanova, L.Z., Tsibizova, T.Y., Samokhin, I.S., Mohammad Anwar, M.S.: «Educational company» (technology): peculiarities of its implementation in the system of professional education. Espacios 39(2) (2018) Tsibizova, T.Y., Postnikov, V.M., Spiridonov, S.B.: Analysis of the impact of technology lectures-visualizations on the results of control measures in various academic disciplines. Perspektivy Naukii Obrazovania 33(3), 358–363 (2018)

Chapter 28

Human Resource Management System Development at Smart University Leyla F. Berdnikova, Natalia O. Mikhalenok, Veronika A. Frolova, Victoria V. Sukhacheva, and Artem I. Krivtsov

Abstract An important development trend of a modern smart organization is effective personnel management. This is due to the need of increasing competitiveness, attracting applicants, expanding the market for educational services and areas of research work, the emergence of new developments, and improving key indicators of the organization. The main goal of the personnel management system is to ensure the quality, rational formation, and development of intellectual resources to achieve the economic efficiency and competitiveness of the smart university. Currently, the role of man in the activities of each organization is of great importance, as it forms the main strategic resource in the competition. Personnel management at the smart university raises a number of controversial questions regarding personnel selection methods, criteria for evaluating them, methods of certification, stimulation and motivation, and formation of career growth which determines the relevance of a scientific article. The article clarifies the main elements of the personnel management system of the smart university. The result of the study is the proposed model for the development of a personnel management system that takes into account the requirements for meeting the performance indicators of a smart university, as well as achieving the goals and objectives of its development strategy. The results were tested during the personnel management of the smart unit of the university on the example of the department.

L. F. Berdnikova (B) Togliatti State University, Togliatti, Russia e-mail: [email protected] N. O. Mikhalenok Samara State Transport University, Samara, Russia V. A. Frolova St. Petersburg State Marine Technical University, Saint-Petersburg, Russia V. V. Sukhacheva St. Petersburg University of Management Technologies and Economics, Saint-Petersburg, Russia A. I. Krivtsov Samara State University of Economics, Samara, Russia © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_28

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28.1 Introduction Modern conditions require the search for new directions to improve the efficiency of the smart organization. The market economy has fundamentally changed the concept of personnel management and the choice of programs and methods for the practical implementation of management tasks to increase the efficiency of activities as a condition for the competitiveness of an economic entity. Particularly noteworthy is the social orientation in personnel policy, changing priorities, and taking into account the interests of personnel, stimulation, and providing labor motivation as a way to increase its effectiveness. One of the significant areas is the improvement of the personnel management system which includes a motivational mechanism on which success in the activities of the smart university largely depends. The ability to manage people, attract highly qualified professionals, develop employees, and to stimulate and motivate them is the main and most important components of personnel policy. Currently, in the economic literature in the study of labor management issues, various terms are used, such as personnel, labor potential, and human resources. In the economic literature, the study of personnel management is the work of many scientists, for example, Bazarov [1], Odegov et al. [2], Catmell [3], Melikhov and Maluev [4], Rasskazov et al. [5], Vesnin [6], Kafidov [7], Berdnikova [8, 9], Glukhov et al. [10]. Currently, significant attention is paid to publications about various activities of smart universities, smart systems, as well as the educational smart environment [11–19]. Modern conditions dictate new requirements for the selection, evaluation, and use of personnel. The need for intellectualization of labor is increasing, the education system is being transformed, and training technologies are improving [8]. This, in turn, entails the need to improve the personnel management system in smart universities. It should be noted that the successful development and competitiveness of the smart university depends on the qualifications and effectiveness of personnel management, forming the most valuable intellectual resources that distinguish it from competitors.

28.2 Statement of the Problem in General Form and Its Connection with Important Scientific and Practical Tasks An increase in the role of personnel and a change in their attitude to it is associated with scientific and technological progress and the expansion of information and communication technologies. The gradual increase in the role of high-tech and high-tech industries, the introduction of robotics, flexible manufacturing complexes based on computer technology, and modern telecommunication facilities has led to a

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reduction in personnel and an increase in the proportion of highly qualified personnel. The importance of conceptual skills is growing (the ability to represent complex processes in a holistic system, to work with computer programs). To ensure the effective operation of the smart university, it is necessary to have highly qualified and creatively thinking staff who are able to quickly respond to changes in the needs of the educational environment, demand, and predict the needs for educational services and developments. Staff is one of the most important resources of a smart university. This differs from other (financial, material, technical, informational) ones in that the employee has the right to refuse the conditions under which he is going to be accepted, to negotiate the amount of wages and bonuses, to raise qualify and reorient professionally, quit at will, and develop new skills. For the purpose of increasing the efficiency of activities, a smart university should be sufficiently provided with personnel, use them rationally, constantly increase the level of labor productivity, and improve working conditions [9]. It should be kept in mind that traditionally, the selection and placement of personnel is made taking into account the content of labor, the role and place of the employee in the structure of the organization, and its compliance with the requirements of the workplace. The main principles of recruitment include: • compliance of the number of personnel with the volume of work performed and services rendered, as well as qualifications and education; • compliance with the degree of complexity of his labor responsibilities; • the formation of the personnel structure taking into account the organization’s strategy; • efficient use of working time; • creating conditions for staff development, such as expanding their professional competencies and increasing labor efficiency. Educational institutions have specifics that require the improvement of personnel management methods. In particular, smart universities are primarily focused on the intellectualization of labor and scientific research, which must be taken into account when creating a personnel management system, determining motivational mechanisms and evaluating personnel efficiency. Competent personnel management is characterized by the degree to which the strategic goals of the smart university are achieved. The effectiveness of using the potential of each employee is largely determined by his ability to perform the necessary functions and the motivation system. In smart universities, such management methods and procedures should be formed and applied that would make it possible to constantly improve personnel and develop their professional competencies. In unity, these methods and procedures are characterized as a personnel management system and are determined by the following parameters: • compliance of staff with the strategy and mission of the smart university; • a constant increase in the efficiency of work with staff (the ratio of costs and results);

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determination of criteria for evaluating personnel management; staffing, determination of needs, number planning; the balance of staff in professional activities and performed labor functions; the intellectual and creative potential of the staff.

Thus, at present, a serious problem arises of creating a management and personnel assessment system that takes into account the features of smart university.

28.3 Presentation of the Main Research Material with Full Justification of the Obtained Scientific Results 28.3.1 Elements of the Personnel Management System of Smart University Effective personnel management is impossible without the active and constant participation of the top management of the smart university in establishing tasks arising from the goals of the institution, modeling professional behavior, creating and implementing personnel management systems, and evaluating their effectiveness. The concept of personnel management is a system of theoretical and methodological views on determining the content, goals, objectives, principles, and methods of personnel management. The personnel management strategy is the priority line of action established by the leadership of the smart university that is necessary to achieve long-term goals of forming a highly qualified, responsible, close-knit team with intellectual abilities and capable of achieving the strategic goals and objectives of the organization. The personnel management system involves the development of goals, functions, organizational structure of personnel management, vertical and horizontal functional relationship of managers and employees in the development, and adoption and implementation of management decisions. Figure 28.1 presents the general elements of the personnel management system of smart university. The personnel management system should be focused on the overall mission and strategy of the smart university, and also take into account its organizational structure. Important elements of the smart university human resources management system include personnel strategy and personnel policy. The personnel strategy implies a set of principles, rules, and goals for working with personnel and takes into account the institution’s development strategy, organizational structure, and employee’s intellectual potential. The personnel policy is represented by a system of goals, principles, forms, and methods of working with personnel based on the personnel strategy of the smart university.

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Smart university Mission and development strategy Organizational structure

HR strategy and HR policy

HR planning

Smart university development

Personnel recruitment and selection

Personnel training and development

Personel assessment

Stimulation and motivation of staff

Technology of effective personnel

Fig. 28.1 General elements of the personnel management system of smart university

A significant component in the personnel management system is the planning of the need for personnel of the smart university. This direction takes into account the movement of personnel. The involvement and selection of personnel involves the search and decisionmaking procedures for selecting the appropriate candidates for the filling of certain positions, taking into account the needs of the smart university. Moreover, various requirements may be established for candidates. So, for example, for the teaching staff, the following criteria may appear: • • • •

degree in the relevant specialty and rank; experience of scientific and pedagogical activity; practical experience in the specialty (area of training); the number of scientific publications (total: in publications indexed in the SCOPUS database, in publications indexed in the database of international scientific citation indexes WEB OF SCIENCE, in publications recommended by the Higher Attestation Commission, etc.); • the number of educational publications; • volumes of research work performed;

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participation in grants; performance of research work with students; the number of protected graduate and doctoral students; training; participation in scientific and scientific conferences, etc.

Training and staff development is an important element not only in personnel management, but in general, in the effective operation of smart university. In the context of increasing management efficiency, the role of personnel assessment is growing with the aim of making subsequent decisions on job movements, reducing inefficient employees, and advanced training or retraining of personnel. The personnel assessment should be aimed at providing information on the quality of personnel and their compliance with the development strategy of the smart university. Evaluation of personnel should be carried out at different stages of personnel management and take into account various goals in particular in the process: • calculation of staffing requirements when assessing existing staff and developing requirements for attracting new staff; • selection of personnel and establishment of compliance of candidates with requirements for vacant positions; • determining the need for staff development with a view to its development; • certification of staff; attestation should be carried out systematically for the analysis of personnel potential and the development of regulatory impacts including the development of decisions on promotion, punishment, dismissal, and motivation of personnel, the formation of a personnel reserve, and planning of personal movements. Important elements of the personnel management system are stimulation and motivation. The motivations that stimulate the employee to take an active part are not only material remuneration, but also the diversity of the content of the work, the possibility of career growth, a sense of satisfaction with the results achieved, increased responsibility, the possibility of taking the initiative, attitude with the team, etc. Motivation is interconnected with labor stimulation, in which the incentives have various benefits, the receipt of which involves labor activity. Thus, the good represents a certain incentive for labor in the event that it forms the motive of labor. Labor stimulation is effective when management bodies are able to achieve and maintain the level of work for which they pay. The purpose of stimulation is not only the incitement of a person to work, but the incentive to do it better and more than that due to labor relations. Incentives include social benefits, low interest loans, career advancement, etc. In smart universities, for faculty members, incentives and motivation include rewards for: • publication activity; • scientific developments;

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• performance of research work; • performance of grants; • management of scientific projects, etc. Technologies for the effective work of personnel should be constantly improved to achieve the smart university development strategy and increase its financial results.

28.3.2 Modeling the Development of a Personnel Management System at Smart University Personnel management should be aimed at achieving effective smart university activities and fair relations between employees. The flexible organization of labor, the selforganization of workers, and collectively their conscious participation not only in the educational and scientific process but also in management becomes an important element in the formation of the personnel management system in the smart university. Human resources management is associated with the organization of joint activities of people with the establishment of coordinated actions within the framework of the institution with the regulation of relations between the individual and the company. As noted earlier, personnel management should begin with the strategic development goals of the smart university which should be based on social development and scientific and technological progress which take into account the needs and priorities of the smart environment as well as the performance indicators of the institution as a whole. The effectiveness of personnel management can be achieved by bringing into compliance with the requirements of the educational market the forms, methods, and procedures of work with personnel existing in the institution, changing goals and objectives, and structures of personnel services. The personnel strategy should be based on the strategic development program of the smart university and take into account the requirements for meeting key performance indicators of the organization. In the personnel management of a smart university, methods should be aimed at developing and using the intellectual potential of an individual, the potential of a team, and holistic social and corporate potential. HR policy should be based on HR strategy. At the same time, the content, forms, and methods of personnel management should orient employees toward activities that would comprehensively affect the socio-economic results of the smart university. The functioning of the smart university in a competitive environment and its long-term development is facilitated by innovative staff who have flexible thinking, intellectual abilities, who can take responsibility and work in groups that carry out scientific development, carry out research work, and transfer their knowledge to students. The personnel management system should form a high competence of personnel and provide incentives based on the assessment of individual labor.

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The effectiveness of personnel management is ensured by the performance indicators of the smart university as well as by achieving the goals and objectives of its development strategy. The model for the development of the personnel management system at smart university is presented in Fig. 28.2.

Smart university development strategy

HR strategy

Smart university performance indicators

HR policy

Organizational mechanisms for personnel management

Forms and methods of personnel management based on objective factors

Forms and methods of personnel management based on subjective factors

Integral methods of personnel development, identification of intellectual resources

HR management performance assessment

Fig. 28.2 Development model of the personnel management system at smart university

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The key elements of assessing the effectiveness of personnel management and its compliance with the development strategy of the smart university include: • the contribution of each employee to improving financial and quality non-financial performance indicators of the organization; • high-quality performance of labor functions of personnel; • the structure of employment and the ratio of categories of workers; • the competence of the staff (the necessary level of qualification and the availability of a scientific degree); • the role of each employee in meeting key performance indicators of the smart university; • volumes of research work performed; • the number of scientific developments obtained by patents; • improving the quality of educational services; • the use of modern educational technologies in professional activities; • receipt by employees of certificates of professional development; • participation of employees in international/scientific conferences and forums; • introduction of foreign scientific and practical experience in professional activities; • performance of research work with students; • organization by employees of the smart university of Olympiads and international scientific conferences; • the degree of interaction of workers in collective productive activities; • the number of scientific and educational works published by employees, etc. Thus, the successful development of a smart university in the long term depends on effective personnel management.

28.4 Conclusions and Next Steps Conclusions. The study revealed that the personnel management system of the smart university should ensure continuous improvement of personnel work methods. 1. The use of scientific and technological progress, new information and communication technologies, and the best professional experience has been considered. In this regard, the article identifies the basic elements of the personnel management system at the smart university. 2. The article reveals the features of each element of the personnel management system in relation to the smart university. 3. The study allowed us to design a model for the personnel development management system of smart university. This model takes into account the development strategy of the organization as well as the requirements for the implementation of key performance indicators to the smart university.

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The elements of the personnel management system and the model for the development of the personnel management system presented in the article can be used in smart universities, research centers, and scientific laboratories. Currently, the developments presented in the article are being introduced into the activities of the smart university departments. The proposed management tools help to increase staff performance and improve the performance of smart university. Next Steps. The next stages in the development of the personnel management system are the development of a grading system for the staff of the smart university and its use to assess the effectiveness of personnel and their role in achieving the organization’s strategic goals.

References 1. Bazarov, T.Y.: Personnel Management, 224 p. Academy, Mexico (2017) 2. Odegov, Y.G., Rudenko, G.G., Babynina, L.S.: Labor Economics: Textbook. In: 2 tons T2, 924 p. Publishing house “Alfa-Press”, Mexico (2007) 3. Catmell, E.: Genius Corporation. How to Manage a Team of Creative People, 400 p. Alpina Publisher, E. Catmell (2020) 4. Melikhov, Y.E.: Personnel Management. In: Melikhov, Y.E., P.A., Maluev (eds) Portfolio of Reliable Technologies, 344 p. Dashkov and Co., Mexico (2018) 5. Rasskazov, S., Rasskazova, A., Deryugin, P.: In: Rasskazov, S., Rasskazova, A., Deryugin, P. (eds.) Corporate Governance, 338 p. Infra-M, Mexico (2020) 6. Vesnin, V.R.: Human Resource Management. In: Vesnin, V.R. (ed.) Theory and Practice, 688 p. Prospect, Mexico (2014) 7. Kafidov, V.V.: Personnel Management, 144 p. Triksta, Academic Project, Mexico (2018) 8. Berdnikova, L.F., Zverintseva, A.S.: Labor Resources: Concept and Main Tasks of Analysis. Karelian Sci. J. 4(17), 50–53 (2016) 9. Berdnikova, L.F., Sherstobitova, A.A., Schneider, O.V., Mikhalenok, N.O., Medvedeva, O.E.: Smart university: assessment models for resources and economic potential. In: Smart Education and Smart e-Learning Smart Innovation, Systems and Technologies, vol. 144, pp. 583–593. Springer, Cham (2019) 10. Glukhov, V.V., Korobko, S.B., Marinina, T.V.: Economics of Knowledge, 528 p. Peter, SPb. (2003) 11. Serdyukova, N.: Algebraic formalization of smart systems theory and practice (Chap. 6). In: Algorithm for a Comprehensive Assessment of the Effectiveness of a Smart System, 6.2.1 The Algorithm of a Complex Estimation of Efficiency of Functioning of the Innovation System, pp. 101 12. Serdyukova, N.A., Serdyukov, V.I., Slepov, V.A, Uskov V.L., Ilyin V.V.: A formal algebraic approach to modelling smart university as an efficient and innovative system. In: SEEL2016, Smart Education and Smart e-Learning, Smart Innovation, Systems and Technologies, vol. 59, pp. 83–96. Springer, Cham (2016) 13. Uskov, V.L., Bakken, J.P., Howlett, R.J., Jain, L.C. (eds.): Smart Universities: Concepts, Systems, and Technologies, 421 p. Springer, Cham (2018). ISBN 978-3-319-59453-8. https://doi. org/10.1007/978-3-319-59454-5 14. Uskov, V.L., et al.: Smart university taxonomy: features, components, systems. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning 2016, pp. 3–14, Springer, Berlin (2016). ISBN 9783319396897. https://doi.org/10.1007/978-3-319-39690-3 15. Coombs, S.: The psychology of user-friendliness: the use of information technology as a reflective learning medium. Korean J. Think. Probl. Solving 10(2), 19–31 (2000). Keimyung University, Korea

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16. Bhattacharya, M., Chatterjee, R.: Collaborative innovation as a process for cognitive development. J. Interact. Learn. Res. 11(3/4), 295–312 (2000). Special Issue on Intelligent Systems/Tools in Training and Life-long Learning. https://www.learntechlib.org/p/8381/. Accessed 15 Mar 2018 17. Bhattacharya, M., Narita, S.: Design of a computer based constructivist tool for collaborative learning. In: Crawford, C., Davis, N., Price, J., Weber, R., Willis, D. (eds.) Proceedings of SITE 2003–Society for Information Technology and Teacher Education International Conference, pp. 3251–3254. Association for the Advancement of Computing in Education (AACE), Albuquerque (2003). https://www.learntechlib.org/p/18686/. Accessed 15 Mar 2018 18. Burlea, A.S.: Success factors for an information systems projects team: creating new context. In: 11th IBIMA Conference, 4–6 Jan 2009, Cairo, Egipt, pp. 936–941 (2009) 19. Glukhova, L.V., Syrotyuk, S.D., Sherstobitova, A.A., Pavlova, S.V.: Smart University Development Evaluation Models—Smart Education and Smart e-Learning, Smart Innovation, Systems and Technologies, vol. 144, pp. 539–551. Springer, Cham (2019)

Chapter 29

Integration of Agile Methodology and PMBOK Standards for Educational Activities at Higher School Anna A. Sherstobitova, Lyudmila V. Glukhova, Elena V. Khozova, and Raisa K. Krayneva Abstract Nowadays, business environment requires “agile” employees who are capable dealing with customers in violative and uncertain society. The research relevance of the study is justified by the necessity of higher educational institutions to design and develop an innovative approach to their activity at the educational market environment featured with strict competition. The aim of the scientific research is to analyze the standardization issues for educational processes. The research methods are based on a systems approach including system analysis and synthesis for the managerial model. The article deals with the innovative processes taking place in the conditions of the economy “4.0” applied for the educational system. The University Marketing and Management Model based on the project management requirements known as the PMBOK standards and agile design technologies is represented. The proposed management model is considered as being useful for strategic planning of smart university development. The model has been implemented and tested at three higher institutions of the Russian Federation.

29.1 Introduction Economy “4.0” characterized by innovative trends in all industries is known as being a powerful source for the development both the educational system and the entire social structure. It should be noted that comprehensive and large-scale innovative A. A. Sherstobitova (B) Togliatti State University, Togliatti, Russia e-mail: [email protected] L. V. Glukhova Volzhsky University named after V.N. Tatishchev, Togliatti, Russia E. V. Khozova Moscow Social-Economical Institute, Moscow, Russia R. K. Krayneva Financial University under the Government of the Russian Federation, Moscow, Russia © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_29

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transformations are complex and consistent processes implemented into all levels of the educational system including the following levels: • macro-level which is including global trends that determine the transformation of the educational system at the state level and represented by the Federal Educational Standards; • meso-level which is including changes determined by regional features of districts and cities; • micro-level which is including innovative processes for educational services provision at the level of a particular educational institution. Many studies [1–5] describe educational innovation as an effective process of creating a new educational environment with a flexible and agile economy and management consisting of stages, phases, and certain regularities that design a system of constant education corresponding the consumer’s requirements and ensuring the appropriate quality of life. The educational innovations are featured by the implementation of transformation and innovation chain: innovations at smart university bring up a competent and agile employee who contributes the intellectual power and project skills into the economic activity and industry initiating the agile and smart activity of the organization in volatile and uncertain market that brings it up to the top in competition. According to the literature review [6–10], the following types of innovations for educational services are distinguished and represented in Table 29.1: innovations in the area of legal and administrative management; innovations in social management; innovations in the organizational and economic management; innovations in pedagogical technologies; innovations in technical and technological support; innovations in educational environment of organization. The latter is relevant to the process of SMART educational systems development. The following results are to be achieved due to the large-scale and complex implementation of innovative processes for educational institutions: • expansion of the educational services market and increasing demand for educational services; • increasing competition among educational institutions; educational process meeting the needs of students and business environment; • implementation of advanced training strategies; getting extra financing from state through grant activity; increasing the remuneration of employees in the education sector, etc. A smart university is characterized by the use of high-tech equipment and new intellectual technologies for educational activities. In contrast to smart university, an intelligent university is featured as a self-study organization targeted to train students for further innovative activities. The level of training at intellectual university meets the external environment.

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Table 29.1 Types of innovations for educational services Innovation types

Characteristics

In the area of legal and administrative management

Use of updated legal and administrative mechanisms for interaction between business and social partners

In the area of social management

Strengthening the connections and influence of stakeholders; increasing innovative mentality of employees at educational institutions

The organizational and economic management

New ways of financing and improving the organizational structure of an educational institution based on the optimal use of both the human resources and real assets

The pedagogical and teaching technology

new techniques, methods of teaching, transition to interactive technologies and interaction between teacher and learner, gamefiction, flipped class, CLIL, etc.

The technical and technological support

Equipping the educational process with modern technical support, telecommunication technologies, digitalization of business and educational process

Interactive educational environment for smart university

Designing and supporting of a smart, educational environment ensuring that the quality of educational activities implemented through modular educational resources and technologies of LOM and MOOCs standards

29.2 Standards for the Smart University Development Intangible resources of smart organization represented by patents, useful models, inventions, rational offers, author’s certificates on software or databases are considered to be very important since they distinguish the essence of smart university from any other competitors at the educational market. Nowadays, the development of smart university is to be based on project activity requiring all the participants to be involved in the project issue and implements required knowledge, skills, and abilities. Hence, there is a question for a project manager: How new knowledge and new soft and hard skills are to be studied and trained in order to reduce the project risks to minimum point and make it competitive at the both educational and industrial markets. The authors follow the ideas of Mrdulyash [11], that there are always two elements of uncertainty in project activities. The first one is an intellectual challenge including a task and the search for issues that have never been studied before. This intellectual challenge stimulates the search for new knowledge and the growth of personal intellectual power for every participant of the project. The second one is a personal challenge related to the necessity of taking responsibility for any part of the

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project according to the trained knowledge and skills. And if there are no project’s participants who can accept these challenges and cope with them, the project activity fails. Therefore, the PMBOK standards, including a body and core knowledge of project management, have been implemented in the students’ project activities at the testing sites, represented by Togliatti State University (TSU), Volzhsky University named after V.N. Tatishchev (OANO VUIT), and Academy of Management (AoM) Samara region.

29.2.1 PMBOK Standard History in Brief Having analyzed studies [12], the following key stages of the “Guide to the Body of Knowledge on Project Management” document can be revealed in Table 29.2. Today project activities are inevitable for smart education. The main PMBOK standards’ feature is paying a lot of attention to stakeholders’ activity as the key management process. The educational stakeholders are known as being the people involved in the project activities who can have both positive and negative attitude Table 29.2 Brief description of PMBOK creation and development features Title

Year of publication

Comments

First edition PMBOK

1996

The need to document to standardize procedures for project activities managing

Second edition PMBOK

2000

Errors have been corrected and necessary knowledge has been clarified

Third edition PMBOK

2004

One of the major changes to the PMBOK® Manual in this issue is the assessment of project management practices based on “generally accepted practices in most projects.” The assessment indicators for project activities have been introduced

Fourth edition PMBOK

2009

A clear distinction between a project management plan and project document has been defined. The widely recognized “triple constraints” for project management has been expanded to six key elements: volume, quality, schedule, budget, resources, and risk

Fifth edition PMBOK

2013

Standardization of terms, processes, inputs, and outputs has been clarified. The essence of floating wave and adaptive life cycle planning has been added to

Sixth edition PMBOK

2017

The role of stakeholders for the project educational managerial activities has been added and considered as being important

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to the short-term and long-term results of project activities and affect the project development.

29.2.2 Educational Management Stakeholder’s Features Education as a field of economic activity has undergone fundamental changes and has been transformed into an area of educational services at the market. Currently, the “educational service” is closely linked to the subject of “buying and selling” in a market economy, and its key figures include sellers and buyers or consumers of educational services accordingly to the law of demand and supply. The features of the “buyers” and “consumers” definitions for the educational services are represented below: 1. buyer and consumer are represented by different groups if the education is paid by parents who are named buyers in this case and by consumers including the buyer’s children who later become students of the chosen higher school; 2. buyers and consumers represented by the same market entities including government and employers who pay for corporate training, etc. Changes occurring in the provision of educational services are to be done due to a number of reasons including: (1) changes in the social and economic system; (2) expansion of forms of ownership in the RF; (3) changes in ideological guidelines; (4) the country’s participation in global economic and socio-cultural processes; (5) the country’s integration into the world educational space; (6) failure of the existing system for professional training to meet the modern realities and requirements; (7) changes in consumer preferences for educational content, methods, place, and form due to the digitalization and generation Z demand, etc. Thus, educational stakeholder management is becoming more and more relevant and important. The designing stakeholder map representing the level of impact and the level of involvement in the smart educational process is known as being very important. Usually, it is designed in the form of a table revealing the task for every participant’s group. It should be mentioned that all the educational stakeholders’ groups are interested in innovative transformation since all of them are driven by the both the financial benefits and perquisites represented in Table 29.3. Thus, the transformation processes implemented in the field of educational services in accordance with the knowledge economy “4.0” educational process should be considered from the standpoint of stakeholders and mutually beneficial cooperation of all the parties of the educational process. To solve this issue, the university’s management is to create a team of professional analysts who have modern tools of business analysis and, by using computer intelligent software, is capable to organize the effective project activity for making the university smarter and more competitive than its competitors at the educational market.

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Table 29.3 Benefits from educational innovations Steakholders

Financial benefits from educational innovation

Human

Financial + perquisites: meeting educational needs, increasing professionalism, salary, social status, career development, and self-development

Organization

Financial + perquisites: competence growth of employees, improvement of general production culture, productivity growth, introduction of technical innovations, profitability increase, and competitiveness of the enterprise

Society

Financial + perquisites: social stability, employment, increase of GDP and GNP, and life standards increase and life expectancy

29.2.3 Agile Methodology (AM) Principles for Smart Education A lot of experts implementing the PMBOK methodology note its effectiveness while dealing with the uncertainty and volatility. According to the expert analytics carried out in tested universities during 2018– 2019, the main reasons for failures in educational projects are the following: 1. The customer’s requirements are variable, and adjustments are made during project implementation (22.3%). 2. There is no possibility of the project’s precise timing due to the probability of unforeseen complications (17.4%). 3. The human factor due to the lack of staff’s hard and soft skills and poor planning (14.7%). 4. Project development based on the intellectual technologies use is known as being an iterating process including repetitive operations (12.8%). According to the above-mentioned statistics, the authors consider the PMBOK standard introduction to the educational facility to be based on flexible project management models and practical use of agile technologies. The experts dealing with AM claim it to be based on the following principles: 1. These are project team or people who are considered to be the key to the project’s success. 2. AM emphasizes the result rather than the full adherence to bureaucratic reporting. The PMBOK is to be strengthened. 3. AM, as being a customer-oriented technology, is considered to be more effective in the current market conditions. 4. AM is flexible for technologies’ changes corresponding the requirements of modern society featured by uncertainty and volatility.

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29.3 Transformational Process Control Model 29.3.1 Model for the University’s Managing and Marketing Performance For managing the transformation at educational processes, the model describing a complex of managerial and marketing performance has been designed. The model is synthesized on the basis of the tested universities’ marketing analysis. The practical importance of the suggested model is in its capability to consider all interested participants’ opinion during the strategic planning for marketing activity for the given university. According to the PMBOK standards, the following model for managing and marketing the university activities has been designed and described by the function from the following formula due to the basic marketing and managerial indicators (MMI): MMI = f (S, V, I, A, RES, PR, PT, CSS, R),

(1)

where S

is the proposed solutions to problems and ways to meet the stakeholders’ needs; V additional value created for them; I ways to ensure the educational institution’s brand identification; A directions for minimizing transaction costs of receiving services; RES range of educational services; PR price range; PT technologies to promote educational services; CSS methods and sales channels for educational services; R complex of relations with different categories of stakeholders. The innovative, integrated character of the suggested model differs from the existing approach in 4P marketing by the integral indicator (MMI) (Fig. 29.1) due its more complex consideration of the analyzed indicators. The figure shows that the marketing policy of an educational institution should be considered according to the following triad: super system, system, subsystem and by finding out the existing cause-and-effect relations between them. Therefore, any smart university is to have a team of business marketing analysts empowered with operational and strategic management skills.

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External Stakeholders S

V

RES

R, MMI

I

M

PR

PT

Internal Stakeholders

CSS

Fig. 29.1 Integration model of the educational institution’s managing and marketing performance

29.4 The Model’s and the Experiment’s Testing 29.4.1 Expert Evaluation of the University Marketing Policy in Accordance with PMBOK Requirements The assessment of the higher institution in Samara region Volzhsky University named after V.N. Tatishchev (OANO VUIT, 2019) revealed that currently, this integration model is at the state of designing and development. It is necessary to pay attention to the value and transaction policy as well as the policy of promoting educational services to a greater extent. For this purpose, the top management of the university is to make a more detailed map of preferences for external and internal stakeholders and discuss the functional and structural model of marketing strategy for the educational institution. Considering “a subsystem of ensuring stakeholders’ interests” [12] in the context of three directions as a providing subsystem: subsystem of attracting stakeholders, subsystem of involving stakeholders, and subsystem of retaining stakeholders, it is necessary to find out between them, a system of causeand-effect relations reflecting interest in the development of educational services. Then, the model for strategic development of the higher education institution has to be designed, assessed, and developed. Figure 29.2 shows the results of the marketing analysis on the results of student recruitment in August 2019. Three tested educational institutions were taken as an example. Marketing analysis was carried out by interviewing representatives of different stakeholder groups, according to the above-mentioned classification in Table 29.1. The analysis was carried out on the expert methods basis. The experts were top managers of each tested higher education institution.

