Smart Education and e-Learning―Smart University: Proceedings of the 10th International Conference on Smart Education and e-Learning (KES SEEL-2023) (Smart Innovation, Systems and Technologies, 355) 981992992X, 9789819929924

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Smart Innovation, Systems and Technologies 355

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

Smart Education and e-Learning— Smart University Proceedings of the 10th International Conference on Smart Education and e-Learning (KES SEEL-2023)

Smart Innovation, Systems and Technologies Volume 355

Series Editors Robert J. Howlett, KES International Research, Shoreham-by-Sea, UK Lakhmi C. Jain, KES International, Shoreham-by-Sea, UK

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. Indexed by SCOPUS, EI Compendex, INSPEC, WTI Frankfurt eG, zbMATH, Japanese Science and Technology Agency (JST), SCImago, DBLP. All books published in the series are submitted for consideration in Web of Science.

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

Smart Education and e-Learning—Smart University Proceedings of the 10th International Conference on Smart Education and e-Learning (KES SEEL-2023)

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

Robert J. Howlett KES International Research Shoreham-by-Sea, UK

Lakhmi C. Jain KES International Selby, UK

ISSN 2190-3018 ISSN 2190-3026 (electronic) Smart Innovation, Systems and Technologies ISBN 978-981-99-2992-4 ISBN 978-981-99-2993-1 (eBook) https://doi.org/10.1007/978-981-99-2993-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 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. From June of 2014, the enthusiastic and visionary scholars, faculties, 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). The members of SEEL international professional research and academic communities actively perform research and share their ideas and research outcomes in various related areas such as smart education, smart e-learning, smart universities, smart classrooms, smart pedagogy, smart campus, and smart educational systems and technology. 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, during June 18– 20, 2014. The information about SEEL conferences in 2015–2022 (with dates and location) and number of accesses to conference proceedings on publisher’s website are given in the table below.

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SEEL conference number

Dates of the conference

Location of the conference

Publisher of conference proceedings

Number of accesses to conference proceedings (as of March 31, 2023)

2nd

June 17–19, 2015

Sorrento, Italy

Springer

118,000+

3rd

June 15–17, 2016

Tenerife, Spain, Springer

124,000+

4th

June 21–23, 2017

Vilamoura, Portugal

Springer

111,000+

5th

June 20–22, 2018

Gold Coast, Australia

Springer

27,000+

6th

June 17–19, 2019

St. Julians, Malta,

Springer

66,000+

7th

June 17–19, 2020

KES Virtual Conference Center, UK

Springer

47,000+

8th

June 14–16, 2021

KES Virtual Conference Center, UK

Springer

35,000+

9th

June 20–22, 2022

Rhodes, Greece Springer

17,000+

The total number of accesses (more than 545,000) to conference publications on link.springer.com clearly demonstrates (1) a well-established popularity of the relatively “young” (just 10 years old) international conference on Smart Education and e-Learning and (2) a keen interest by international researchers, practitioners, faculties, administrators, and Ph.D. students to SEEL conference’ main topics and publications. The jubilee SEEL conference—the 10th international KES conference on Smart Education and e-Learning (SEEL-2023)—will be held at Rome, Italy, in both inperson and online modes during June 14–16, 2023. The main common topics of the SEEL-2023 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, security; research projects, best practices, and case studies on smart education; partnerships, national and international initiatives and projects on smart education; economics of smart education;

Preface

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• Smart e-Learning (SmL cluster): Smart e-learning: concepts, strategies, and approaches; Massive Open Online Courses (MOOC); Small Personal Online Courses (SPOC); 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 assurance; faculty development and instructor’s skills for smart e-learning; research, design, and development projects, best practices and case studies on smart elearning; standards and policies in smart e-learning; social, cultural, and ethical dimensions of smart e-learning; 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 selforganization 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; smart phones 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; 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 healthcare; smart wealth; smart life-long learning; smart city; national and international initiatives and projects; smart society. The main theme of the SEEL-2023 international conference is Smart University. During 2012–2022, the members of the international SEEL professional research and academic communities worked hard together with a strong belief that traditional universities should be transformed into smart universities (SmU). Based on (a) outcomes and findings of dozens of research, design, and development projects, completed by the members of SEEL academic and research communities in 2012–2023 and (b) corresponding publications at the SEEL conference proceedings in 2014–2022, we arrived with a firm understanding of various distinctive smart features— or, smartness levels—of SmU, including (1) sensing and data collecting, (2) inferring or data processing and generating information, (3) self-analysis and information processing, (4) adapting to new conditions/ restrictions/limitations, (5) anticipating and getting knowledge, and (6) self-optimization or selforganization and active use of obtained knowledge. Particularly: 1. “Sensing” smartness feature deals with SmU’s ability to automatically use various sensors and monitoring/control devices (robots) to identify, recognize, understand, and/or become aware of various events, processes,

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2.

3.

4.

5.

6.

objects, phenomenon, etc. that may have impact (positive or negative) on SmU’s operation, infrastructure, or well-being of its components—students, faculty, staff, software and hardware systems, computer network, etc. “Inferring” (or, logical reasoning) smartness feature deals with SmU’s ability to automatically make logical conclusion(s) on the basis of raw data, processed information, observations, evidence, assumptions, and/or established/implemented rules. “Self-learning (self-exploration, self-assessment, self-analysis, selfdiscovery, self-description)” smartness feature deals with SmU’s ability to automatically obtain, acquire, or formulate new or modify existing knowledge, experience, or behavior to improve its operation, business functions, performance, effectiveness, etc. “Adaptation” smartness feature deals with SmU’s ability to automatically modify its teaching/learning strategies, administrative, safety, technological, and other characteristics, infrastructure, network, systems, etc. to better operate and perform its main business functions such as teaching, training, e-learning, safety, management, maintenance, and control, etc. “Anticipation (awareness)” smartness feature deals with SmU’s intelligence and predictive analytics software systems’ ability to automatically collect raw data, process it in real time, and predict what is going to happen and how to address a specific event. “Self-organization (self-optimization, reconfiguration, restructuring, and self-recovery)” smartness feature deals with SmU’s ability to automatically change its internal structure (components), self-regenerate, and self-sustain in a purposeful (non-random) manner under appropriate conditions but without an external agent/entity (A note: Self-protection, self-matchmaking, and self-healing are a part of self-organization.).

This year, several dynamic sub-groups in our international research and academic communities proposed in-depth discussion on a number of emerging research topics in smart university and smart company/business. We strongly support those pioneering initiatives and are very thankful to the organizers and chairs of the following special sessions at the SEEL-2023 international conference: • IS01: Smart Pedagogy: Innovative Ways to Increase the Effectiveness of Teaching Strategies and Maximize Student Learning Outcomes (chair: Prof. Jeffrey P. Bakken); • IS02: Smart Education: Development Models, Technologies, and Social Issues (co-chairs: Prof. Shyan-Ming Yuan and Assoc. Prof. Li-Xian Chen); • IS03: Smart Universities: Paradigms and Mechanisms for Digital Transformation of Education and Economy (chair: Prof. Natalia A. Serdyukova); • IS04: Smart Company: Digital Transformation of Social, Training, Management, and Business Systems (co-chairs: Prof. Lyudmila V. Glukhova and Prof. Svetlana A. Gudkova).

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We would like to thank many scholars—members of the SEEL-2023 International Program Committee—who dedicated many efforts and time to make SEEL international conference a success, namely: • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

Prof. Luis Anido Rifon (University of Vigo, Spain), Prof. Jeffrey P. Bakken (Bradley University, USA), Prof. Adriana Burlea Schiopoiu (University of Craiova, Romania), Prof. Nunzio Casalino (Guglielmo Marconi University and Luiss Business School, Italy), Dr. Seyeoung Chun (Chungnam National University, South Korea), Prof. Robertas Damasevicius (Kaunas University of Technology, Lithuania), Prof. Lyudmila V. Glukhova (Volga Region State University of Service, Russia), Prof. Svetlana A. Gudkova (Togliatti State University, Russia), Dr. Peter Ilic (University of Aizu, Japan), Dr.-Ing. Prof. h. c. Dr. Karsten Henke (Ilmenau University of Technology, Germany), Prof. Daocheng Hong (East China Normal University, China), Prof. Alexander Ivannikov (Russian Academy of Sciences, Russia), Prof. Aleksandra Klasnja-Milicevic (University of Novi Sad, Serbia), Dr. Gara Miranda Valladares (University of La Laguna, Spain), Prof. Isidoros Perikos (University of Patras, Greece), Prof. Petra Poulova (University of Hradec Kralove, Czech Republic), Prof. Natalia A. Serdyukova (Plekhanov Russian University of Economics, Russia), Dr. Ruxandra Stoean (University of Craiova, Romania), Prof. Masanori Takagi (Iwate Prefectural University, Japan), Dr. Heinz- Dietrich Wuttke (Ilmenau University of Technology, Germany), Prof. Yoshiyuki Yabuuchi (Shimonoseki City University, Japan), Prof. Shyan-Ming Yuan (National Yang Ming Chiao Tung University, Taiwan), Prof. Vladimir I. Serdyukov (Bauman Moscow State Technical University, Russia), Prof. Aleksandr A. Rudenko (Saint Petersburg State University of Architecture and Civil Engineering, Russia), Prof. Marina V. Malashchenko (Southern Federal University, Russia), Prof. Aidar A. Ayupov (Kazan Federal State University, Russia), Prof. Alexander D. Nemtcev (Volzhsky University named after V. N. Tatischev, Togliatti, Russia), Prof. Dmitry L. Savenkov (Togliatti State University, Russia), Prof. Marina A.Vengranovich (St. Alexius Orthodox Institute of Volga Region, Russia), Prof. Anna A. Sherstobitova (Togliatti State University, Russia), Dr. Valery M. Kaziev (Kabardino-Balkarian State University, Russia).

We are indebted to international collaborating organizations that made the SEEL2023 international conference successful, specifically:

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• • • •

KES International (UK), InterLabs Research Institute, Bradley University (USA), Science and Education Research Council (COPEC), and World Council on System Engineering and Information Technology (WCSEIT).

One of the advantages of the SEEL international conference—as a part of the Smart Digital Futures (SDF) 2023 multi-theme hybrid conference—is that it is organized in conjunction with several other conceptually close high-quality conferences, including Agents 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 keynote presentations and session presentations, 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 professional contributions presented at the 10th international KES conference on Smart Education and e-Learning with the Smart University as the main conference theme this year. It contains high-quality peer-reviewed papers that are grouped into several interconnected parts: • • • • •

Part I—Smart Education, Part II—Smart e-Learning, Part III—Smart University, Part IV—Smart Education: Case Studies and Research, and Part V—Smart Business/Company: Case Studies and Research.

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, 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, smart e-learning, and smart university areas. Peoria, IL, USA Shoreham-by-Sea, UK Selby, UK June 2023

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

Contents

Part I 1

2

Smart Education

Smart Education: Students’ Perception of Hybrid Learning in Graduate Computing Curriculum . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vladimir Uskov

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Gaining Insight into Adoption of Immersive Technologies in Higher Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jose Garcia and Ekaterina Prasolova-Førland

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3

Innovative Methods for Smart Education: Hybrid Approach . . . . . . Svetlana A. Gudkova, Elena N. Korneeva, Raisa K. Krayneva, Svetlana P. Azarova, and Aigul U. Samuratova

4

Impact of Offline and Online Lecture Formats on Student Satisfaction with the University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Takumi Kato

5

Possibilities of Using Multimedia Technologies in the Learning Process for Children with Special Needs . . . . . . . . . . . . . . . . . . . . . . . . . Natalya Prokofyeva, Sabina Katalnikova, Viktorija Ziborova, and Andrejs Semrjakovs

Part II 6

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45

55

Smart e-Learning

Online Ed.D. Program Development: A Program Level Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Erik A. Dalmasso and Jeffrey P. Bakken

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Using CAI to Provide Early Literacy Instruction for All Learners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haya Shamir, Erik Yoder, and David Pocklington

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xii

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9

Contents

Online Doctoral Faculty Engagement: Building Connections Through Authenticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Erik A. Dalmasso and Jeffrey P. Bakken

89

Reliability Issues with At-Home Assessment During the COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haya Shamir, Erik Yoder, and David Pocklington

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10 Student Support in an Online Environment: Doctoral Student Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Erik A. Dalmasso and Jeffrey P. Bakken Part III Smart University 11 Organizing the University 4.0: New Goals and Insights to Promote the Digital Transformation of Higher Education Institutions to Succeed Next E-learning Era . . . . . . . . . . . . . . . . . . . . . . 121 Eleonora Veglianti, Elisabetta Magnaghi, Nunzio Casalino, Alessandro Gennaro, and Marco De Marco 12 Systematic Approach to Project Management at Smart University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Leyla F. Berdnikova, Svetlana I. Sotskova, Irina V. Kalashnikova, Elizaveta I. Gnatishina, Ekaterina A. Afonichkina, and Elena A. Khramova 13 The Concept of New-Generation Lecturers for Smart Universities: Case Study and Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Svetlana A. Gudkova, Inga V. Treshina, Marina V. Malashchenko, Tatiana S. Yakusheva, and Marina V. Dayneko 14 Organizational and Methodological Support of the Strategic Analysis of the Resource Potential of Smart University . . . . . . . . . . . . 153 Leyla F. Berdnikova, Natalya A. Igoshina, Andrei S. Vasilchuk, Lyubov K. Shamina, Iuliia A. Anisimova, and Anastasia Yu. Malyarovskaya 15 Decision-Making Training for Students and Managers Using Data Science and Smart Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Anna A. Sherstobitova, Bella V. Kazieva, Lyudmila V. Glukhova, Valery M. Kaziev, and Elena I. Koroleva 16 Algorithm for Strategic Management of Project Activities in Smart University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Leyla F. Berdnikova, Olga S. Aksinina, Elena V. Shchepotkina-Marinina, Elena A. Borgardt, Oksana A. Lugovkina, and Pavel A. Kabanov

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17 Ecosystems for Higher Education and Society . . . . . . . . . . . . . . . . . . . . 183 Svetlana A. Gudkova, Elena N. Korneeva, Raisa K. Krayneva, Irina V. Khristoforova, and Aizhan Omarova Part IV Smart Education: Case Studies and Research 18 Mathematical Simulation for Quality Management in State System of Higher Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 Lyudmila V. Glukhova, Svetlana D. Syrotyuk, Svetlana A. Gudkova, Anna A. Sherstobitova, Sabina Sh. Palferova, and Anton A. Gudkov 19 Quasi-Fractal Logic Usage in Risk Management Models of Smart Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Natalia A. Serdyukova, Vladimir I. Serdyukov, Olga A. Kusminova, and Svetlana I. Shishkina 20 Research and Simulation of Cryptocurrency Market as an Innovative Use Case for Emerging Smart Education . . . . . . . . 221 Anna A. Sherstobitova, Bella V. Kazieva, Olga A. Kusnetsova, and Tatiana V. Polteva 21 Literary Tourism: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 ˇ Miloslava Cerná, Anna Borkovcová, and Petra Poulová Part V

Smart Business/Company: Case Studies and Research

22 A Model of Quality Management System for Digital Economy . . . . . 251 Anna A. Sherstobitova, Elena V. Kargina, Svetlana E. Vasilyeva, and Ekaterina N. Zolotareva 23 A Model for Business System Infrastructure’s Vulnerability Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 Lyudmila V. Glukhova, Olga A. Filippova, Svetlana D. Syrotyuk, Svetlana A. Gudkova, and Yuliya S. Munirova 24 Marketing Research for Regional Development . . . . . . . . . . . . . . . . . . 273 Anna A. Sherstobitova, Elena V. Kargina, Slavyana O. Shanogina, and Natalya A. Nesmeyanova 25 Digital Technologies for Quality Management in Integrated Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 Anna A. Sherstobitova, Elena N. Korneeva, Raisa K. Krayneva, Manchuk T. Bayetova, and Azyk A. Orozonova

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26 Innovative Marketing Strategy for Industry . . . . . . . . . . . . . . . . . . . . . 293 Anna A. Sherstobitova, Elena V. Kargina, Varvara V. Danshina, and Olga A. Filippova Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305

About the Editors

Dr. Vladimir L. Uskov 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, 9 chapter books and more than 375 papers in international journals and conference proceedings. Dr. Robert J. Howlett 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; and 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. Dr. 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, cooperation and teaming. Involving around 5000 researchers drawn from universities and companies worldwide, KES facilitates international cooperation and generate 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. xv

Part I

Smart Education

Chapter 1

Smart Education: Students’ Perception of Hybrid Learning in Graduate Computing Curriculum Vladimir Uskov

Abstract Advanced hybrid teaching and learning are aimed to take advantage of the strengths of both online and face-to-face learning environments to (a) create a dynamic, highly effective and engaging educational experience for students and (b) provide the highest quality of student learning outcomes, i.e., a portfolio of excellent analytical, technical, managerial, business communication and team working skills pertaining to a specific advanced course and/or topic. The described ongoing research, design and development project at the Department of Computer Science and Information Systems and The InterLabs Research Institute at Bradley University (IL, USA) is aimed to use a systematic approach and identify, analyze, design, develop, implement, teach, test, analyze and recommend various components of an optimal hybrid course and courseware in graduate computing curriculum. Based on obtained feedback from 91 graduate students in computer science graduate courses, (a) a total of 87.5% of graduate students “Agreed” or “Strongly agreed” to take other hybrid graduate courses in the future even if face-to-face or online versions of those courses are offered at the same time, and (b) a total of 74.9% of graduate students “Agreed” or “Strongly agreed” to recommend taking computer science graduate courses in hybrid mode to classmates, teammates, friends and/or co-workers.

1.1 Introduction. Hybrid Teaching and Learning Future defined by hybrid models of teaching/learning. The Chronicle of Higher Education published a paper in 2022 entitled “The Faculty and Student Experience, Year 2030”; it particularly reads: “According to the architect and futurist Buckminster Fuller, in 1900 the amount of knowledge doubled every 100 or so years. By the mid1940s, it was every 25 years. Now it’s approximately every 12 months, and with V. Uskov (B) Department of Computer Science and Information Systems, Bradley University, Peoria, USA e-mail: [email protected] The InterLabs Research Institute, Bradley University, Peoria, USA © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_1

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increased computing power and artificial intelligence, the amount of knowledge may soon double every 12 h. This ever-changing explosion of knowledge and the technology that accompanies it is going to affect the way college students learn, what they learn, and where they learn. … But it’s not a far-fetched speculation that, by the next decade, attending college classes in a classroom, face to face with classmates will be a luxury. More classes will be taught online, and more students will receive degrees without ever setting foot on a campus. Fewer student families willing to pay for room and board will force some of the colleges with better brand names to invest heavily in online teaching—not just for adults looking for an advanced degree while they work or raise a family, but also for students who may not have the time or money to relocate and dedicate themselves full-time to study” [1]. In 2022, 60 + highly ranked experts of the EDUCAUSE Expert Panel identified the top six technologies and practices that will shape the future of postsecondary teaching and learning. Three out of 6 top practices deal with hybrid/remote learning, including: (1) mainstreaming hybrid/remote learning modes, (2) hybrid learning spaces and (3) professional development for hybrid/remote teaching [2]. Both online learning (e-learning) and hybrid learning have gained popularity, especially in North America with around 55% of the courses changing from traditional classes to either online or hybrid classes. Online and hybrid classes are the future of education, and with the pandemic, the growth of these mediums has been faster than previously predicted. They are highly accessible, non-restricting and engaging than traditional classes [3]. Online modalities, which include online synchronous, hybrid, HyFlex and multimodal models, gained traction during the pandemic and are becoming important factors in the online learning landscape. Nearly 99% of chief online officers (COOs) surveyed project that online elements will feature prominently in the typical student experience within the next three years. Survey co-author Richard Garrett, Eduventures Chief Research Officer at Encoura, observed that “online leaders anticipate significant acceleration in online adoption across students and institutions over the next few years” and added that “online leaders see a future defined by hybrid models” rather than solely online offerings [4]. Goal of hybrid education. The goal of hybrid education is to take advantage of the strengths of both online and in-person learning environments to create a dynamic, highly effective and engaging educational experience for students. As a result, a welldesigned hybrid course often involves a highly effective combination of in-person and online class activities, tasks, courseware, assignments, discussions, group projects, hands-on and lab activities, tests and exams. The optimal combination of traditional face-to-face (F2F) and online course components is aimed at the highest quality of student learning and training outcomes—a portfolio of analytical, technical, managerial, business communication and team working skills pertaining to a specific course and/or topic. Definition of hybrid course. The definition of hybrid education (or, blended education) used by universities worldwide can vary, but, in general case, it refers to a teaching model that combines elements of both F2F and online teaching and learning.

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In accordance with Stanford University [5], “… the term hybrid specifically describes a course where some sessions take place in person and some sessions take place fully online. While the in-person meetings may often include blended teaching elements, hybrid courses set the expectation that all students will engage in some parts of the course in person and in other parts of the course through remote, fully online participation. … Students meet regularly in person throughout the term, but there are a significant number of scheduled sessions where students meet online, or work asynchronously instead. In contrast, the following are not hybrid courses: (1) using Canvas or other learning management systems to organize course materials, assign and collect student work electronically, and facilitate online discussions, (2) recording course sessions as a study aid or for students to make up for missed classes, according to your course’s policies, (3) allowing students to join a class by Zoom on occasions when they are not able to come to class, according to your course’s policies, (4) holding an occasional session online using Zoom when the instructor is traveling, and (5) having a guest speaker join the class by Zoom”. In accordance with University of South Florida [6], “Hybrid classes can include an online component of 50–79% of the total hours offered during the semester, both synchronous and asynchronous. The remaining hours are conducted in the classroom”. In accordance with University of Wisconsin–Green Bay [7], “A hybrid course is a course where the content is taught using face-to-face and online or “time-out-ofclass” learning modes either synchronously or asynchronously. Less than 75 percent of the instruction and interaction occurs via electronic communication, correspondence or equivalent mechanisms, with the faculty and students physically separated from each other”.