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Fig. 29.2 Comparative diagram of the expert evaluation for marketing policies of the tested higher education institutions

According to the figure, the internal diagram reflecting the assessment of the OANO VUIT activity shows that its top management has to reconsider the existing approaches in this direction. It is necessary to assess the use of the means and methods of educational stakeholder’s communication development. It is necessary to plan and regulate the transformation processes implemented in the educational services accordingly to the knowledge economy “4.0” and the development of smart education. Systemic activities in the sphere of marketing for educational services and introduction of educational innovations are to be undertaken by educational managers at the above-mentioned educational facility. Marketing policy in other universities is planned more efficiently and reflects a sufficient level of strategic planning of the higher education institution.

29.4.2 Expert Assessment for Students’ Project Activity Based on Agile Methodology and PMBOK Standards According to Fig. 29.3, the period of two years (2018–2019) has been assessed and analyzed due to the collected data. The new knowledge and skills gained from the flexible AM and PMBOK standards implementation have been assessed. The qualification scale suggested by Glukhova et al. [13, 14] has been used. Based on the results of the measurement, the evaluations characterizing the achieved level of project activities skills have been assessed and analyzed. An assessment scale for the above-mentioned indicators K i has been designed and tested. According to the task for the assessment K i , the indicator is to be analyzed.

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90 80 70 60 50 40 30 20 10 0 High

Good

Fig. 29.3 Result of the experiment

If K i corresponds the intervals K i < 0.6, it means unsatisfactory level or 0.6 ≤ K i ≤ 0.7, and it means low level. If the K i corresponds the intervals 0.7 < K i ≤ 0.8, it means satisfactory level; 0.8 < K i ≤ 0.9, means good level; 0.9 < K i ≤ 1, means high level. The result of experiment conducted at the Togliatti State University for students of the Finance, Economics, and Management Institute revealed that 84% of students obtained a good level of mastery and 16% ones have been assessed as a high level of hard and soft skills mastery due to the PMBOK and AM implementation in the educational process (Fig. 29.3).

29.5 Conclusions and Future Trends Conclusion: The performed research and analysis and the obtained outcomes enabled us to make the following conclusions: 1. Application of PMBOK 6 version standards in integration with AM and technologies is considered to be relevant and important for smart university. Otherwise, the higher school’s competitive advantages can be lost or under evaluated at the educational market. 2. It is necessary to introduce AM and PMBOK principles into the educational process. In the experimental groups, high levels of knowledge and skills for project activities have been achieved. 3. The suggested integration model of transformation processes management for educational systems provides a practical value based on the fact that contrary to the widely known 4P’s marketing tool, and it takes into account an additional level of stakeholders’ satisfaction integrated with PMBOK standards. Future trends can be represented by the following steps:

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1. Development of stakeholder’s management on the base of collection, analysis, and the stakeholder mapping design revealing the level of the stakeholders’ impact and involvement in the process of university development. 2. AM and PMBOK standard implementation into the educational processes of engineering and prototyping will increase the speed for information processing and fact-based management decisions. 3. Study and suggest criteria and indicators for the educational innovations’ effectiveness and performance.

References 1. Agamirzian, I.R., e.t.l.University inc.: from academic corporation to intellectual manufacturing. University Management: Practice and Analysis. 22(4), 126–134 (2018). (In Russ.) 2. Uskov, V.L., et al.: Smart pedagogy for Smart Universities. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning 2017, pp. 3–16. Springer (2017). https://doi.org/ 10.1007/978-3-319-59451-4. ISBN 978-3-319-59450-7 3. Uskov, V.L., Bakken, J.P., Howlett, R.J., Jain, L.C. (eds.): Smart Universities: Concepts, Systems, and Technologies, p. 421. Springer, Cham (2018). ISBN 978-3-319-59453-8. https://doi. org/10.1007/978-3-319-59454-5 4. Shikhnabieva, T., Beshenkov, S.: Intelligent system of training and control of knowledge, based on adaptive semantic models. In: Smart Education and e-Learning 2016, pp. 595–603. Springer International Publishing (2016) 5. Firsova, I.A., Vasdieva, D.G., Litvinov, A.V., Chemova, O.E., Telezhko, I.V.: Trends in the development of the global energy market. Int. J. JF Energy Econ. Policy 9(3), 59–65 (2019) 6. Bogdanov, I., Ishkildina S., Korneeva, E.: Centres of advanced professional training of personnel as the leaders of technologization: a case study of Russian regions. In: Proceedings of the 4nd International Conference on Social, Economic end Academic Leadership (ICSEAL) (2019). https://doi.org/10.2991/icsbal-19.2019.31 7. Gudkova, S.A., Osadchikova, E.V.: Comparative analysis of the concepts of competitiveness specialist. Azimuth Sci. Res.: Pedagog. Psychol. 6(2, 19), 38–41 (2017) 8. Nemtsev, A.D., Glukhova, L.V.: Concept of the managers activity standardization in the digital economy conditions (in Russian). Vestnik of Volzhskiy university named after V.N. Tatishchev 2(1), 165–175 (2018) 9. Glukhova, L.V., Nemtsev, A.D.: Use of some risk-management instruments for the project activity management (in Russian). Vestnik of Volzhskiy University named after V.N. Tatishchev 3(34), 145–155 (2015) 10. Tarasova, A.N., Korneeva, E.N., Kraineva, R.K., Gudkova, S.A.: Pitfalls and drawbacks in engineering education in Russia. J. Appl. Eng. Sci. 1, 43–51 (2019) 11. Mrdulyash, P.B.: The practice of development planning in the format of strategic sessions. Univ. Manage.: Pract. Anal. 23(1–2), 155–164 (2019). (In Russ.). https://doi.org/10.15826/ umpa.2019.01-2.013 12. Glukhova, L.V., Nemtsev, A.D.: Some aspects of training VUCA managers for SMART systems. Vestnik of Volga University named after V.N. Tatishchev 2(3), 22–30 (2019) 13. Glukhova, L.V., Syrotyuk, S.D., Sherstobitova, A.A., Gudkova, S.A: Identification of key factors for a development of SMART organization. Smart Innov., Syst. Technol. 144, 595–607 (2019) 14. Glukhova, L.V., Yarygin, O.N., Syrotyuk, S.D.: Qualimetric approach to the evaluation of the knowledge increase level based on the Boolean algebra tools. Balt. Humanit. J. 5(1, 14), 158–161 (2016)

Chapter 30

Intellectual Resources in the Development of Smart University Leyla F. Berdnikova, Natalia O. Mikhalenok, Svetlana V. Pavlova, Oksana G. Gortcevskaia, and Artem I. Krivtsov

Abstract Modern conditions are closely related to the development of information technology, new approaches to telecommunications, and digitalization of business. The emergence of a new digital generation of people requires a universal transition to smart education. With an increase in the intelligence of work, a need arises for knowledge management which focuses on the strategic development of the smart university and involves the effective use of its intellectual potential. Intellectual resources represent a unique capital, which occupies a leading position in the development of the smart university. Modern practice demonstrates the emergence of a new branch of the science of management and a new type of management activity that meets the requirements of the knowledge economy; this is the management of intellectual resources. Current market conditions pose new challenges for the development of a smart university; the definition and management of its intellectual resources are what determines the relevance of a scientific paper. The concept of intellectual resources is revealed in the paper, and the features of applying this category to a smart university are highlighted. The basic principles of managing the intellectual resources of a smart university have been clarified. The result of the study is the refinement of the conceptual apparatus of the intellectual resources of the smart university, as well as the development of an algorithm for managing the intellectual resources of the smart university. The results were tested in the management of intellectual resources of the smart unit of the university on the example of the department. L. F. Berdnikova (B) Togliatti State University, Togliatti, Russia e-mail: [email protected] N. O. Mikhalenok Samara State Transport University, Samara, Russia S. V. Pavlova St. Petersburg National Research University Information Technologies, Mechanics and Optics—ITMO University, Saint-Petersburg, Russia O. G. Gortcevskaia St. Petersburg State Marine Technical University, Saint-Petersburg, Russia A. I. Krivtsov Samara State University of Economics, Samara, Russia © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_30

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30.1 Introduction Modern conditions of digitalization, improvement of technologies, and approaches to understanding resources determine the directions for the development of scientific and innovative activities of various organizations and institutions. Particular attention is paid to the formation of an innovative society, the basis of which is the generation of ideas, the formation, dissemination, and application of knowledge that characterize a certain intellectual potential of an individual business entity. At the same time, smart universities are of particular importance in this process. By smart university, we mean the university of the future focused on modern technologies, intellectual, and innovative development [1]. Modern business conditions, along with financial, technical, and material resources at smart universities, allocate especially significant resources, intellectual ones. It is intellectual resources that represent the key vector in the innovativeness and competitiveness of the smart university. In the economic literature, quite a lot of work is devoted to the study of the concept of “intellectual resources,” for example, Odegov et al. [2], Glukhov et al. [3], Lavrentiev and Sharina [4], Berdnikova [5, 6], and Milner [7]. The research, design, and development outcomes of various projects on smart universities, smart systems as well as smart educational environment are presented by various authors, for example, Serdyukova [8], Serdyukov et al. [1, 9–14]. We are observing the transformation of the education system, its improvement, taking into account the emergence of smart technologies, and the intellectualization of labor [5]. It should be noted that at present, a unified approach to the definition of intellectual resources has not been developed and insufficient attention is paid to the basics of their management at smart universities. In general, the intellectual resources of a smart university can be represented as a system of accumulated knowledge on developments, technologies, and scientific discoveries which can be disseminated among students and effectively used in further innovative activities.

30.2 Statement of the Problem in General Form and Its Connection with Important Scientific and Practical Tasks In the period of digitalization of the economy and with the growth of scientific and technological progress, there is a need for the development and implementation of innovative demanded products, services, and technologies that have qualitatively new characteristics. However, for the implementation of innovative activities, the economic entity must have the necessary resources for this. Among the activities of a smart university, intellectual resources play a special role among such resources. The

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identification of opportunities for the successful development of the smart university is facilitated by intellectual resources. They represent the most valuable resource in the possession of highly qualified personnel who are able to generate and implement ideas aimed at the effective functioning of the smart university. This paper is devoted to solving the problem of improving the management of intellectual resources, in particular, clarifying the principles and developing an algorithm for managing intellectual resources at a smart university. Relying on the principles and algorithm for managing intellectual resources specified in the paper, smart university can increase the development efficiency by basic criteria such as quality of educational services, the growth of research work increase in scientific research, and an implementation of innovation. This paper pays significant attention to the expansion of the conceptual framework regarding the terms “intellectual resources,” “intellectual potential of an employee,” and “intellectual potential of a smart university.” Figure 30.1 defines the role of intellectual resources in the activities of a smart university. Thus, the intellectual resources of the smart university are involved in creating an educational smart environment, scientific research, carrying out research work, and the formation of the innovative potential of the smart university. In this regard, special approaches to the management of intellectual resources in smart universities are needed.

Intellectual resources of smart university

Educational smart environment

Scientific developments

Research work

Fig. 30.1 Role of intellectual resources in the activities of the smart university

Innovation potential

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30.3 Presentation of the Main Research Material with Full Justification of the Obtained Scientific Results 30.3.1 Refining the Principles of Intellectual Resource Management of a Smart University Currently, many terms are used to characterize the intelligence of work, for example, intellectual resources, intellectual capital, intellectual potential of an individual, and intellectual potential of an organization, and others. The concept of “intellectual” is a derivative of the word “intelligence.” The term “intelligence” comes from the Latin “intellectus” and means “mind, reason; man’s cognitive ability,” and the concept of “intellectual” refers to the mental life of a person, to reason, or intellect. Also, “intellectual” is characterized as spiritual, mental, or characterized by a high level of development of intelligence. Thus, the intellect is a mental ability, the mental principle in a person, and the intellectual is mental, and spiritual with highly developed intelligence. According to Odegova et al., “intellectual potential is a multifactorial, multistructural concept, combining human, educational, scientific, informational, organizational, industrial, innovative, investment, institutional, and social aspects, which together form the intellectual prerequisites for the scientific, technical, economic, and social development of a company, a region, and the country as a whole” [2]. Lavrentiev and Sharina emphasize that “intellectual potential is a combination of theoretical knowledge, practical experience, and individual abilities of workers engaged in the creation of innovations in industrial enterprises and organizations” [4]. In our opinion, in general terms, the term “potential” is a set of capabilities and resources of a smart organization that ensure the continuity and effectiveness of its activities. We believe that for the innovative development of the smart university, it is necessary to separate the intellectual potential of the employee and the intellectual potential of the institution. The intellectual potential of the employee is determined by the totality of the employee’s characteristics, namely the level of education, qualifications, professional knowledge, experience, practical skills, creative and intellectual abilities, and creative thinking which can contribute to the innovative development of the smart university. The intellectual potential of a smart university is the presence of effective use of workers with a high level of education and qualifications with professional knowledge, experience, practical skills, creative and intellectual abilities, creative thinking, able to develop innovative projects and implement them, and implement the innovative activities of the smart university. The presence of the intellectual potential of the institution is a fundamental element necessary for the implementation of innovative activities but not the only one. For the development of a smart university, one should possess other resources, the

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material and technical base, financial, technological, energy resources, etc. However, the intellectual potential in this case takes the leading place. In general, intellectual resources can be represented in the form of knowledge bases of the organization’s personnel and intellectual technologies as new means of generating unique knowledge and high-tech educational environments, including new “smart” data access technologies [6]. Using the capabilities of the industrial Internet, modern universities are moving to the category of “smart university.” We believe that the mechanism of managing intellectual resources of a smart university should be based on certain principles. The main task of the intellectual resource’s management mechanism is to ensure the effective development of the smart university. In Fig. 30.2, we clarify the principles of managing the intellectual resources of a smart university.

Refined principles of intellectual resource management smart university

Principle of continuity - creating the necessary conditions to ensure continuous and efficient use of intellectual resources Principle of complexity - creation of an integrated management system in which all elements are a single mechanism aimed at the development of intellectual resources

Principle of regularity - the management system must solve the problems of not only the current, but also the long-term development of intellectual resources

The principle of scientific validity - methods and means of managing intellectual resources should be scientifically sound and proven in practice Principle of system - intellectual resources management should be systemic and take into account changes in the external and internal environment The principle of stimulation and motivation - in the management of intellectual resources, justified methods of stimulation and motivation should be applied

Principle of effectiveness - methods and methods of managing intellectual resources should be economically justified and effective

Fig. 30.2 Refined principles of intellectual resource management smart university

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30.3.2 Visualization of the Results of a Statistical Study The management of intellectual resources and the development of the smart university which occupies an important place in high-tech and knowledge-intensive industries are significantly affected by macroeconomic factors: economic, political, legislative, social, demographic, and others. Currently, the industry of high-tech and knowledge-intensive industries requires the development and needs intellectual resources. Statistics in 2018 show a decrease in the share of high-tech and high-tech industries in the GDP of the Russian Federation [15] (Fig. 30.3). Figure 30.3 shows that in 2018, the share of high-tech and knowledge-intensive industries in the GDP of the Russian Federation decreased to the levels in 2015 and amounted to 21.1%. To a greater extent, such dynamics of indicators were affected by macroeconomic factors. Figure 30.4 shows the share of value added by high-tech and knowledge-intensive industries in the GRP of the constituent entities of the Russian Federation by Federal Districts [15]. Figure 30.4 shows that the largest share of the added value of high-tech and knowledge-intensive industries in GRP is accounted for by the Volga Federal District—23.9% in 2016–2017. The smallest share of the added value of high-tech and knowledge-intensive industries in GRP is accounted for by the Ural Federal District—12.3% in 2016–2017. 2014 22 21.5 21 20.5

2015 2016 The share of high-tech and knowledgeintensive industries in the gross domestic product, %

2017 2018

Fig. 30.3 Share of high-tech and knowledge-intensive industries in the gross domestic product of the Russian Federation Far Eastern Federal District, % Siberian Federal District,% Ural Federal District, % Volga Federal District, % North Caucasian Federal District, % Southern Federal District, % Northwestern Federal District, % Central Federal District, %

2017 2016

0.0

5.0

10.0 15.0 20.0 25.0 30.0

Fig. 30.4 Share of value added by high-tech and knowledge-intensive industries in the GRP of the constituent entities of the Russian Federation by Federal Districts [15]

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2018

High-tech types of economic activity,%

2017 Medium-tech economic activities,%

2016 2015

High-tech economic activities, %

2014

Total, % 0.0

2013 20.0

40.0

60.0

80.0

100.0

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Fig. 30.5 Share of domestic expenditures on research and development in priority areas for the development of science and technology in the total amount of domestic expenditures on research and development in the whole of the Russian Federation

The share of domestic expenditures on research and development in priority areas for the development of science and technology, in the total volume of domestic expenditures on research and development in the whole of the Russian Federation [16] is presented in Fig. 30.5. The main provisions of the concept regarding the transition of Russia to innovative development form a new document, a long-term forecast of scientific and technological development of the Russian Federation until 2025. Based on this document, an increase in Russia’s share in the global economy, as well as an increase in the share of innovative products in the total industrial products, confirms the need to develop smart universities as a strategically important element in the formation of their own intellectual resources that can transfer knowledge, experience, and skills and build the ability of graduates for their effective self-realization in a professional environment.

30.3.3 Intellectual Resource Management Algorithm In the management of intellectual resources, various methods and algorithms can be applied. No management model is typical for all organizations. Methods and algorithms for managing intellectual resources depend on the industry and the characteristics of the organization. In the industry, algorithms for managing intellectual resources and innovations are focused on reducing costs and increasing the scale of production. In the sectors of the banking sector, consulting services, and design organizations, the management of intellectual resources is based on information and communication technologies. High-tech industries (electronics and aerospace) are focused on research and development. In their activities, the management of intellectual resources is based on obtaining innovative breakthroughs in nanotechnology and microelectronics. The specifics of the functioning’s of the smart university require new approaches to managing its intellectual resources.

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In order to develop a smart university in the management of intellectual resources, it is necessary to adhere to improved approaches. Based on the study, we propose an algorithm for managing intellectual resources with the goal of developing a smart university (Fig. 30.6). The proposed algorithm involves the assessment and forecasting of the external and internal environment; as a result of data verification, setting goals and objectives for managing intellectual resources are necessary. The next step is the selection of methods for managing intellectual resources and forecasting the results. Next, the process of managing intellectual resources is implemented. The next step is to evaluate the results of using intellectual resources. If the results do not reach the planned value, then it is necessary to move again to the choice of methods for managing intellectual resources and forecasting the results. If the results of the use of intellectual resources reach the planned values at the appropriate stage, Entrance

Assessment and forecasting of the internal environment

Assessment and forecasting of the external environment

no

Verification of results

no

Verification of results yes

yes

Setting goals and objectives for managing intellectual resources

Choice of methods for managing intellectual resources, forecasting results

Intellectual resource management implementation no Assessment of the results of the use of intellectual resources no yes

Monitoring the implementation of predicted indicators of the use of intellectual resources and the adjustment of measures Exit

Fig. 30.6 Intellectual resources management algorithm for the development of smart university

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then we should proceed to monitoring the implementation of indicators of the use of intellectual resources and, if necessary, adjusting measures. In our opinion, the intellectual resources of a smart university should be considered as a holistic and an economically necessary system, reflecting a complex set of connections between the structural elements of the institution’s intellectual field which requires qualified personnel with professional knowledge, experience, practical skills, creative and intellectual abilities, creative thinking, and contributing to the development of smart university. In the modern economy in the context of business digitalization, the assimilation and dissemination of new knowledge will be possible only with an adequate change in the forms and methods of managing intellectual resources, which at different stages and at an increasing pace, will become the vehicles of the technological revolution, science, and innovation, and will be an important element in the development of smart university.

30.4 Conclusions and Next Steps Conclusions. The study showed that the management of the intellectual resources of smart university requires the improvement of principles and algorithms that take into account the features of its development. 1. The paper clarifies the basic principles of intellectual resource management in smart universities, taking into account the features of the development of the smart environment. 2. The study conducted a statistical study of the share of high-tech and knowledgeintensive industries in the GDP of the Russian Federation, the results of which confirm the need to develop these areas and improve the intellectual resource management system at smart university. 3. The paper proposes an algorithm for managing intellectual resources in a smart university which can increase the efficiency of its development and taking into account the needs of both external and internal environment. The proposed principles and the algorithm for managing intellectual resources can be applied both at the level of a separate structural smart unit and in general at smart university. These management tools help to increase the level of development of the smart university, improve the quality of smart education, and achieve the planned indicators for the use of intellectual resources. Next Steps. To expand the tools for managing intellectual resources, it is necessary to establish key indicators of the effectiveness of the use of intellectual resources at the smart university in the following areas: educational smart environment, scientific developments, the performance of research work, and formation of the innovative potential of smart university.

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References 1. Serdyukova, N.A., Serdyukov, V.I., Slepov, V.A, Uskov, V.L., Ilyin, V.V.: A formal algebraic approach to modelling smart university as an efficient and innovative system. In: SEEL2016, Smart Education and Smart e-Learning, Smart Innovation, Systems and Technologies, vol. 59, pp. 83–96. Springer, Cham (2016) 2. Odegov, Y.G., Rudenko, G.G., Babynina, L.S.: Labor Economics: Textbook. In: 2 tons T2, 924 p. Publishing House “Alfa-Press”, Mexico (2007) 3. Glukhov, V.V., Korobko, S.B., Marinina, T.V.: Economics of Knowledge, 528 p. Peter, SPb. (2003) 4. Lavrentiev, V.A., Sharina, A.V.: The intellectual potential of the enterprise: concept, structure and direction of its development. Creative Econ. 2(26), 83–89 (2009). http://www. creativeconomy.ru/articles/2173/ 5. Berdnikova, L.F., Sherstobitova, A.A., Schneider, O.V., Mikhalenok, N.O., Medvedeva, O.E.: Smart university: assessment models for resources and economic potential. In: Smart Education and Smart e-Learning Smart Innovation, Systems and Technologies, vol. 144, pp. 583–593. Springer, Cham (2019) 6. Berdnikova, L.F., Zverintseva, A.S.: Labor resources: concept and main tasks of analysis. Karelian Sci. J. 4(17), 50–53 (2016) 7. Milner, B.Z.: Innovative Development: Economics, Intellectual Resources, Knowledge Management, 624 p. INFRA-M, Mexico (2009) 8. Serdyukova, N.: Algebraic formalization of smart systems theory and practice (Chap. 6). In: Algorithm for a Comprehensive Assessment of the Effectiveness of a Smart System. In: The Algorithm of a Complex Estimation of Efficiency of Functioning of the Innovation System, p. 101 9. Uskov, V.L., Bakken, J.P., Howlett, R.J., Jain, L.C. (eds.): Smart Universities: Concepts, Systems, and Technologies, 421 p. Springer, Cham (2018). ISBN 978-3-319-59453-8. https://doi. org/10.1007/978-3-319-59454-5 10. Uskov, V.L., et al.: Smart university taxonomy: features, components, systems. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning 2016, pp. 3–14, Springer, Cham (2016). ISBN 9783319396897. https://doi.org/10.1007/978-3-319-39690-3 11. Coombs, S.: The psychology of user-friendliness: the use of information technology as a reflective learning medium. Korean J. Think. Probl. Solv. 10(2), 19–31 (2000). Keimyung University, Korea 12. Bhattacharya, M., Chatterjee, R.: Collaborative innovation as a process for cognitive development. J. Interact. Learn. Res. 11(3/4), 295–312 (2000). Special Issue on Intelligent Systems/Tools in Training and Life-long Learning. https://www.learntechlib.org/p/8381/. Accessed 15 Mar 2018 13. Bhattacharya, M., Narita, S.: Design of a computer based constructivist tool for collaborative learning. In: Crawford, C., Davis, N., Price, J., Weber, R., Willis, D. (eds.) Proceedings of SITE 2003–Society for Information Technology and Teacher Education International Conference, pp. 3251–3254. Association for the Advancement of Computing in Education (AACE), Albuquerque (2003). https://www.learntechlib.org/p/18686/. Accessed 15 Mar 2018 14. Glukhova, L.V., Syrotyuk, S.D., Sherstobitova, A.A., Pavlova, S.V.: Smart University Development Evaluation Models—Smart Education and Smart e-Learning, Smart Innovation, Systems and Technologies, vol. 144, pp. 539–551, Springer, Cham (2019) 15. Federal State Statistics Service of the Russian Federation—Access Mode: https://www.gks.ru/ folder/11186?print=1 (date of the application: 30.12.2019) 16. Federal State Statistics Service of the Russian Federation—Access Mode: https://www.gks.ru/ folder/11189 (date of the application: 30.12.2019)

Chapter 31

VUCA-Managers Training for Smart Systems: Innovative and Organizational Approach Lyudmila V. Glukhova, Anna A. Sherstobitova, Elena N. Korneeva, and Raisa K. Krayneva Abstract The term “VUCA” represents a four-letter acronym including definitions and contents of volatility, uncertainty, complexity, and ambiguity. These are the features of a modern business environment in which managers have to face and deal with. The area of research is designing and developing the managers’ training according to the qualification requirements revealed in both the educational and professional standards accordingly to social and business demands. Nowadays, modern, effective managers are known as being highly stress-resistant, flexible, creative, and having available hard and soft skills for managing risks in unstable situations. Smart business environment and a university’s activities are based on the ideas of digitalization increasing the social demand for employees who are ready to process large amounts of information, multi-dimensional analysis of data, and identifying intelligent management solutions quickly while meeting the situation of uncertainty and risk. The goal of the research is to review some relevant innovative means and organizational methods of students’ training for being smart and effective managers in conditions of volatility, uncertainty, complexity, and ambiguity. Modern means and methods of personnel training in the digitalization age on the basis of professional standards are considered and revealed. The VUCA-manager training models are designed according to the theoretical framework of the research and modern requirements.

L. V. Glukhova (B) Volzhsky University named after V.N. Tatischev, Togliatti, Russia e-mail: [email protected] A. A. Sherstobitova · E. N. Korneeva Togliatti State University, Togliatti, Russia e-mail: [email protected] R. K. Krayneva Financial University under the Government of the Russian Federation, Moscow, Russia e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_31

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31.1 Introduction and Literature Review Modern business environment is based on agile methodology and approach. The competitive businesses should introduce capabilities of both the managers and employees in dealing with unpredictable situations. The analysis of recent publications [1–4] proves the digitalization of all business structures and the increased demand for staff who is ready to deal with the latest, high-tech products, and technologies in unstable environment. According to educational and professional standards, these are state higher institutions of the Russian Federation that are to design and develop the smart environment inside and outside the company. This may include new, smart technologies, smart infrastructures, and smart organizations. Thus, the smart organization is considered to be a complex system functioning in a particular environment and interacting with other systems. This approach is justified by the general theory of systems implemented and later developed by American researchers Bertalanfi et al. [5]. Nowadays, smart organization can be represented as a self-learning organization where the team of performers is ready for changing their activity quickly by adapting to the relevant conditions of the environment. The employee’s ability to introduce innovations and make flexible decisions based on agile methodology and approach in conditions of uncertainty and structural complexity is considered to be the key skills for the company’s competitiveness. The concept of smart organizations and their functioning, key indicators of success and efficiency of their functioning in the conditions of globalization, as well as management tools to assess the dynamics of development levels for smart organizations and their transformation, are revealed in studies of Burlea, A. S., Burdescu D. D., and some other researches [6]. All the abovementioned researches’ claim to smart organization including the team of performers with available hard skills and soft skills including creativity, stress resistance, ability to self-development, and adaptation to complex conditions of uncertainty and risk during the digitalization age.

31.2 Basic Research Results 31.2.1 Basic Concepts of VUCA-Managers Training for SMART Organizations According to the management methods and approaches described in the studies of foreign [7, 8] and Russian authors [9, 10], a new management paradigm called “Management 3.0” is considered to be attractive and profitable due to its peculiarity of applied use of computer-aided design, programming methods, and modern computer control technologies [11]. Nowadays, IT experts integrate agile approach in the management sphere and in project management. However, the Russian business environment, due to the lack of qualified staff, does not fully meet the challenges of

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the external smart environment. The research problem is the designing of the VUCAmanager’s training model targeted to the students’ hard and soft skills development and claiming to make the graduates competitive while dealing with the VUCAreality requirements and issues. Taking into the consideration the conclusions of M.A. Matushkin and some other modern studies [11, p. 92], the authors believe that represented definition of VUCA-reality formed with the first letters of the words “volatility,” “uncertainty,” “complexity,” “ambiguity” reveals both the main features of modern smart environment and challenges for scientists and methodologists while designing and developing bachelors’ and masters’ educational programs.

31.2.2 Core Functions for Smart Environment and VUCA-Manager Training According to the management rules, the processes of instability and uncertainty are to be carefully analyzed and identified for reducing risk threats while performing the working tasks by a team. Management tools are to be discussed and selected for monitoring key indicators in the current market conditions. To reduce the complexity and ambiguity of the requirements in external smart environment, structural analysis and synthesis tools are also required to design the risk management models for the VUCA-managers training. Figure 31.1 presents the process of designing the generalized labor function (GFP) and application of the professional standard (PS 08.018) for training employees in risk management. The model represents the correlation between the designed generalized labor function “Risk Analysis and Assessment” and the generalized labor function “Development of Individual Functional Areas of Risk Management.” It shows

Risk Analysis and Assessment

Methodology of Risk Management

Federal Standard 38.03.02 Management

A/01.5 A/02.5 A/03.5 A/04.5

General Labor Function for risk management in VUCA environment

Professional Standard 08.018 Development of separate function for risk management B/01.6 B/02.6 B/03.6 B/04.6

A/05.5

Bachelors' Skills

Masters' Skills

Labor Function simulation

The teaching staff

Assessment Base

Fig. 31.1 Model of the designing the generalized labor function for risk management in separate functional areas

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the transformation of skills and competencies classified as A/01.5-A/06.5 into B/01.6-B/04.6. On the left side of Fig. 31.1, the entrance shows the required knowledge, skills, and competencies that are to be developed on the basis of secondary vocational education according to professional standards. They are included in the generalized work function “Risk Analysis and Assessment.” The proposed model cannot be used for training on the basis of secondary education due to the required competencies are not available at the entrance. On the right side of Fig. 31.1, the output representing the formed labor functions is to be analyzed. The arrows included at the bottom define the supporting element for the above-mentioned model and included both the university staff engaged in training of VUCA-managers and test tasks base containing a set of situations, tasks, tests. The designed model claims to provide the required level of VUCA-managers competence for risk management in smart environment.