1.2 Literature Review: Quality of Learning Outcomes in Hybrid Courses Versus Traditional Face-to-Face and Online Courses Based on the outcomes of our literature review of 2021–2023 (i.e., after COVID19 time period) publications about quality of hybrid education, in general, there are limited research and research data/findings/outcomes available on the quality of hybrid courses in comparison to F2F and online courses. Moreover, the literature review clearly shows that the obtained and presented results may significantly vary depending on: • type of educational institutions (K12, college, university, training center, etc.), • type of college/university (state or private), • type and level of course (introductory or advanced, undergraduate or graduate, high-enrollment or low-enrollment), • discipline and/or major (e.g., computer science (CS), information technology, data science, math, engineering, history, nursing, arts),

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• required use of advanced technology, hardware and software systems in a course, • faculty experience in design and development and teaching of hybrid and fully online course, and faculty knowledge of learning theory, and • learning objectives, expected learning outcomes and student to-be-gained analytical, technical, management and communications skills from a specific course. Several studies have shown that the quality of hybrid courses can be comparable to or even surpass that of traditional F2F courses. Engineering and computer science education. Egbue et al. [8] make the following conclusion based on surveys of students in the research project entitled “COVID-19 and the New Normal in Engineering and Computer Science Education: Students’ Perspectives on Online and Hybrid Education (2022)”: “Of the 120 students across the three universities, a majority (50.8%) predict that the future teaching format in universities will be mostly hybrid, 32.5% indicate mostly face-to-face formats will be used while only 10.8% predict a mostly online format. The prediction about mostly hybrid teaching being utilized by universities in the future is supported by students’ comments about the ability of hybrid teaching formats to combine the benefits of online and face-to-face teaching”. Mechanical engineering education. Bubacz et al. [9] in their research project in mechanical engineering education made the following conclusion: “Day, evening, and graduate students were surveyed at the beginning and end of the semester to investigate their preference regarding face-to-face, online, and hybrid modes of teaching. To verify the survey results, six quizzes were given to seven different classes. Based on survey results at the beginning of semester, face-to-face hybrid and online were preferred mode of teaching respectively. Surveys at the end of semester showed that face-to-face, online, and hybrid were the preferred mode of teaching, with hybrid being more favorable than online in the perspective of qualitative and quantitative analysis”. Business education. Moodie [10] in research project in business education concludes that “Concept of hybrid mode education is spreading. Little research compares hybrid teaching modes to online and all in person (AIP) teaching modes. Nearly all this research assumes that there is no difference in the students entering AIP, hybrid, or online sections of a course. This study used data from four years of all the courses in the Coles College of Business at Kennesaw State University. The results showed that for all demographics, students in hybrid course sections earned higher final course grades than those in online sections, which in turn, earned better final grades than those in AIP sections”. Medical education. Bock et al. [11] in their research project in medical education entitled “Effectiveness of face-to-face, blended and e-learning in teaching the application of local anaesthesia: a randomised study” conclude: “The aim of the study was to compare the effectiveness of face-to-face, blended and e-learning in teaching in local anaesthesia by assessing students’ knowledge gain, performance of practical skills and satisfaction with the course. This study indicates that blended learning

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improves the learning outcome for theoretical knowledge in teaching local anaesthesia more than either face-to-face learning or e-learning alone. Furthermore, the blended learning approach is highly appreciated by the students. For acquiring practical skills, this study shows that blended learning is as effective as other teaching methods”. Problems identified. Unfortunately, the aforementioned and multiple additional analyzed 2021–2023 publications about quality of hybrid teaching/learning/courses do not provide systematic information about research outcomes on 1. optimal integration of F2F and online components in hybrid graduate courses in graduate computing curriculum (“Perhaps the most important and challenging aspect of designing and teaching hybrid courses is to properly integrate in-person and online activities. Designing an effective hybrid course does not simply entail adding an online component to a face-to-face course, and results can be less than ideal if both elements are implemented independent of one another” [12]); 2. student perception of hybrid learning in graduate computing curriculum, and 3. overall quality of hybrid teaching in graduate computing curriculum as a part of smart pedagogy—a pedagogy that strongly supports “smartness” levels and smart features such as (1) adaptivity, (2) sensing, (3) inferring, (4) anticipation, (5) self-learning, and (6) self-organization [13].

1.3 Project Goal and Objectives Project goal. The overall goal of the ongoing research, design and development project at the Department of Computer Science and Information Systems (CSIS) and the InterLabs Research Institute at Bradley University (Peoria, IL, USA) is to (1) use a systematic approach and identify, analyze, design, develop, implement, teach, test, analyze and recommend various components of an optimal hybrid course and courseware in computing graduate curriculum, and (2) a set of innovative teaching approaches for hybrid teaching that strongly support (a) the concepts of smart pedagogy and (b) main features (i.e., smartness levels) of smart education and e-learning. Project objectives. In order to achieve project goal, the project team selected the following objectives for the current stage (Aug 2020–Mar 2023) of the designated project: • design, develop, teach and test at least 3–4 courses in graduate computing curriculum in hybrid mode for about 80…100 graduate students; • get student feedback on various aspects of hybrid learning by graduate CS students, including (1) “optimal” organization of graduate hybrid course, (2) a distribution of F2F and online components in an “ideal” hybrid graduate course, (3) use of advanced educational technology and industrial software systems in graduate hybrid courses in computing curriculum, etc.;

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• perform student formative and summative surveys to get student perception about quality of hybrid learning in graduate computing curriculum; • get student and faculty opinion about “smartness” features of hybrid courses, including adaptation, sensing, inferring, self-learning, anticipation and selfoptimization; • get student recommendations about the improvements of design, development, teaching and offering of hybrid courses in graduate computing curriculum. A summary of up-to-date project findings and outcomes is presented below.

1.4 Research Outcome #1: Proposed Organization of Hybrid Courses in Graduate Computing Curriculum 1.4.1 “Fundamentals of Software Engineering (FSE)” Graduate Course In Fall-2020, Fall-2021 and Fall-2022 semesters, the author taught the “Fundamentals of Software Engineering” graduate courses in hybrid mode for a total of 91 graduate students. Course goal. The goal of the “Fundamentals of Software Engineering” graduate course is to introduce fundamentals of software engineering (SE), including the software analysis and design concepts and methodologies, techniques, tools and environments, software engineering process models, software testing and validation, software development life cycle (SDLC) and its phases, basics of human computer interaction (HCI) and graphic user interface (GUI) or user experience (UE), and software quality concepts. Course main topics. Course main topics include but are not limited to (1) software product and software engineering, (2) software engineering process models, (3) software analysis modeling, (4) software design modeling, (5) GUI/UE concepts and design principles, (6) software quality concepts, functions and metrics, (7) software testing concepts, (8) software project management, (9) metrics of software products, processes and projects and (10) emerging trends and advanced topics in software development/engineering. Advanced software systems used in this course in lab sessions and course project. Students should master technical skills with modern advanced computeraided system engineering (CASE) systems that serve as standard in software development industry, including at least Microsoft Visio, IBM Rational and SmartDraw commercial systems, and UMLet and Visual Paradigm open-source (free) systems.

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Course project (CP) to simulate software engineering process and team-working in industry. A mandatory multi-aspect and multi-task CP with a real-world assignment and industry-focused set of required activities (such as team working, SE diagrams, active use of CASE systems, CP Executive Summary, CP Business Report, CP video presentation, CP professional evaluation) is an essential part of this course. CP in this course closely simulates the main SE tasks, phases, activities and outcomes as they are required by and conducted/implemented in software development (SD) industry in the USA. It is aimed to help students to obtain and master the required SE analytical, technical, business communication, team working and management skills, and be ready to join the SD industry. Combination of online and F2F course activities. About 70% of course activities are in online (asynchronous and synchronous) modes, including: (1) pre-recorded video lectures in Canvas learning management system (LMS), (2) pre-recorded video tutorials in Canvas LMS for homework and lab assignments, (3) two online tests in Canvas LMS and (4) synchronous (in real time) online individual and all-class meetings (such as virtual office hours and advising) on ZoomPro platform. About 30% (in terms of class time) of course activities are in F2F mode, including mandatory: (1) three lab sessions, (2) midterm and final exams and (3) CP presentations. However, the designated course activities in F2F mode provide students with a total of 400 out of 500 (or, 80%) of points available in this course.

1.4.2 “Software/Information Systems Development Project Management (SPM)” Graduate Course In Fall-2022 and Spring-2023 semesters, the author taught the “Software/Information Systems Development Project Management” advanced graduate courses in hybrid mode for a total of 64 graduate students. Course goal. The goal of this course is to introduce fundamentals of software/information systems’ development project management, including (1) project management (PM) overview (systems approach, framework activities) and (2) project initiation and planning methods (scope, human resources, time and cost management, quality planning, risk management and procurement management) in accordance with the Project Management Body of Knowledge (PMBOK) framework. Course main topics. Course main topics include but are not limited to (1) PMBOK framework, (2) project knowledge areas and process groups, (3) project initiation management, (4) project scope management, (5) project scheduling and time management, (6) project humane resources and materials management, 7) project cost management, (8) project quality management, (9) project risk management, (10) project communications management and (11) project procurement management.

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Advanced software systems used in this course in lab sessions and course project. Students should master technical skills with modern advanced PM systems that serve as standard in SD industry, including at least Microsoft Project, SmartDraw, Microsoft Excel—Advanced Level (to calculate project NPV, ROI, IRR, PTP and other parameters). Course project (CP) to simulate software engineering processes and teamworking in industry. A mandatory multi-aspect and multi-task CP with a specific real-world assignment and industry-focused set of required activities (team working, PM plans, active use of PM systems, creation of CP Executive Summary, CP Business Report, CP Executive Video Presentation, CP professional evaluation) is essential part of this course. CP in this course closely simulates the main PM tasks, phases, activities and outcomes as they are required by and conducted/implemented in SD industry in the USA. It is aimed to help students to obtain and master the required analytical, technical, business communication, team working and management skills, and be ready to join the SD industry. Combination of online and F2F course activities. About 70% of course activities are in online (asynchronous and synchronous) modes, including: (1) pre-recorded video lectures in Canvas LMS, (2) pre-recorded video tutorials in Canvas LMS for homework and lab assignments and (3) synchronous (in real time) online individual and all-class meetings (virtual office hours and advising sessions) on ZoomPro platform. About 30% (in terms of class time) of course activities are in F2F mode, including mandatory: (1) three lab sessions, (2) midterm exam and (3) CP presentations. However, those course activities in F2F mode provide students with a total of 400 out of 500 (or, 80%) of points available in this course.

1.5 Research Outcome #2: Student Feedback Obtained About Quality of Hybrid Courses in Graduate Computing Curriculum Students have been asked to provide the instructor with a formative feedback about various aspects of quality of the designated hybrid courses in graduate computing curriculum. The obtained student feedback is summarized in Tables 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9 and 1.10 (A note: The different numbers of respondents (155 and 91) in the tables below are due to the fact that students in Spring-2023 graduate courses submitted before-the-semester and mid-semester surveys to date, but not the final, or, after-the-semester, surveys.). Legend used in Tables 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9 and 1.10: StrDis— “Strongly Disagree”, Dis—“Disagree”, Neutr— “Neutral”, StrAgr— “Strongly

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Agree”, Agr + StrAgr—a sum of values in corresponding “Agree” and “Strongly Agree” columns.

1.5.1 Student Feedback About Pre-course Experience in Hybrid/Online Courses See Table 1.1.

1.5.2 Student Feedback About Hybrid Course Organization See Tables 1.2, 1.3, 1.4 and 1.5.

1.5.3 Student Feedback About Overall Quality of Hybrid Course Taken See Table 1.6.

1.6 Research Outcome #3: Student Opinion About Hybrid Learning and Recommendations for Improvements 1.6.1 Student Post-course Opinion About Hybrid Courses in Graduate Computing Curriculum (in General) See Table 1.7.

1.6.2 Student Preferences and Recommendations for the CSIS Department in Terms of F2F, Hybrid and Totally Online Courses in Graduate Computing Curriculum See Tables 1.8, 1.9 and 1.10.

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Table 1.1 Student’s initial experience in hybrid learning (number of respondents—155) StrDis (%)

Disag (%)

Neutr (%)

Agree (%)

StrAgr (%)

Agr + StrAgr (%)

8.3

10.4

16.7

31.2

33.3

64.6

… already took hybrid courses 18.7 in the past

16.7

12.5

16.6

35.4

52.0

… did not take any online course yet

25.0

14.6

2.1

31.3

27.1

58.3

… already took online courses 39.6 in the past

12.5

6.3

25.0

16.6

41.6

… did not take any course that 12.5 is based on active use of advanced educational technology and industrial software systems relevant to this CS graduate course

16.7

16.6

22.9

31.3

54.1

… already took courses that require an active use of advanced technology and software systems relevant to this course

29.2

25.0

20.8

10.4

14.6

25.0

… (so far) preferred to take my graduate courses in face-to-face (F2F) mode only

20.8

6.2

14.6

35.4

22.9

58.3

… (so far) preferred to take 18.7 my graduate courses in hybrid mode only

12.5

22.9

27.1

18.7

45.8

… (so far) preferred to take as 16.7 many of graduate courses in online mode as possible (a note for international students on F1 visas: “Under the condition that a student meets immigration/visa requirements”)

14.6

18.7

22.9

27.1

50.0

Q1: Before this hybrid course, I … … did not take any hybrid course yet

1.7 Hybrid Learning/Teaching and Main Features of Smart Education Based on obtained student feedback, a well-designed and organized hybrid graduate course in computing curriculum provides the main features of smart education, including:

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Table 1.2 Student feedback about hybrid course organization in terms of a distribution of online/F2F and synchronous/asynchronous course activities (number of respondents—91) Q2: Do you agree that the proposed course organization (i.e., listed course components in designated modes) were helpful for you to succeed in this hybrid course? StrDis (%)

Disag (%)

Neutr (%)

Agree (%)

StrAgr (%)

Agr + StrAgr (%)

15 course lectures: pre-recorded video lectures in asynchronous online mode

10.4

6.3

10.4

33.3

39.6

72.9

2 course tests: in synchronous online mode

16.7

10.4

14.6

35.4

22.9

58.3

12.3

25.0

57.7

82.7

11.1

54.2

34.8

89.0

4.2

35.4

54.2

89.6

3 lab practicum sessions: in F2F mode

4.2

1 course project presentation: a required pre-recorded CP video presentation in asynchronous online mode with a required posting of video on YouTube Midterm and final exams: in F2F mode

4.2

2.1

(1) adaptation (to various innovative teaching strategies and learning styles, to student’s individual learning pace, to student timetable/activities/duties, etc.); (2) sensing (awareness) of student behavior, perception and academic performance in F2F and online course components using, for example, multiple tools in the Canvas LMS and/or other systems to support graduate studies; (3) inferring (logical reasoning) of obtained raw data and getting meaningful information using, for example, Canvas LMS or other learning analytics systems [14, 15], about student progress in a course, a prediction of student academic performance in the course, development of student’s technical, analytical, management and business communication and team working skills; (4) self-learning (or, self-discovery) of optimal hybrid course structure and features based on student feedback and academic performance, optimal distribution of face-to-face and online course activities, creative comparison of learning outcomes in hybrid, face-to-face and online courses; (5) anticipation of student behavior, progress, academic performance, questions, concerns, etc. in future graduate hybrid courses based on active utilization of obtained new knowledge/experience in hybrid teaching, and (6) self-optimization or self-configuration (or, self-restructuring) of F2F and online components in the “ideal” hybrid courses in graduate computing curriculum based on course’s goal, main topics, required courseware, professional commercial software and/or hardware to be used in a course, required to-be-obtained technical, analytical, management, business communication and team working skills.

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Table 1.3 Student feedback about hybrid course organization in terms of a required use of Canvas LMS and its various sections (number of respondents—91) Q3: Do you agree that a required use of the Canvas LMS and its designated sections was helpful for you to succeed in this hybrid course? StrDis (%)

Disag (%)

Neutr (%)

Agree (%)

StrAgr (%)

Agr + StrAgr (%)

Required active use of the Canvas LMS (in general)

4.2

29.2

66.7

95.8

“Preliminaries and Conventions” section in Canvas LMS

8.3

33.1

58.6

91.6

Detailed course syllabus 2.1 with week-by-week schedule of course activities, tasks, submission deadlines in Canvas LMS

8.3

33.3

56.2

89.5

“Announcements” section (with weekly updates) in Canvas LMS

4.2

31.2

64.6

95.8

Well-structured 15 learning modules (with web links to related videos, files, pages, assignments, submission deadlines, support documents and files) in Canvas LMS

6.2

35.4

58.3

93.7

4.2

22.4

71.1

93.5

20.7

74.8

95.5

10.4

25.0

56.2

81.2

6.3

39.6

54.1

93.7

Online asynchronous pre-recorded video lectures for every learning module in Canvas LMS with weblinks to YouTube

2.1

Online asynchronous pre-recorded video tutorials for homework assignments and lab practicum assignments in Canvas LMS with weblinks to YouTube

2.1

2.1

Online asynchronous pre-recorded video lectures and video tutorials for lab sessions in the Panopto system (that is integrated with the Canvas LMS)

4.2

4.2

“Assignments” section (with detailed homework, lab, and course project assignments) in Canvas LMS

(continued)

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Table 1.3 (continued) Q3: Do you agree that a required use of the Canvas LMS and its designated sections was helpful for you to succeed in this hybrid course? StrDis (%)

Disag (%)

Neutr (%)

Agree (%)

StrAgr (%)

Agr + StrAgr (%)

Required submission of course homework and lab assignments through Canvas LMS

6.3

4.2

35.4

54.2

89.6

Required submission of CP 2.1 reports and files—presentation, executive summary, business report, PM plans and/or SDLC diagrams, etc.—through Canvas LMS

4.2

4.2

35.4

54.2

89.6

Required online tests in Canvas LMS

2.1

2.1

6.3

31.2

58.3

89.5

24/7 availability of up-to-date scores/grades for all course assignments in Canvas LMS

2.1

2.1

4.2

20.8

70.8

91.6

1.8 Conclusions. Future Steps Conclusions. The obtained up-to-date project findings and outcomes enabled the author to make the following conclusions. 1. The described ongoing research, design and development project at the Department of Computer Science and Information Systems and The InterLabs Research Institute at Bradley University (IL, USA) clearly demonstrated a great potential of systematic approach’s use to identify, analyze, design, develop, implement, teach, test, analyze and recommend optimal structure and various components of a highly effective hybrid courses in graduate computing curriculum. 2. In Fall-2020, Fall-2021 and Fall-2022 semesters, the author taught the “Fundamentals of Software Engineering” graduate courses in hybrid mode and obtained feedback from a total of 91 graduate students. In Fall-2022 and Spring-2023 semesters, the author taught the “Software/Information Systems Development Project Management” graduate courses in hybrid mode and obtained feedback from a total of 64 graduate students. Formative surveys contained 60 + questions about various aspects of hybrid courses in graduate computing curriculum; the detailed summative outcomes of those surveys are presented in Tables 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9 and 1.10. 3. One of the most important research outcomes to-date is as follows: If a hybrid course is well designed, structured, organized and taught by experienced faculty, then

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Table 1.4 Student feedback about hybrid course organization in terms of supporting learning environment and activities (number of respondents—91) Q4: Do you agree that the following supporting environment and activities were helpful for you to succeed in this hybrid course? StrDis (%)

Disag (%)

Active use of CS&IS computer labs (including, Br160 Smart Classroom/Lab) in asynchronous in-person mode for student individual work (in general)

Agree (%)

StrAgr (%)

Agr + StrAgr (%)

4.2

41.4

54.4

95.8

Neutr (%)

Required active use of professional (both, commercial and free) industrial CASE and/or PM systems in this course

2.1

2.1

27.1

68.7

95.8

Availability of university library (with course reserves, laptops checkout, research help, room and table/desktop reservations, trial databases and software systems, etc.) and university Technology HELP Desk

12.5

4.2

37.5

45.8

83.3

Mandatory all-class virtual meetings on Zoom (approximately 1 time per month)

8.3

2.1

10.4

22.9

56.3

79.2

By-request (optional) individual virtual office hours and advising (weekly) on Zoom platform

12.5

6.3

2.1

31.3

47.9

79.2

(a) a total of 87.5% of graduate students “Agreed” or “Strongly agreed” to take other hybrid course(s) in the future even if F2F or online versions of those courses are offered at the same time; (b) a total of 74.9% of graduate students “Agreed” or “Strongly agreed” to recommend taking hybrid courses to classmates, teammates, friends and/or co-workers (the details are available in Table 1.7). 4. Based on student feedback (Sect. 1.7 above), a well-designed and organized hybrid course in graduate computing curriculum provides valuable contributions to the main features of smart education, including (1) adaptation (to various innovative teaching strategies and learning styles), (2) sensing (awareness) of student behavior, perception and academic performance in F2F and online course components, (3) inferring (logical reasoning) of obtained raw data and getting meaning

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Table 1.5 Student feedback about frequency of use of required technology and systems in hybrid course (91 respondents) Q5: How many times per week (on average) did you use this course-related technology and/or software system to succeed in this hybrid course? 1 time (%)

2–3 times (%)

About 5 times (%)

Canvas LMS (“Modules” section)

8.3

33.3

50.0

Canvas LMS (“Announcements” section)

8.3

83.3

8.3

50.0

50.0

8.3

75.0

8.3

33.3

33.3

33.4

University email system for emails related to course activities CASE systems (SmartDraw, Microsoft Visio, IBM Rationale, UMLet, VisualParadigm Video capturing and recordings system like free ScreenCast-O-Matic during the 2nd part of semester to prepare required video reports

8.3

More than 5 times (%) 8.3

full information about student progress in a course, including a prediction of his academic performance in the course, (4) self-learning (or, self-discovery) of optimal hybrid course features based on student feedback and academic performance, (5) anticipation (or, use of new obtained knowledge/experience in teaching hybrid course in the future) and (6) self-optimization and/or selforganization and configuration of F2F and online components in the “ideal” hybrid graduate courses in computing curriculum. 5. Based on the obtained detailed student feedback from 90 + computer science and information systems graduate students (Tables 1.6, 1.7, 1.8, 1.9 and 1.10), we are confident that (a) a well-designed hybrid course (hybrid teaching/learning modes) in graduate computing curriculum provides at least the same (if not higher) level of quality of learning and learning outcomes as F2F and/or online course, and adequately prepares graduates to real-life working/self-training environment in industry, and (b) future education (including, smart education) in higher education institutions will be primarily defined by hybrid models of teaching/learning rather than solely F2F and/or online offerings.

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Table 1.6 Student feedback about quality of hybrid course taken and its components (91 respondents) in comparison with quality of F2F and online modes of teaching/learning Q6: Do you agree that this hybrid graduate course … Agree (%)

StrAgr (%)

Agr + StrAgr (%)

4.2

43.8

50.0

93.7

6.2

8.3

30.7

54.8

85.5

0.0

0.0

12.5

14.6

73.0

87.5

… did not provide any problems/difficulty to manage time for F2F course components

6.3

4.2

2.1

39.6

47.9

87.5

… provided you with good level of connectivity between course’s F2F and online components

6.3

0.0

10.4

39.6

43.8

83.3

… provided you with the same level of learning quality (theoretical concepts, lab experience, etc.) as in totally online course?

6.3

6.3

33.3

14.6

39.6

54.2

… course online components were helpful to understand learning content

10.4

6.3

8.4

45.2

25.0

70.2

… did not provide any problems/difficulty to manage time for the online course components

6.3

4.2

4.2

37.6

47.9

85.5

… online course components (lectures, tests, cp) were not difficult to understand and follow

8.3

12.5

14.6

29.2

35.4

64.6

StrDis (%)

Disag (%)

Neutr (%)

… provided you with the same level of learning quality (in terms of theoretical concepts and methods) as in F2F mode?

2.1

0.0

… provided you with good level of control of pace of your own learning?

0.0

… provided you with the same level of quality of learning professional CASE systems as in F2F mode?

Future Steps. Based on (a) obtained project’s data, findings and outcomes and (b) hybrid teaching of designated hybrid courses in graduate computing curriculum, the future steps in this research, design and development project are as follows: (1) identify a set of additional innovative teaching approaches for hybrid teaching/learning that strongly support (a) the concepts of smart pedagogy and (b) smartness levels and smart features of smart education and e-learning;

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Table 1.7 Your overall opinion about hybrid courses taken (91 respondents) Q7: Based on my gained experience in this hybrid graduate course, I … StrDis (%)

Agree (%)

StrAgr (%)

Agr + StrAgr (%)

6.2

39.6

47.9

87.5

4.2

20.8

25.0

50.0

74.9

Disag (%)

Neutr (%)

… would recommend taking hybrid courses to my friend, relative, and/or co-worker

6.3

… will take other hybrid course(s) in the future even if F2F or online versions of those courses are offered at the same time … missed in-classroom interactions, exercises, discussions and brainstorming sessions

14.6

12.5

25.0

20.8

27.1

47.9

… missed an opportunity to attend in-person office hours

29.2

16.6

14.6

12.5

27.1

39.6

… missed proposing/sharing/discussing my ideas/solutions to/with my classmates

47.9

27.1

10.4

6.3

8.3

14.6

… would like to have a pre-course orientation session about hybrid learning

8.3

27.1

16.7

22.9

25.0

47.9

… would like to have a 37.5 pre-course training about use of Canvas LMS in hybrid course

20.8

4.2

12.5

25.0

37.5

… would like course instructor to have a combination of virtual and in-person office hours (with approx. 50–50 split)

6.2

14.6

20.8

16.7

41.7

58.3

24.8

16.7

6.3

16.7

35.2

51.9

31.2

12.5

8.3

8.3

39.6

47.9

… would like CSIS Labs to be open: • 7:00 AM–11:00 PM weekdays only • 7:00 AM–11:00 PM every day

(2) implement the developed framework of advanced hybrid course structure and courseware in additional courses in graduate computing curriculum (e.g., Agile Software Engineering, Advanced Topics in Software Engineering) and get detailed student feedback about quality of hybrid learning in those courses.