31.2.3 Model for the VUCA-Managers Development The suggested model is designed according to both external and internal environment requirements represented by the Federal State Educational Standard (FSES) and the professional standard (PS). The model of the general labor function (GLF) is shown in Fig. 31.2. The required set of hard and soft skills is to be taken into consideration. Figure 31.3 presents a fragment for the generalized labor function (GLF) assessment in the section of classification characteristics (B/01.6-B/04.6) based on the test assignment bank (TAB). The statistical processing of the results proved the achieved level of labor functions assimilation as a percentage of each evaluated characteristic.

Requirements of the Federal State Educational Standard (FSES)

External Environment Professional Standard Requirements (PS)

Bank of formed Soft Skills and Hard Skills (TAB)

Internal Environment

Designing knowledge and skills according to General Labour Function from the PS (GLF)

Fig. 31.2 Set of hard and soft skills

New skills

Development

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Development of risk impact measures for individual activity.

23,6

32,4 Documentation of risk management process and adjustment of risk registers within individual business processes. Providing methodological assistance and support to the risk management process

8,3 35,7

Development of a methodological and regulatory framework for risk management system

Fig. 31.3 A ssessment of the TAB-based GLF

The diagram shows the decoded results. The diagnostic process proves that the labor function covering the process of documentation for the revealed uncertainty and risk situations has been mastered. The insufficient level of knowledge and skills acquiring has proved the necessity for standardization process involving all responsible persons for risk management methodology implementation in educational process. The training process for the generalized labor function has been adjusted and practiced. In the digital age, the issue of knowledge intellectualization and designing the personal indicators base for skills and abilities allowing to get further experience in vulnerabilities blocking is known as being very important. The implementation of professional standards reflecting the functional responsibilities is considered to be a must have for smart environment due to the claimed possibility for risk decreasing at smart organizations dealing in VUCA-environment. However, many scientists and methodologists define the necessity for information security standards learning and its implementation for students studying managerial training programs. The model to solve the problem is considered below.

31.2.4 Information Security Standards Model The standard document analyzed and studied in the paper (GOST R 57628-2017) represents a technique including requirements for achievement of targeted indicators, namely construction of a profile of information security protection for business structures is possible. Table 31.1 reveals components for the initial level of the knowledge-base, which should be formed at the stage of the methodology study described in GOST R 57628-2017 (Information Technologies. Methods and means of ensuring security) and corresponding to the requirements of the professional standard (PS 08.018). Table 31.1 shows a fragment of compliance with the requirements of GOST R 15408.1-2012 and the knowledge, skills, and abilities that have been achieved. This table can be considered as a model for VUCA-managers training at a smart university and for their further work at VUCA-environment.

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Table 31.1 Designing the information security protection at the enterprise (GOST 15408.1-2012) Main content of knowledge

Acquired skills and experience

1. Knowledge in the field of operational management and planning of requirements to information security for each specific user

1. The skills of modeling and designing profiles for the protection of information assets in stages 2. Creation of a profile for protection and information security (RSP) 3. Gaining experience in step-by-step description of a security task (FSIB)

2. Creation of a terminology vocabulary in the field of trust levels

4. Skills to determine the cause–effect relationships between terminology, confidence levels, and instrumental means 5. Skills to justify and select management tools to assess compliance between goals, objectives, problems, and requirements for the security of information assets

31.2.5 Skills of Information Security Profiles Designing Figure 31.4 shows the results of the survey conducted by the graduates from the “management organization” faculty at the end of 2019. The analysis of the results showed an insufficient level of knowledge and formed competencies and skills for designing simple profiles for the information assets protection at the workplace. One of the basic reasons is considered to be the lack of “interfaces” with the requirements of professional standards in the training process. However, the chart of evaluation characteristics in 2019 showed some improvement in the results of the formed level of competencies for the graduates in all analyzed parameters Security Policy

Threats and vulnerabilities

Information Security Tasks

Information Security Protection Profile

Security Audit

Aims and objectives of the security protection system

Fig. 31.4 Analysis of the results of personnel training for the information security profiles at workplaces

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due to the adjustment of training material for such subjects as “Production Management,” “Information Management,” “Strategic Management,” and “Innovation Management.” The basic concepts of the information security systems, the designing and development of security policy of threats and vulnerabilities are studied. However, the realities of digital economy allow the authors to obtain a conclusion on the necessity of introduction of a subject “standardization for managers” in the process of the managers’ training. Some ideas of implementing the requirements of the standard for the designing and development of information assets protection profiles are represented below.

31.3 Smart Organizations’ Security Policy for VUCA-Environment 31.3.1 The Algorithm of Security Policy for SMART Organizations In the conditions of further digitalization of the business environment, there is a constant lack of personnel with the skills to identify risks and to reduce them at all stages of business activity. In this regard, the authors define the necessity to introduce the professional standards’ requirements into the training process of employees involved in management processes at various levels. Thus, the knowledge profile for training the experts for protection of information assets can be improved due to the designing and implementation of the concepts including “security audit,” “security tasks,” and “profile of protection of information security.” The following algorithm (Figs. 31.5 and 31.6) is considered to be effective: Step 1. Preparatory Phase. Step 2. Basic Stage. Step 3. Final Stage. The fragments of the stages are represented below according to the state requirements of GOST R 57628-2017. This algorithm reveals the author’s vision of risk management training process in an unstable environment. The algorithm was tested in 2019 while training VUCAmanagers for joint venture “Avtovaz.”

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1. Preparation Phase 1. Review of the requirements of GOST (15408.1, 15408.2, 15408.3) for the construction of the Security Polic y for the object of investigation. 1.1 Formation of terminology vocabulary. 1.2 Creating a database of ready-made solutions. 1.3 Identification of information security assessment objects. 1.4 Algorithm building Identification of possible threats and risks. 2. Buildin g the tar get tree and the task tree. 3. Study of the requirements of GOST R 57628- 2017 3.1 Analysis of requirements to the construction of information protection profiles (IPR). 3.2 Analysis of typical information protection tasks (IP). 3.3 Analysis of basic techniques for di agnosing threats to Assessment Objects (AO). 4. Study of the existing document of the object of study "Security Policy" 4.1 Analysis and identification of noncompliances with GOSTR 5728-2017 requirements. 4.2 Analysis of identified non-compliances and reasons for their occurrence. 4.3 Strategic and operational planning

Fig. 31.5 Algorithm of forming skills for building safety profiles (a fragment)

Basic Stage 1. Analysis of the existing level of trust. 2. Identifying security issues. 3. Defining formal and informal security requirements. 4. Assessment of existing risks and threats

5. The choice of threat analysis methodology. 6. Identification and specification of threats. 7. Analysis of negative actions and their causes. 8. Identification and specification of conducted Information Security Policies. 9. Identification of security problems. 10. Formation of the purposes of increase of level of safety.

Fig. 31.6 Method of forming skills of building information security profiles. The main stage. (a fragment)

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31.3.2 Document “Information Security Protection Profile” for SMART Organizations The document “profile of information security protection,” formed for activity of organizational structures, contains the following kinds of logically interconnected information created as a result of stage-by-stage performance due to the following algorithms: 1. defining the security need corresponding to the identified problems and issues; 2. revealing threats from both the external and internal environment; 3. designing the “tree of purposes” for safety of objects and designing and assessing the “tree of possible risks” for the objects; 4. designing a logic correlation and swap between the “tree of requirements” and the “tree of decisions” where functional requirements for trust and safety are stated. Thus, the level of the enterprise’s confidence to the taken measures on information protection designing can be assessed. The acquired skills of the protection profiles designing will significantly reduce the risks of information losses in SMART organizations.

31.4 Conclusion and Future Steps The described multi-aspect research and project of students’ training for VUCAenvironment conducted at Togliatti State University is considered as being relevant to modern social requirements. It is aimed at active use of systematic approach to analyzing and then designing model for VUCA-managers training in the digitalization age due to constantly changing requirements of smart society and smart business environment. The obtained research outcomes enabled us to make the following conclusions. Organizational aspects 1. The model of managers’ training during the digitalization age and VUCA environment is proposed. The model is considered to be distinguished due to the fact that the training is conducted with the integrated approach according to the educational and professional standards’ requirements and security standards for information assets of the company. The required VUCA-manager skills (soft skills and hard skills) are formed according to the demand of the external smart environment. 2. VUCA-manager skills for rapid adaptation to the volatility, uncertainty, complexity, and ambiguity of modern society and decreasing possible risk situations. Innovational aspects

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3. An example of the designing and developing information security profiles for smart organizations is shown. The algorithm “profile of information security protection” is offered and developed. 4. The correspondence of required and formed competences in the described training process is shown. Next steps. VUCA-manager training process is to be based on the test tasks database verifying the availability of the formed competencies and constantly monitoring their level. The knowledge-base for VUCA-manager is to be created.

References 1. Uskov, V.L., Bakken, J.P., Howlett, R.J., Jain, L.C. (eds.): Smart Universities: Concepts, Systems, and Technologies, p. 421. Springer, Cham (2018). ISBN 978-3-319-59453-8. https://doi. org/10.1007/978-3-319-59454-5 2. Serdyukova, N., Serdyukov, V.: Algebraic Formalization of Smart Systems. Theory and Practice. Springer, Switzerland (2018) 3. Uskov, V.L., et al.: Smart university taxonomy: features, components, systems. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning 2016, pp. 3–14. Springer, Cham (2016) 4. Shikhnabieva, T., Beshenkov, S.: Intelligent system of training and control of knowledge, based on adaptive semantic models. In: Smart Education and e-Learning 2016, pp. 595–603. Springer, Berlin (2016) 5. Uskov, V.L., Bakken, J.P., et al.: Building smart learning analytics system for Smart University. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning 2017, pp. 191– 204. Springer, Berlin (2017). https://doi.org/10.1007/978-3-319-59451-4. ISBN 978-3-31959450-7 6. Burlea, A.S., Burdescu, D.D.: An integrative approach of e-Learning: from consumer to prosumer. In: Smart Education and e-Learning 2016. Smart Innovation, Systems and Technology, vol. 59, pp. 269–279. Springer, Switzerland (2016). https://doi.org/10.1007/978-3-31939690-3 7. Nandram, S., Bindlish, Puneet, K. (eds.): Managing VUCA Through Integrative SelfManagement: How to Cope with Volatility, Uncertainty, Complexity and Ambiguity in Organizational Behavior (Management for Professionals). Springer, Berlin (2017) 8. Apello, J.: Management 3.0 Leading Agile Developers, Developing Agile Leaders. Person/Addison-Wesley Professional, Boston (2011) 9. Drugova, E.A., Kalachikova, O.N.: Understanding the Process of Decision-Making in Universities in a VUCA-World. University Management: Practice and Analysis. 23(1–2), 81–92 (2019). (In Russ.). https://doi.org/10.15826/umpa.2019.01-2.006 10. Glukhova, L.V., Mitrofanova, Y.S.: Digitalization of economy and the particularities of its application in an integrated facility’s activity. Bull. Volga Region State Univ. Serv. 4 (2017) 11. Matushkin, M.A.: Methods and tools of an enterprise management in VUCA-reality conditions (in Russian). SGSEU Bull. 5(74), 92–95 (2018)

Chapter 32

Economic and Organizational Aspects of University Digital Transformation Tatiana N. Popova, Yana S. Mitrofanova, Olga A. Ivanova, and Svetlana B. Vereshchak

Abstract The article discusses the economic and organizational aspects of the university digital transformation in the transition to Smart University. It is proposed to understand digital transformation as one of the university digital maturity levels. Digital maturity levels such as initial digitalization and digital manageability are also highlighted. The university digital transformation objects (data, university processes, user interfaces and people) are defined. The management system importance in the process of university digital transformation is indicated. The article also offers conceptual models for managing the university digital transformation in the form of managed Markov chains that take into account the educational environment specifics. The transformation processes modeling is carried out by the iterative optimization method. It is also considered the technological process of managing the university transformation based on digital technologies. The developed and proposed models and methods of decision-making in the university digital transformation management system take into consideration the management process versatility, as well as the degree of its uncertainty. The proposed models can be applied in the project offices activities to manage the university digital transformation and the transition to Smart education concept. The experience of creating an organizational infrastructure for digital transformation is considered on the example of Russian universities.

T. N. Popova · Y. S. Mitrofanova (B) Togliatti State University, Togliatti, Russia e-mail: [email protected] O. A. Ivanova · S. B. Vereshchak I.N. Ulyanov Chuvash State University, Cheboksary, Russia © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_32

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32.1 Introduction The digital economy and the development of Industry 4.0 ideas have an impact on all human activity areas, especially education. Nowadays, the most competitive universities will be those that can transform into Smart universities based on digital technologies and train staff for the digital economy. It will create a modern infrastructure in Russia and around the world for the Smart society concept development. It should be noted that the key point of universities’ digital transformation is changing business processes or reengineering business processes, where digital and Smart technologies are the key tools (Fig. 32.1). Only a well-built management system can start the transformation process, where the mechanism for making optimal management decisions has been worked out and the appropriate management principles have been selected. Today, the most significant trends in modern management can be identified in the digital space, which include the use of flexible management methods, the development of organizational and personal competence in project management, and the introduction of specialized management technologies [1, 2]. It can also be noted that project management is the driver that can dramatically increase the efficiency and effectiveness of universities and economy digital transformation.

Management System

University Processes

Business Process Reengineering

Digital Transformation

Digital Technologies

Fig. 32.1 Transition process through digital transformation to Smart University

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32.2 Levels of University Digital Maturity

User Interfaces

People

Fig. 32.2 Levels of university digital maturity

Stage 3. Digital Transformation

Data

Stage 2. Digital Manageability

University Processes

Stage 1. Initial Digitalization

Digital transformation is a catalyst for forming and achieving the transition goals from one university development state to another, more qualitative one [3, 4]. The digital transformation completes the transition from a classic university form to a Smart university. We can emphasize the following university digital maturity levels during this transition: initial digitalization, digital manageability and digital transformation. At the same time, it should be noted that during the transition from one level of maturity to another, the transformation objects are not only university processes, but also data, user interfaces and people (Fig. 32.2). At the initial digitalization stage, the university has already implemented information systems that automate the main and auxiliary university processes, digital accounting should be introduced, digitize data and work with digital data and its generation organized. Users of information systems and portals (teachers, management, students and support staff) at the initial digitalization stage must have basic skills to work on a computer and in specific information systems (customer relations management (CRM), enterprise resources planning (ERP), learning management system (LMS) and others) installed at the university. Digital controllability stage is characterized by common information base presence, where all the university data, implemented consistent infomodel data and integrity rules, fixed digital track (Learning Record Store (LRS) and other tools), organized by processes automatic execution with Key Performance Indicator (KPI) formation, organized paperless document circulation using electronic digital signature. Personal offices and desktops with all actions notifications and tracking have been created for all educational process participants. All digital tools at the stage of digital manageability have mobile and ergonomic interfaces. University digital

Smart Universit y

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tools (teachers, management, students and support staff) at the digital manageability stage must have digital transformation skills of information, work with knowledge management tools, analytics tools and be able to build digital communications. The next level of digital maturity (digital transformation) is characterized by Smart tools introduction for work with big data. Using learning analytics tools and providing robotic processes (using of expert systems tools and artificial intelligence tools, neural networks and chat bots), all interfaces are integrated and intelligent services are created. Digital profiles of teachers, students, employees and management were created [5, 6]. A managing digital profiles tool has been implemented and is working. In our point of view, these are one of the main signs of the university digital transformation while switching to a Smart university. To form these digital maturity levels, it is necessary to use advanced university management technologies that will speed up the process of making management decisions and help you to choose the right strategy for the transition to a Smart university.

32.3 Conceptual Models of University Digital Transformation Management The task of developing and applying models for managing university digital transformation is a rather difficult scientific problem, starting with the setting the problem and ending with the choice of mathematical apparatus. The university management system has its own specific features. The university as a system is either organizational or “active,” where elements may have their own goals that do not coincide with the university global aim, which requires taking into consideration these element activities while modeling. In our opinion, it is necessary to consider the university digital transformation management system as a technological management process that has certain features.

32.3.1 Constraints Modeling to the Managing Process Let us consider the technological process of managing the transformation of the university based on digital technologies. Restriction 1 Firstly, the technological process of management consists of sequential actions where we take into consideration either modern requirements of standards or information technologies of management process based on the structural analysis methodology. The process becomes statistically manageable, adding the ability to quantify the completion of each of the subprocesses, including, for example, KPI measurements. Digital transformation management processes can be considered from four sides,

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where the structural analysis method can be used to assess the quality of each of the processes functioning. Restriction 2 Secondly, it is necessary to take into consideration the results of dependence between management stages. The results of each previous stage of the university digital transformation management process directly or indirectly affect the next stage results or the educational system quality. Restriction 3 Thirdly, if there is a probability that the results of each digital transformation management subsequent stage depend on the previous one, then the entire process will have no aftereffect and can be represented as a Markov chain. At any stage of digital transformation management, the university management can manage the whole process influencing the system state and transition probabilities. Markov chain is a discrete sequence of states (conditions); each of them is taken from a discrete state space (finite or infinite) that satisfies the Markov property. The Markov property tells us that at any given time, the conditional distribution of future process states (conditions) with the specified current and past states (conditions) depends only on the current state, but not on past states (conditions).

32.3.2 Modeling of a Generalized Control Model Based on Markov Chains In common, the generalized model of managing the university digital transformation can be represented by the following expression: find the optimal control actions (strategies) for a complex dynamic process that meet the final quality vector (standard) requirements with limited material and time resources. Optimization models provide information about the most favorable combination of control factors that can be used to form a management strategy and make decisions. To optimize the organizational structure of university digital transformation management, it is recommended to use the method of structuring goals and functions based on the concept of a system where the environment and goal setting are taken into account [7, 8]. Speaking about all above, we suggested that while developing a digital transformation management system for university and making management decisions about choosing a particular strategy, we can use mathematical models in managed Markov chains form. The main feature of such chains is the ability to control (within certain limits) transient probabilities in order to increase the random process efficiency. The necessary elements of mathematical models in this case should be: • finite set of management strategies—K {N }; (K n ) • transition matrices P[s] , related to a particular strategy (decision) K n ;

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(K n ) • income (expense) matrices R[s] that reflect this process effectiveness.

Thus, this system functioning process described by managed Markov chains looks like this: • if the system is in the state i ∈ s and the decision K n ∈ K {N }, is made, then it receives income ri (K n ); • the system state at the next time (step) is determined by the possibility of Pi j (K n ), that is, the probability that the system will go from the state i ∈ {s} to the state j ∈ {s}, if the solution K n ∈ K {N } is selected. Obviously, the income per n steps is a random variable that depends on the initial state and the quality of decisions, which is estimated to be the average of the total income (in finite time) or average revenue per time unit (at infinite time).

32.3.3 Modeling the Method of Strategic Management of the University Digital Transformation into a Smart Organization The strategy π refers to the sequence of solutions π = (U1 , U2 , . . . , Un ), where Un = K 1 , K 2 , . . . , K n , Un ∈ K [N ] —is the control vector. The strategy imposition means a complete description of the specific decisions is made at all process stages, depending on the process state at that moment. If in the sequence (vector) π all U are the same, then this strategy is called stationary; that is, it does not depend on the step number. In other words, a stationary strategy means that the solution is only related to the state the system is in, but does not change over time. The optimal strategy will be that one that minimizes the total expected revenue for all i i n. To determine the optimal strategies in the theory of managed Markov chains, two methods have been developed. These are recurrent and iterative. The recurrent method is used most often with a relatively small number of “n” steps. Its idea is based on the application of the Bellman principle and consists in the sequential optimization of income at each step using a recurrent equation of the following type: ⎡ Vi (n + 1) = max⎣qiK + K

N 

⎤ ) ⎦ Pi(K j V j (n) ,

(1)

j=1

where Vi (n + 1)—is the total expected revenue for (n + 1) steps if the system is in state i;  (K ) (K ) N qiK = j=1 Pi j ri j —directly expected revenue, i.e., revenue at one step if the process started from the ith state; V j (n)—the value of the total expected revenue for the n-preceding steps, if the process started from the jth state.

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So, this method is similar to the dynamic programming method, but in contrast to it takes into account the process randomness, that is why it is called the stochastic dynamic programming method.

32.3.4 Modeling of Transformation Processes Using Iterative Optimization The iterative optimization method is used for unlimited number of steps. In addition, the important advantage of this method is its close connection to linear programming methods, which allows extensive use of existing information systems for automating the finding optimal solutions process. The main difference between the iterative method and the recurrent one is a linear dependence assumption of the total revenue on the number of steps n. This assumption is based on the property of regular chains to have constant transition probabilities when n increases (limit probabilities). For clarity, we present the total revenue dependence on the number of steps “n” for managed Markov chains with two states S1 u S2 . Vi (n) can be described by equations Vi (n) = ng + Vi (0). It is easy to notice that in this representation, the amount of directly expected revenue qi is replaced by g, that is gi (n) = const. In the managed Markov chains theory, the value Vi (0) is called a weight, since the difference V1 (0) − V2 (0) shows the average gain from what state the process is in at the beginning (regardless of the chosen strategy). Thus, you can write the following equality: qi =

n 

Pi j Vi (n − 1) ≈ ng + Vi (0).

(2)

j=1

The iterative method is based on this assumption. Its essence is reduced to the sequential determination of the maximum values of sums n 

Pi j (ng + Vi )

j=1

with different strategies (solutions). Thus, the process of finding optimal solutions (strategies) is reduced in this case to the procedure either for determining weights or for improving the solution. So, let us sum up the following simulation results. There are many random factors involved in managing the university digital transformation, and therefore, the process of functioning of this management system is random. Developed and proposed models and methods of decision-making in university digital transformation management system should take into account the process versatility, as well as the

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degree of its uncertainty. In our opinion, the most convenient model for modeling transformation control is a mathematical model in the form of controlled Markov chains. The proposed models can be applied in the project offices activities to manage the university digital transformation and the transition to Smart education concept.

32.4 The Experience of Creating an Organizational Infrastructure for Digital Transformation We will consider the experience of creating an organizational infrastructure for digital transformation on the example of Togliatti State University (TSU) and other Russian universities. Togliatti State University is a university that has passed the implementation stage of digital manageability and entered the digital transformation stage. Figure 32.3 shows the digital infrastructure of TSU. The university has a consistent information model and rules for all data integrity, automatic execution of end-to-end processes with the KPI calculation of each process is implemented, all interfaces are mobile and ergonomic; the staff has the digital transformation of information skills. The university has accumulated digital data for analytic and Learning Analytic tools and other Smart Analytic tools are being implemented, robotic processes (using expert systems tools and artificial intelligence system, implemented chat bots) are partially provided, and all interfaces are integrated and intelligent services have been created. The experience of digital transformation should be replicated in other Russian universities. One of the tools for transferring the experience of digital university creating was the “Charter about educational space digitalization” [9]. This document was formulated and signed by 29 Russian universities on July 16, 2019, as a part of the educational intensive “Island 10-22” at Skolkovo Institute of Science and Technology. The document lays down the principles of Russian IT tools market forming for universities with a set of rules that encourage integration into a single digital space and provide technical capability. The document was formulated by universities initiative group: Togliatti State University, Omsk State Technical University and “Moscow Institute of electronic technology”. To develop the formulated Charter ideas, the IT services heads of more than 50 universities signed a protocol confirming their intention to actively develop professional communication in the universities digitalization field. The “Charter about the educational space digitalization” contains the following principles: • to ensure the data formats unification generated by universities in the course of their activities, as well as the integration protocols unification for the management and support services developed by them for different types of university activities; • to promote networking and the best online courses spreading, the digital services practice using and tested (proven) solutions for building the digital architecture of universities;

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Sites TSU Educational portal of TSU

Student’s personal account

Gateways between the database systems

LMS Rosdistant (Moodle)

Teacher’s personal account Employee’s personal account

GosInsp 1С: Accounting department DB

ERP «Galaxy» (managing plans, students, academic performance, staff, payroll, admission campaign, paid tuition)

Information system «Departments»

Corporate portal «Bitrix24»

1С: Paperwork The reporting system and personal account managers

Fig. 32.3 Information infrastructure of Togliatti State University

• to create an accounting system for databases, new intellectual services and forecast systems to ensure the universities activities as the intellectual activity results having their own authors and owners, and to ensure compliance with copyright and property rights to protect intellectual property in this area. Those who signed this charter and the educational process participants who have joined it believe that its unconditional implementation will provide all necessary prerequisites for successful integration and effective interaction of Russian universities in the interests of developing the digital knowledge economy, as well as for Smart society building. Collective universities developments in the digital transformation framework will be placed on the research university platform basis “Creating an ontological model

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“Digital University””. You can log into the service using the link https://eduservices. 2035.university/#login [10]. The services of marketplace is a platform solution that provides formalized rules for placing and describing digital ecosystem services based on a single markup model.

32.5 Conclusion and Next Steps Conclusions. The obtained outcomes enabled us to make the following conclusions: 1. In this study, it was suggested to understand the digital transformation as one of digital maturity university level, following the primary digitalization and digital controllability. 2. The objects of university digital transformation were defined. There were set aside such data as the university processes, user interfaces and people. 3. There were suggested the conceptual models of digital transformation university management as controlled Markov chains, taking into consideration the specifics of educational environment. 4. The technical process of university transformation management was considered on the base of digital technologies. 5. Proposed models can be used in the project offices activities for the university digital transformation managing and the transition to Smart education concept. Next steps. Studies and findings revealed that the next steps of our project will be the practical usage these models in Togliatti State University project office, that will afford to speed up the digital digitalization and raise the management quality of this process.

References 1. Glukhova, L.V., Mitrofanova, Y.S.: Digitalization of economy and the particularities of its application in an integrated facility’s activity. Bull. Volga Region State Univ. Serv. 4 (2017) 2. Mitrofanova, Y.S.: Modeling the assessment of definition of a smart university infrastructure development level. Smart Innov., Syst. Technol. 144, 573–582 (2019) 3. Uskov, V.L., et al.: Smart university taxonomy: features, components, systems. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning 2016. Springer, Cham (2016) 4. Uskov, V.L., Bakken, J.P., Howlett, R.J., Jain, L.C. (eds.): Smart Universities: Concepts, Systems, and Technologies, 421 p. Springer, Cham (2018). ISBN 978-3-319-59453-8. https://doi. org/10.1007/978-3-319-59454-5 5. Burlea, A.S., Burdescu, D.D.: An integrative approach of e-Learning: from consumer to prosumer. In: Smart Education and e-Learning 2016. Smart Innovation, Systems and Technology, vol. 59, pp. 269–279. Springer International Publishing, Switzerland (2016). https://doi.org/ 10.1007/978-3-319-39690-3

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6. Aleksandrov, A.Y., Ivanova, O.A., Vereshchak, S.B., Getskina, I.B.: SMART university in digital learning space. In: Proceedings of the 34th International Business Information Management Association Conference, IBIMA (2019) 7. Mitrofanova, Y.S.: Modeling smart learning processes based on educational data mining tools. Smart Innov., Syst. Technol. 144, 561–571 (2019) 8. Serdyukova, N., Serdyukov, V.: Algebraic formalization of smart systems theory and practice (Chap. 6). In: Algorithm for a Comprehensive Assessment of the Effectiveness of a Smart System, 6.2.1 The Algorithm of a Complex Estimation of Efficiency of Functioning of the Innovation System, p. 101. Smart Innovation, Systems and Technologies, vol. 91. Springer International Publishing, AG (2018) 9. Charter about the educational space digitalization: https://www.tltsu.ru/hartiya/. Last accessed 2019/12/26 10. The services of marketplace. Homepage, https://eduservices.2035.university/#login. Last accessed 2019/12/25

Chapter 33

Security by Design Development Methodology for File Hosting Case Ilya Danenkov, Daria Kolesnikova, Aleksandr Babikov, and Radda Iureva

Abstract The use of “security by design” (SbD) approach in smart university systems can increase university’s cybersecurity. The paper presents an application of SbD approach to the development of Web applications as well as the key points, features, advantages, and disadvantages of SbD approach. A comparative analysis of measures to counter the cybersecurity threats in various approaches to the system’s design is presented.

33.1 Introduction With development of Web technologies and increase of Internet coverage, the number of companies using various Web applications for promotion has increased. Such applications have become one of the main tools for sale of products and services. In these circumstances, difficulties in ensuring security are stated since the greater functionality and complexity of the system lead to increased number of vulnerabilities. According to Positive Technologies statistics [1] in 2018, a number of attacks have increased compared to the previous years and have reached 67%. The most common vulnerabilities are associated with insufficient authorization, ability to download or read arbitrary files, as well as the ability to embed SQL code. All these problems can be avoided by designing and implementing security mechanisms at the development stage. This is more modern approach compared to the classical security approach, which involves taking security measures into account after putting the system into operational and, as a rule, using many proprietary solutions, which in turn greatly complicate security management [2]. Paper [3] provides a discussion about a development of methodology, which can evaluate critical adaptive systems by reviewing dynamic architectures in real time. The results of developed methodology evaluation indicate that the methodology

I. Danenkov · D. Kolesnikova · A. Babikov · R. Iureva (B) ITMO University, Saint-Petersburg, Russia e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_33

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gives a basis to determine adaptive system correction functioning by automatically generating and reviewing assurance cases.

33.2 The Principles of SbD Approach According to classical approach, development of system security is ensured after the system is implemented. Typically, third-party frameworks, libraries, and software are used for security ensuring. However, only the developer is engaged in improving tools, which in turn can create a problem if the developer did not respond to new vulnerabilities on time. In this case, the system will be at risk, and the only way is to change the decision, which entails additional costs. SbD approach involves consideration of security problems at all stages of system life cycle and implementation of security mechanisms in it with the minimum required number of third-party solutions. There is a need for develops appropriate qualifications in secure coding, which increase cost of development in early stages, but decrease spending on cybersecurity in the future [4]. SbD approach is based on the following principles [5, 6]. 1.

2. 3.

4.

5. 6.

7.

8.

Minimize surface area. Attack surface means number of potentially vulnerable objects in the computer system. Each addition of new features in application potentially increases risk of vulnerabilities. Minimizing attack surface area leads to reduction in number of vulnerable points without loss of application functionality. Establish secure defaults. Default settings are determined with the most secure values to reduce the number of vulnerabilities. Principle of “Least privileges.” Each user of the system should have only that number of privileges as he needs to perform necessary tasks. Each excess privilege can increase level of information security risks of application. Principle of “Defense in depth.” This principle assumes that there is protection at all system levels, despite attacker used vulnerabilities of elements in the system to penetrate and carry out an attack. Securely Fail. Application crash prevents the invoked operation, thus preventing use of errors for penetration. Not trusted services. Many organizations use capabilities of other companies for their business processes. They may have different security policies, so all information that comes from external systems must be carefully validated. Separation of duties. The same system element cannot be responsible for execution of action and its control. Thus, it is impossible to perform both actions by one user. Avoid security by obscurity. Security of critical elements should not depend on the concealment of details, if this is the only security measure. Otherwise, such system becomes completely unreliable.

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9.

Keep security simple. It is needed to avoid complex decisions and solutions where it is possible to use a simpler one. 10. Fix security issues correctly. Incorrect fixing of security issues can lead to even more significant problems.

33.3 Results Description of Studied System 33.3.1 Description of Studied System The developed system is a file hosting with Web interface based on its server. The main purpose of such system is to create application using SbD method and its principles. Application is created using open-source cross-platform environment Node.js in the Visual Studio Code IDE. The main elements of Web application are presented in Table 33.1. Communication diagram of developed Web application is presented in Fig. 33.1. This is the simplest version—it shows how files can be inserted in the system. To check reliability of the application, several tests were implemented. Tests were made using Kali Linux (Debian-based Linux distribution aimed at advanced penetration testing and security auditing). There were run the next tests: 1. W3af is attack and audit framework aimed to test and exploit Web application vulnerabilities. To scan the system, we used w3af_console and its main plugins: audit, crawl, infrastructure, and output. 2. Plugins were also used as Pykto and Hmap; Pykto is searching for vulnerable references, and Hmap identifies Web server and its parameters. 3. OWASP ZAP—is a tool to examine HTTP requests and responses. Such instruments can help to find vulnerabilities such as missing headers or tokens, and also, it finds SQL injections and XSS. 4. Metasploit helps to verify vulnerabilities, manage security assessments, and improve security awareness. Table 33.1 Main elements of developed Web application Element

Group

Function

Web interface

Frontend

Ensuring user interaction with the system

NGINX

Backend

Intercessor between modules; token management

Access control

Backend

Check access privileges

File processing

Backend

File check; file archiving

Set/get file

Backend

Add/receive files

User manager

Backend

Adding/removing new users

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Fig. 33.1 Communication diagram

Fig. 33.2 OWASP ZAP scanning report

The results of scanning developed Web application are presented in Fig. 33.2. All tests showed that risk level is not high, and the application can run properly.

33.3.2 Securing Web Applications with an SbD Approach To ensure the security of Web applications, the SbD approach pursues the following goals and uses the following means to achieve them. 1. Event log to record actions of system entities for active investigations of security incidents. 2. Access control, encrypted connection, and password protection to save from unauthorized access. 3. Mechanism of user authentication to establish user’s rights. 4. File verification to prevent attacks by spoofing or modifying (or modified) files. 5. Architectural separation and minimization of functions by elements ensure that it is impossible to perform and hide malicious action through modification of one element.

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6. Information minimization is used to hide redundant information about processes and system configuration. 7. Token mechanism for checking validity of user’s request. The main feature of the developed system is the approach that is used as the base of development process. The alternate security tactics and patterns are the first thought. But in comparison with the original method, the used tactics and functions were modified. The aim of such modifications was to guarantee secure use and storage of information.

33.3.3 Comparative Analysis of Approaches in the Developed System To analyze approaches in the developed system, it is necessary to compare measures to counter threats. Threats are taken from “OWASP Top 10-2017” [7]: • • • • • •

code injection, authentication flaws, confidential data disclosure, XML external entities, access control flaws, incorrect security settings, and cross-site scripting (XSS) using components with known vulnerabilities.

An example that implements the standard approach to security is Dropbox file hosting, the security measures of which are described in the document “A Dropbox Whitepaper” [8]. Measures for SbD approach are taken from the OWASP documentation [9]. As a result of comparison, it was found that standard approach implemented in Web application has some differences compared to original SbD approach; a summary is presented in Table 33.2. The developed system involves two approaches: standard solution and SbB approach. Creation of secure and fulfill application needs implementation of measures that can protect the system, but not make it complicated. Therefore, during the work, we used methods from SbD approach that can definitely protect our system. For example, to save confidential data, we used only data encryption during transmission, while in SbD approach, data is encrypted not only during transmission, but also on the server. These modifications secure the system, but do not complicate it. A feature of the developed application is the declaration of security methods (security through obscurity). The widely used file hosting tool Dropbox does not declare used method, which is bad practice in implementing information security. Since one cannot be sure that some methods actually are implemented.

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Table 33.2 Comparative analysis of approaches in the developed system Threat

Description

Standard solution

Security by design

Approach in the developed system

Code implementation

Unverified data is sent to the interpreter as part of a command or query

Validation of fields

Parameterized queries; screening of input data; minimized privileges

SQL/NoSQL is not used

Authentication failure

Incorrect implementation of authentication mechanisms

OAuth 2.0; two-factor authentication

Account blocking mechanism; protected transfer/storage of passwords

Account blocking mechanism; protected transfer/storage of passwords

Disclosure of confidential data

Confidential data requires additional security measures

Data encryption on the server and during transmission

Data encryption on the server and during transmission

Data encryption during transmission

Incorrect security settings

Occur due to the use of standard security settings

Not used

Secure options enabled by default

Secure options enabled by default

Using components with known vulnerabilities

Components run with application privileges

Not used

Not used

Not used

XML external entities

Poorly configured XML processors transform references to external entities

Not used

Disable external entities or use of XML parsers

XML is not used

Cross-site scripting (XSS)

XSS occurs when application adds unverified data to new web page without proper verification

Not used

Data validation; HTML attribute reset; escaping untrusted JS data; reorganization of HTML markup

Data validation; HTML attribute reset; escaping untrusted JS data; reorganization of HTML markup

Lack of access control

The actions by authenticated users are often incorrectly controlled

Not used

Approach declares careful access control, minimization of privileges

Not used

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33.3.4 Developed Application as Smart Technology Currently, university-based learning involves a combination of the physical and digital—class handouts combined with communal online platforms. But implementation of such technologies is not an easy task. When it comes to data storage on electronic devices, not all given solutions can be used. Traditional systems cannot give high level of security that is needed to store special documents such as intellectual property or personal information. Such application can be used during the study process. The main aim of this system is to allow file and data sharing among specific number of people. Access to uploaded files can be given only by author or users that have such privileges. With the development of technology, universities have a need to create programs that will ensure the implementation of the smart processes occurring during studying. One of the main tasks of such applications is to provide distance learning opportunities. In addition, simplifying the data exchange process is also an important task. The developed application can allow a certain circle of people to share files, with the possibility of ensuring integrity and their security.

33.4 Conclusions and Next Steps Conclusions. Web application using the SbD approach was modeled. The modeling process showed that for narrower tasks, the SbD approach should be modified; however, the general principles of the security methods remain unchanged. In the future, it is planned to implement a simulated system. It can be concluded that SbD approach is reliable way to build and protect the system from the beginning of its development. But the method has to be modified; to obtain holistic product, developers should investigate special changes to the given approach. The performed research identified evolution and development tendencies and obtained research findings and outcomes enabled us to make the following conclusions: 1. SbD approach can be used to create application and provide high level of security. 2. The developed application was created using security through obscurity approach. 3. Web application is a file hosting service that gives opportunity to store and share files, and collaborate on projects. 4. Such application can be introduced in smart universities system to ensure data communication among students and lectors. The security of such tool guaranties safe store of important data and files.

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5. Leading universities try to create systems to store and share data using own local hosts, and developed application can be investigated as one of them, but with insurance of its security and reliability. 6. The next generation of smart university systems should pay more attention to implementation of “smartness” abilities of products and tools. Next steps. Various directions for future work have been identified in light of the research results. SbD methodology can be implemented in different systems; one of the ways is to investigate it into smart processes in universities. Such implementations can help to avoid informational attacks and to sense and to process data, and develop special facilities in order to make smart education. The planned nest steps in these projects are: 1. The application will be developed further to make it user friendly and easy to use. 2. Developed application will be investigated as a smart technology in university studying process. Acknowledgements This work is financially supported by Government of Russian Federation (Grant 08-08) and by the Ministry of Science and Higher Education of Russian Federation, passport of state contract no. 2019-0898.

References 1. Web application vulnerabilities: statistics for 2018. Mar 2019. https://www.ptsecurity.com/wwen/analytics/web-application-vulnerabilities-statistics-2019/ 2. Cisco: Annual Cybersecurity Report. Cisco Systems, Inc, February 2018. https://www.cisco. com/c/dam/m/hu_hu/campaigns/security-hub/pdf/acr-2018.pdf 3. Koelemeijer, D.: Enhancing the cyber resilience of critical infrastructures through an evaluation methodology based on assurance cases. In: Proceedings of the 22nd International Conference, KES-2018, Belgrade, Serbia. Procedia Computer Science, vol. 126, pp. 1779–1791 (2018) 4. The Economic Impacts of Inadequate Infrastructure for Software Testing. RTI Health, Social and Economics Research. National Institute of Standards and Technology Acquisition and Assistance Division, May 2002 5. Security by Design Principles. OWASP Foundation, Inc., August 2016. https://www.owasp.org/ index.php/Security_by_Design_Principles 6. Howard, M., LeBlanc, D. (eds.): Writing Secure Code (Developer Best Practices). Pearson Education (2003) 7. OWASP Top 10—2017 The Ten Most Critical Web Application Security Risks. OWASP Research, Dec 2017. https://www.owasp.org/images/7/72/OWASP_Top_10-2017_%28en%29. pdf.pdf 8. Dropbox Business Security. A Dropbox Whitepaper. Dropbox Business, 2019. https://www. dropbox.com/static/business/resources/Security_Whitepaper.pdf. Accessed 29 Oct 2019 9. The OWASP Cheat Sheet Series. https://github.com/OWASP/CheatSheetSeries. Accessed 29 Oct 2019

Part VII

Smart Education, Smart Universities and Their Impact on Students with Disabilities

Chapter 34

Smart Universities: Gesture Recognition Systems for College Students with Disabilities Jeffrey P. Bakken, Nivee Varidireddy, and Vladimir L. Uskov

Abstract In a highly technological society, smart universities, smart classrooms, and smart education are the wave of the future. Of many distinct features, one of those is its ability of adaptation to and smooth accommodation of various types of learners/students in on-campus classrooms as well as for students with remote/online access. These types of environments can benefit students/learners, regular students, and special students, i.e., students with various types of disabilities including physical, visual, hearing, speech, cognitive, and other types of impairments. This paper presents the outcomes of an ongoing research project aimed at systematic identification, analysis and testing of available open-source and commercial gesture recognition systems rating those that could significantly benefit college students with disabilities in highly technological environments. Based on a careful analysis of open-source and commercially available products, we identified and recommended the top gesture recognition systems for implementation in smart universities.

34.1 Introduction: College Students with Disabilities Smart universities (SmU) and smart classrooms (SmC) have the ability to create multiple opportunities for students to learn material in a multitude of ways. In addition, these environments can give students access to learning content in new and innovative ways that they normally would not have. Although not designed or even conceptualized to benefit students with disabilities, this concept would definitely J. P. Bakken (B) The Graduate School, Bradley University, Peoria, IL, USA e-mail: [email protected] N. Varidireddy · V. L. Uskov Department of Computer Science and Information Systems and InterLabs Research Institute, Bradley University, Peoria, IL, USA e-mail: [email protected] V. L. Uskov e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_34

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have an impact on the learning and access to material for students with all different types of disabilities. In accordance with the U.S. Department of Education, National Center for Education Statistics [1], in 2011–2012, 9.0% of all undergraduate students in the age range of 15–23 enrolled in postsecondary institutions in the USA were students with some form of disability, 11.3% of those aged 24–29, and 12.4% of those aged 30 and older. As a result, on average, about 10% of the college student populations in all US institutions are identified with disability. Many of these students need some form of technology to be successful in this environment. In general, students in colleges/universities may experience a variety of different categories of disabilities; they include but are not limited to: (1) learning disabilities, (2) speech or language impairments, (3) health impairments, (4) psychological/neurological impairments, (5) hearing impairments, (6) physical/mobility/motion/orthopedic disabilities, and (7) visual impairments. Software systems can benefit students with disabilities by providing them equal access in the classroom and different learning environments. These systems can also help them learn more efficiently and effectively and in many cases allow them to interact better with their professor and classmates. Where traditional classrooms do not specifically address software systems and how students with disabilities could be impacted, the implementation of specific advanced software systems in smart universities and smart classrooms would address these learning barriers from the perspective of universal accessibility—providing greater learning opportunities for all students in smart learning environments—including students with disabilities [2–7].

34.2 Gesture Recognition (GR) Systems: Literature Review 34.2.1 GR Systems in Use by Students with Disabilities: Examples The College of William and Mary proposes SignFi to recognize sign language gestures using Wi-Fi. There is a huge barrier between the deaf community and people that do not understand or know little about sign language. A sign language recognition system would help break this barrier. There are some sign language recognition systems using cameras or Kinect and Leap Motion, but they are subject to lighting conditions and distance to the software system. SignFi uses Wi-Fi to make this possible and can recognize 276 sign gestures, which involve the head, arm, hand, and finger gestures, with high accuracy. The average recognition accuracy of SignFi is 98.01, 98.91, and 94.81% for the laboratory, home, and lab + home environment, respectively. For 7500 instances of 150 sign gestures performed by five different users, the recognition accuracy of SignFi is 86.66%, thereby proving it to be a very useful sign language recognition system [8].

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In an effort to bridge the communication gap between American Sign Language (ASL) speakers and people with hearing, the University of Washington developed gloves that translate sign into text or speech. Sensors in the gloves record hand position and movement and send the data via Bluetooth to a central computer that analyzes the data through various sequential statistical regressions. When a match with a gesture is found, the corresponding word or phrase is played through a speaker [9]. SCEPTRE is a powerful tool used to translate gestures performed in real time, while also being flexible enough to be fully personalized to be used as a platform for gesture-based HCI. The system is envisioned to be used in two primary use cases: user-to-user communication and user-to-computer interactions. The tool solves the problem with communication and collaboration between deaf people and hearing people [10].

34.2.2 GR Systems in Use by Regular Students: Examples The Massachusetts Institute of Technology (MIT) designed and implemented a speech and GR system to control a PowerPoint presentation using the Microsoft Kinect. This system focuses on the identification of natural gestures that occur during a PowerPoint presentation, making the user experience as fluid as possible. It can navigate through a PowerPoint presentation and has a limited control over slide animations. The incorporation of speech commands gives the user an additional level of precision and control over the system [11]. North Carolina State University developed an ambient light-based GR system called LiGest. The key novelty of LiGest is in its high robustness against changing lighting conditions, changing user positions and orientations, and even changing users. LiGest works with all type of light sources and does not require any control over them. The general idea behind LiGest is that when a user performs different gestures, the shadows of the user move in unique patterns. LiGest first learns these patterns using training samples and then recognizes unknown samples by matching them with the learnt patterns. To capture these patterns, LiGest uses a grid of light sensors deployed on the floor. LiGest achieved an average accuracy of 96.36%, and currently, the software is extended to support very low illuminance levels and also to the walls by deploying and experimenting with sensors on walls [12]. Gesture has been used for control and navigation in Cave Automatic Virtual Environments (CAVEs). CAVE is an immersive virtual reality environment invented at the University of Illinois, Chicago. But it has been reapplied and is currently being used in a variety of fields. Researchers can use the CAVE system to conduct their research topics in a more accessible and effective method. For example, CAVEs were applied on the investigation of training subjects on landing a F-16 aircraft [13, 14]. National Taiwan Normal University designed a lecture theater-based student gesture analysis system. This is a new attempt to apply image processing techniques to help the teacher to notice some behaviors of the students in the classroom. This

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system is intended to automatically track the behavior of students in a classroom and is a revolutionary way of collecting data for educational research [15]. Adam Mickiewicz University used the Kinect sensor from Microsoft to identify the movements and gestures of the user. This interface was used by them in teaching chemistry in a middle school and high school by developing a virtual chemical laboratory, which is based on a system of hand movements. The students emphasized that working in a virtual chemistry laboratory resulted in an increased sense of self-efficacy and self-confidence while working in a real laboratory [16]. The University of Colorado Boulder proposed a low-cost low-power wristbandform hand GR system utilizing capacitive sensing technique. This is an opensource system which includes low-power, low-cost hardware components and a user-friendly software stack. This system will be available for users and developers to customize various hand gesture sets and integrate into third part application, from computer remote command to video game controller [17]. Carnegie Mellon University used XWand, a wireless UI device that enables styles of natural interaction with intelligent environments. The XWand system exploits human intuition, allowing control of everyday objects through pointing and gesturing [18]. Tsinghua University developed a micro-hand GR system which uses microdynamic hand gestures to achieve human–machine interaction. Ultrasonic active sensing, pulsed radar signal processing, and time-sequence pattern recognition have been used for micro-hand GR [19]. The University of Central Florida developed a virtual classroom environment, TeachLivE for teacher training, reflection, and assessment purposes. They developed an immediate feedback application that is presented to the participants in one of the study settings. It provides visual cues to the participant in front of the tracking sensor any time that teacher exhibits a closed stance. They used the Microsoft Kinect sensor and its full body tracking data stream to develop our real-time gesture feedback application [20]. Central China Normal University developed a teaching platform (StarC) with GR technology, which contains two functions, namely lesson preparation and teaching. Furthermore, 16 kinds of gestures were proposed to instantiate the corresponding teaching functions. The platform was applied in a teaching reform project in Suzhou City, and the pretest and posttest scores of experimental group and the control group were analyzed. The results indicated that students’ achievement was much more significant in experimental group than that in the control group. It concluded that the StarC platform can better promote teaching than the traditional multimedia used in classroom teaching [21].

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34.3 Research Project Goal and Objectives During this analysis related directly to “GR systems for college students with disabilities” and “GR assistive technology for college students,” no publications that provided a classification of available open-source and commercial GR systems using a set of evaluation criteria (functions, technical platform, SWOT analysis, price, etc.) and their ranking in terms of functionality and usability by university/college students could be identified or located. Project goal. The overall goal of this research project was to identify the best open-source and commercially available GR software systems for university/college students with disabilities. Project objectives. The objectives of this project included but were not limited to: (1) identification of colleges/universities in the USA and what GR systems they provide for college students with various forms of disabilities; (2) analysis of available open-source and commercial GR systems and identification of the best systems to implement with college students with various forms of disabilities; (3) the identification of the top three open-source and commercial GR systems; (4) creation of recommendations for university/college student assistance centers in terms of GR systems to be implemented to benefit college students with various forms of disabilities. The obtained up-to-date research outcomes and findings are presented below.

34.4 Research Project Outcomes 34.4.1 GR Systems in Use by Universities/Colleges: Best Examples We analyzed available publications about GR systems that are used by student assistance centers at top US universities, and, additionally, at University of Technology Sydney, Australia. A summary of our research outcomes are presented in Table 34.1. (A note: Due to the limits of the current paper, the references to all GR systems analyzed are omitted in these papers; they are available in the GR bibliography at http://cs-is1.bradley.edu/uskov/GR/).

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Table 34.1 A list of GR systems that are used by student assistance centers at top universities (those centers usually serve students with disabilities) Name of the university or college

Commercial or open-source GR systems in use

MIT—Massachusetts Institute of Technology

GR systems in use (as they are described in publications) • Kinect • Rapidnition • Gesture Recognition Toolkit • Ergonomic Micro-Gesture Recognition And Interaction Evaluation • Wearable in-air gesture recognition system • Multi-signal gesture recognition • Sixth Sense

University of Texas—Austin

• Leap Motion Controller • Myo Gesture Control Armband

North Carolina State University

• Kinect

• LiGest

Eastern Kentucky University

• Leap Motion Controller

•

Augsburg University

• FUBI • NovA—nonverbal behavior analyzer • Social signal interpretation (SSI)

College of William and Mary

• SignFi

University of Washington

• AllSee • Sign Aloud Gloves

Arizona State University

• Spectre • MirrorGen • TranslatAble (IN-development)

Carnegie Mellon University

• XWand

University of Notre Dame

• Leap Motion

Ohio State University

• Kinect • Leap Motion • Myo Armband

University of Technology—Sydney, Australia

• Leap Motion • Kinect

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34.4.2 Obtained and Expected Benefits of Using GR Systems The performed systematic analysis of the above-mentioned publications and demo versions and user reviews of the available GR systems enabled us to arrive with a list of the following reported, obtained, or expected potential benefits for the end users. • The use of gesture-based control mechanisms allows a user to engage with a virtual environment and manipulate information instinctively. • GR systems will allow people with disabilities to interact more easily with computers. • Not only is the nature of gestures universal, and more natural than operating a mouse or keyboard, but it could be a valuable tool in maintaining and focusing students’ attention and promoting an interactive classroom. • GR technology will allow students to perform extremely accurate simulations of tasks that can require expensive equipment. From driving a forklift to wielding a scalpel, there will be no limitations to what you can learn with gesture control technology. • Using GR technology, gaming environments can, and are being developed to, promote activities that improve social skills, involves teamwork, and allows users to solve problems through collaboration. This, in turn, promotes a method of teaching which is student-focused rather than teacher-centered. • GR systems can automatically identify and track human behavior in the classroom and are a revolutionary way of collecting data for educational research. Student gestures, which include raising the right hand, raising the left hand, raising two hands, lying prone, standing up, and normal can be identified. • GR systems will allow communication between those who speak with sign language and those who do not. This is also helpful to people who speak different sign languages—there are more than 300 sign languages practiced around the world. The Kinect, coupled with the right program, can read these gestures, interpret them, and translate them into written or spoken form, then reverse the process and let an avatar sign to the receiver, breaking down language barriers more effectively than before. • Just as speech recognition can transcribe speech to text, certain type of GR software can transcribe symbols represented through sign language into text. • Controlling a computer through facial gestures is a useful application of GR for users who may not physically be able to use a mouse or keyboard. • Gesture control technology engages learners through movement, facilitating active learning, which in turn increases the academic and social performance of students. • A lecturer could explain a science concept or a practical laboratory in an easily understandable way. Moreover, for hands-on work such as applied engineering, design, or construction, gesture-based computing may offer new ways to interact with immersive 3D content and allow students and staff to investigate immersive scenarios. • Leap Motion introduced Orion, a hand tracking for virtual reality—into the product development. Orion tracks the user’s hands and then projects the image in the

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• •

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virtual reality environment, so the users will see their virtual hands in front of them, which augments the user’s presence in the virtual world: Users can interact with virtual objects as if they were in the real world. Hand tracking has the potential to be a turning point in the interactive virtual reality, which can lead to the major use in the education environment, changing the way people learn. GR systems can be used to control PowerPoint presentations with gestures. GR technology can be used to develop virtual tours, allowing users to navigate their way across campus simply by using their hands and the Leap Motion device. In an unassuming room at Ohio University’s Athens campus, students have the power to travel virtually anywhere on Earth and dive into virtual cities. The “Holodeck,” as it is popularly called, uses projectors, Google Earth, and the Leap Motion Controller to generate three interactive walls—which can become an IMAX-style chalkboard, Chinese calligraphy practice, Minecraft exploration platform, and much more. Kinect sensors monitor the audio data for a class and signal the instructor when he or she is talking too much. Kinect being moved around like a camera, recording the depth of everything it sees and building up a full 3D map of the room and every object in it is called KinectFusion. Among the applications for this suggested by the Microsoft Research team: “Extending multi-touch interactions to arbitrary surfaces; advanced features for augmented reality; real-time physics simulations of the dynamic model; novel methods for segmentation; and tracking of scanned objects” are a few. Kinect Math—A kinesthetic learning experience allows teachers to make abstract mathematical concepts more interactive through using the Kinect. Students can manipulate graphs, variables, and more.

34.4.3 GR Software Systems Analyzed There are several available GR systems that could be implemented in a highly technological smart classroom at a smart university. The research findings and analysis outcomes of analyzed GR systems are presented in Tables 34.2 and 34.3.

34.4.4 Top GR Systems Identified One of the research team members downloaded, installed, tested, and analyzed the functionality of almost all the open-source systems and demo versions or trial versions of most of the commercial GR systems. The outcomes of analysis findings as well as the final ratings of those systems are given in Table 34.4 for open-source GR systems and Table 34.5—for commercial GR systems.

Main functions

• AMD gesture control enables to control the features on AMD powered computer with simple, intuitive gestures by leveraging the computer’s webcam • Waving control for things like maps and system controls • Enables to use hand to skip tracks and advance movies • Supports Microsoft PowerPoint slides • AMD Gesture Control works with many applications like Adobe Acrobat, Windows Media Player, and Windows Photo Gallery • Compatible only with AMD Processors

• Hand movements are recorded by a webcam and translated into pointer movements • Gestures or voice commands are accepted to emulate the actions that are usually performed with a regular mouse or a touchpad: clicks, double-clicks, drags, and scrolls • People with disabilities use head movements to control the computer • Pointer speed, motion acceleration, menu timeout, movement cutoff, and other settings can be adjusted to fit the user’s needs

Name of the system

AMD gesture control

NPointer 2.0

Table 34.2 Open-source GR systems analyzed

Neurotechnology

AMD

Company developer

(continued)

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Main functions

• The full body interaction framework (FUBI) is a framework for recognizing full body gestures and postures in real time from the data of a depth sensor such as the Microsoft Kinect with OpenNI/NiTE or the Kinect SDK • FUBI distinguishes between four gesture categories: static postures, linear/angular movements, combination of postures and movements, and symbolic gestures • FUBI includes a GUI application which allows testing the recognizers, looks at all the information FUBI offers regarding the sensor streams and user tracking, changes and tests the filter values, and starts mouse emulation for freehand interaction and bind gestures to key or mouse events, e.g., for clicking • It records gesture performances and can generate valid XML file • It supports to add gestural interaction by using gesture symbols • FUBI provides buttons and a swiping menu to implement freehand GUI interaction

• An interface for controlling the mouse pointer using finger gestures • It works with a standard or even an integrated webcam • It needs two different color markers on fingers to control mouse movements • User can do all sorts of actions that are done using a mouse including click, double-click, right-click, scroll, and drag-n-drop

Name of the system

FUBI

Ishara—Mouse Control with Gesture

Table 34.2 (continued)

Developed by Saikat Basak

Augsburg University

Company developer

(continued)

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Main functions

• Easystroke is a gesture recognition application for X11 which can execute predefined actions based on “gestures” that are drawn on the screen. X11 is a windowing system for bitmap displays, common on Unix-like operating systems. It provides the basic framework for a GUI environment: drawing and moving windows on the display device and interacting with a mouse and keyboard • Easystroke will execute certain actions if it recognizes the stroke; currently, easystroke can emulate key presses, execute shell commands, hold down modifiers, and emulate a scroll wheel • Easystroke tries to provide an intuitive and efficient user interface, while at the same time being highly configurable and offering many advanced features

Name of the system

Easystroke

Table 34.2 (continued) Developed by Thomas Jaegar

Company developer

(continued)

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Main functions

• The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, C++ machine learning library designed for real-time gesture recognition • The input to the GRT can be any N-dimensional floating-point vector—this means we can use the GRT with cameras, Kinect, Leap Motion, accelerometers, or any other custom sensor built • The GRT features a large number of algorithms that can be used to: recognize static postures, recognize dynamic temporal gestures (such as a swipe or tap gesture), perform regression, and (i.e., continually map an input signal to an output signal, such as mapping the angle of a user’s hands to the angle a steering wheel should be turned in a driving game) • The toolkit also includes a large number of algorithms for preprocessing, feature extraction, and post-processing

Name of the system

Gesture Recognition Toolkit

Table 34.2 (continued) The Gesture Recognition Toolkit is available under an MIT license

Company developer

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Main functions

• Kai is a smart wearable interaction device which allows the user to interact with digital devices using gestures • Kai recognizes the smallest of finger movements which can make and allow the user to communicate with devices and programs seamlessly • Kai’s dev kit allows the user you to integrate Kai with a wide range of solutions across software and hardware ecosystems • Kai can be used for gaming, to control presentations with gestures, to control hardware, to have a real-time experience with VR, and to interact with objects designed in 3D • The control center app in this device allows to pair any action on the computer with simple gestures

• Reads the electrical activity of muscles and the motion of arm and allows to wirelessly control technology with hand gestures • Myo for presentations allows to use gestures to control Microsoft PowerPoint and Apple Keynote slideshows. Gestures such as making a fist to control a digital pointer and rolling fist to zoom in on slides • Can be used as a replacement to the mouse and keyboard; Myo turns the user’s hand into a pointing device, with the finger-tapping gesture replacing the mouse click • Myo uses only five basic gestures to control various applications • Myo works along with a desktop app called Myo Connect, which includes a number of options, including an Armband manager that lets you calibrate and modify gesture settings and an application manager that is designed to activate and disable individual apps and connectors • Myo Gesture Control Armband is used in University of Texas, Austin, and Ohio State University

Name of the system

Kai Gesture Controller

Myo Gesture Control Armband

Table 34.3 Commercial GR systems analyzed

(continued)

Thalmic Labs

Vicara

Company developer

34 Smart Universities: Gesture Recognition Systems for College … 405

Main functions

• The Leap Motion Controller lets the user interact directly with digital content on PCs using bare hands • Leap Motion’s hand tracking technology runs on mobile processors with low latency and high accuracy • Leap Motion can be embedded directly into any VR/AR headset—no gloves or handheld controllers needed • Hands initialize faster and track better against complex backgrounds and extreme lighting conditions • Several free apps for desktop and virtual reality can be downloaded on the Leap Motion Gallery • It provides real 3D interaction: 135-degree field of view creates a wide interactive space between user and computer • Leap Motion is used at Ohio State University, University of Technology Sydney, Eastern Kentucky University, and University of Texas, Austin

• DeviceSense offers simple and natural touch-free control over device functions by detecting the user’s finger or hand motions and converting them into commands • DeviceSense software is offered as an SDK for simple integration by OEMs • DeviceSense is compatible with devices existing camera standard CMOS sensors and supports all major operating systems • The software is lean and requires very little computing resources

Name of the system

Leap Motion Controller

DeviceSense

Table 34.3 (continued)

Eyesight

(continued)

Leap Motion

Company developer

406 J. P. Bakken et al.

Main functions

• Kinect is a motion sensing input device which allows the user to control and interact with a range of apps using gestures and spoken commands • The device features a RGB camera, depth sensor, and multi-array microphone which means it can provide full body 3D motion capture, facial recognition and voice recognition • Kinect is capable of simultaneously tracking up to six people, including two active players for motion analysis with a feature extraction of 20 joints per player • Kinect is used at North Carolina State University, Ohio State University, University of Technology Sydney, Los Angeles, Unified School District (California), Chicago Public Schools (Illinois), Houston Independent School District (Texas), Scottsdale Unified School District (Arizona), Flagstaff Unified School District (Arizona), Fairfax County Public Schools (Virginia), and Loudoun County Public Schools (Virginia), Porter-Gaud School in Charleston, South Carolina • *) Kinect for Windows is no longer available. Azure Kinect will replace it

Name of the system

Kinect

Table 34.3 (continued) Microsoft

Company developer

34 Smart Universities: Gesture Recognition Systems for College … 407

408

34.4.4.1

J. P. Bakken et al.

Top-Ranked Open-Source GR Systems

See Tables 34.4.