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Table 1.8 Student post-course preferences in terms of taking courses in graduate computing curriculum in F2F, hybrid or online modes (91 respondents) Q8: Overall, based on my experience in this hybrid graduate course, I prefer to … StrDis (%) … take my graduate CS courses in F2F and hybrid modes

Agree (%)

StrAgr (%)

Agr + StrAgr (%)

6.3

35.4

45.8

81.2

Disag (%)

Neutr (%)

12.5

… take my graduate CS courses in only hybrid and totally online modes

18.8

8.3

8.3

24.5

40.2

64.7

… take my graduate CS courses in only F2F mode

33.3

4.2

12.5

18.8

31.3

50.0

Table 1.9 Student post-course recommendations to the CSIS department in terms of hybrid courses in CS graduate curriculum (91 respondents) Q9: Overall, based on my experience in this hybrid course, I recommend the CS&IS Department to …

… offer this hybrid graduate course in regular 15-week long semester with one 3-h long class every week

StrDis (%)

Disag (%)

6.3

2.1

… develop and offer more hybrid CSIS graduate courses 2.1 in • Fall and/or Spring semesters 4.2 • January, May and Summer terms

Neutr (%)

Agree (%)

StrAgr (%)

Agr + StrAgr (%)

20.8

70.8

91.6

6.3

10.4

18.7

62.5

81.2

2.1

8.3

31.3

54.2

85.4

Table 1.10 Student post-course recommendations to the CS&IS department in terms of distribution of online and F2F course activities/tasks in hybrid graduate course in computing curriculum (91 respondents) Q10: Overall, based on my experience in this hybrid course, I recommend the CS&IS Department to … … use the following distribution of online and F2F components in hybrid graduate course

80% in online and 20% in F2F modes

70% in online and 30% in F2F modes

50% in online and 50% in F2F modes

30% in online and 70% in F2F modes

66.6%

16.6%

12.5%

4.2%

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Acknowledgements The author would like to thank Ms. Swetha Mellachervu and Ms. Hemanthi Veeramachaneni—CSIS graduate students and research assistants—for data processing of student surveys and valuable contributions to this research, design and development project.

References 1. The Faculty and Student Experience, Year 2030, The Chronicle of Higher Education: Trends Snapshot. https://connect.chronicle.com/rs/931-EKA-218/images/FacultyStudentExperie nce_Cisco_TrendsSnapshot_v3.pdf?aliId=eyJpIjoiYkUwaEpIanllMWcyUFdpZiIsInQiOiI xMFJIVjh2Snl1Y0RJSVFQOUpkT3BBPT0ifQ%253D%253D 2. 2022 EDUCAUSE Horizon Report: Teaching and Learning Edition. https://library.educause. edu/resources/2022/4/2022-educause-horizon-report-teaching-and-learning-edition 3. Ertu˘grul, T.U.: What is the Difference Between Hybrid and Online Learning? Dan Institute (2021). https://daninstitute.com/blog/what-is-the-difference-between-hybrid-and-onlinelearning/ 4. Garrett, et al.: CHLOE 7: Tracking Online Learning from Mainstream Acceptance to Universal Adoption (2022). https://encoura.org/project/chloe-7/ 5. Stanford Teaching Commons. https://teachingcommons.stanford.edu/teaching-guides/ble nded-and-hybrid-teaching-guide/frameworks-blended-and-hybrid-teaching/what 6. University of South Florida: Starting your Program Online or Hybrid. https://www.usf.edu/int ousf/programs/online-hybrid.aspx 7. University of Wisconsin—Green Bay. Graduate Catalog. https://catalog.uwgb.edu/graduate/ general-information/academic-rules-regulations/mode-of-instruction/ 8. Egbue, O., Al-hammoud, R., Khan, A.: COVID-19 and the new normal in engineering and computer science education: students’ perspectives on online and hybrid education. In: Proceedings ASEE 2022 Annual Conference, Minneapolis, Minnesota, June 26–29, 2022 (2022) 9. Bubacz, M., et al.: Potentials and Limitations of Face to Face and Hybrid Teaching Modes. American Society for Engineering Education (ASEE) (2021). https://sites.asee.org/se/wp-con tent/uploads/sites/56/2021/04/2021ASEESE41.pdf 10. Moodie, D.R.: Comparing the outcomes of the different teaching modes: all-in-person, hybrid, and online for different student demographics in a business school. Online Learning 25(4), 362–387 (2021). https://doi.org/10.24059/olj.v25i4.2298 11. Bock, A., Kniha, K., Goloborodko, E., et al.: Effectiveness of face-to-face, blended and elearning in teaching the application of local anaesthesia: a randomised study. BMC Med. Educ. 21, 137 (2021). https://doi.org/10.1186/s12909-021-02569-z 12. University of Colorado – Boulder, Hybrid Course Design, https://www.colorado.edu/assett/ faculty-resources/resources/hybrid-course-design#:~:text=Hybrid%20courses%20(also%20k nown%20as,online%20discussions%2C%20or%20activities). 13. Uskov, V.L.: Smart pedagogy: concepts, components, outcomes. In: Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning—Smart Pedagogy, pp. 3–16. Springer, June 2022, 535 p. (2022). ISBN: 978-981-19-3111-6. https://doi.org/10.1007/978-981-19-3112-3 14. 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://doi.org/10.1109/EDUCON.2019.8725145. https://ieeexplore.ieee.org/Xpl ore/home.jsp 15. 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). IEEE, Dubai, UAE, pp. 1370–1376 (2019). https://doi.org/10.1109/EDUCON.2019.8725237. https://ieeexplore.ieee.org/Xplore/home.jsp

Chapter 2

Gaining Insight into Adoption of Immersive Technologies in Higher Education Jose Garcia and Ekaterina Prasolova-Førland

Abstract Immersive technologies such as virtual, augmented and mixed reality, also referred as extended reality (XR), can be integrated into education in several disciplines to facilitate learning experiences that might be difficult or unfeasible at a particular time or place. Our group manages an XR lab that supports educators in integrating immersive technologies in their courses. We investigated how and why those educators used immersive technologies to inform how to improve further adoption. The results show the value of having a contact person that actively communicates the available resources to educators, the need for training in immersive technologies, the need for technical support and importance of logistics for accessing hardware as well as organising educational sessions with students. We propose that the results from this work can inform understanding of technology adoption in higher education and help to design strategies to reduce the digital divide.

2.1 Introduction Educators often need to consider how to improve delivery of learning material to their students. They might often resort to complementary educational tools to help them better achieve their learning goals. Often these complementary resources included laboratory practices, invited talks by experts or visits to locations or facilities related to the concepts being studied. The restrictions in face-to-face teaching experienced during the pandemic’s lockdowns in some countries between 2020 and 2021 also showed the need for remote teaching resources. In many cases, teaching during that period was carried out through teleconferencing applications and collaboration apps that enable communication between educators and their students [1]. In such case, it J. Garcia (B) UAS Technikum Wien, Vienna, Austria e-mail: [email protected] E. Prasolova-Førland Norwegian University of Science and Technology NTNU, Trondheim, Norway e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_2

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is reasonable to assume that the decision to use specific tools and technologies in their teaching was strongly influenced by the limitations in face-to-face teaching at the time. However, it is often difficult to determine the attitude towards new technology in teaching and how this is adopted and integrated in the day-to-day educational process. Immersive technologies (virtual, augmented, mixed reality, VR/AR/MR), also called Extended Reality (XR), have previously demonstrated their potential to facilitate learning in different contexts, e.g. supporting collaborative learning in metaverse or providing immersive experiences in subject-specific applications [2–5]. Despite immersive technologies been around for long, they are still seen by many educators as a novelty. However, we propose that the major challenge for adoption, rather than familiarity or knowledge about immersive technologies, is providing educators with clear, concise and trustable information that helps to understand the potential of educational immersive technologies, what is involved in adopting it and how can they integrate it into their teaching. A starting point to make that adoption process more straightforward is looking into adoption practices to gain insight into the criteria used by educators in the decision to integrate immersive technologies in their courses. We approached this task by investigating adoption of immersive technologies in higher education at the Norwegian University of Science and Technology (NTNU). The context of the research is in an institution where there is an established XR lab (Innovative Immersive Technologies for Learning/IMTEL XR lab) with equipment available for lecturers and students as well as expertise in the domain. This article contributes to the field by investigating the integration of immersive technologies in higher education courses and reflecting on the findings. In this article, we present an extended description of a qualitative study into adoption of immersive technologies as previously presented in [6], and its results and discuss lessons for educators interested in exploring or integrating immersive technologies in their teaching.

2.2 Adoption of Technologies for Education There are several examples in the literature where behavioural intention to use, attitude and other factors are used to investigate adoption of technology. Those factors are part of the widely adopted Technology Acceptance Model (TAM) [7] and following variations of the TAM model [6–8]. The model is useful for testing user acceptance and getting insight into the acceptance process and revealing potential barriers to adoption [9]. One can consider factors that are inherent to the teaching process and those related to the technology. It is important that the educator has a positive perception on how the adoption of a technology might affect their work as well as the consequences for their students [10]. Implementation of XR can be expensive or difficult in comparison with other tools used by educators. Lack of adequate XR infrastructure can negatively affect a decision of adoption [11, 12]. Additionally, educators who are not familiar with XR technology can feel underprepared to use it

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and require training. If such training is perceived as unavailable, it becomes a barrier for adoption [13].

2.3 Context of Immersive Tech at the University This study was carried out at NTNU with campuses in three different cities. The research focuses on use of XR only at Trondheim campus. There, the university has a main campus and several minor campuses and buildings distributed over the city. The XR lab run by the authors with the focus on educational immersive technologies is located at the Dragvoll campus, which can be reached within 10 min from the main campus and 20 min from city centre using public transportation. This information is relevant in order to understand some findings presented in later sections. It is not uncommon that some universities have buildings spread over a city. The IMTEL XR lab (https://www.ntnu.edu/imtel/) is a dedicated space for XR technology. It has an area of approximately 70 m2 , divided in a main room and four cubicles. The cubicles have HTC Vives or Valve Index headsets permanently installed and ready to be used. Visitors can also use Oculus Quest 1 and 2, HoloLens 1 and 2 and Magic leap headsets, Virtuix Omni treadmill and other equipment. Entry access to the lab is restricted, so visitors must coordinate with the lab manager. The research group leader often organises visits and reaches out to lecturers to make them aware of services provided by the lab. The XR lab is presented in the university’s website and appears sporadically in news bulletins that are sent to lecturers’ inboxes digitally by the university.

2.4 Qualitative Study Methodology It is challenging to gain insight into the different considerations from an expert in their decision-making. In this study, we were interested in finding what can help other educators decide to integrate immersive technologies into their teaching. This is the reason for selecting a qualitative method. It is difficult to obtain similar insight using quantitative studies. This work uses a grounded theory approach to investigate the adoption of XR in higher education. Grounded theory is a way of carrying out qualitative analysis to support building a theory, not linked to specific types of data or research lines [14].

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2.4.1 Qualitative Study The study was designed to understand the considerations from an educator when adopting immersive technologies. We aimed to capture the characteristics of immersive technologies within specific contexts which is necessary to study the adoption and diffusion of the technologies due to their changing nature [15, 16]. Semistructured interviews were carried out using an interview guide. The interviews were conducted via zoom between December 2020 and June 2021. The use of remote interviews was strongly influenced by temporary restrictions present at the time due to the pandemic. All interviews were carried out in English. English is not the first language of most of the participants. The interviews were transcribed verbatim and were recorded in audio. Data collection and storage was approved by the Ethical approvals Norwegian Centre for Research Data. The interview process was guided by the main research question: How to support integration of XR into educational practices at our university? This question was detailed through a subset of questions. The first sub-question considered the status, needs and barriers to adoption at the university. The second sub-question focused on the guidelines, resources and recommendations needed to overcome barriers/support integration into academic plans.

2.4.2 Interview Script The interviewer followed a script of five questions to guide the interview. The first question was: “What are your experiences with using immersive technologies for teaching at the VR lab or somewhere else?” The second question was: “How do you think immersive technologies support your teaching and how can they be integrated in your teaching plan?” The third question was: “Do you see any limitations to use immersive technologies for teaching? What are the barriers to adoption?” The fourth question was: “What help and support do teachers need to overcome barriers and integrate immersive tech in their teaching (guidelines, resources, teacher training, toolkits, technical assistance).” The final and fifth question was: “How do you think is the adoption of immersive technologies affected by COVID crisis? Other thoughts on integrating immersive tech in teaching at our institution?”.

2.5 Study Results The interviews were transcribed, and thematic analysis was applied to the collected data. Eight active higher education teachers from NTNU were interviewed. All persons interviewed had visited the XR lab at least once and were previously known

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to the members of the research team. Five male and three female educators participated in the study. No other demographics were collected from the participants. The researchers determined to have reached data saturation when the eighth participant was interviewed. The thematic analysis identified the following themes: Preconditions for immersive technologies, Immersive experience, Logistics of visits to immersive technologies facility, Students’ group size, Awareness/knowledge of repositories/resources, Use/Creation of 360° films, Awareness of immersive technologies, Champion for immersive technologies, Convenience/expectations regarding immersive technologies, Immersive technologies and pedagogy and independent use of immersive technologies. In the following, we shortly detail the identified themes. Preconditions for immersive technologies. We identified factors that educators held as important in their decision to use immersive technologies more regularly or intensively in their teaching such as cost of equipment, knowledge or expertise to set up or use immersive technologies, knowledge and expertise to find or create immersive technologies content and educator’s motivation for using or integrating immersive technologies into their teaching. Some participants refer to the cost of equipment as a factor limiting further use. Some would like to have the equipment available at their site but see cost as a barrier. The following is a statement from a participant in Interview 6: “I think, there’s a cost barrier. It would be very interesting for us to have some of the same technologies on site, in our department.” Most participant feel they do not have enough expertise or knowledge to set up equipment for immersive experience with students. Participants in their majority felt that they lack competences to create content. Some are aware of sources related to their own discipline or have been involved in the development of immersive content by providing expertise in their disciplines to the team in the XR lab. Several participants were motivated to use immersive technologies to solve a problem in their teaching that was difficult to address with other alternatives as, e.g., identified in Interview 1: “I use this virtual reality immersive technologies for two reasons: one is for climate change studies, and one is for fieldbased education. For climate change it’s making it much more concrete, showing what will the effects be.” Immersive experience. Educators want the students to have a particular experience when using immersive technologies and gain specific benefits from their educational activity. Some educators chose immersive technologies to generate a conversation with their students through their reaction to the immersive experience. Interviewee 4 commented: “I thought that this could be a really good thing for my students to get a real-life experience, and be able to connect the chemistry with what is actually going on in the world.” A valid teaching goal is for the students to become aware and familiar of technological options. It was also important to allow students to form an opinion of what could be achieved with the technology as specified by the teacher in Interview 3: “I always try to put some technology part into my teaching.” As another teacher elaborates in Interview 6: “I think understanding immersive technologies, and testing out how they work, and how they change the way we understand or perceive surroundings and the world and so on, is extremely important.”

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A use case was mitigating consequences of field trips being cancelled due to pandemic restrictions. Educators did not expect to replace the field trip, but it was a way to limit the negative effect of the restrictions. One of the educators commented in Interview 2: “For example last year when the corona came, we made seven days of digital field course in this Erasmus class instead of going to Trondheim.” One educator was part of a project developing an immersive application for teaching neuroscience and elaborated on his motivation in Interview 8: “The main motivation was that there is a serious lack of skilled people. Skilled teachers with the skills to do this kind of teaching who have enough overview that they can look at a brain and recognise things and also look at variations of brains. That was a main motivation, the second one was also to make it easier to explain to lay people.” Logistics of visits to immersive technologies facility. It is still common that educators do not have their own equipment, but that there are lab facilities where it can be used. In that context, it is important to understand how educators see organising a lab visit to use immersive technologies. Some educators highlighted the need to coordinate with students when the immersive technology equipment is not in the campus where they usually receive lectures. Sometimes that requires organising transportation. Sometimes some students decide not to show up for the class if it is in another campus unless it is compulsory as exemplified in Interview 4: “the students look at it a sort of extra thing. Maybe they see the lectures as the “real” classes. So, some of them didn’t show up for the virtual reality lab. So, I think, at least what I have to do, is to make it compulsory to go to that lab.” Most participants found it easy to organise visits, but two of the teachers had to arrange for payments between departments for lab assistants facilitating the visits. They see that as a barrier for more frequent use of the facility. This is detailed in Interview 3: “It was a little bit of administration organization, because assistants were used, and they get a payment, and it should come from another department.” The logistics were not always negative as described in Interview 6: “the logistics were quite straight forward and easy. I always experienced a large degree of service mindedness, openness, inclusiveness. This was basically just a couple of emails, and some structuring.” Students’ group size. A recurrent comment was about the number of students that could simultaneously use the technologies during visits to the VR lab. Some educators pointed out to the need to find a place nearby for the students that cannot be in the VR lab during a visit. That was already an issue before the pandemic. As discussed in Interview 4: “when you book in a time at the VR lab that, we need additional rooms close to the VR lab.” It is also expressed in Interview 4: “I had to make sort of a logistic around, since I can’t have my whole student group in the lab at the same time.” It was highlighted by some as problematic being unable to have all students at one place. They need to plan activities for students waiting to take part in the immersive experiences. This was further detailed in Interview 1 “as long as we have many stations, and they can watch what their peer students are experiencing, it’s ok. But it shouldn’t be too much waiting.” Also pointed out in Interview 5: “if all my

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students at the same time could watch the same film, that would be nice. If you have enough headsets.” Awareness/knowledge of repositories/resources. Most teachers are not aware where to find content relevant for their needs, repositories or open-source sites for content. This is exemplified in Interview 5: “knowing whether Within (app) was the best place to go for videos, or if there are other ones too, that can be useful” and further elaborated in Interview 4: “perhaps there’s already a lot of tools here that I’m not aware of.” One of the educators created own documentation for an application in which development they participated, to be used for a local science festival. Use/Creation of 360 films. 360° VR videos are relatively easy to create and offer a lot of possibilities for content creation. Several teachers have used 360° videos in VR and some have been involved in creating videos. Many of the participants used applications available from the app Within. The films fitted with the topics they needed to teach. As elaborated in Interview 5: “I wanted the students to watch these films, that were connected to the topic that we were talking about. Like children in displaced situations, or war, refugee children., they also tried games.” One teacher uses film creation as a learning activity and has also made own 360° videos. This is exemplified in Interview 3: “I think at least those who had some experience with 360 videos before the (Covid) crisis, they saw the potential to develop it further. But for people starting from scratch, I think it could be quite heavy. Because we made some small films, maybe totally 20 min of VR films for this teaching last spring, and every five minute of film needed 24 h of computer work to just make up the last part of sewing the four different films together.” Awareness of immersive technologies. Educators are often not aware who has expertise or equipment at their institutions, or maybe they are not aware or don’t remember how to access such resources. For example, as one participant explains in Interview 6: “I was looking around, who was, who had set up labs that work with immersive technologies, and then I came across you.” Some educators point out that it is often difficult to know how to use the technology in their courses without some guidance. As exemplified in Interview 5: “I was advised to have a look at the Within-app. I found some videos that I thought were useful. I talked with VR lab leader and discussed whether there were any programs that could be useful for us, and she suggested two apps.” Champion for immersive technologies. The participants were asked about how they came to use the XR lab facility. Their answers show that reaching out and direct contact by the XR lab leader plays an important role in the teachers’ adoption of the technology. This points towards the need for a champion of immersive technologies at the institution. The lab leader is the first point of contact and principal way of organising activities in the lab. As exemplified in Interview 2: “I think it started with the connection between VR leader (name replaced) and Lecturer (name replaced) at the Department of Geography, and then the ball rolled further on.”

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Convenience/expectations regarding immersive technologies. Unsurprisingly, educators would prefer to have equipment closer to the location where they usually teach. Several of the participants are in campuses apart from the campus where the XR lab is located. All participants are in the same city. Nonetheless those in other campuses prefer to have equipment available at their own campus. As pointed out in Interview 3: “I think I have mentioned the most important. And that is close connection to the campus you are teaching, easy access to technical support, economy, easy booking of room.” The participants expect or wish to have support or being able to call for assistance if something does not work. As mentioned in Interview 1: “I think some students helping, would be needed for most people. Maybe some easy things could be done by themselves, but then they need to have some good guidance, and maybe training.” Immersive technologies and pedagogy. Using immersive technologies in education requires teachers to identify adequate pedagogical approaches. Approaches such as experiential or active learning are unsurprisingly favoured for use with immersive technologies. Most educators wanted students to have an experience that clarifies theoretical concepts or interactive experiences where students have to complete a task. As exemplified in Interview 7: “I do believe that the students will in a different way manage to reach the learning outcomes, in a much better way.” Independent use of immersive technologies. It is important that educators can work independently with immersive technologies. All participants agreed that technical support is useful, desirable and necessary. One of the participants suggested how that support could be: “It could be a document, explaining the steps needed. It could be also some short instructional videos.” Some educators showed reluctance to work without technical support. One educator will not use immersive technologies if it requires to do setup and solve technical problem on their own “I would have tried to use it more if I had more knowledge about it.” The analysis of the interviews shows understanding of the benefits of immersive technologies and appreciation for what can be achieved with it in higher education. The technologies are used as a supplement to the courses. All visitors expressed to be satisfied with the support received during the visits. Administrative issues and group size limitations were highlighted by the participants.

2.6 Discussion The results of the study provide criteria and practical considerations for adopting immersive technologies in higher education. The XR lab makes XR hardware and XR expertise available to the educators in social sciences, art, medicine and technical subjects. Educators can bring groups of students to the lab to participate in immersive

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learning experiences, with the students having an active role in interpreting content and associating it with their curriculum. An affordance of immersive technologies is the ability to carry out activities that are unfeasible or difficult in real life. In that sense, our lab supported using immersive technologies to substitute activities that were unfeasible or impossible during the COVID pandemic such as field trips. Educators at our institution acknowledge our role in supporting them in their visits with students and offering expert advice in selecting content as well as technical support. There is also acceptance among the educators concerning the need for courses where they can acquire competences in use of immersive technologies. In relation to this, our lab introduced a course for university educators to gain competences in immersive technologies and participates on several Erasmus + projects developing guidelines and resources on the use of XR in teaching and learning. The study’s findings highlight concerns from educators about self-efficacy, access to equipment, logistics and administrative issues. Additionally, there are technical aspects that are well known for the XR research community [13]. One of them is the difficulty of having enough headsets for a large group of students for simultaneous participation in an activity in immersive environments. Despite having several high-end headsets in the lab, it would take a considerable investment and larger available space to have enough working stations for large group of students (more than 10 students). Our group is currently part of a consortium investigating low-cost VR. A feasible solution could be to determine the optimal ratio between number of headsets and students. An important aspect is to develop appropriate practices for sharing resources across different departments and study programs. Another wellknown challenge is access to content. Our lab has developed a repository of XR educational resources, such as software code that can be reused in future projects. Our experience showed us a need for curating the software over time. Recent development in the AI field with ChatGPT, Midjourney, Nvidia Omniverse and similar opens interesting possibilities for facilitating development of immersive content (3D models) and simpler apps by end users without programming experience. An additional interesting finding is that educators at our university mostly find out about the XR lab from the lab leader’s direct action. That suggests that existing communication channels are not as effective and alternative communication channels might be desirable to increase the number of educators adopting immersive technologies. The role of actively communicating the existence of resources is therefore currently very important. Our lab has attempted to address this challenge by arranging regular events “Innovation days” highlighting the potential of immersive technologies for teachers, students and broader public. It is important to highlight that we focused on the experience of those who adopted immersive technologies. We decided not to survey the whole educator staff.