34.4.4.2

Top-Ranked Commercial GR Systems

34.5 Conclusions. Future Steps Conclusions. The performed research helped us to identify the current status of GR systems available for college students with disabilities. The obtained research outcomes and findings enabled us to make the following conclusions: 1. The research on GR systems for students with disabilities is very limited. It is almost impossible to find any publications on the Internet with direct relevance Table 34.4 Gesture Recognition Toolkit (GRT) open-source GR system: brief analysis outcomes Name of the system

Main functions

Main most system’s important features and functions

• The GRT features a large number of algorithms that can be used to: recognize static postures, recognize dynamic temporal gestures (such as a swipe or tap gesture), perform regression (i.e., continually map an input signal to an output signal, such as mapping the angle of a user’s hands to the angle a steering wheel should be turned in a driving game) • The toolkit also includes a large number of algorithms for preprocessing, feature extraction, automatic gesture spotting, and post-processing

Strengths and opportunities

• The GRT API integrates well with machine learning universe(Python) the same way it works in object-oriented universe(C++) • It is compatible with any type of sensor or data input • It is easy to rapidly train with user’s own gestures • Gesture recognition system can be deployed on custom, embedded hardware

Possible weaknesses and threats

• Requires technical knowledge to maintain and support the product

Technical platform

• Windows • OS X • Linux

Price (if any)

• Free

Ranking

1

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Table 34.5 Microsoft Kinect commercial GR system: brief analysis outcomes Name of the system

Main functions

Main most system’s important features and functions

• Kinect is a motion sensing input device which allows the user to control and interact with a range of apps using gestures and spoken commands • The device features a RGB camera, depth sensor, and multi-array microphone which means it can provide full body 3D motion capture, facial recognition, and voice recognition • Kinect is capable of simultaneously tracking up to six people, including two active players for motion analysis with a feature extraction of 20 joints per player

Strengths and opportunities

• Can do both voice recognition and face recognition • Very adaptable for wheelchair users as well • Portable

Possible weaknesses and threats

• Sensitive to external infrared source(sunlight) • Cannot detect crystalline or highly reflective objects

Technical platform

• • • • • •

Price (if any)

• $399 USD

Colleges/universities that currently use this system

• • • • • • • • • • •

Ranking

1

Cloud compatible 32-bit (×86) or 64-bit (×64) processors Dual-core, 2.66-GHz or faster processor USB 2.0 bus dedicated to the Kinect 2 GB of RAM Graphics card that supports DirectX 9.0c North Carolina State University Ohio State University University of Technology Sydney Los Angeles Unified School District (California) Chicago Public Schools (Illinois) Houston Independent School District (Texas) Scottsdale Unified School District (Arizona) Flagstaff Unified School District (Arizona) Fairfax County Public Schools (Virginia) Loudoun County Public Schools (Virginia) Porter-Gaud School in Charleston, South Carolina

410

2. 3. 4.

5.

J. P. Bakken et al.

to this topic. The research on GR systems for university/college students needs to be implemented. We downloaded, installed, tested, analyzed, and evaluated five (5) open-source (Table 34.2) and five commercial (Table 34.3) GR systems. We ranked all analyzed open-source and commercial GR systems—the research outcomes are presented in Tables 34.4 and 34.5. Based on our evaluation, the top open-source GR system is Gesture Recognition Toolkit (GRT) (Table 34.4), and the top commercial GR system is Microsoft Kinect (Table 34.5). Those systems are strongly recommended by our research team for implementation and active use in smart classes of smart universities. More research needs to be completed that directly focuses on the perception of GR systems by actual college students with motion/mobility disabilities.

Next steps. The next steps of this research, design, and development project deal with 1. More implementation, analysis, testing, and quality assessment of GR systems by actual college students with gesture disabilities. 2. Implementation, analysis, testing, and quality assessment of GR systems in everyday teaching of classes in smart classrooms. 3. Organization and implementation of summative and formative evaluations of local and remote college students and learners with and without disabilities with a focus to collect sufficient data on quality of GR systems. 4. Creation of a set of recommendations (technological, structural, financial, curricula, etc.) on what GR systems universities should get (purchase, if needed) and install to benefit college students with and without disabilities at smart university.

References 1. U.S. Department of Education, National Center for Education Statistics: Digest of Education Statistics, 2015 (2016-014), Chapter 3 (2016). https://nces.ed.gov/fastfacts/display.asp?id=60 2. Bakken, J.P., Uskov, V.L, Kuppili, S.V., Uskov, A.V., Golla, N., Rayala, N.: Smart university: software systems for students with disabilities. In: Uskov, V.L., Bakken, J.P., Howlett, R.J., Jain, L.C. (eds.) Smart Universities: Concepts, Systems, and Technologies, pp. 87–128. Springer (2017). 425 p., ISBN: 978-3-319-59453-8 3. Bakken, J.P., Uskov, V.L., et al.: Smart university: software/hardware systems for college students with severe motion/mobility issues. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (Eds.) Smart Education and e-Learning 2019, pp. 471–487. Springer (2019). 643 p., ISBN: 978-98113-8260-4 4. Bakken, J.P., Uskov, V.L. et al.: Analysis and classification of university centers for students with disabilities. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (Eds.) Smart Education and e-Learning 2019, pp. 445–459. Springer, June 2019. 643 p., ISBN: 978-981-13-8260-4 5. Bakken, J.P., Uskov, V.L, Penumatsu, A., Doddapaneni, A. Smart Universities, Smart Classrooms, and Students with Disabilities. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (Eds.) Smart Education and e-Learning 2016, pp. 15–27. Springer, June 2016, 643 p., ISBN: 978-3-319-39689-7

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6. Bakken, J.P., Uskov, V.L. et al.: Text-to-voice and voice-to-text software systems and students with disabilities: a research synthesis. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (Eds.) Smart Education and e-Learning 2019, pp. 511–524. Springer, June, 643, p., ISBN: 978-981-13-82604 (2019) 7. Uskov, V.L, Bakken, J.P., Pandey, A., Singh, U., Yalamanchili, M., Penumatsu, A.: Smart university taxonomy: features, components, systems. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (Eds.) Smart Education and e-Learning 2016, pp. 3–14. Springer, June 2016, 643 p. (2016) 8. Ma, Y., Zhou, G., Wang, S., Zhao, H., Jung, W.: SignFi: Sign Language Recognition Using WiFi. Computer Science Department, College of William and Mary, USA (2018). http://www. cs.wm.edu/~yma/files/SignFi2018authorversion.pdf 9. Sign Aloud Gloves: https://interactiveaccessibility.com/news/signaloud-gloves#. XKIxfJhKjIX 10. Paudyal, P., Lee, J., Banerjee, A., Gupta, S.K.S.: Sceptre-A Pervasive, Non-Invasive, and Programmable Gesture Recognition Technology, IMPACT Lab, Arizona State University. https:// impact.asu.edu/2017/11/sceptre/ 11. Chang, S.M.: Using Gesture Recognition to Control PowerPoint Using the Microsoft Kinect. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (2013). https://dspace.mit.edu/handle/1721.1/85410 12. Venkatnarayan, R.H., Shahzad, M.: Gesture Recognition Using Ambient Light, North Carolina State University. https://raghavhv.wordpress.ncsu.edu/files/2018/05/RaghavVenkatnarayanGestureRecognitionUsingAmbientLight.pdf 13. Kenyon, R.V.: The CAVE Automatic Virtual Environment: Characteristics and Applications. University of Illinois—Chicago. https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/ 19960026482.pdf 14. Turk, M.: Gesture Recognition, Computer Science Department, University of California http:// www.cs.ucsb.edu/~mturk/pubs/Gesture%20Recognition%20Chapter.pdf 15. Fang, C.-Y., Kuo, M.-H., Lee G.-C., Chen, S.-W.: Student Gesture Recognition System in Classroom 2.0, Department of Computer Science and Information Engineering, National Taiwan Normal University. http://www.csie.ntnu.edu.tw/~violet/publicationlist/2011CATE.pdf 16. Robert, W., Jagodzinski, P.: Virtual Laboratory—Using a Hand Movement Recognition System to Improve the Quality of Chemical Education (2017). https://onlinelibrary.wiley.com/doi/epdf/ 10.1111/bjet.12563 17. Truong, H., Nguyen, P., Bui, N., Nguyen, A., Vu, T.: DEMO: Low-Power Capacitive Sensing Wristband for Hand Gesture Recognition. University of Colorado Boulder (2017). http:// mnslab.org/nambui/papers/2017_S3_Wristband_Demo.pdf 18. Wilson, D., Wilson, A.: Gesture Recognition Using the XWand. Assistive Intelligent Environments Group, Robotics Institute, Carnegie Mellon University. http://citeseerx.ist.psu.edu/ viewdoc/download?doi=10.1.1.1.2780&rep=rep1&type=pdf 19. Sang, Y., Wang, Q.: Micro Hand Gesture Recognition System Using Ultrasonic Active Sensing Method. Tsinghua University (2016). https://www.sigport.org/sites/default/files/docs/Micro_ hand_gesture_recognition_spm_submit.pdf 20. Barmaki, R., Hughes, C.E.: Towards the Understanding of Gestures and Vocalization Coordination in Teaching Context (2016). http://www.educationaldatamining.org/EDM2016/ proceedings/paper_179.pdf 21. Chen, Z., Feng, X., Liu, T., Wang, C., Zhang, C.: A Computer-assisted Teaching System with Gesture Recognition Technology and Its Applications, National Engineering Research center for E-Learning, Central China Normal University (2017). https://dl.acm.org/citation.cfm?id= 3134848

Chapter 35

University Centers for Students with Disabilities: A Pilot Study Carrie Anna Courtad and Jeffrey P. Bakken

Abstract The vast majority of US universities and colleges have specialized centers to support students with disabilities. These centers have various names (Student Accommodation Centers, Student Access Services, Disability Resources), but the goal of these centers is to support students with disabilities who are obtaining a postsecondary education. The previous research reviewed 30 universities that had listed information about their student center in the public domain (through Web sites). This research will move beyond the review of Web-based pages by surveying those employees who are in charge of these centers at the 30 institutions in addition to targeting public 4-year regional universities who not have a publicly accessible webpage. This paper discusses future research and implications from results of the pilot study and the need to further investigate university accommodation centers.

35.1 Introduction More and more US college age students with disabilities are taking classes in higher education than in previous years. According to the National Center for Education Statistics (NCES) in 2015–2016, 19.4% of undergraduate students reported having a disability [1]. NCES statistics from 2011 to 2012 recorded that 9.0% of all undergraduate students of ages 15–23 enrolled in postsecondary institutions in the USA were students with some form of a disability, and this is an increase from 10% in previously reported data. U.S. Legal Requirement. Access to higher education for students with disabilities is a civil right afforded by the Americans with Disabilities Act (ADA) of 1990

C. A. Courtad (B) Department of Special Education, Illinois State University, Normal, USA e-mail: [email protected] J. P. Bakken The Graduate School, Bradley University, Peoria, USA e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_35

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and Section 504 of the Rehabilitation Act of 1973. These legal acts mandate that universities and colleges in the USA make every reasonable effort to provide appropriate assistance, services, and accommodations to students with disabilities by providing auxiliary aids at no cost to the student. While the institution has a directive to meet the enrolled college student’s need, the responsibility is on the student to seek these out these aids and find a mutually agreeable solution for the need. The Office of Civil Rights (OCR) and US Department of Education [2] have identified possible auxiliary aids to be the following: • • • • • • • • • • • • • • • • • •

taped texts; telephone handset amplifiers; assistive listening devices or systems; telecommunications devices for deaf persons; closed caption decoders; Braille calculators, printers or typewriters; note takers; open and closed captioning; interpreters; voice synthesizers; readers; specialized gym equipment; videotext displays; television enlargers; calculators or keyboards with large buttons; talking calculators; electronic readers; reaching device for library use.

The above list clearly indicates the need for technology as a way for students with disabilities to access institutions of higher education (IHE). As a result of more students who are identified as having a disability attending IHE, these students are seeking support for these auxiliary aids often through university centers for students with disabilities, students’ access or student accommodation services located at the campus of the university. On average, about 10% of the college student population needs to use assistive technology on campus—various software applications, tools, systems and devices, electronics and hardware that will facilitate their learning and social life on campus and, especially, in a technological Smart University [3].

35.2 Previous Work and Research Project Goal and Objectives The previous work. The previous project analyzed and classified university centers for students with disabilities based on types of students with disabilities served and

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types and quality of software systems provided for special students by those centers. Bakken et al. [4] reviewed the publicly posted information of 30 academic centers across the nation. These centers were either called student accommodations, student access or center for students with disabilities. Bakken and colleagues using public domain information reviewed and analyzed the type of student these centers served and technology to support these students. Project’s objectives. To continue the very important work and to extend the previous study, a pilot study has been completed. The previous study ranked centers based on public domain information. It was suspected that there could possibly high-quality centers “invisible” to the previous investigation because the Web site was incomplete. Therefore, the objectives of this project were to: 1. develop a survey investigating the technology available to students with disabilities, disabilities types, the financing and staffing for these centers; 2. deploy the pilot of survey to understand the technology housed in at universities; 3. see if there was a general mismatch between their online presences and the daily work of the centers.

35.3 Research Project Outcomes 35.3.1 University Centers for Students with Disabilities Analyzed and Classified This pilot study was created to study student accommodation centers based on the university setting. Using the previous work completed by Bakken et al. [4], survey-based methods were used to attempt to deeply investigate the student support/accommodation centers located in the USA. Bakken and colleagues used public information about the assistive technology supports for students with disabilities. They relied on the university accommodation centers, webpages to determine the quality and quantity of assistive technology at institutions of higher education. Therefore, the nature of the data was dependent on the quality of the institution’s webpage for the accommodation center. The current study was an attempt to dig beyond the work of Bakken and colleagues by surveying directors of support centers. An email invitation was sent to 30 institutions. After the initial invitation, three more reminders or invitations went out in the next two months. Two emails were never delivered, two invitations did not qualify because they were not four year institutions, two never finished the survey and three completed the entire survey.

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Table 35.1 Students served in centers Number of student at institution

IHE 1

IHE 2

IHE 3

18,107

33,461

5200

Number of students served

944

2622

550

Number of students served three years ago

795

1540

651

Change in students served

Table 35.2 Percent of students by disability category

+149 more

+1082 more

−101 less

IHE 1

IHE 2

IHE 3

LD

18

11

17

ADD/ADHD

29

25

25

Deaf or HH

3

2

2

LVB

3

1

1

Physical impairment Mental health impairment Autism

3

1

24

24

24

27

2

4

1

504

0

0

0

Multiple impairment

0

0

0

Unknown

0

0

0

18

32

3

Other

35.3.2 Survey Results A summary of data obtained from surveying directors of student support centers are presented in Tables 35.1, 35.2, 35.3 and 35.4.

35.4 Discussion of Obtained Outcomes Incident disabilities. Table 35.2 indicates across the three institutions over a third of the students have high incidence disabilities such as learning disabilities (LD) or attention-deficit hyperactivity disorder (ADHD) or attention deficit disorder (ADD). These labels have a tendency to be “unseen” in the classroom yet impact greatly how a student learns. In turn, it would reason that the technologies the IHE centers use are tools to aid in learning for those labels. Most used technology by student assistance centers. The most used technologies are technologies that change the nature of reading and writing. These are technologies that read text to students, technology that turns voice into text and notetaking tools that do a combination of both. These technologies would tend to lend the best benefit for students with LD, ADD and ADHD. It was interesting to note

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Table 35.3 Equipment available and notes IHE 1

IHE 2

IHE 3

Text-to-speech/or screen reading software (e.g., JAWS, Kurzweil, Read and Write, Intel Reader)

Yes

Yes, less than 1% of students served

Yes, 1% of students served Currently only JAWS. A site license for Kurzweil is in the works, at which time the percent of users will increase to 17%

Speech-to-text (e.g., Dragon, Mac dictation, Google voice)

Yes

Yes, 1% of students served

Yes, 2% of students served (Dragon)

OCR software (e.g., Adobe Reader, ABBYY Fine Reader)

Yes

No answer

Yes. We OCR everything for our students

Mouse clicking software or mouse input alternatives (e.g., Joystick, Trackball, Camera Mouse)

Yes

No answer

Yes, available when needed

Organizational and graphic organizer tools (e.g., Inspiration, Draft Builder)

No

No answer

No

Magnification (e.g., CCTV, Zoom Text)

Yes

Yes, about 1.5% of students served

Yes, available when needed

Ergonomic/alternative keyboards (e.g., Big Keys, EZ Reach

Yes

No answer

Yes, available when needed

Note-taking device or software (e.g., Smart Pen. Sonocent)

Yes

Yes, 10% of students served

Yes, 9% of students served, both Smart Pen and Sonocent are in use

Amplification devices (FM systems or sound field)

Yes

Yes, 1% of students served

Yes, 1% of students served

Motion Detection Devices (e.g., Quha Zono, Headmouse Extreme

No

No answer

No

Eye-tracking device (e.g., Tobi ATI, EyeTracker)

No

No answer

No

Sip-and-puff systems (e.g., Jouse3, Origin Instruments)

No

No answer

No

Proofreading software (e.g., Ginger, Grammarly, GHOTIT)

No

No answer

Yes, available when needed (continued)

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Table 35.3 (continued) IHE 1

IHE 2

IHE 3

Dyslexic font or software (e.g., BeeReader, Dyslexie font)

No

No answer

No

Math-related software (e.g., Math Talk, Math Simulation)

No

Yes, less than 1% of students served

Yes, 1% of students served

that at least one institution indicated that the most used tool is not only for students with disabilities but also for the general population by having text-to-speech support universally installed on university laboratory computers. Providing this type of open access to all could be a potential standard for a Smart University. It would allow any student to decide if this was a tool that could help them in learning material. Least used technology. Of the institutions that answered the question “What is the piece of equipment that you have that is the least used?” they named the CCTV. The CCTV looks like a large TV or computer monitor that magnifies traditional print. Several reasons for this effective, but low use tool could be the reason as to why a center would be in possession but not frequently used. The CCTV magnification was a powerful tool for those with low vision before other magnification devices. It takes traditional print in a book or worksheet and projects it to screen allowing a user to see a very enlarged print. However, the tool is not very portable, it is very expensive and there are many “off-the-shelf tools” that provide the same support without the drawbacks. It is likely that a CCTV would not be considered effective, or easy to use, and enhancement of the student’s performance could be easily obtained through other tools. These factors would all be considerations for high-level abandonment of this technology piece [5]. Interestingly enough, the IHE that did not answer this question indicated that about 1% of their students are using magnification of some kind, but, it is unclear the type of magnification. Complicated systems for students. More complicated systems (e.g., eye gaze, sip and puff systems) are used by less students but also might be due the fact that those systems are used in everyday living regardless if a student is attending a university. These systems are required beyond the learning environment and are highly personalized. Complicated systems such as eye gaze, sip and puff and motion detector devices are an important part of daily life functions for those limited mobility in limbs. Because these are items that are needed beyond a learning environment, it would stand to reason that if a student needed this type of technological support, they would come with those items already present. Change in number of students served. Two out of the three intuitions in the pilot study indicated an increase. Perhaps the institution serving less students with disabilities than three years ago is experiencing enrollment fluctuation. This could be one reason; or, it could also be students who are coming more often with their own assistive technology already in place. Students can add extensions to the browsers that will read text aloud. Other large-scale software programs have accessibility built

IHE 1

As needed for student’s use on campus

As it gets outdated, we send retired equipment to campus property control

Voice Dream Reader (text-to-speech software) for audio exams. We keep this loaded on an iPad for students, which we give them when they come to take an exam

CCTV’s

8–9

Question

How do you obtain new equipment?

How do you retire old equipment?

What equipment at your center is utilized the most?

What equipment do you still have that is utilized the least?

How many full time staff do you have including yourself?

Table 35.4 Equipment and funds

10

No answer

Software: Read and Write Gold is on all campus laboratory computers

If it is clearly outdated, not being used, or broken

When we need it

IHE 2

4–5 (continued)

CCTV, magnification cameras

Sonocent and Smart Pens for students, Abbyy Fine reader for creation of e-text. I find most students come to school with their own assistive technology at this point

Follow university protocols (surplus typically), or donation to local groups that work with individuals with disabilities

We purchase as needed. For lending equipment (e.g., Smart Pens), we try to keep two on hand, so we do not run out. For small purchases ( 0, γ j = const. It means that students with higher grades have more solid knowledge. 7. We can accept such hypothesis that the amount of student’s knowledge he obtained independently is not forgotten and it is proportional to the average level of students’ knowledge obtained in all disciplines (courses), that is

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Y j (t) = b j

n 1 X s (t0s ) = b j U, j = 1, 2, . . . , n, n s=1

(40.4)

where b j is the coefficient of proportionality, which matches the assumption that students with higher grades devote more time to studying the disciplines (courses) independently. Statutes 1–7 and formulas (40.1)–(40.4) say that the total amount J(t) of residual knowledge at moment time t ≥ max(t01 , t02 , . . . , t0n ) can be expressed by the formula of the following form: J (t) =

n 

J j (t),

(40.5)

j=1

where J j (t) is defined by formula (40.2). If we put (40.3) and (40.4) in (40.2), we get 

    γ t − t0 j J j (t) = ηa j U exp − + b j U E t − t0 j , j = 1, 2, . . . , n, (40.6) U

where t0 = max t0 j , U = j

1 n

n

X s (t0s ).

s=1

The Internet expansion and the accumulation of large amounts of data in the early 2000s caused the rapid growth of interest in data mining methods, thus leading to them being researched, distributed, and developed [7, 8]. The LMS developed in parallel with data mining; for example, the first version of the popular learning management system Moodle was released in 2002. These technologies made it possible to obtain very detailed information about the students’ behavior that did not fit into the traditional educational statistics framework [9, 10]. At the same time, the international conferences on using the artificial intelligence methods in education (International Conference on Artificial Intelligence in Education, International Conference on Intelligent Tutoring Systems and others) [8] started hosting regular seminars on the development of analysis methods for this new type of educational data. On the other hand, the traditional educational process digitalization connected with the introduction of electronic journals, students’ record books, and so on is taking place. The appearance of public repositories such as the Pittsburgh Science of Learning Center’s DataShop and National has led to the further development of the digital technologies application in the sphere of education.

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40.3 The Modeling of Residual Knowledge Dynamics The simplest probabilistic characteristic of the dynamics of residual knowledge is the mathematical expectation of the total amount of knowledge in the considered area of training, that is M[J (t)] =

n 

    M J j (t) ∀ t ≥ max t0 j j

j=1

(40.7)

where M […] is the symbol of the “mathematical expectation” operation. The quality of knowledge (reliability, stability) can be characterized by the dispersion of the total volume, that is, the value of n       2 D (J (t) − M J j (t) D[J (t)] = M (J (t) − M[J (t)] =

(40.8)

j=1

where D […] is the symbol of the variance operation. The values can be roughly calculated based on the laws of probability  theory  and mathematical statistics formulas after simplifying the formulas for M J j (t) . It follows from (40.6) that           γ t − t0 j + b j M[U ] , ∀ t > max t0 j . M J j (t) = ηa j M U exp − j U (40.9) 

If you know the probabilities of estimates P2 j , P3 j , P4 j , P5 j for the discipline “j,” then M[U ] ≈

5 n 1   , s Ps j = U n i=1 s=2

(40.10)

where U is the static expectation score and M […] is the probability of getting an average grade of “s” in discipline “j” by a control group of students. Therefore, the second term included in (40.9) is equal to: n n 5  bj   bj   M[X s (t0s )] = s Ps j = b j M[U ]. M bjY = n s=1 n j=1 s=2

(40.11)

It is more difficult to calculate the value of the first term, since it is necessary to γ find the expectation of a nonlinear function U e− U . Using an approximate formula, it follows from (40.9) and (40.10) that

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  γ t − t0 j  exp − . M J j (t) ≈ ηa j U + bjU  U 





(40.12)

In general, the meanings of mathematical expectation and dispersion are easier to calculate by the method of statistical modeling. It is necessary to solve the problem of identification of the following parameters in the proposal model: η aj γ Ps j bj t0 j

the coefficient of perception of information per unit of time; the number of training hours spent on the studying of information on the studying direction in the discipline (course); the relative coefficient of forgetting information determined experimentally; the probability of getting the mark k in the discipline (course), A j , s = 1, 2, 3, 4, 5; the coefficient of proportionality of the volume of knowledge absorbed independently in the discipline, A j determined experimentally; the time moments of the course completion on the discipline (course) A j .

The statistical model for the calculation of expectation function in a dynamic of   residual knowledge volume for ∀ t ≥ t0 = max t0 j consists of the following blocks:   1. The modeling of independent discrete random dimension U j = X j t0 j . The implementation of this item can be done by the collecting of initial statistical information about students’ marks received while studying some disciplines (courses) A j , j = 1, 2, . . . , n. In fact, this statistical model can be fundamental for possibly determining the meaning (role) of statistical estimation of any other parameters, including the laws of probability distribution. Nevertheless, the accuracy of the initial data and the influence of subjective factors call into question the appropriateness of such calculations and the calculation of statistical estimates of probabilities Ps j , s = 1, 2, 3, 4, 5; j = 1, 2, . . . , n. Let us have Nn as a set of statistical values and u i j of random variables U j (i-test number) obtained as a result of this point. 2. The statistical point calculation of the expectation function is done according to the formula

N 1  Ui j (tk ), j = 1, 2, . . . , n, M J j (tk ) ≈ N i=1





for the meanings t0 , t1 = t0 + h, t2 = t0 + 2h, . . . , tm = t0 + mh = t, where [t0 , t] is an interval of forecasting the dynamics of the decrease in the amount of knowledge obtained for each of the studied disciplines (courses). 3. Calculation of statistical estimates of mathematical expectation in the total amount of residual information for time points by the formula

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n 1 M[J (tk )] ≈ J(tk ) = J j (tk ), k = 0, 1, . . . , m. n j=1

4. Calculation of statistical estimates of variance of total residual information for time moments t0 , t1 , . . . , tm by the formula D[J (tk )] =

n 2 1  J j (tk ) − Jj (tk ) n j=1

and the calculation of statistical (marks) points of probabilities Ps j , s = 1, 2, . . . , n. It should be noted that in this mathematical model for estimating the dynamics of residual knowledge, we can use the information that is available in the information systems of smart university. For practical implementation, only additional characterizing the experiments are required to determine the parameters speed of perception, the speed of forgetting, and the possibility of self-studying the discipline (course) j. By controlling the dynamics of residual knowledge, it is possible to arrange unacceptable gaps in curricula while planning sequences of logically related training courses (disciplines).

40.4 Results We assessed the possibility of residual knowledge management in forming the required competencies. We studied the impact of each of the disciplines studied by students (for each course of study) on the formation of the required knowledge, skills, and abilities in the practical application of future professional activity. Four areas of activity were considered: project, research, organizational, and management, and technology. For each of the disciplines, tables have been compiled for each course of study to generate the required amount of knowledge, skills, and competencies. We will form the model of competence management on the first course. Let us have four competencies according to the state standard: project, research, managerial, and technological. Let us denote them conditionally K m (m = 1, 2, 3, 4) and put them in a reciprocal correspondence study of the basic 12 disciplines, in accordance with the curriculum Di. The suggested table (Table 40.1) estimates the given set of 12 disciplines (Di)corresponds the required number and educational quality level of trained competences for the experimental student group in accordance with the curriculum. Let us summarize, in the table, the correlation of disciplines with the competencies being formed and obtain a matrix of information completeness. Let us call the internal content of the table “information matrix.” It reflects the true state of correspondence of the learning process to knowledge accumulation. The analysis of information shows an approximately equilibrium characteristic of the importance of the formed

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Table 40.1 Formation of knowledge volume, skills, and abilities of trainees Disciplines

Competencies Project activity

Research activity

Management activity

Technological activity

D1

*

*

D2

*

*

*

*

D3

*

*

*

D4

*

…

*

D10

*

D11

*

*

*

D12

*

*

*

*

Volume of knowledge

0.97

1.13

0.95

0.87

volumes of competencies for each of the activities. The formed volume of knowledge at the first year in those disciplines, which are included in the curriculum, is close to one (0.97; 1.13; 0.95; 0.87). However, this syllabus can be adjusted, since the research and development type of activity is formed with an excess and the technological type with an insufficient amount of knowledge. The disciplines chosen for the formation of these competencies do allow achieving the required level of competencies at this stage of training.duq It is important to preserve the applicability of the formed volume of competence filling in subsequent courses of study and possible correction of the sequence of “readable” disciplines in the courses of study, in order to ensure applicability and logical relationship of the obtained knowledge in all the previous and subsequent courses (the principle of divergence, implemented by one of the authors [11, 12] from 1998 to present in training). It should be noted that the type of information field completely coincides with the classical type of cybernetic system (black box) (Fig. 40.1), and the result of the black box is disclosed through structural schemes. In the control model for each block of disciplines (filling the competence), the probability of achieving the goal was obtained = 99.5%, and for each of the blocks in parallel, γ = 95%. Provision of such parameters is achieved in supposition that accumulation of knowledge in time is subject to normal law of distribution and with probability close to one. Then, the course program will be assimilated by 95% of students (the value = 95% was taken by us initially). The normal distribution law allows us to simulate the curve for knowledge assessment for the given period. Turning to the normal law of distribution, it is possible to receive a curve of accumulation of knowledge for a year. The graphs of competence formation by types of activity are presented in Fig. 40.2.

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Fig. 40.1 Scheme of the information field 1,20000 1,00000 0,80000 0,60000 0,40000 0,20000 0,00000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Project (loss function)

Research (loss function)

Organizational - management (loss function)

Industrial and technological (loss function)

Fig. 40.2 Competency charts (project)

The evaluation of residual knowledge for each type of activity is presented in Fig. 40.3. Similarly, for 2–5 courses, functions reflecting the loss of knowledge at the end of the training period were also obtained, from which management models were derived. Thus, the theoretical substantiation of the possibility of management of knowledge accumulation processes was confirmed in practice.

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Fig. 40.3 Cumulative function of knowledge loss

40.5 Conclusions and Next Steps Conclusions. 1. The described models, aimed to estimate student residual knowledge, can be used by smart universities to build individual educational trajectories, while correcting curricula, in preparation for an independent knowledge estimation, for preparation for the accreditation examination or in preparation for the passage of professional and public examination. 2. The received data can be collected and processed by educational data mining tools. Next steps. 1. Formation of the structure of the database, which accumulates information about the reasons for the insufficient knowledge formation. 2. Development of test database to assess knowledge formed during students’ activity. This solution will make it possible to adjust the existing trajectory of learning at smart university.

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References 1. Serdyukova, N., Serdyukov, V.: Algebraic Formalization of Smart Systems. Theory and Practice. Springer Nature, Switzerland (2018) 2. Vasiliev, V.N., Ruzanova, N.S.: An approach to capacity planning of a local service provideras an element of internet infrastructure. In: Proceedings of the DPW 97—98.—Petrozavodsk University Press (1998) 3. Glukhova, L.V.: Management Methodology of the Enterprises Innovation Activity Using the Methods of the Structural Analysis and Synthesis: Monograph. In: L. V. Glukhova. Publishing House of the Institute of Commerce and Law, Moscow. 165 c. (2010) 4. Agrawal, R., Gollapudi, S., Kannan, A., Kenthapadi, K.: Study navigator: an algorithmically generated aid for learning from electronic textbooks. JEDM-J. Educ. Data Min. 6(1), 53–75 (2014) 5. Mitrofanova, Y.S.: Modeling the assessment of definition of a smart university infrastructure development level In: Sherstobitova, A.A., Filippova, O.A. (eds.) Smart Innovation, Systems and Technologies. T. 144. C. 573–582 (2019) 6. Glukhova, L.V., Mitrofanova, Y.S.: Digitalization of economy and the particularities of its application in an integrated facility’s activity. In: The Bulletin of the Volga Region State University of Service. № 4 (2017) 7. Uskov, V.L., Bakken, J.P., Howlett, R.J., Jain, L.C. (eds.): Smart Universities: Concepts, Systems, and Technologies, 421 p. Springer, Cham (2018) 8. Uskov, V.L., et al.: Smart university taxonomy: features, components, systems. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning 2016. Springer, Cham (2016) 9. Aleksandrov, A.Y., Ivanova, O.A., Vereshchak, S.B., Getskina, I.B.: SMART university in digital learning space. In: Proceedings of the 34th International Business Information Management Association Conference, IBIMA (2019) 10. Aleksandrov, A.Y., Ivanova, O.A., Vereshchak, S.B.: SMART-university: new opportunities for individuals with disabilities. In: Proceedings of the 34th International Business Information Management Association Conference, IBIMA (2019) 11. Aleksandrov, A.Y., Ivanova, O.A., Vereshchak, S.B., Getskina, I.B.: Modern concepts of the quality management system in higher education: Russian practice and international experience In: Proceedings of the 33rd International Business Information Management Association Conference, IBIMA 2019: Education Excellence and Innovation Management through Vision (2020) 12. Mitrofanova, Y.S.: Modeling smart learning processes based on educational data mining tools In: Mitrofanova, Y.S., Sherstobitova, A.A., Filippova, O.A. (eds.) Smart Innovation, Systems and Technologies. T. 144. C. 561–571 (2019)

Chapter 41

Smart Algebraic Approach to Analysis of Learning Outcomes Natalia A. Serdyukova, Vladimir I. Serdyukov, Sergey S. Neustroev, Elena A. Vlasova, and Svetlana I. Shishkina

Abstract One of the problematic issues of education is a comparative analysis of the results achieved by students in the learning process. The results may vary, for example, in breadth, in depth of knowledge, different areas of mathematics can be mastered by the same student at completely different levels, etc. The proposed smart algebraic approach that uses a probabilistic approach, semantic networks, and marked graphs for the analysis of learning outcomes allows you to measure learning outcomes, taking into account the structure of the knowledge system, rank of students according to their level of knowledge, evaluate their strengths and weaknesses, identify gaps among students in knowledge, and create recommendations on the teaching methodology. Smart algebraic model of learning outcomes analysis uses algebraic formalization of systems and probabilistic estimation methods.

41.1 Introduction Smart algebraic model of learning outcomes analysis differs from the traditional model of knowledge assessment, and in that, it makes it possible to evaluate such characteristics of students, such as the breadth and depth of knowledge, to build a measure of deviation of the knowledge system, skills, and abilities of the student from the knowledge system, taking into account the structural characteristics of the knowledge system. The student performs the test work, and the solution is displayed on the semantic network of the knowledge system [1, 2]. Then, the solution is presented in the form of a marked graph with (a) vertices that represent mathematical N. A. Serdyukova (B) Plekhanov Russian University of Economics, Moscow, Russia e-mail: [email protected] V. I. Serdyukov · E. A. Vlasova · S. I. Shishkina Bauman Moscow State Technical University, Moscow, Russia e-mail: [email protected] V. I. Serdyukov · S. S. Neustroev Institute of Education Management, Russian Academy of Education, Moscow, Russia © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_41

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concepts used by the student to solve a problem and (b) edges that represent mathematical actions performed by the student in the process of solving the problem. The solution presented by the student represents the path in the graph. The initial vertex of this path is determined by the condition of the problem and the last vertex by the result of the solution. While constructing an estimate, for example, weights can be associated with each edge. Each intermediate vertex corresponds to a certain stage of solving the problem with the weight attributed to the edge of the graph path which is defined as the proportion of the estimate for solving the problem and the percentage of its solution. The optimal solution corresponds to a path whose sum of weights is minimal. One can use a different approach based on probability theory.

41.1.1 Traditional Text Tasks. Construction of a Solution Estimation Algorithm Traditionally, in tests, there are tasks of different difficulty levels. Example 1—Text tasks. Sketchily, the stages of solving such problems can be represented by a scheme.

41.1.2 Construction of a Monitoring Algorithm for the Progress of the Problem Solving. Learning Outcome Control Options In this case, the following skills are checked in the steps outlined below for solving the problem: 1. Skills of analysis and processing of initial information: the ability to analyze external information and write down a block diagram of a problem solution or a verbal, mathematical model to solve a problem. Score of this skill is a1 . 2. Orientation skills in the knowledge system: the assessment parameters are the breadth of knowledge of solution methods and the depth of knowledge of problem-solving methods. Score of this skill is a2 . 3. Technical skills for solving the problem, such as the ability to build the logic of solving the problem, the ability to find the range of admissible values, the skills of researching functions, and the knowledge of the properties of elementary functions. Score of this skill is a3 . 4. The ability to make simple mathematical models and the ability to make elementary identical transformations. Score of this skill is a4 . 5. Computational skills for solving problems: ability to make transformations and calculations. Score of this skill is a5 .

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6. The ability to analyze the solution founded and to verify the credibility of the solution by comparing the results with input data and known facts. Score of this skill is a6 .

41.1.3 Algorithm Construction for Probabilistic Assessment of the Progress of the Problem Solution Assessment of knowledge is always probabilistic in nature since it cannot be absolutely accurate. Let us build a model of probabilistic estimation of the problem solution progress. Let  = {a1 , a2 , a3 , a4 , a5 , a6 } be a space of elementary outcomes and P() be a set of all subsets of the set . Then, P() = P(), ∪, ∩, \ is a finite σ -algebra, so a probability measure p: P() → [0, 1] can be constructed, that is, the function which satisfies the following two conditions: 1. p() probability of a true event is 1 ∞= 1—the   ∞ 2. p i=1 Ai = i=1 p(Ai ). In our case, the number of elements of σ -algebra P() is equal to |P()| = 26 . If || = n, then |P()| = 2n . The function p can be determined, in essence, by use of one of the laws of distribution of a discrete random variable. We obtain the following scheme for evaluating the solution of a text problem here from the following stages: First stage. Assessment for the correct passage of the first stage of the solution is p(a1 ). Let us give the necessary explanations. The first, basic skill in solving a text problem is the ability to translate a situation into a mathematical language, i.e., the ability to build a mathematical model. Second stage. Assessment for the correct passage of the second stage of the solution is p(a2 ). Third stage. Assessment for the correct passage of the third stage of the solution is p(a3 ). Fourth stage. Assessment for the correct passage of the fourth stage of the solution is p(a4 ). Fifth stage. Assessment for the correct passage of the fifth stage of the solution is p(a5 ). Sixth stage. Assessment for the correct passage of the sixth stage of the solution is p(a6 ). An estimate of the solution to the text problem presented by the student can be represented as an element of the σ -algebra P(). Let us consider an example. Let us write down all of the elements of σ -algebra P(), for the case  = {a1 , a2 , a3 , a4 , a5 , a6 }. These are:

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A1 = ∅, A2 = , A3 = {a1 }, A4 = {a2 }, A5 = {a3 }, A6 = {a4 }, A7 = {a5 }, A8 = {a6 }, A10 = {a1 , a2 }, A11 = {a1 , a3 }, A12 = {a1 , a4 }, A13 = {a1 , a5 }, A14 = {a1 , a6 }, A15 = {a2 , a3 }, A16 = {a2 , a4 }, A17 = {a2 , a5 }, A18 = {a2 , a6 }, A19 = {a3 , a4 }, A20 = {a3 , a5 }, A21 = {a3 , a6 }, A22 = {a4 , a5 }, A23 = {a4 , a6 }, A24 = {a1 , a2 , a3 }, A25 = {a1 , a2 , a4 }, A26 = {a1 , a2 , a5 }, A27 = {a2 , a3 , a4 }, A28 = {a2 , a3 , a5 }, A29 = {a2 , a3 , a6 }, A30 = {a3 , a4 , a5 }, A31 = {a3 , a4 , a6 }, A32 = {a1 , a2 , a3 , a4 }, A33 = {a1 , a2 , a3 , a5 }, A34 = {a1 , a2 , a3 , a6 }, A35 = {a1 , a2 , a3 , a4 , a5 }, A36 = {a1 , a2 , a3 , a4 , a6 } With the mildest assessment of the solution to the problem (all disputed issues are decided in favor of the examiner), elementary events {a1 , a2 , a3 , a4 , a5 , a6 } can be considered as independent ones. Then, the assessment of the solution to the text problem lies in the interval [0, 1]. The maximum score for solving a text problem can be equal to 1. The general case. One can represent the stages of solving other tasks proposed for knowledge control in a similar way. In the general case, the scheme for constructing a probabilistic assessment of the solution of the test problem runs as follows. We single out and describe, in detail, the stages of solving the problem. This is a qualitative part of assessment of the solution of a problem of a certain type. Let us suppose that n stages of the solution of the problem are distinguished, and an estimate for the correct passage of the ith stage of the solution of the problem is p(i), i = 1, . . . , n. Let  = {a1 , . . . , an } be a space of elementary outcomes, and P() is the set of all subsets of the set . Then, P() = P(), ∪, ∩, \ is a finite σ -algebra, so one can construct a probability measure. p: P() → [0, 1] Now, the number of elements in σ -algebra P() equals to |P()| = 2n . The function p can be determined, in essence, using one of the laws of distribution of a discrete random variable. An estimate of the solution to the text problem presented by the student can be written as an element of the σ -algebra P(). With the mildest assessment of the solution to the problem (all disputed issues are decided in favor of the examiner), elementary events {a1 , . . . , an } can be considered as independent ones, so every element of σ -algebra P() corresponds to a certain error pattern made by the student in solving the test problem. Then, the assessment of the solution to the text problem lies in the interval [0, 1]. The maximum score for solving a text problem can be equal to 1. Let us consider one more example.

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Example 2—Tasks to investigate functions and to construct graphing. According to the tasks to investigate functions, the following stages can be distinguished: 1. Knowledge of the simplest properties of elementary functions: here, one can distinguish the following seven simplest stages: the domain of definition of the function, the domain of values of the function, monotonicity, convexity, differentiability, continuity, and periodicity. If the content were to be specified, then it may include knowledge of the basic formulas, knowledge of the graphs of elementary functions, the ability to perform simple transformations of graphs, the student’s computational skills. 2. Orientation skills in the knowledge system: the assessment parameters are the breadth of knowledge of solution tasks methods and the depth of knowledge of solution tasks methods. 3. The ability to analyze the solution found and to verify the verisimilitude of the solution by comparing the results with the input data and known facts. In this example,  = {a1 , a2 , a3 }, |P()| = 23 = 8. Usually, the total score for solving all test tasks is presented in the form of an arithmetic weighted average of the grades obtained for solving each of the examination task. However, from the point of view of measurement theory, the estimate obtained in this way is not reliable, since it is not invariant in the transformation of scales. In fact, such an assessment of knowledge, as in the examples 1 and 2, represents the ranking of students’ decisions according to the positions listed in general form in the “General case” section. A separate, big question is the question of how to evaluate the width and depth of knowledge and other assessment parameters that characterize to some extent the quality of knowledge shown by the student in solving test tasks.

41.2 Main Results. Formalization of the Concept of “Task” in the Learning System Suppose now that the knowledge system is represented as a semantic network. Now, let us represent a semantic network of knowledge in the form of a graph [3]. Let us now formalize the concept of a “task.” Let the knowledge system S be represented as the graph Γ . A task is a path in a graph. Let us recall the definition of a path in a graph. A path in a graph is a sequence of vertices in which each vertex is connected to the next edge. More precisely, we can define it as follows. Definition 1 [4]. Let Γ be an undirected graph. A path in Γ is such a finite or infinite sequence of edges and vertices

w = (. . . , a0 , E 0 , a1 , E 1 , . . . , E n−1 , an , . . .), that every two adjacent edges E i−1 , E i , have a common vertex ai .

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A path in a graph can be considered as a special case of the route. The set of tasks in the knowledge system S will be denoted by T = {Ti |i ∈ I }, the path corresponding to the problem Ti , i ∈ I, will be denoted by W (Ti ), i ∈ I , and the set of paths in the graph Γ , corresponding to the set tasks T = {Ti |i ∈ I }, will be denoted by W (T ) = {W (Ti )|i ∈ I }. One can formalize the knowledge, skills, and abilities of a particular student using the concept of graph homomorphism.

41.2.1 Formalization of the Concepts of “Knowledge,” “Proficiency,” and “Skills” of a Particular Student in the Knowledge System S Let {α|α ∈ Λ} be the set of students that should learn the knowledge system S. Definition 2 Let f α : Γ → Γ be a homomorphism of a graph Γ into itself. Then, the image f α (Γ ) is a student α, α ∈ Λ, knowledge system and the image f α ({W (Ti )|i ∈ I }) is the proficiency to solve the tasks of the student α, α ∈ Λ. Let us recall that a homomorphism of the graph Γ is such a mapping f : Γ → Γ of the vertices of the graph Γ into the set of vertices of the graph Γ under which the incidence relation is preserved. Let us now show how to formalize attempts to solve the task by a student. To do this, one should embed the graph Γ , representing the knowledge system S, into the complete graph Γ S . By the graph Γ S , one constructs the free group G Γ (free) whose van Kampen diagram corresponds to the graph Γ S . By the path W (Ti )| in the graph Γ S , one selects the corresponding path to it, part D(G Γ ), in the van Kampen diagram and H (G Γ ) in the group G Γ , then closes H (G Γ ) up to the subgroup G(W (Ti )), which one calls the set of attempts to solve the task Ti . After that, we construct a homomorphism f i : G Γ → G(W (Ti )). Then, the kernel of this homomorphism Kerfi , is a measure of the deviation of the student’s knowledge system image from the knowledge system. Now, let us apply the Erdös-Renyi algorithm to the graph D(G Γ ). From the property “almost all graphs are connected,” which follows from the Erdös-Renyi algorithm, one gets that it is impossible to establish the knowledge gap areas for students by probabilistic methods. Nevertheless, despite this, it is possible to significantly increase the adequacy of assessments of the student’s knowledge system using probability-theoretical methods.

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41.3 Main Results. The General Case. Algorithms for Constructing a General Probabilistic Assessment of a Test Consisting of Several Tasks, and Ranking Students by Knowledge Level The assessment offered by us is a probabilistic one. Let us assume that the process of the correct solution of the ith task of the pedagogical test can be divided into ni consecutive stages. Let us denote by pi j the probability of a random event consisting in the fact that the jth stage of solving this task will be performed correctly (provided that the previous stages of this task are also performed correctly), j = 1, n i . Then, the probability of the opposite event, namely that the j stage of solving the i task is performed incorrectly (provided that the preceding stages of this task were completed correctly), is equal to qi j = 1 − pi j . Let us assume that both of these probabilities are nonzero. The solution of the ith task will be correct if all n i stages of its solution are performed correctly. Let us set the random variable X i , the numerical values of which correspond to the number of successively correctly performed stages of solving this problem (starting from the first). Then, the smallest value of this random variable X i = 0 will correspond to the case when the first stage of solving the ith task of the pedagogical test was performed incorrectly, and the largest value X i = n i corresponds to the case when all the stages of solving this problem are completed correctly. Therefore, this problem is solved correctly. The distribution series of the random variable X i is presented in Table 41.1. It follows from this table that the probability of solving the ith task of the pedagogical test is equal to Wi =

ni 

pi j .

j=1

Table 41.1 Random distribution of X i

Random variable X i Value

Probability

0

pi (0) = qi1

1

pi (1) = qi2 · pi1

…

…

k

pi (k) = qi(k+1) ·

…

…

ni

pi (n i ) =

n i s=1

k

pis

s=1

pis

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Accordingly, the probability of the opposite event, which consists in the fact that the i problem of the pedagogical test will be solved incorrectly, has the form Q i = 1 − Wi .

(41.1)

Let us consider the results of the control works solution. For ease of understanding the proposed approach, we shall assume that the solution of any of the problems can be represented as a path on the graph, and this path is the only one. The set of states of the system in which the student’s testing process will develop by moving from one state to another can be represented as a directed graph as follows. If one assigns sequence numbers to the tasks of the pedagogical test, then we can associate any of the graph states with an m-dimensional row vector, the serial number of the component will correspond to the serial number of the task, and the value of the component will correspond to the number of correctly completed steps. We shall call this row vector a state vector. Then, the initial state of the system, from which the testing process will begin to develop, will be the following one: (0, 0, . . . , 0). From this state, the process can go into one of m different states that form the first group of states. A sign that allows one to select all the states belonging to the first group is that the sum of the components of the vectors of such states is equal to unity. From the state of the first group, the process can go to one of the states of the second group. Signs that allow one to highlight all the conditions of the second group, in which the transition is possible, are the following: • The sum of the components of the vectors of such states is equal to two; • The value of the ith component of the state vector of the second group is less or equal to n i . The transition from the state of the first group to the state of the second group is possible only if the difference between the corresponding state vectors of the second and first groups is equal to a vector belonging to the first group of states. If n i ≥ 2, i = 1, m, then the number of states of the second group will be equal to m · (m − 1). From the state of the second group, the process can go to one of the states of the third group. Signs that allow you to highlight all the conditions of the third group, in which the transition is possible, are the following: • The sum of the components of the vectors of such states is equal to three; • The value of the ith component of the state vector of the third group is less than or equal to n i . The transition from the state of the second group to the state of the third group is possible only if the difference between the corresponding state vectors of the third and second groups is equal to the vector belonging to the second group of states.

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If n i ≥ 3, i = 1, m, then the number of states of the second group will be equal to m · (m − 1) · (m − 2), and so on. Herewith, the number of states in each new group will firstly increase and then decrease. The process will develop until it falls into an absorbing state: (n 1 , n 2 , . . . , n m ), m n i th a group of states consisting of one state. which will be i=1 The peculiarity of the student testing process is that it will be one of the possible ways in a directed graph. Moreover, each of the vertices of the graph on this path, starting from the original one, can be the end of this path. Let us consider the results of solving the tasks of the pedagogical test as independent events. If the student’s testing process, starting in the initial state of the system, ends in it (which can happen when none of the steps in solving the tasks of the pedagogical test is completed correctly), then the implementation of such a test result is possible with probability 1. The implementation of other test results is possible only with a probability less than 1. The probability will be less that the student receives a finite test result, and the more significant his success, that is his knowledge level will be higher. Let the student, who completed the test correctly, performed: k1 stages in solving the task of a pedagogical test with ordinal number 1; k2 stages in solving the task of a pedagogical test with ordinal number 2; …………………………………………………………………………; km stages in solving the task of a pedagogical test with ordinal number m. In other words, during the test, the student has reached the state (k1 , k2 , . . . , km ) which became the final state in the process of testing. Then, the probability of achieving such a result in the testing process will be possible is equal to m 

pi (ki ),

i=1

which will be less than 1. The lowest probability has the condition (n 1 , n 2 , . . . , n m ), This probability is equal to m  i=1

pi (n i ),

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This allows one to rank students tested, based on the probabilities of the results they obtained when performing the pedagogical test: the lower the probability, the higher is student’s ranking. This approach can be used if probabilities of the correct completion for solving each of the tasks stages of the pedagogical test are known. Otherwise, the practical use of this approach should be preceded by a preparatory period during which appropriate values of these probabilities are first set by experts (usually in decimal notation with two decimal places). Then, a pedagogical experiment is conducted in order to collect statistics on the results of the student’s decisions of the pedagogical test tasks that are of interest for us. This experiment continues until the posterior values of the probabilities of the events which are of interest to us, calculated by the Bayes formula, assume stable values. After that, we can proceed to the practical use of the approach described. The described method of experimental determination of the posterior probabilities of events is commonly called the Bayesian approach [5–7]. Let us note that during the practical use of the proposed approach to the analysis of learning outcomes, new statistical data will appear. Furthermore, they can be used to calculate and refine the posterior values of the probabilities of the correct completion of the stages for solving each of the pedagogical test tasks. If desired, the above approach can be extended to problems that may have several solutions by renumbering the paths corresponding to these solutions on the graph and setting the criterion of their preference.

41.4 Conclusions We have shown that from the property “almost all graphs are connected,” which follows from the Erdös-Renyi algorithm, one gets that it is impossible to establish the knowledge gap areas for students by probabilistic methods. Nevertheless, despite this, it is possible to significantly increase the adequacy of assessments of the student’s knowledge system using probability-theoretical methods, as it is noted in Sect. 41.3. The article is of theoretical nature, and it is supposed to use the recommendations developed in it in practice later on.

References 1. Serdyukov, V., Serdyukova, N.: Quasi-fractal model of the semantic knowledge network as the basis for the formation of a pedagogical test. In: Proceedings of the International Conference on the Development of Education in Eurasia (ICDEE 2019). https://doi.org/10.2991/icdee-19. 2019.9 2. Serdyukova, N., Serdyukov, V., Neustroev, S., Shishkina, S.: Assessing the reliability of automated knowledge control results. In: 2019 IEEE Global Engineering Education Conference (EDUCON), pp. 1453–1456. American University in Dubai, Dubai, UAE (2019)

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3. Palagin, A., Krivoy, S., Petrenko, N.: Conceptual graphs and semantic networks in systems of processing of naturally-language information. Math. Mach. Syst. (3), 67–79 (2009) 4. Kuznetsov, O., Adelson-Velsky, G.: Discrete Mathematics for the Engineer. Energia, Moscow (1980) (in Russian) 5. Lobach, V.: Bayes Time Series Forecasting Based on Models in the State Space. Belarusian State University, Minsk, Belarus. http://www.elib.bsu.by 6. Lbov, G., Nedelko, V.: Bayesian approach to forecasting problem solution based on experts’ information and data table. RAS Rep. 357(1), 29–32 (1997) 7. Bishop, C.: Pattern recognition and machine learning. In: Jordan, M., Kleinberg, J., Schoellkopf, B. (eds.) Information Science and Statistics, 2006. Springer Science + Business Media, LLC (2006)

Chapter 42

Taxology in Smart University Economics: New Approaches to Teaching Taxation Natalya V. Serdyukova and Ivan M. Kolpashnikov

Abstract As it is mentioned in the “Knowledge management as foundation of smart university” paper by L. Maciaszek, K. Marciniak, “functioning in an era of knowledge is forcing organizations to manage this valuable resource in an exact way, and very frequently, activities of organizations are dependent on application of knowledge.” Sometimes, even this means “to be or not to be for an enterprise.” These obstacles touch every sphere of the activity of an enterprise. Within the work, we are going to more thoroughly examine the processes of tax planning and of preparation of competitive tax specialists for a modern enterprise, national or multinational. Regarding the scope of our work, description of the new methods of teaching taxation, we can conclude that in the Russian Federation, traditionally, taxation in the higher school is taught on the bases of theory, ordinary oral lections, the sources for which are legislative norms, books, paper methodological materials on the subject, and solving of practical cases. These methods represent standard background, but for now, do not correspond to modern challenges, clauses of conduction of business, especially global, and the needs of knowledge-based economy. In this work, we observe new direction of preparation of tax specialists. Taxology is aimed at optimization of tax function of the companies and is established and actively promoted by the Thomson Reuters Corporation within the world (in the USA, Australia, and Europe) since 2014. Also, we estimate the perspectives of the development of this direction of training of tax specialists in the Russian Federation.

N. V. Serdyukova (B) Institute of Business Studies, The Russian Presidential Academy of National Economy and Public Administration, Moscow, Russian Federation e-mail: [email protected] I. M. Kolpashnikov Thomson Reuters Corporation, Moscow, Russian Federation © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_42

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42.1 Introduction. The Need and Reality of Smart Processes in the Modern Society In modern society, there are changes associated with the emergence of the idea of Specific, Measurable, Achievable, Relevant, Time bound (SMART) organization of modern society in all areas of its functioning. SMART functioning of the society implies the digitalization of the processes taking place in the society and the active use of IT technologies. Such concepts as smart goals, smart structures, smart university, and smart economics appear. In this regard, one of the most important issues, both with the economy and with the theory of education, arises: training specialists in the field of smart specializations in various fields of human activity, creating smart teaching methods for this, and developing innovative methodologies. This should be based on mathematical models and digitalization to ensure the reliability of the proposed solutions. We will discuss in more detail smart solutions used within teaching of taxation in the Russian Federation and abroad—the scope of taxology. Since 2002, taxology has been engaged in solving complex and non-trivial issues of corporate taxation [1, 2]. In the sphere of taxation, it is rather new notion. Taxology is understood as a complex discipline, preparing specialists in the sphere of IT technology and taxation. Development of international business and transactions intensify globalization, the growth of huge holding companies, restriction of tax legislation, and condition of the necessity of collection of information about tax legislation of different jurisdictions in online regime. Options, which help companies to receive such information operatively, help companies to strengthen their competitive advantages due to fast feedback and significant economy on consultancy services (Fig. 42.1).

Fig. 42.1 Divergent contemporary tax challenges, standing behind the multinational companies

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In this regard, it is necessary to look for innovative methodologies and smart teaching technologies, in particular, in disciplines of an economic nature, corresponding to the realities and development trends of modern society.

42.2 Problem Statement The main problem of the present paper is preparing the program for academic course “Taxology” for economic universities.

42.2.1 History of Development of the New Direction of Preparation of Taxologists Training, especially in economic disciplines, should be closely linked to practice since it is the economy that ensures the functioning of society in accordance with the time. The main issue discussed in this article is the training of specialists in the field of taxation that are able to work in the conditions of the emerging smart economic society. Step by step, the focus in the international practice moves from traditional consultancy services to the solving of controversial questions in taxation by the competent departments of the holding companies. Such departments obtain and exploit special IT programs which facilitate tax function. Most known and demanded among such programs are: • bases of international tax legislation IBFD; • Thomson Reuters programs, including: – Checkpoint World (analysis of international tax obligations, legislation, structuring and modeling of cross-border operations, tax planning), – ONESOURCE Transfer Pricing (preparation of three-tier package of transfer pricing documentation online), – ONESOURCE Benchmark (analysis of profitability of the companies for transfer pricing a taxation purposes); • KPMG–BEPS Automation Tool (for preparation of country-by-country report) (Fig. 42.2). Such programs are produced by: • International Bureau of Fiscal Documentation (IBFD): one of the leading organizations in the sphere of tax expertise and independent research in taxation; • Thomson Reuters: the largest provider of information and technological business decisions in the world for government tax services and tax departments of the largest commercial banks and companies within the Russian Federation and in the world. Thomson Reuters decisions are deeply integrated in the system

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Fig. 42.2 Example of tax function optimization program—Thomson Reuters ONESOURCE transfer pricing

of risk analysis and preparation of the different reports and tax administration. Among the Thomson Reuters users are the Federal Tax Service of the Russian Federation, Ministry of Finance of the Russian Federation, Committee of the Government Revenues of the Republic of the Kazakhstan, Ministry of Taxes of the Azerbaijan Republic, Internal Revenue Service of the USA, Her Majesties Revenue and Customs of the United Kingdom, Rosneft, Gazprom, Gazprombank, LUKOIL, Russian Railway Systems, Norilsk nickel, Surgutneftegas, Rosatom, System corporation, Sberbank, VTB, and Openbank; • KPMG: largest consulting firm, the member of Big 4; • Government bodies and businesses more often face with the questions of effective tax administration and organization of control over transactions of holding companies, leading their activity in several jurisdictions. In the whole world, taxation is going through cordial transformation due to the rapid integration of modern informational systems and technological decisions in accountancy, control and tax administration processes.

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42.2.2 Russian Federation. Training of Specialists in the Field of Taxation Training of specialists in the Russian Federation is carried out by the largest universities of the country, such as: • Financial University under the Government of the Russian Federation; • Russian Academy of national economy and public administration under the President of the Russian Federation; • National University “Higher school of Economics”; • Plekhanov Russian University of Economics, and many others not so great. Thus, in the Russian Federation in particular, a situation formed when hundreds of tax specialists are prepared and perfectly understand legislation in action and have wide practical experience, but, however, are weakly oriented in the sphere of modern technologies and decisions. Lots of IT specialists are prepared who orient well in informational systems and decisions, but do not possess experience and understanding of taxation processes. This fact was defined as “gap between tax industry and technology.” Mentioned tendencies sourced as a base for active development of direction of preparation of specialists named taxologists which obtain knowledge and competences on the edge of taxation and IT. This category of tax specialists is just starting to form, and there is a lack of them. First, programs of preparation of these specialists in the USA, Australia, and Europe within universities are just starting to form. The methodological support of the course by now is sourced by the Thomson Reuters programs Checkpoint World and ONESOURCE Transfer Pricing. Students of the course teach not only the theory, but also practical work with modern IT decisions for taxation. In Russia, the national university “Higher School of Economics,” Plekhanov Russian University of Economics started preparation of such courses.

42.3 Main Results 42.3.1 The Necessity of the Discipline “Taxology” in a Modern Smart University. The Function of the Taxologist Within Modern Company Knowledge of the modern approaches to the formation of a tax function within an enterprise and taxation technologies is necessary for the evaluative formation of the course in any university tending to correspond to the name of smart. Therefore, currently in the Russian Federation, more and more attention is paid to the training of specialists in the field of taxation.