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2.7 Conclusion Our XR lab offers educators at our university the possibility to integrate immersive technologies in their courses as well as to provide technical advice in any projects where they envision the use of XR technologies for learning. The lab is aimed for multidisciplinary support of education. The findings of the study motivate action in two main directions in order to facilitate greater adoption and integration in teaching. Firstly, a decentralisation approach is necessary. This means making resources available to educators in other geographical localisations and not underestimating the logistics of moving students across the city between campuses. Additionally, it is desirable to establish as joint resource centre which can provide access to educational resources, best practices, technical support and XR applications. Though ethical and privacy considerations were not explicitly mentioned by our participants, this is a growing concern in the XR field, both at our university and internationally. Our university has been engaged in several rounds of consultations regarding GDPR and purchase and use of XR equipment such as Meta’s Oculus Quests. The XR lab/research group described in the paper is a partner in a new Horizon EU project (XR4HUMAN, https://xr4human.eu/) that aims at developing a European roadmap for ethical, safe and inclusive use of XR, that would have long-term consequences for the adoption of immersive technologies as well. Further research is needed on the topic of ethical [17] and inclusive adoption of XR technologies in higher education. Acknowledgements The authors would like to thank the participants for volunteering in the data collection. This work was funded by a grant from the Faculty of Social and Educational Sciences, at NTNU.

References 1. Jacques, S., Ouahabi, A., Lequeu, T.: Synchronous e-learning in higher education during the COVID-19 pandemic. In: 2021 IEEE Global Engineering Education Conference (EDUCON), pp. 1102–1109. IEEE (2021) 2. Akçayır, M., Akçayır, G.: Advantages and challenges associated with augmented reality for education: a systematic review of the literature. Educ. Res. Rev. 20, 1–1 (2017) 3. Hu-Au, E., Lee, J.J.: Virtual reality in education: a tool for learning in the experience age. Int. J. Inno. Educ. 4(4), 215–226 (2017) 4. Pellas, N., Kazanidis, I., Konstantinou, N., Georgiou, G.: Exploring the educational potential of three-dimensional multi-user virtual worlds for STEM education: a mixed-method systematic literature review. Educ Inf Technolo. 22, 2235–2279 (2017) 5. Radianti, J., Majchrzak, T.A., Fromm, J., Wohlgenannt, I.: A systematic review of immersive virtual reality applications for higher education: design elements, lessons learned, and research agenda. Comp Educ 147, 103778 (2020) 6. Garcia Estrada, J., Prasolova-Førland, E.: Improving adoption of immersive technologies at a Norwegian university. In: 2022 8th International Conference of the Immersive Learning Research Network (iLRN), pp. 1–5. IEEE, May (2022) 7. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, Sep 1, pp. 319–340 (1989)

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8. Koutromanos, G., Mikropoulos, T.A.: Mobile augmented reality applications in teaching: a proposed technology acceptance model. In: 2021 7th International Conference of the Immersive Learning Research Network (iLRN), May 17 (pp. 1–8). IEEE (2021) 9. Gregory, S., Scutter, S., Jacka, L., McDonald, M., Farley, H., Newman, C.: Barriers and enablers to the use of virtual worlds in higher education: an exploration of educator perceptions, attitudes and experiences. J. Educ. Technol. Soc. 18(1), 3–12 (2015) 10. Sugar, W., Crawley, F, Fine, B.: Examining teachers’ decisions to adopt new technology. J. Educ. Technol. Soc. 7(4), 201–213 (2004) 11. Häkkilä, J., Colley, A., Väyrynen, J., Yliharju, A.J.: Introducing virtual reality technologies to design education. Seminar. Net 14(1), 1–12 (2018) 12. Matsika, C., Zhou, M.: Factors affecting the adoption and use of AVR technology in higher and tertiary education. Technol. Soc. 67, 101694 (2021) 13. Lee, M.J., Georgieva, M., Alexander, B., Craig, E., Richter, J.: State of XR & immersive learning outlook report 2021, Walnut, CA, Immersive Learning Research Network (2021) 14. Strauss, A.L.: Qualitative analysis for social scientists. Cambridge University Press, Jun 26 (1987) 15. Marks, B., Thomas, J.: Adoption of virtual reality technology in higher education: an evaluation of five teaching semesters in a purpose-designed laboratory. Educ. Inf. Technol. 27(1), 1287– 1305 (2022) 16. Choudrie, J., Dwivedi, Y.K.: Investigating the research approaches for examining technology adoption issues. J. Res. Pract. 1(1), D1 (2005) 17. Wang, M., Yu, H., Bell, Z., Chu, X.: Constructing an Edu-metaverse ecosystem: a new and innovative framework. IEEE Trans. Learn. Technol. 15(6), 685–696 (2022)

Chapter 3

Innovative Methods for Smart Education: Hybrid Approach Svetlana A. Gudkova , Elena N. Korneeva , Raisa K. Krayneva , Svetlana P. Azarova , and Aigul U. Samuratova

Abstract The development of intelligent and digital technologies explains the need for innovative learning tools in educational systems. They are a lot of modern digital tools for educational activities, which in many foreign works are described as smart technology (SmT), smart learning (SmL), smart environment (SmE) and smart pedagogy (SmP). These technologies and platforms are successfully applied in the new generation of smart universities (SmU), convincingly proving the effectiveness of modern e-learning. The aim of the study is to develop a model for training intellectual resources in higher education. The novelty of the proposed solutions includes the hybrid approach based on the integration of crowdsourcing management tools with ED-Tech and CLIL technologies for their implementation into education export programs and further evolution and maturation of smart education.

3.1 Introduction Modern conditions of educational consumerization require from educational institutions to focus their training processes on the stakeholders’ requirements [1]. Thus, the main directions of educational technology development can evolve in three directions: (a) developing EdTech as a high-tech platform for innovation in education; (b) developing EdTech to expand b2b-type market services and introduce project-based approach in the educational environment (confidently promoted by PMI standards) and startups to form confident skills; (c) investing in their own development of intellectual potential. It is possible to state that such modern accents S. A. Gudkova (B) · E. N. Korneeva Togliatti State University, Togliatti, Russia e-mail: [email protected] E. N. Korneeva · R. K. Krayneva · S. P. Azarova Financial University Under the Government of the Russian Federation, Moscow, Russia A. U. Samuratova Makhambet Utemisov West Kazakhstan University, Uralsk, Kazakhstan © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_3

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allow us to talk about the manifestation of digital maturity for the country and its key areas. It is usually assessed by various indicators, including the “Global Innovation Index” (GII), which reflects the sustainable development of innovative ideas in the economy, business and education. Currently, according to external GII assessments conducted by foreign experts, the Russian Federation ranks 45th, being in the group of countries with a moderate income level [2–4]. The authors consider that one of the key reasons that the global innovation index does not develop at a fast pace is the insufficient level of education. The results of such lag are visualized by the indicator “we do not have time to train the personnel ahead of time”, but we train them “on the run-up” [5]. Therefore, the main goal of this study is to analyze the available means of innovation management in educational systems, which allow a faster rate of mastering new means and methods of information processing in order to further transform the obtained knowledge into new knowledge, hard and soft skills. It can influence the global innovation index for any country. It should be mentioned that nowadays there are a lot of foreign studies devoted to the digitalization and development of EdTech—as a new digital platform used for higher education pedagogy. These are various smart technologies (SmT) and elements of smart learning (SmE) widely presented in these studies [6–8]. The papers reveal that more and more participants of networking are coming to solve various tasks that require joint communication and collective discussion of problems. Each new participant brings his own vision for application of those or other methods, or forms of mutually advantageous cooperation. It can be noted that crowdsourcing is recognized by many scientists as the systemic factor of effective management, which initiates motivation and the search for innovation. Since crowdsourcing can be considered as an effective innovative model for organizing digital information and educational interaction for higher education [9–12]. There are also many studies devoted to the use of CLIL technologies for the implementation of the university export activity and its transformation into an open university, competing with other educational institutions in the international educational market [13, 14].

3.1.1 Research Problem, Goals and Objectives The problem of the study is the lack of tools to manage the export of educational programs, which does not allow rapid and mobile access to the international market of educational services. The inadequacy of the development of educational programs in a foreign language arises due to the reasons identified in the presented study: (1) low or inadequate level of foreign language skills of the teaching staff to the extent required to prepare the required educational and methodological resources. These conclusions are considered in some modern studies [15–18].

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(2) the need to master EdTech and promote them in educational activities; (3) the need to study the features of CLIL as an internationally proven tool for hybrid approach and conducting classes of various types in a foreign language; (4) the need to integrate EdTech with CLIL to organize hybrid learning processes in the context of digital transformation. Therefore, the main goal of the study is to find management tools for collaborative projects in education. Such a tool, in our opinion, is crowdsourcing. Research objectives: (1) To study the possibilities of crowdsourcing as a management and hybrid tool for managing intellectual and other organizational and information resources; (2) To study the features of CLIL as a hybrid technology for rapid transfer of knowledge in a foreign language in the process of professional training of various specialists; (3) To perform a review of EdTech tools used in higher education; As a result of solving the tasks, propose a set of author’s models for solving the identified problem.

3.1.2 Hypothesis The hypothesis of the research lies in the assumption that crowdsourcing can be effectively applied for the team work of experts in the educational systems targeted for the strategic development of higher education institutions and presented in the form of educational projects. The authors consider the hybrid approach for project as an activity to develop export educational programs.

3.2 Theory for Research A review of theoretical sources showed that the study is based on the following key theoretical framework. Crowdsourcing is a new model that implements an innovative way of delegating authority to a multitude of executives, using the means of the Internet and other digital communications. The authors of the article see the peculiarity of using the crowdsourcing model in different interpretations. For example, Malashchenko [17] in her work sees the possibility of involving linguistic students in the preparation of audiovisual translations as examples of presenting educational material. Gudkova S.A. and Yakusheva T.S. propose to train students in linguistic analysis of texts in order to form the skill

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of translation interpretation of various content or presentation using audio accompaniment of the results of their scientific activities [8], or by means of additional targeted training based on EDTech and CLIL methodology [9]. The integration of crowdsourcing, EdTech and CLIL is the basis of knowledge export to the international educational market. EdTech is a complex name for modern means which are used in smart universities to organize learning processes. In the studies V. Uskov [4–6, 19] and his followers reveal a lot of examples for it. We can add that Togliatti State University, in addition to smart technologies, also uses IIoT tools, which are implemented in the form of robotic complexes, highly intelligent automated systems (CATIA), specialized technologies, such as machine learning (E-Learning) used for the employer-sponsored training. CLIL is known as Integrated Learning of Subjects, Content and Knowledge through English. The necessity of applying this methodology is proved by the need to develop an English-language version of those sciences that are studied at universities for international students who can get education remotely [20]. Below the authors consider the key results for the tasks. The solution of the first problem “To study the possibilities of crowdsourcing as a management tool for managing intellectual and other organizational and information resources” is shown in Tables 3.1, 3.2 and in Figs. 3.1, 3.2 by the author’s models. Table 3.1 Reasons for participation in the university crowdsourcing project in the implementation of hybrid approach for CLIL and EdTech tools (fragment) Reasons for participating in the university crowdsourcing project

The share of respondents who indicated the main reason, % Togliatti State University