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Let us describe the features of terminology in the field of training tax specialists. The term “taxologist” was first introduced by the Thomson Reuters Corporation in 2014 and concerned a preparation of tax specialist, who, in spite of knowledge in the sphere of taxation, possesses knowledge and skills in the sphere of work with advanced automated programs of tax reporting. Tax technician and taxologist are two different types of specialists. A tax technician can be a specialist who has an experience in the sphere of informational technologies, has gained taxation skills in a course of introduction of the ERP system [3], and IT decision for correspondence with tax legislation or tax system. This also can be any tax specialist who gained IT skills in the course of integration of the mentioned systems. Taxologist is a specialist in taxation with complex knowledge and skills in modern technologies (software of leading specialists), uses them for maximization of the effectiveness of tax functions, and can propose more complex and innovative approach to business [3]. Taxologist is responsible for integration of IT projects in the field of taxation, introduction of the ERP system, and management of tax technologies and projects. Taxologists also act as the architects of decisions of tax function of the enterprise in the sphere of creation of tax function of the enterprise, propose general visualization of business processes, and control on the level of separate aspects of integration of IT tax decisions. The work of taxologist is often carried out with tax technicians on mutual bases, including independent consultants, which all work under taxologist’s management. We can find examples when integration of IT technologies in the sphere of taxation is delegated on outsource bases to the provider of IT technologies. However, in this case, companies often faced with additional time and labor costs on interaction between tax service and the representatives of the provider of services (Fig. 42.3).

42.3.2 Mathematical Model of the Course “Taxology” The program proposed in Sect. 42.3.2 and its block diagram of specialist training must be accompanied by the following mathematical disciplines: • • • • • • •

higher algebra and mathematical analysis, discrete mathematics, operations research, probability theory and mathematical statistics, economic statistics, econometrics, basics of IT technology.

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Fig. 42.3 Example of preparation of two kinds of the reports (internal and external) CBC report with the use of ONESOURCE transfer pricing of Thomson Reuters

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B

Fig. 42.4 Fractal methodic of the course “Taxology”

A

D

C

E

The theoretical unit (general theory of taxation) should be integrated with mathematical disciplines, the teaching of which requires emphasis on the application of these disciplines in the field of taxation [4]. The main question in teaching every discipline is the question about the structure of the course one should master. So, because of scatter of needed disciplines, we propose to use fractal pedagogy, and more precise, fractal methodic, in teaching the taxology course [5–8]. The essence of such approach is in the fact that fractal methodic can provide the optimal structure of the course and the possibility to detail every part of the course that one needs. Let us construct a model of the system of knowledge necessary for the specialization of taxology in the form of an algebraic quasi-fractal system [8]. Let us distribute the various areas of the knowledge system necessary for the specialization of taxology according to the levels of quasi-fractal. The simplest structure of such distribution can be as presented in Fig. 42.4. Each vertex of the triangle corresponds to one of the following blocks of discipline: taxation block, IT block, and mathematics block. So, such structure of the course provides the teacher with the opportunity to manage the depth and width of the studied knowledge system.

42.3.3 Perspectives of Development of the Direction of Preparation of Tax Specialists “Taxology” The tax system, which was designed for an era of trade in physical goods, sharply evolves due to the rise of digital technology, new business models, and e-commerce. We assume that along with the digitalization of the economy in the course of approximately nearest 10 years, taxology will become one of the most demanded specialties in the sphere of taxation not only in the whole world, but also in the Russian Federation. In the Russian Federation, this sphere is not yet mastered by any educational institution and by now represents free and perspective niche. In present time, Thomson Reuters Corporation, together with the leading universities of the Russian Federation, works out questions on cooperation in preparation

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of taxologists in the frames of long-term program. The program will incorporate following blocks: • theoretical block (general theory of taxation); • examination of the work with Thomson Reuters programs Checkpoint World and ONESOURCE Transfer Pricing; • solving of practical tasks on the bases of the mentioned programs, and • digital control over the knowledge of the students. Possibility of creation of digital rank system of estimation of the knowledge of the students on the subject with the use of 100-point mark and separation of this mark on points for creativity, theoretical knowledge (estimation of the growth of the qualification of student). Also, there are plans to include master classes on taxology in the programs of preparation of tax and finance specialists of the School of Finance of the Russian National Research University “Higher School of Economics” in October 2020.

42.4 Conclusion. Next Steps Conclusion. The proposed taxology course is of great importance both for smart e-learning and for the economics of a smart university, since it is based on a new methodology for the comprehensive training of specialists. Next steps. We plan the following next steps in this project: 1. a development of a detailed work program for the theoretical course taxology, containing plans for practical exercises conducted in the laboratory classes of Thomson Reuters, and 2. regular monitoring of students’ performance, and the development on this basis of student rating.

References 1. Maciaszek, L., Marciniak,K.: Knowledge management as foundation of smart university. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Proceedings of the 2013 Federated Conference on Computer Science and Information Systems 2013, pp. 1255–1260. IEEE. ISBN 978-1-46734471-5 (2013) 2. TAXOLOGY Company: https://taxology.ru/eng/teamnews2009/19 3. Enterprise Resource Planning: https://tax.thomsonreuters.com.au/taxologist/ 4. Serdyukova, N.: Optimization of Tax System of Russia, Parts I and II. Budget and Treasury Academy, Rostov State Economic University (2002) (in Russian) 5. Warnecke, H.: Revolution in der Unternehmerkultur: Fraktale Unternehmen. Springer, German Edition (1999) 6. Madzhuga, A., Sabekia, R., Sinitsyna, I., Salimova, R., Sadaeva, I.: Fractal pedagogy: theoretical and methodological preconditions of formation and development. Prof. Educ. Russ. Abroad 2(22), 71–80 (2016)

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7. Binsztok, A., Leja, K.: University as a fractal organization of knowledge. In: Annual Conference on Higher Education Management and Development in Central, Southern and Eastern Europe, 26–28 Nov (2006) 8. Serdyukov, V., Serdyukova, N.: Quasi-fractal model of the semantic knowledge network as the basis for the formation of a pedagogical test. In: Proceedings of the International Conference on the Development of Education in Eurasia ICDEE 2019. https://www.atlantis-press.com/ proceedings/icdee-19/articles (2019)

Chapter 43

Quality Assessment of Modular Educational Resources for Smart Education System Yana S. Mitrofanova, Olga A. Filippova, Svetlana A. Gudkova, and Elena V. Ivanova Abstract Modular educational resource is defined as an autonomous set of studying and training materials that consist of information, practical, and controlling components for the discipline and created by teaching staff according to the thematic elements of subjects claimed to the professional curriculums. Modular educational resources are known as being complete interactive multimedia products aimed at solving and dealing with educational issues. The unified information model for metadata based on the Learning Object Metadata (LOM) standard implementation provides an effective usage of different electronic educational resources in its full compliance with the modern federal program’s requirements targeted the educational environment for digitalization. The problem of the study is to represent the existing expert methods chosen as the basic ones for modular resources’ quality assessment. The methods of modular educational resources and the algorithm of its implementation, as well as a set of assessing quality indicators are revealed. The suggested methods have been tested on the Togliatti State University’s sites and proved their reliability and effectiveness.

43.1 Introduction Educational standards in the Russian Federation focused mainly on setting up both the level of graduate training and assessment of an individual learner’s educational achievements. The federal standards provide the guidelines for the development of the education system by revealing the requirements for employee’s hard skills and soft skills expected by the businesses and society. Thus, Federal State Educational Standard serves as the basis for analysis and assessment of both regional education systems’ tasks and individual students’ achievements. Y. S. Mitrofanova (B) · O. A. Filippova · S. A. Gudkova Togliatti State University, Togliatti, Russia e-mail: [email protected] E. V. Ivanova I. N. Ulyanov Chuvash State University, Cheboksary, Russia © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_43

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New goals of education, its new content, and teaching methods require fundamentally different approaches to the problem of monitoring and evaluation. The scientific ideas of classic methodological school represented by Sheldon et al. [1] revealed the holistic phenomenon of the educational process. It means that the goals, the content of education, teaching methods, control, and learning performance are considered as being the interrelated and integrated features. That could mean that change of some links in the educational process leads to its other components changing. Nowadays, due to globalization, the requirement for the learning process integrity and its components has expanded significantly because of educational content’s possibility of being chosen from Internet resources [2–5]. Nowadays, higher education institutions are allowed to design their own educational modules corresponding to the both state and regional business requirements and those ones assessed as being successful are to be included in the Unified Educational Portal of the country. In this regard, the approach of N. A. Serdyukova and V. I. Serdyukov to the designing and development of intellectual rating system for world universities [6, pp. 70–71] representing the stability of international rating systems could be considered as relevant. The set of parameters allowing to link qualitative and quantitative characteristics of rating systems has been designed. According to the World University Rankings and smart systems, all the assessing indicators are divided into the following groups: 1. 2. 3. 4. 5.

Teaching and learning environment. Research-volume, income, reputation. Citation, influence, authority. Income due to performance and production activities: innovations. International image.

The first group considers the learning environment designing (30%). The second group assesses the level of research activities and the planned income due to the activity (30%). The third group assesses the authority of the university according to the citations’ volume of the higher school’s employees and professors (30%). The other two groups are given only 10% and they allow monitoring and estimating the university’s performance at educational market. According to the study, this is the first group of indicators that could be considered as the motor for the other categories’ development due to the fact that University educational environment supported by the set of qualitative modular educational resources affects the total university rating in both the international scientific and educational areas.

43.2 Modern Approaches to Smart Educational Environment The necessity for a qualitative approach to the smart educational environment design is represented in studies of many Russian and foreign researchers [7–10].

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The results of the annually held international conference (KES) highlighted the growing demand for international e-learning design and its further development based on massive open online courses (MOOCs) and smart infrastructure [11–14]. Modern education emphasizes the necessity for designing, development, and implementation of open educational modular multimedia system (MOOCs) into the higher school system. It requires a new system architecture design and development by unifying the structure of electronic educational products and developing the unified software environment. It should be noted that the content and educational technologies are to be aimed at advanced education where the level of graduates’ knowledge and the level of their skills development goes ahead and meets the society and businesses demands.

43.2.1 Modular Educational Structures Figure 43.1 shows the structure of the smart environment based on modular educational resources implementation. The aggregate content is divided into modules corresponding to thematic elements and components of the learning process. At the same time, each module may have an analogue or a prototype which differs due to its content elements, methodology, and teaching technology. According to the digitalization standards, the requirements for both the software and hardware tools supporting the educational process are increasing very fast. A lot of researches define the “no limits for content” approach as the leading for the new generation of modular educational environment. There are some other requirements for the modular educational structures including the following: the learning content of an e-learning module can be as complex as the teacher wants according to the educational targets and learners’ skills; the possibility of network distribution; electronic training modules are to be open for changes, additions, and complete modernization; software solutions in the new generation EER modules are to be based on interpreted languages and the possibility of personally-oriented training is to be implemented through the usage of existing ELM variants or by the modules’ upgrading. Fig. 43.1 Structure of modular educational resource

Training unit (Mij) for the thematic element i - thematic elements j - variatives

I11

P13

C12

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43.2.2 Teaching by Using Modular Electronic Resources in Smart Environment Digitalization and Internet technologies at higher schools provide an opportunity for unlimited and low cost replication of educational information through its fast and targeted delivery. The learning process becomes interactive; the demand for both the students’ self-studying and the learning process’ intensity has dramatically increased. Thus, the following challenges for employees in smart education system can be identified: • environment for the individual student’s education and development through the individual learner’s pathway designing; • cooperation among students and coaching staff including expert communities and social business networks for achieving an optimal level of differentiation and individualization during the learning process; • designing the modular educational content in collaboration and co-authorship with all the participants of educational activities. The quality education control is to be shifted toward self-control and selfassessment conducted by students, teachers, and businesses in integration and collaboration. Thus, modern education is considered to become publicly discussed and open to external assessment for its further development.

43.3 Methodology of Educational Resources Quality Assessment Introduction of information technologies in the educational process is known as “being trendy” for modern education. The teachers in smart universities are to use modular electronic educational resources in classrooms, and students use them for self-studying at home following the “flipped classes” trend. In this regard, the issue of evaluating the quality of electronic educational resources used in education is considered to be a relevant one. E-learning educational resources (EERs) are products created due to the educational and information technologies integration and the EER’s quality is assessed by two groups of indicators: 1. traditional approach, which is mandatory for the whole educational process. Such teaching methods include a differentiated approach to students, mathematical and statistical methods of the learning process modeling, and forecasting its results. 2. innovative approach characterizing the EER’s quality in terms of its special capabilities. For example, by means of electronic textbooks, innovative teaching methods, intellectual analysis of data for quality assessment of the training process, the use of expert systems, the use of LMS, etc.

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The basic approach to EER quality assessment implies that a worthy electronic resource should meet both traditional and innovative indicators. In terms of traditional criteria, an e-learning resource should include the following features: fully comply with the adopted educational program; designing the educational content that meets modern scientific knowledge and ideas; be adequate to modern methodology in terms of content presentation (in particular, the principle of “simple to complex”), general principles of presentation, etc. There are three basic types of electronic learning modules (ELMs) corresponding to each thematic element of a subject: I-type module for obtaining information; P-type module for performing practical tasks; C-control and assessment module. The I-part is represented and assessed according to the text content, animations, video fragments, interactive models, and tasks. The P and C-types module are assessed on the base of practical tasks and measuring models. Quality assessment of e-learning resources by traditional criteria is a well-established process. As for innovative indicators, they are considered to be the most significant object for consideration.

43.3.1 Modular Educational Resources’ Assessment by Innovative Quality Indicator According to the innovative quality assessment indicator, both the EERs and ELMs should correspond the following requirements: 1. Contain all components of the parts for the educational process. In other words, teachers and students should be able to obtain the necessary information, practice it, and to control the achievements. It is obvious that traditional educational resources rarely cope with all the above-mentioned three tasks. 2. Be interactive, i.e., include active forms of learning and thus provide more opportunities for learners. The necessary level of e-learning resources innovative quality is achieved by using special pedagogical tools. The main one among them is interactivity process, i.e., the ability to receive a quick and relevant feedback on all your actions. Another important pedagogical tool is multimedia including the educational content as audio and video components. This is an irreplaceable method when it is necessary to provide visualization for some scientific topics. Modern qualitative e-learning resources include the simulation technology that is modeling various processes and further visualization of these processes. All the above-mentioned facts require high-quality electronic educational resources and special methods for their assessment.

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43.3.2 Methodology of Modular Resources Quality Assessment The quality assessment methodology for modular educational resources consists of a sequence of steps. Since smart education defines certain requirements to educational resources, the method can be presented as a set of stages: Stage 1: Evaluation of content modularity, which represents structured educational resources including the necessary knowledge and activity elements that form the required competencies in the relevant training direction; Stage 2: Evaluation of module’s structure for learning, designing, and improving the required knowledge and competences; Stage 3: Assessment of the modular educational resources and their focus on the certain individual characteristics designing and development because it is important for success of educational activities; Stage 4: Evaluation of the module’s metadescription completeness which is necessary for its automated search in appropriate repositories; Step 5: Evaluation of an intelligent automated module search system and its availability based on their metadescriptions; Stage 6: Evaluation of the possibility of autonomous use of the module in any sequence, its transformation, the use of individual elements, etc., to form a personal trajectory for learning. Stage 7: Analysis of the obtained evaluation characteristics and the recommendations’ designing for the learner to master the personal learning path. In the study, expert methods are used to collect and analyze the evaluation characteristics.

43.3.3 Requirements for Quality of Modular Educational Resources Table 43.1 shows a fragment of quality approach for the modular educational resources assessment on the basis of expert method and indicators (according to idea of [9]).

43.3.4 Content Quality Assurance Requirements The educational value of an e-learning module for smart universities consists of its innovative qualities assessment and the content evaluation according to the traditional indicators including compliance with modern scientific ideas of the subject area; the

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Table 43.1 Range of assessed indicators (fragment) Indicator

Expert 1

Expert 2

…

Expert n

Average score

Providing all components of educational activities, taking into account individual preferences

O11

O12

…

O1n

O1 = k=1n k , where O1 –assessment of the first indicator; Expk– Expert assessment by expert k

Implementation of active forms of training

O21

O22

…

O2n

O2 = k=1n k , where O2 –assessment of the second indicator

…

…

…

…

…

n

n

Exp

Exp

educational content compliance with the State Educational Standard; compliance with the basic values of society. Additional requirements are represented below. Requirement 1. Necessity and sufficiency. The content of an educational elearning module (ELM) should be adequate, relevant, and complete. The content qualities should correspond to interactivity standards and multimedia usage as a tool for active learning methods by increasing the efficiency of educational activity sufficiently. Requirement 2. The adequacy of the resulting data to the unified requirements. The unified requirements define six elements of the SCORM RTE data model, which in the course of ELM playback receive some values reflecting the results of the student’s work with the given module. Algorithms for the resulting data calculation should be constructed in such a way that it is possible to make an objective picture of quantitative and qualitative evaluations of the student’s actions. Requirement 3. Variability of modules is achieved due to differences in content (different learning objects/processes, alternative scientific views), different ways of content’s presenting, and differences in technological solutions. Variants may differ from each other by the content’s presentation (e.g., the ratio of postulates to evidence); the methodology (e.g., due to a different set of previous knowledge); the nature of the learning activity (e.g., solving a computational problem or an experiment, test or control exercise on a simulator); the technology of presentation of learning materials (e.g., text or audiovisual series); the availability of special opportunities (e.g., for those who have poor hearing/vision). Anyway, the content’s originality is to be 70% at least.

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43.4 Mathematical Support for the Modular Educational Resources’ Quality Assessment To monitor and evaluate the modular educational resources’ quality in the smart education system, the following expert assessment methods are suggested to be used:

43.4.1 The Generalized Expert Evaluation Simulation While considering the expert team assessing (X exp ) and evaluating any modular educational resource, then x j —represents the assessment of the j expert, j = 1 . . . m = 1, m—the amount of experts (43.1): m j=1

X exp =

xj

m

,

(43.1)

Sometimes, it is necessary to determine whether a factor (object) is important (essential) in terms of a criterion. In this case, it is necessary to determine the weight of each factor. One of the methods for determining weights can be represented as the follows X i j —estimate of the factor i given by the jth expert, i = 1 . . . n, j = 1 . . . m, n—number of compared objects, m—number of experts. Then, the weight of the ith object, calculated by the estimations of all the experts (wi ), is equal to (43.2): m wi =

j=1

wi j

m

,

(43.2)

where wij represents the weight of the ith object calculated by the jth expert is equal: xi j wi j = n i=1

xi j

,

(43.3)

where j = 1, m, i = 1, n. In the case of several experts’ participation in a survey, differences in their assessments are inevitable, but the magnitude of the difference is important. A group assessment can only be considered sufficiently reliable if there is a good consistency in the individual experts’ responses. Statistical characteristics are used to analyze the scatter and consistency of assessments. The average square deviation is calculated by the known formula (43.4):

σ =

   m (x j ex p − x)2   j=1 m−1

,

(43.4)

43 Quality Assessment of Modular Educational Resources …

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where x j is the evaluation given by the j expert; m is the number of experts. Coefficient of variation (V ) is usually expressed as a percentage: V =

σ xex p

· 100% ,

(43.5)

Specific approaches to consistency checking are used when evaluating objects using the ranking method. In this case, the result of the Expert Advisor’s work is ranking, which is a sequence of ranks (for example, for the Expert Advisor j): x1 j , x2 j , . . . , xn j . Consistency between the rankings of two Expert Advisors can be determined by using the Spearman’s Rank Correlation Coefficient: ρ =1−

6

n 6 i=1 (xi j − xik )2 d i2 = 1 − , n(n 2 − 1) n(n 2 − 1)

n

i=1

(43.6)

where xi, j represents the rank given to the i-object by j expert; xik -means the rank given to the i-object by k expert; di -means the difference between the ranks given to i-object.

43.4.2 Requirements for Expert Evaluation Simulation The consistency assessment for experts’ views is considered to be the most important requirement. Then, it is necessary to determine the consistency in the rankings of a large number of experts; the total rank correlation coefficient for the group consisting of m experts is calculated according to the Kendall Concordance Factor formula: W =

12S , − n)

m 2 (n 3

(43.7)

where m number of experts in a team, n number of factors, S the sum of squares of rank differences (deviations from the average). ⎞2 ⎛ n m   1 ⎝ S= xi j − m(n + 1)⎠ 2 i=1 j=1

(43.8)

It should be noted that the subtracted elements in brackets represent the average sum of ranks received by i objects from experts. The W-factor changes in the range from 0 to 1. Its parity to one means that all the experts have assigned the same ranks to the objects.

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43.5 Results 43.5.1 Quality Assessment of Modular Educational Environment by Expert Group On the basis of the suggested and considered above mathematical apparatus, an expert assessment has been made and the following targeted groups have been assessed: 1. modular educational resources, their availability at smart university for all studied sciences in «Rosdistant» system; 2. quality of the modular educational environment and its compliance to the international educational environment; 3. ranking of the tested higher education institution at the international rating scale. Taking as a basis, the conclusions about rating categories represented by Serdyukova and Serdyukov [9, pp. 70–71], the expert estimation of the university educational environment conformity according to the first group of assessment indicators rating has been carried out (Fig. 43.2). The estimation of conformity has shown that the university still should work over all estimated categories for completely corresponding to the world rating. Ranking of the received estimations (K-calculated factor) on level of conformity (high (0.75 ≤ K ≤ 1.0), average (0.50 ≤ K ≤ 0.74), and low (K < 0.5) has shown that only the first index (0.77) gets to a zone of high values that allows to judge about a favorable infrastructure for employees of scientific higher education institutions. Two indexes (the third one (0.31) and the fourth one (0.38) get to a low values zone and demand from the university’s management much work to improve the indicators. The second (0.71) and fifth indicators (0.73) fall into the satisfactory values’ zone. Category I "Teaching and learning environment' Standard Indicator 30

Togliatti State University

25 20

15

15 10 5 0

11,6

4,5 3,3

6 2,25 0,7

2,3

Fig. 43.2 Expert evaluation of the educational environment quality

2,25 1,6

43 Quality Assessment of Modular Educational Resources …

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According to the assessed indicators analysis, there is a low quality level for some indicators. Nevertheless, the assessment shows the university’s steps for the development and dealing with the threats to meet the world requirements for smart universities.

43.5.2 Quality Assurance Assessment of Modular Educational Resources The quality assessment of modular educational resources has been carried out by the experts from the tested university. The content of the intellectual technologies represented by the modular resources has been assessed accordingly to their application and implementation into the educational process. The levels of intellectual infrastructure implementation and the weight coefficients of each component influence have been taken into account. To assess and consider the university’s intellectual infrastructure development, the following formula is used (43.9). n  k   βi j × si j

S=

j=1 i=1

n

,

(43.9)

The following results have been received: the use of intellectual platforms 0.764; the use of intellectual technologies 0.659; the use of intellectual knowledge management systems 0.682; teachers’ accessibility to use intellectual technologies 0.845; the use of mobile devices in the learning process 0.526; and the use of e-learning tools 0.573. Thus, it can be concluded that “smart” infrastructure of modular educational resources at the experimental site can be considered as satisfactory ones (Fig. 43.3).

Using e-learning tools

100% 80%

0,573

Using mobile devices in training

0,526 0,845

60%

0,682

40%

0,659

20%

0,764

Availability of teachers (faculty) using intellectual technologies Using intelligent knowledge management systems Use of intellectual technology Using intelligent platforms

0% 2018-2019

Fig. 43.3 Evaluation of the modular educational resources quality by intellectual technologies

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All the above-mentioned quality assessment models have been designed and developed for the competitive performance of smart university system at the international educational environment. Having been tested at the university, they are considered as being relevant and efficient for further smart university’s development.

43.6 Conclusions and Next Steps Conclusions. The suggested mathematical support for the modular educational resources evaluation for smart education system has proved that the expert methods’ implementation in educational systems provides reasonable conclusions revealing the university’s development assessment and the obtained results can be used in the following directions: 1. The university’s rating assessment in the system of international educational resources. The assessment makes it possible to define existing problems at the educational electronic modular and their compliance with the existing resources at the international level. 2. The compliance of the current smart university infrastructure with international standards can be assessed and monitored due to the suggested and tested simulations. 3. Methods of designing modular educational resources and the algorithm for their implementation as well as complex quality indicators have been revealed. Next steps. The next steps in this project deal with: 1. A design and development of an expert system. This system is needed for a faster and more effective preliminary forecasting of possible educational results and corrective actions for both (a) the university’s employees, and (b) students during their educational activities. 2. New evaluation indicators assessing both the efficiency and reliability of new modular electronic resources are to be designed and implemented at the educational and managerial process for smart university.

References 1. Sheldon, K.M., Osin, E.N., Gordeeva, T.O., Suchkov, D.D., Sychev, O.A.: Evaluating the dimensionality of self-determination theory’s relative autonomy continuum. Pers. Soc. Psychol. Bull. 43(9), 1215–1238 (2017) 2. Kinnebrew, J.S., Loretz, K.M., Biswas, G.: A contextualized, differential sequence mining method to derive students’ learning behavior patterns. JEDM-J. Educ. Data Min. 5(1), 190–219 (2013) 3. Mallavarapu, A., Lyons, L., Shelley, T., Slattery, B.: Developing computational methods to measure and track learners’ spatial reasoning in an open-ended simulation. JEDM-J. Educ. Data Min. 7(2), 49–82 (2015)

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4. Miller, L.D., Soh, L.-K., Samal, A., Kupzyk, K., Nugent, G.: A comparison of educational statistics and data mining approaches to identify characteristics that impact online learning. JEDM-J. Educ. Data Min. 7(3), 117–150 (2015) 5. Agrawal, R., Gollapudi, S., Kannan, A., Kenthapadi, K.: Study navigator: an algorithmically generated aid for learning from electronic textbooks. JEDM-J. Educ. Data Min. 6(1), 53–75 (2014) 6. Serdyukova, N., Serdyukov, V.: Algebraic Formalization of Smart Systems. Theory and Practice. Springer Nature, Switzerland (2018) 7. Bates, A.W.: Teaching in a Digital Age: Guidelines for Designing Teaching and Learning. Tony Bates Associates (2015) 8. Mitrofanova, Y.S., Sherstobitova, A.A., Filippova, O.A.: Modeling the assessment of definition of a smart university infrastructure development level. (2019). https://doi.org/10.1007/978-98113-8260-4_50. Retrieved from www.scopus.com 9. Serdyukova, N.A., Serdyukov, V.I., Uskov, A.V., Slepov, V.A, Heinemann, C.: Algebraic formalization of sustainability ranking systems for evaluating university activities: theory and practice. In: SEEL2017. Springer, Cham (2017) 10. Mitrofanova, Y.S., Sherstobitova, A.A., Filippova, O.A.: Modeling smart learning processes based on educational data mining tools. (2019). doi: https://doi.org/10.1007/978-981-13-82604_49. Retrieved from www.scopus.com 11. Uskov, V.L., Bakken, J.P., Howlett, R.J., Jain, L.C. (eds.): Smart Universities: Concepts, Systems, and Technologies, 421 p. Springer, Cham (2018) 12. Uskov, V.L., et al.: Smart university taxonomy: features, components, systems. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning 2016, pp. 3–14. Springer, Cham (2016) 13. Burlea, A.S., Burdescu, D.D.: An integrative approach of e-Learning: from consumer to prosumer. In: Smart Education and e-Learning 2016. Smart Innovation, Systems and Technology, vol. 59, pp. 269–279. Springer International Publishing, Switzerland (2016) 14. Uskov, V.L., Bakken, J.P., et al.: Learning analytics based smart pedagogy: student feedback. In: Uskov, V., Howlett, R., Jain, L. (eds.) Smart Education and e-Learning 2018. Smart Innovation, Systems and Technology. Springer, Cham (in print, this volume) (2018)

Chapter 44

Soft Skills Simulation and Assessment: Qualimetric Approach for Smart University Svetlana A. Gudkova, Tatiana S. Yakusheva, Anna A. Sherstobitova, and Valentina I. Burenina Abstract The article represents the application of the qualimetric approach to the diagnostics of the soft skills level which is considered to be necessary for both the university graduates and the regional development according to the “societysmart university-enterprise” triad. Qualimetric approach is represented by Taguchi methods implementing signal-to-noise ratio for assessment noise factors that reduce the quality level of the research object. For the practical application of Taguchi’s ideas, the methods of mathematical statistics and probability theory are used. The expert methods are also used to monitor and assess the level of the graduates’ soft skills. The expertise was proposed and tested on the base of some departments in Togliatti State University. The key concepts of ABC-analysis were used and revealed. The assessment of the targeted soft skills for technical faculties was conducted. The additional models suggested by the authors determine such characteristics as “the coefficient of unrealized learning opportunities” and “the cost of achieving a given level of quality of education.” The suggested simulations allow supervisors at smart university to clearly indicate the required didactic units for the educational purposes. The considered models and the achieved results allow authors to determine the focus of management and educational influence on the competences and soft skills training that level turned out to be insufficient. The educational process in a smart university can be adjusted and improved due to the above-mentioned methods.

44.1 Introduction Nowadays, there is a necessity for both the graduates and employees who are able to be flexible and adapt to the new conditions of a changing world and professional environment very quickly. The competent staff has to be ready for changes by relevant S. A. Gudkova (B) · T. S. Yakusheva · A. A. Sherstobitova Togliatti State University, Togliatti, Russia e-mail: [email protected] V. I. Burenina Moscow State Technical University named after N.E. Bauman, Moscow, Russia © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_44

527

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implementation of the both hard and soft skills. In this regard, the soft skills modeling and training students according to the suggested model can be considered as the long-term target for smart universities. According to the theoretical base, soft skills are understood as a number of nonspecific but essential for career development skills including team building, communication at international business environment, cognitive flexibility data processing, and etc. These skills are considered to be a “must have” element for person’s successful and effective participation in the work process and career development. They are closely related to personality, management, and communication skills. Analysis of the current state of research in the field of smart learning has shown that one of the central issues in the modern system of higher education is the modeling and training the common scientific outlook among graduates of universities on the basis of the implementation of metaproject potential in the conditions of rapidly changing market needs and fierce competition [1–4]. The authors of works [5] consider soft skills as an integration of both the social and communicative skills that allows us to work effectively in a smart environment and solve professional problems. Nowadays, literacy and mastery of a foreign language are considered to be as a crucial component of professionalism in the context of globalization and international discourse. However, the process of learning foreign languages becomes a means which provides both the skills of intercultural communication and soft skills that include teamwork, emotional intelligence, tolerance, and management skills including planning, analysis, and control. All the above-mentioned categories contribute to further professional socialization and career development of graduates and employees.

44.2 Theoretical Research Base and Literature Review The assessment of the existing competences and skills is an important direction in the modern educational systems. This issue is studied and described in the works of a lot of scientists [6]. To test and assess the level of skills and competences formation, a qualimetric approach focused on measurement can be studied and suggested. The term “qualimetry” (from Latin quails-quality and from ancient Greek metrioto measure) means a relatively new scientific discipline that studies the methodology and problems of dynamic development and complex quantitative assessments of quality for any object, subject, phenomena, and processes [7]. Qualimetric approach is based on expert methods: assessment, methods of self-assessment, method of group expert assessments, as well as the apparatus of mathematical statistics [8, 9]. A lot of scientists define that expert evaluation and assessment are carried out according to a certain algorithm, which includes the following stages: preparatory part; main part, related to the activities of the working, expert, and technical groups; final part, aimed to discussing the results of the examination and assessment ranking and making a decision.

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44.2.1 The Soft Skills Competence Model for Smart University According to the authors’ idea, the way of performing the work related to the modeling and formation of skills will be called as a technology. Technology (x i ) is defined by three components: axiomatics of actions (methods), thesaurus of concepts (attributes), and events. Thus, any technology is represented by a tuple-structured data including the multitude of methods, the multitude of attributes, and the multitude of events > (44.1).   xi = Mix , Aix , Six

(44.1)

This three-component model is based on the object-oriented approach, which can be named as a basic feature of modern economy due to the development of informatization and digitalization of modern society. This approach uses the concept of classes. Classes are defined as a set of entities united by a certain structure and behavior. Another important indicator in the object-oriented approach is its inheritance, which allows teachers and test-developers in smart universities to create derivative classes (classes of heirs) based on all methods and elements of the base class (parent’s class). Thus, a lot of time is saved when the possibility of automated intellectual control is studied and then implemented, for example, when forming a database or knowledge bases for the future expert system, which is still under development. Figure 44.1 shows a fragment of a teacher’s and learner’s activities in this aspect.

Didactic Unit

Educational Program Teacher Executing the Educational Program

Educational Program Creating

Education Performer

Student

Staff

Fig. 44.1 Object-oriented approach to the implementation of soft skills training technology

530 Fig. 44.2 Fragment of the semantic concepts network

S. A. Gudkova et al. Task Solves

Employee

Defines has got

Uses

Soft skills

Creates Technology

The semantic approach to the development of knowledge bases and databases that allows lecturers evaluating the level of soft skills development has been studied and implemented.

44.2.2 Soft Skills Semantic Network Simulation The presented models are applicable in the system of life cycle management of career development training programs to form and maintain the necessary set of competencies through the processes of professional training, seminars, and workshops. According to the gist of the research, for some scientists, the theory should be preceded by the choice of the main concepts, the modeling of integrative connections between them, and the choice and clear formulation of axioms and the conclusion of additional concepts on the basis of the main ones [10]. The main concepts of soft skills theory include the following elements: a specialist or a graduate in the smart environment, technology, task, and skills. Thus, in dealing with the tasks “Participation in an international project” or “The International Audit,” the following competences for employees can be required and defined: Emotional intelligence and cognitive flexibility; time management; stress resistance; cooperation and teamwork; foreign language communication and social networking skills; tolerance; use of digital technology; and self-presentation. A fragment of the semantic network showing the relationship between the concepts used in the description is shown in Fig. 44.2. All the above-mentioned competences are studied, then trained, and assessed during the professional training and workshops.

44.3 The Qualimetric Approach to the Assessment Methods Qualimetric approach for educational and professional purposes is based on the qualimetric methods application. The authors consider the basis of the index qualimetry

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531

developed by Subetto [7] as the key note while developing training and workshops for assessment the graduates and employees skills. In order to increase the efficiency of educational process management in the evaluation system, along with traditional quantitative indicators in technologies, the following indicators are usually used: the coefficient of unrealized opportunities, the level of formed competences, and the quality indicator, calculated according to the laws of probability theory and mathematical statistics, as a result of testing and examinations. When considering the maximum quality level with all the highest scores, it is necessary to analyze the scatter of all scores in the quality objectives relative to the maximum score. This indicator reflects the variance (D(m)). D(m) =

n 

(a − xi )2 ∗ pi ,

(44.2)

i=1

where: D(m) represents dispersion; α—average value from the set of analyzed values; x i —the specific value to be analyzed under the number i; pi —probability of occurrence of the analyzed value x i . The mean square deviation (σ (α)) reveals the range of values (α) by the formula (44.3):   n  (a − xi )2 ∗ pi , σ (α) = 

(44.3)

i=1

These characteristics of the distribution of discrete random variables can be used to assess the learning outcomes of the developed test or examination systems. Tests are understood as a set of questions, the answers to which are evaluated in points, i.e., by discrete values. At the same time, k max is the largest number of points in the test, k min is the smallest. Mathematical expectation of k-test results is calculated by the formula (44.4): m=

k 

(kmax − i h) ∗ pk−i , where h =

i=0

kmax − kmin k

(44.4)

where pi is the statistical frequency of the wi of this result. Quadratic deviation of the obtained result from the maximum value of k max is based on the formula (44.5):   k  σ (kmax ) =  (i h)2 ∗ pk−i , i=0

(44.5)

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From Taguchi’s point of view, it determines the value of “noise” (dispersion) relative to the best result, and the mathematical expectation m is the value of “signal.” Signal-to-noise ratio will characterize the unrealized capabilities of the test subject in the range from k max to k min . The calculation of the unrealized quality indicator can be calculated by the formula (44.6): σ (kmax ) , m

γ (kmax , kmin ) =

(44.6)

For the convenience and the rationing procedure, the following formula can be applied (44.7): γ =

α · γ (kmax , kmin ), 0 ≤ γ  ≤ 1 1−α

(44.7)

min . Then, the coefficient reflecting the quality of the obtained knowledge where α = kkmax (or the coefficient of implementation of the possible quality of training) will be defined as r(α) (44.8).

r (α) = 1 − γ n , 0 ≤ r (α) ≤ 1,

(44.8)

where γ n —the normative value of the unrealized quality of education. Here, the normative value of the unrealized quality of education is usually equal to 5% of the margin of error at 95% of the quality level. The costs of achieving maximum efficiency in one process cycle (from r(α) to 1) can be calculated using the following formula (44.9):  Si,a = S 1 +

α 1−α

· γ (kmax , kmin ),

(44.9)

where S is the cost per unit of maximum quality of training per technological cycle. Then, the total cost of achieving a given level of maximum quality for m different elements formed during k technological cycles can be calculated by the formula (44.10). p Si,α (k, m)

=

m k   j=1 i=1

 Si 1 +

α γi, j (kmax , kmin ) , 1−α

(44.10)

where γ i,j is considered as the coefficient of unrealized learning opportunities for element i in the cycle j.

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44.4 The Weight Coefficients for Expert Assessment Methods Before any assessment, it is necessary to determine which of the proposed competencies are considered to be the most important and necessary for the current assessment. For this purpose, it is necessary to apply the ABC-analysis consisting of the weights indicators. This analysis is carried out with the participation of experts. Each expert makes assessment according to his or her experience and knowledge. The assessment of all qualities is conducted by three evaluation factors (ABCs) according to a 5-point scale: In this case: A defines how often this competence is used in the assessment of the general cultural competences level, including {1–2}—means that this competence is never used; {3–4}—the competence is rarely used; {5}—the competence is always used for assessment. B defines how much this competence influences the assessment of the general cultural competences level, including: {1–2}—weak impact on the overall assessment; {3–4}—average impact on the overall assessment; {5}—strong impact. C defines whether it is possible to deal without the assessed competence, including: {1–2}—means that it is impossible; {3–4}—means that it is temporarily possible; {5}—means that it always possible. To calculate the quality weight coefficients, the following formulae can be used: (44.11 and 44.12): P Q = A · B · C,

(44.11)

P Qi , λi =

P Qi

(44.12)

i

where A, B, C—expert opinion on the selected scale; λi weight coefficient of the whole group; PQ—assessment of group qualities [9]. Based on the obtained assessments, followed by the assessment of the whole group competences, the weight coefficient of each competence can be calculated (Table 44.1). Figure 44.3 represents the assessment of soft skills level for graduates studying the professional development integrated program “Production Management in

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Table 44.1 Weight coefficient calculation for soft skills in smart university λ

Competence

A

B

C

PQ

Creativity

5

5

1

25

0.136

Social networks

3

3

2

18

0.098

Team building

5

4

1

20

0.109

Decision making

5

5

1

25

0.136

Negotiating

5

5

1

25

0.136

Presentation and self presentation

3

3

1

9

0.049

Cognitive flexibility

3

4

1

12

0.065

Data processing

2

3

2

12

0.098

Communication in international environment

3

3

2

18

0.098

Project management

5

4

1

20

0.109

Total

39

39

13

184

105 100 95 90 85 80

A21

A20

A19

A18

A17

A16

A15

A14

A13

A12

A11

A9

A10

A8

A6

A7

A5

A4

A3

A2

A1

75

Fig. 44.3 Student’s assessment

automobile industry.” The team including twenty-one graduates has been tested and evaluated. It is a cross program designed by professors of Togliatti State University according to the targets and requirements of HR Department at Public Company “AvtoVAZ,” which is considered to be both the leader of Russian automotive industry and the major employer in Samara region. It can be seen that the quality of the students’ training is higher than 85%. Table 44.2 represents the results of the soft skills mathematical modeling according to the targeted and studied didactic units. Table 44.3 represents the results of the soft skills mathematical modeling according to the targeted and studied didactic units.

3 3

Executive attitude to work and responsibilities, demonstration of goal-setting skills Participation in public works, ability to engage in dialogue

Possesses a culture of thinking, ability to perceive information, analyze and summarize it, set goals and choose how to achieve them

1

3

5

2

Experts

Assessment criteria

Competencies

Table 44.2 Expert assessment of soft skills (the fragment)

3

3

3

5

5

4

5

5

5

3.8

4.2

Average rating

0.48

0.53

mij

4.01

PQi

44 Soft Skills Simulation and Assessment: Qualimetric … 535

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Table 44.3 Total assessment of soft skills

Competence

% of subject

Creativity

100

Social networks

95

Team building

100

Decision-making

97

Negotiating

100

Presentation and self presentation

100

Cognitive flexibility

100

Data processing

95

Communication in international environment Project management

100 92

5% 19%

52%

10/10

9/10

8/10

7/10

24%

Fig. 44.4 Soft skills training performance, %

Figure 44.4 represents the result of soft skills modeling and training in June 2019. Mathematical expectation, the coefficient of unrealized opportunities, and the quality indicator calculated on the basis of dependencies (44.4), (44.6), and (44.8) for this group, make up: m = 35.33, γ n = 0.193, and r = 0.807. The results show a high quality of soft skills training. Mathematical modeling of the assessment and monitoring the effectiveness of soft skills training programs for students and employees allow to obtain quantitative indicators. The suggested mathematical modeling allows supervisors to clearly indicate the required didactic units for the educational purposes. According to the assessment results, it can be seen that the student’s total group indicators give 39 points while targeted 50 points, that provides the supervisor information about mathematical deviations and fluctuations from the standard and makes both the tutors and students look for corrective steps for achieving the targeted competences.

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44.5 Conclusion and Further Prospects for the Practical Application of the Study The method of group expert assessment is considered to be as one of the effective methods for planning, modeling, and assessing the training programs for employees’ professional development. The study is relevant to the modern society and business requirements of the employee who is competent to work at International Professional environment in spite of the volatility, uncertainty, complexity, and ambiguity. The suggested qualimetric methods and expert systems can be used for both (a) the assessment and forecasting process, while modeling the educational environment, and (b) employees’ professional development at smart university. For the further study expert systems prediction and assessment the quality level of soft skills competencies is to be considered.

References 1. Uskov, V.L., Bakken, J.P., Howlett, R.J., Jain, L.C. (eds.): Smart Universities: Concepts, Systems, and Technologies, 421 p. Springer, Cham (2018). ISBN 978-3-319-59453-8. https://doi. org/10.1007/978-3-319-59454-5 2. Uskov, V.L., et al.: Smart university taxonomy: features, components, systems. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning 2016, pp. 3–14. Springer, Cham (2016) 3. Glukhova, L.V., Syrotyuk, S.D., Sherstobitova, A.A., Pavlova, S.V.: Smart university development evaluation models. In: Smart Innovation, Systems and Technologies, vol. 144, pp. 539–549 (2019) 4. Gudkova, S.A., Yakusheva, T.S., Sherstobitova, A.A., Burenina, V.I.: Modeling of scientific intercultural communication of the teaching staff at smart university. In: Smart Innovation, Systems and Technologies, vol. 144, pp. 551–560 (2019) 5. Burlea, A.S., Burdescu, D.D.: An integrative approach of e-Learning: from consumer to prosumer. In: Smart Education and e-Learning 2016. Smart Innovation, Systems and Technology, vol. 59, pp. 269–279. Springer International Publishing, Switzerland (2016). https://doi.org/ 10.1007/978-3-319-39690-3 6. Shikhnabieva, T., Beshenkov, S.: Intelligent system of training and control of knowledge, based on adaptive semantic models. In: Smart Education and e-Learning 2016, pp. 595–603. Springer International Publishing, Switzerland (2016) 7. Subetto, A.I.: Qualimetry: a small encyclopedia. Issue 1. St. Petersburg. IPC SZIU - Phil. RANEPA, 2015, 244 pc (2015) 8. Serdyukova, N.A., Serdyukov, V.I., Uskov, A.V., Slepov, V.A, Heinemann, C.: Algebraic formalization of sustainability ranking systems for evaluating university activities: theory and practice. In: SEEL2017. Springer, Cham (2017) 9. Glukhova, L.V.: One of the approaches of the index qualimetry to the evaluation of the quality of the information system of specialists training. In: Vestnik of the Kostroma State University named after N.A. Nekrasov, vol. 5, № 1, pp. 119–122 (2007) 10. Glukhova, L.V., Yarygin, O.N., Syrotyuk, S.D.: Qualimetric approach to assessment of the level of knowledge increment on the basis of Boolean algebra tool. Baltic Humanitarian J. 1(14), 158–161 (2016)

Chapter 45

Simulation for Evaluating the Feedback Effectiveness at e-Learning University System Olga A. Kuznetsova, Sabina S. Palferova, Svetlana A. Gudkova, and Oksana A. Evstafeva Abstract In the paper, the authors consider topical issues of the effectiveness evaluating for the existing smart environment at the university. The article focuses on the issues of its sufficiency to ensure the quality of the learning process. The models of risk assessment and risk management are considered. Feedback in e-learning is known as being very important for smart environment due to its influence on investment attractiveness of the university and quality of smart education. The authors use a well-known mathematical apparatus to substantiate the conclusions. The practical significance of the obtained results is in defining the factors that can decrease the risk of lost quality of education in e-learning and smart environment. The obtained results were tested at the university for students training. The issue of feedback in e-learning was considered on the base of statistical data from “Rosdistant” smart environment for students studying economics and management.

45.1 Introduction Nowadays, the university’s smart environment can be represented as a set of hightech tools ensuring the learning process according to the quality level and standards. The automated system where students receive assignments and monitor their level of leadership and participation in obtaining knowledge, a set of programs, tests, methodological recommendations, etc., is considered as the examples of these tools. One of the most important aspects of its purpose is the ability to monitor both the quality of the educational process and the quality of various learning methods and approaches [1].

O. A. Kuznetsova (B) · S. S. Palferova · S. A. Gudkova Togliatti State University, Togliatti, Russia e-mail: [email protected] O. A. Evstafeva Murmansk Arctic State University, Murmansk, Russia e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 V. L. Uskov et al. (eds.), Smart Education and e-Learning 2020, Smart Innovation, Systems and Technologies 188, https://doi.org/10.1007/978-981-15-5584-8_45

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While considering smart educational environment of Togliatti State University, two main technologies of education can be distinguished including both the traditional and distance-learning approaches. Each of the above-mentioned ways has its own disadvantages and advantages depending on the capabilities, skills, and preferences of the students. Therefore, distance-learning technology (e-learning) provides students a range of opportunities and advantages including the ability to choose an individual learning path, the ability to take tasks and tests regardless of the schedule, the ability to study the content without the need for classroom activities. Thus, the targeted knowledge is transferred due to the student’s own hard and soft skills.

45.1.1 Research Problem The research problem can be reprinted as studying the mathematical methods and models that can be used to identify and evaluate negative factors in the organization of e-learning educational process by using smart technologies. The effective management of the smart environment is impossible without an assessment of the consequences of adverse events for the organization. To ensure the competitiveness of the university, it is necessary to have an increasing inflow of students. Only in this case, the reliability of the existence of the team and the entire organization as a whole will be ensured. The preliminary analysis of risks of students’ dissatisfaction due to the low level of the quality of education at the introduction stage is considered to be especially important because it reveals the necessary corrective activity. For acceptance of such decision, various methods can be used. The degree of combination of these methods is determined by various circumstances, including those that characterize the professionalism of specialists. The analysis of the most frequently used methods of risk research has shown that the probabilistic and expert methods of assessment can be considered as the most relevant and valid ones. These methods are implemented in the study for the model of risk assessment of students’ dissatisfaction. It allows teachers and managers at smart university to assess the effectiveness for the chosen educational technology on the base of the targeted audience study.

45.1.2 Objectives of the Article and Research Methods Nowadays, there are many researchers studying the similar issues in different cases and directions [1, 2]. However, there are many issues, connected with the low level of quality in education, that are still controversial. Thus, the issues of whether the intensity of students’ visits to educational content should be assessed while defining the quality of educational process, or whether there is a demand for practical solutions to non-standard situations in e-learning are still discussed and studied.

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The aim of the work is to design a probabilistic model of the students’ dissatisfaction risk due to the low-level quality in smart education in order to define the lowest level of risk, and consequently the smallest possible damage to the higher school facilities. In these conditions, the concept of risk should be considered as more complicated one. It is an activity related to overcoming uncertainty in the situation of inevitable choice where it is possible to quantitatively and qualitatively assess the probability of the both the expected result achieving and failure or deviation from the goal. The following main elements that make up its content should be highlighted using the concept of “risk”: 1. the possibility of deviation from the intended purpose for which the chosen alternative was carried out; 2. the probability of achieving the desired result, and 3. the possibility of both the material, moral, and other losses associated with the implementation of the chosen alternative in conditions of uncertainty [3].

45.1.3 Conceptual Research Ideas The concept of the suggested issue is based on the ideas and conclusions represented by N. A. Serdyukova and V. I. Serdyukov in their studies [4]. The following rules can be considered as key points for designing the algorithm and achieving one of the strategic targets of any higher school facility. “Let’s highlight the peculiarities of risk beginnings: Breaking the links of the system. Breakthrough of minima of system elements functioning. Breakthrough in H of interrelations of high levels of hierarchy in the centers of processing of generalized information and development of solutions. Breakthrough of information channels between the blocks of the system or their overload, as it leads to difficulties in the exchange of information between the blocks of the system. Violation of the reliability of information used by the system. Disruption of the quality of executive bodies’ work and reduction of resources lead to weakening of management and, as a consequence, to distortion of the entire dynamic process in the system. Management of the system is possible on condition of reliable forecast of the situation development high level of external influences on the system”. Adhering to the goal, the mathematical model of probabilistic risk assessment by using different approaches to the weights of factors can be designed.

45.2 Quality of Education and Students’ Dissatisfaction: Mathematical Model of Risk Assessment The risk of students’ dissatisfaction with the quality of education is understood as the risk of the educational organization to lose consumers because of their negative

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reaction to the elements of the smart environment used for the learning process. The authors consider the risk acceptable if it does not exceed the previously planned level (γ = 0.05%). This value is justified by the mathematical statistics requiring reaching the 95% level of the results significance. Because of mathematical modeling for the learning process, the desired quality indicators are to be obtained. A product of perfect quality always reacts equally to the consumer’s influences (signals). However, if there are different reactions obtained in response to the same signal, then there is less than ideal quality [5]. With regard to smart education, there is a set of certain set of training conditions, psychological and pedagogical norms that should be followed and observed. If the planned level of training or targeted knowledge with the required set of personal characteristics and students’ skills is achieved, then such educational system is known as being the ideal one. The methods of mathematical simulation and experimental planning are to be used in order to minimize differences in obtaining final results and to increase the possibility of obtaining the targeted knowledge and guaranteed result for educational systems.

45.2.1 Mathematical Model for Risk Assessment To design a mathematical model for risk assessment, it is necessary to quantify the purpose of the study represented by a quantitatively defined parameter called the target function or response function. The main requirements to the parameters are the following: 1. the parameters are to be measurable and reveal the effectiveness for the object of study; 2. the parameters are to be quantitative, unambiguous, and statistically significant; 3. the parameters are to be simple and recognized by an understandable meaning. The main task for designing the smart educational environment is to determine the variety of conditions and criteria used by researchers for a choice. Different ways of the pedagogical system managing allowing to get a discernible state of the educational environment are to be considered. For this purpose, the risk factors should be revealed. Stochastic Approach for Risk Factors Identification. In this paper, the authors adhere to the following definition: a factor is a measured variable that at some point takes a certain value and corresponds to one of the possible ways of influencing the object of study [6]. To design a mathematical model of stochastic risk assessment, a number of risk factors that were assessed by consumers (students) of smart education because of sociological research and polling were defined. These include the following five risk factors: 1. dissatisfaction with working desktop and navigation in the Rosdistant environment;

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2. lack of direct communication with the teacher; 3. postponement of a teacher’s answering the student’s questions, which means that the immediate relevance of the question is lost; 4. discrepancy of lecture material of electronic content with theoretical questions of test tasks; 5. insufficient students skills for solving practical tasks bringing the significant time losses while solving test tasks. The identified five factors were additionally studied and systematized for defining the links between them. When formulating and defining the factors, the authors were guided by the following requirement: absence of correlation between any two factors and compatibility of the factors. The Response Function Simulation for Different Object States. Once the response function y and the set of factors {xi } determining the set of states ν of the object for the study have been determined, it is necessary to establish the correspondence between the set of values of factors and the values of the output parameter y: y = f (x1 , x2 , . . . , xn ),

(45.1)

where y is the output parameter; xi —factors, i = 1, 2, …, n. The final goal of the experiment is to determine a set of optimal values of factors and to study the factor space in the vicinity of this set. In accordance with the planned program, the intervals of factors variations and the number of values to be tested in the experiment are to be determined. The number of such values determines the number of levels for each factor. The most common case of planning at two levels, when the values corresponding to the upper and lower limits of the range of factors variation are used in the experiment. They are indicated by (+1) and (−1) or simply, (+) and (−). Such designations create great convenience for the content presentation and calculations. Levels written in this form are called as coded one. Pilot plans where all factors vary at two levels, are called Type 2k plans, where k is known as being the number of factors. Plans at two levels are used whenever it is possible [6]. In order to select a suitable plan, it is necessary to formulate a criterion of its optimality depending on the goal set. In the case of a linear model, the criterion is the orthogonality requirement of the plan. Orthogonality allows us to obtain independent estimation model coefficients, which is very important for the interpretation of the equation. Orthogonality is a matrix for which the matrix of normal equations of the method of least squares is diagonal. The orthogonality condition of the plan looks like: n  n=1

 xin x jn =

0, i = j, i, j = 1, . . . , k, n, i = j,

(45.2)

where k is the number of factors; n—number of experiments. Because of meeting these requirements, the dispersions of coefficients are not only minimal but also

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equal to each other [5]. All this creates ideal conditions for statistical analysis. Since it is necessary to obtain not only model coefficients, the plan should be unsaturated, i.e., the difference between the number of experiments and the number of desired coefficients f > 0, where f is the number of degrees of freedom. In this case, besides estimating all the unknown constants, it is also possible to check the adequacy of the chosen mathematical model. Applying the Least Squares Method. Calculation of coefficients is a problem solved by the least squares method. Thanks to the optimal organization (orthogonality); the solution is very simple. The least squares method is used to find unknown polynomial coefficients approximating the initial function. If the degree of the polynomial is not set a priori, the calculations are performed several times, gradually increasing the degree of the polynomial until the obtained model becomes adequate. To solve the problem, an X-matrix, a matrix of experimental conditions, consisting of the number of columns corresponding to the number of unknown coefficients and the number of rows corresponding to the number of experiments, is compiled. A Y-matrix of the values of the desired function obtained because of experiments is also compiled. In general, there will be the following matrix expression: ⎡

x01 ⎢ x02 X =⎢ ⎣... x0n

x11 x12 ... x1n

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

⎤ ⎡ ⎤ xk1 y1 ⎢ y2 ⎥ xk2 ⎥ ⎥, Y = ⎢ ⎥ ⎣...⎦ ...⎦ xkn yn

(45.3)

The matrix X is rectangular, with dimensions [n · (k + 1)], n > k. Using the rule of least squares, we obtain a matrix system of normal equations of:

 X T · X · B = X T · X,

(45.4)

where X T —transposed matrix of experimental conditions; B—matrix-column of required coefficients of polynomial mathematical model or regression equations; (X T · X )—matrix of coefficients of normal regression equations. As is known, (X T · X ) will be a square nondegenerate matrix of the order (k + 1). Therefore, it is possible to find a reverse matrix (X T · X )−1 for it. From Eq. (45.2), we can find the matrix-column B by multiplying the left part of Eq. (45.4) by (X T · X )−1 : (X T · X )−1 · (X T · X ) · B = (X T · X )−1 · X T · Y.

(45.5)

Taking into account that (X T · X )−1 · (X T · X ) = E (unit matrix), we get the expression for the required matrix of coefficients: B = (X T · X )−1 · X T · Y.

(45.6)

This is the solution to the problem of finding the regression for equation coefficients.

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In the expanded form from formulas (45.1)–(45.4), the expression for the regression coefficients is received bi =

k 

Ci j

j=0

n 

(X im Ym ) ,

(45.7)

m=1

where C ij —the elements of the reverse matrix. If an orthogonality condition is applied to the matrix of experiment X , then the matrix (X T · X ) will be diagonal. The inverse elements of the diagonal matrix are equal to the inverse values of the corresponding elements of the straight matrix. This circumstance allows us to use the simplest calculation formulas when planning experiments and make the operation of matrix rotation practically in mind. Besides, it gives an opportunity to estimate all regression coefficients independently of each other.

45.2.2 Planning the Experiment The example of the planning an experiment for two controlled factors x1 , x2 can be considered. The linear dependence of the species can be defined according to the formula: U = b0 + b1 x1 + b2 x2 .

(45.8)

Transition from real variables to coded ones is determined by the ratio xi =

N − xiN xi0 , Ji

(45.9)

N —natural value of zero (average) level where xi is the coded value of the factor; xi0 N of the factor; xi —natural current value of the factor; J i —numerical value of the variation interval. Coding is a linear transformation of the coordinates for the factor space—the transfer of the origin of coordinates to the zero point of the plan and the selection of scales on the axes in units of intervals of variation. Identification of the upper level with the “plus” sign and the lower level with the “minus” sign leads to the standard form of the planning matrix using only signs. For two factors, the complete factor experiment will take the form of 22 = 4 [3, pp. 91–93]. The planning matrix for this factorial experiment is shown in Table 45.1. The conditional variable x0 , identically equal to +1, is introduced to calculate the free term of the regression equation. Taking into account the values of encoded variables and by using the values of expressions (45.6) and (45.7) for function (45.8), the following values of coefficients

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Table 45.1 A complete factor experiment for two factors Experiments

Factors

yi

x0

x1

x2

1

+

+

+

y1

2

+

+

–

y2

3

+

–

+

y3

4

+

–

–

y4

b0 , b1 , b2 (45.10) can be obtained. b0 =

1 (y1 + y2 + y3 + y4 ), 4 i=1

b1 =

1 (y1 + y2 − y3 − y4 ), 4 i=1

b2 =

1 (y1 − y2 + y3 − y4 ). 4 i=1

4

4

4

(45.10)

Similar calculations were made in our study for the five risk factors for students’ dissatisfaction with the quality of education. Simulating the Possibility of Risks. To determine the yi values of the response function, the expert technology is to be used. To coincide with the start of all rating scales, mathematical expectations of xi, j factors x j of each Expert Advisor are determined: m 1  xi j , M(xi j ) = m j = m i=1

(45.11)

where xi j —jth factor evaluation by ith expert; m—the number of experts. As a result, the average square deviations of all factors for each Expert Advisor are found. The mathematical expectation of the calculated values σi is defined as follows: Calculate the adjustment factor  j = σσ∗j and correct the expert estimates for each factor by using the formula βi j = (xi j − m j ) j + M(m j ).

(45.12)

Assuming that at the first stage, the contribution of all factors to the formation of risk is equal, we obtain a directly measured value (yidirect ) of the probability of risk:

45 Simulation for Evaluating the Feedback …

yidirect =

547 n 1 βi j . n j=1

(45.13)

45.3 Results In order to determine the probability of the studied risk, 349 students of the Institute of Economics, Finance, and Management at Togliatti State University were surveyed and polled. The assessment of the level of each of the five factors identified at the beginning of the work is given in Table 45.2.

45.3.1 Risk Assessment The above-mentioned factor experiment and expert technology have been used for modeling the hypothetical model of risk level dependence on five controlled factors represented like the following: Y = 0.162 + 0.089x1 + 0.263x2 + 0.327x3 + 0.092x4 + 0.067x5 and b0 = 0.162, b1 = 0.089, b2 = 0.263, b3 = 0.327, b4 = 0.092, b5 = 0.067 where all the above-mentioned criteria represent weights for the relevant factors. Using this model, it is possible to rank the factors according to the degree of their influence on the probability of risk situations occurrence, and consequently achieve the target by developing appropriate corrective measures. Table 45.2 Assessment of the level of risk factors for students’ dissatisfaction with the quality of education Student

Risks factors x1

x2

x3

x4

x5

1

0.5

0.3

0.9

0.3

0.2

2

0.2

0.7

0.2

0.3

0.1

…

…

…

…

…

…

349

0.2

0

0.5

0.8

0

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45.3.2 Assessing the Level of Acceptable Risk To estimate the level of acceptable risk level, a confidence interval Jβ was designed by the formula:   σ σ Jβ = θ − tβ,k √ ; θ + tβ,k √ , n n

(45.14)

where θ —represents the students’ risk assessment; σ —represents average value of mean square deviations by factors; n—number of students surveyed; tβ,k = 2 at the level of trust β = 0.95 and k = n − 1—number of degrees of freedom distribution according to t-distribution (Student distribution) (n > 15). According to the results of the experiment, the following interval was obtained (0.089; 0.463).

45.4 Conclusions and Future Steps The described on-going multi-target research at Togliatti State University is aimed at active use of mathematical apparatus and simulation for reducing the risks while teaching the remote students since e-learning is known as being an important feature for smartness of modern university. The obtained research outcomes enabled us to make the following conclusions: 1. The results of the experiment show that the majority of controlled risk factors take on the value of