Financial University

West Kazakhstan University

Average

Grant application

14.3

20.3

22.8

19.1

Competition for the best e-learning 22.5 tool

17.1

15.5

18.4

Competition for the best training platform

15.4

20.4

14.2

16.7

The possibility of scientific publication

20.6

15.1

19.7

18.5

Gaining personal experience in an open university

14.4

15.2

16.8

15.5

Acquisition of new knowledge and skills

9.5

7.3

5.8

7.5

The productive need for university development

3.3

4.6

5.2

4.4

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Table 3.2 Competency-based model of the benefits of crowdsourcing management Benefits for the university

Benefits for performers

New competence

Mobilization of intellectual resources for project activities

Motivation of acquiring new knowledge, experience, skills and abilities relevant to the university

Intellectual capacity index

Networking solutions for specific tasks by means of EdTech, SmT

Getting experience of working with EdTech and SmT The project activity skills and team working skills at the initial stage

Hard Skills Soft Skills Adaptive Skills Digital Skills Technology Skills PMI Skills

Practice of obtaining the required results

Motivation for professional and scientific activity Experience of collective decision-making on the basis of intellectual work and technology

Development of a digital maturity index Development of an index of intellectual potential Development of international communication skills

25

20

15

10

5

0 Grant application

Competition for the best e-learning tool

Competition for the The possibility of best training scientific publication platform

Togliatti State University

Financial University

Gaining personal experience in an open university

Acquisition of new The productive need knowledge and skills for university development

Kyrgyz National University

Average

Fig. 3.1 Need for participation in the crowdsourcing model Stakeholders' demands

Hard Skills & Soft Skills

CLIL

PMI

GII Нard Skills+Soft Skills+ Digital Skills+Technology Skills+Adfptive Skills

Crowdsourcing EdTech

SmT, SmP, SmE, SmI

Resourse

Executive team: intellectual resources of the university + voluntary helpers + other resources

Fig. 3.2 Crowdsourcing management model for hybrid approach of EDTech and CLIL

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3.3 Our Results 3.3.1 The Crowdsourcing Model of EdTech and CLIL Integration Management in Higher Education Institutions We considered the collaborative model of two universities: Togliatti State University (TSU), Fina University (FU) and Makhambet Utemisov West Kazakhstan University (KNU). All the universities take part in open education development programs. The project was a crowdsourcing one, as the main goal was to find volunteer teachers with high intellectual potential and English-language communication skills in a professional and scientific environment. The results of the survey and peer review yielded the following data, which are shown in Table 3.1. The reasons for selecting participants in project activities and the proportion of respondents who chose one or another position were assessed. The analysis of average evaluations showed that the external beneficiaries that they could get as a result of gaining experience in project activities under the conditions of new tools of educational environment turned out to be an attractive motivation. Figure 3.1 shows the results of comparative analysis. The dotted line shows the average values. The figure shows that almost all of the interviewed employees have realized the need to participate in project activities to bring the university to the status of “open university”. This gives the right to compete in international educational projects and to host foreign students. It is in this aspect that the crowdsourcing model is the most attractive, because it is designed to manage the remote work of a multitude of intellectual resources involved in solving the target problem. Figure 3.2 shows the authors’ vision of the application of the crowdsourcing model. This model was proposed and tested by the authors during the development of educational contents in a foreign language for the employer-sponsored training of university students to work in high-tech production. The figure shows that the crowdsourcing tool combines the requirements of the stakeholders and the existing competencies of teachers. They are shown by the block of forming professional (Hard Skills) and extra professional (Soft Skills) competences and skills. New competences are formed as a result of collective work motivated by acquiring new knowledge and skills and teamwork experience. The advantages of the presented model are shown in Table 3.2. A fragment consisting of several steps is shown, each of which refers to a different stage of project activities managed by crowdsourcing. The table shows only the results that have already been achieved by the authors of this publication. The experiment was conducted over different (in terms of duration) time and in different directions (2017–2022): scientific, educational and methodological, employer-sponsored training.

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The improvement of the model (Fig. 3.1) is currently considered and studied. The solution of the second problem “To study the features of CLIL as a technology of rapid transfer of knowledge in a foreign language in the process of professional training of various specialists” will be discussed below.

3.3.2 Simulation of the Innovative Development Trajectory for the University: CLIL Technology and the Export-Educational Activities of the University The authors understand the export of educational services in higher education as the organized work on preparation and dissemination of such educational content in a foreign language, which would be relevant and for the stakeholders of international educational services markets and which would form a certain educational, methodological and scientific status of the university in the world educational space. To implement the possibility of providing such services, it is necessary to have developed and approved export educational programs with all the educational and methodological support of their content and placement, providing access, real-time feedback, etc. Under the export educational program is understood a complex set of educational characteristics, allowing to broadcast or export the formed knowledge in different discourses of communication. For example, when implementing the interaction of “teacher-student”, “student-educational Internet-environment”, “mentor at the enterprise—university graduate”, “faculty of the university—enterprise specialists” and so on. Since 2019 Togliatti State University has been actively working on the implementation of export educational programs on the basis of educational collaboration of the university with the real sector of the economy. However, despite the high educational status of the university students, there are some factors that can reduce the effectiveness of educational activities. They were especially evident during the pandemic. Figure 3.3 shows the most common problems that were identified in distance learning (SmE) students. As we can see, the biggest problem is the “lack of opportunity for personal communication with the teacher”, which is the most time-consuming for students when studying science cycle and foreign language. There are also a number of “painful” points for teachers in the organization and implementation of programs of strategic development of the university through the export of knowledge, taking into account the realities and conditions of the digital society. Sociological survey and expert activity of the authors of the article identified several significant problems, without solving which it is impossible to effectively solve the project on export activity.

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30,0

TSU

KNU

FU

25,0

20,0

15,0

10,0

5,0

0,0 Lack of personal contact with the teacher

Technical failures of distance learning

Significant financial costs

No lively interaction and Lack of self-motivation and A detached assessment of exchange of experience self-discipline material

The depersonalization of faculty and students

Fig. 3.3 Analysis of problems while implementing hybrid approach for EdTech and SmT in SmE

The most significant ones are the low level of foreign language proficiency among the teaching staff of technical areas of training and insufficiently high skills of interaction with IT and effective use for the solution of the set tasks among the teachers of humanitarian areas of training. To solve the identified problem points in one of the analyzed universities (Togliatti State University), the university developed and implemented the program of additional professional education on the use of CLIL methodology to implement the program of educational content creation and further export of knowledge. Table 3.3 shows a fragment of the professional development program implemented in 2019–2022 on the basis of TSU. Table 3.3 Crowdsourcing model for hybrid approach in CLIL and EdTech (fragment) Participant

Responsibilities

Participation Results in the project, %

Foreign Language Instructor with CLIL skills

Selection of content, training, implementation of feedback

40

Selection of lexical and grammatical units. Simulation of thesaurus for the subject area, the content design

Special subject teacher with B1 proficiency in English

Content realization, consulting activities in the profile of the discipline

40

Selection of lexical units. Simulation of thesaurus for the subject area, Content Planning

CLIL certified coach

Educational and methodological function, coordinating

15

The competence and skills description

Stakeholders

Assessment

5

Expert assessment for the content

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The table shows fragmentarily selected types of work at the initial stage of activity. The algorithm of organization of work with CLIL technologies in the enlarged form consists of seven stages. It is shown below in other paper.

3.4 Conclusions and Trends 1. Nowadays, Russian universities should comply with the requirement for their performance at the domestic and international educational markets and meet the requirements both for the educational and scientific export activities. Dealing up with the challenge is based on the development and fostering of scholars’ foreignlanguage professional competence for creating and promoting their courses for specific professional purposes and educational projects that are competitive at the international market. 2. The novelty of the study is represented by the author’s vision for the importance of new innovative and hybrid approaches including crowdsourcing, EDTech and CLIL for knowledge export activities of the university; as well as fostering their hard skills and soft skills based on scientific communication in international society. 3. Practical value is revealed in its possibility of increasing the university competitiveness in the international educational market through the development of author’s courses or workshops as well as providing educational collaborations and academic mobility. 4. The authors believe that effective implementation of knowledge export contents is possible only due to collaborative projects of experts possessing both hard and skills based on effective integration EDTech.

References 1. Development of export potential of the Russian education system: priority project passport. http://static.government.ru/media/files/DkOXerfvAnLv0vFKJ59ZeqTC7ycla5HV.pdf 2. Export of Russian educational services: Statistical Collection. Issue 8/Ministry of Science and Higher Education of the Russian Federation—M.: Center for Social Forecast and Marketing, 2021, 536 p 3. Kuntsman, A.A.: Transformation of internal and external business environment in the conditions of digital economy [Electronic resource]. Access mode: http://uecs.ru/index.php?option= com_flexicontent&view=items&id=4131 (reference date: 14.08.2021) 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). https://doi.org/10.1007/978-3-31959454-5 5. Uskov, V.L., Bakken, J.P., Aluri, L.: Crowdsourcing-based learning: the effective smart pedagogy for STEM education. In: Proceedings of 2019 IEEE Global Engineering Education Conference EDUCON, April 9–11, 2019, Dubai, UAE, IEEE (in print)

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6. 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, pp. 191–204. Springer. ISBN 978-3-319-59450-7 (2017). https://doi.org/10.1007/978-3-319-59451-4 7. Adamko, A., Kadek, T., Kosa, M.: Intelligent and adaptive services for a smart campus visions, concepts and applications. In: Proceedings of 5th IEEE International Conference on Cognitive Infocommunications, November 5–7, 2014, Vietri sul Mare, Italy, IEEE (2014) 8. 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) 9. Gudkova, S.A., Yakusheva, T.S., Vasilieva, E.A., Rachenko, T.A., Korotenkova, E.A.: Concepts of educational collaborations and innovative directions for university development: knowledge export educational programs. In: Smart Innovation, Systems and Technologies, vol. 188, pp. 305–315 (2020) 10. Sherstobitova, A.A., Gudkova, S.A., Kazieva, B.V., Kaziev, K.V., Kaziev, V.M., Yakusheva, T.S.: University innovative networking in digital age: theory and simulation. In: Smart Innovation, Systems and Technologies, vol. 240, pp. 293–303 (2021) 11. Cornell University, INSEAD, WIPO: |2021. globalinnovationindex.org 12. Aghion, P., Antonin, C., Bunel, S.: The Power of Creative Destruction: Economic Upheaval and the Wealth of Nations. The Belknap Press of Harvard University Press, Cambridge, MA (2021) 13. Gudkova, S.A., Emelina, M.V.: Teaching foreign languages in higher school for social studies and science students and professional mobility. Human. Balkan Res. 1(3), 17–20 (2019) 14. Gudkova, S.A., Glukhova, L.V., Syrotyuk, S.D., Krayneva, R., Filippova, O.A.: Validating development indicators for smart university: quality function deployment. In: Smart Innovation, Systems and Technologies, vol. 240, pp. 241–252 (2021) 15. Gudkova, S.A., Dayneko, M.V., Yashchenko, N.V., Burenkova, D.Yu., Treshina, I.V.: Managerial approach for foreign language learning and fostering in a smart university environment. In: Smart Innovation, Systems and Technologies, vol. 240, pp. 395–405 (2021) 16. Glukhova, L.V., Sherstobitova, A.A., Korneeva, E.N., Krayneva, R.K.: Vuca managers training for smart systems: innovative and organizational approach. In: Smart Innovation, Systems and Technologies, vol. 188, pp. 361–370 (2020) 17. Malashchenko, M.V.: Linguocultural features of the media course of education: analysis of Anglo-American sources. In the collection: Linguistics of the future: new trends and prospects. Proceedings of the International Scientific Conference, pp. 334–338 (2019) 18. Sherstobitova, A.A., Glukhova, L.V., Sergeeva, I.G., Tihanova, N.Y.: The remote process support for collaborative work. In: Smart Innovation, Systems and Technologies, vol. 144, pp. 631–641 (2019) 19. Uskov, V.L., Bakken J.P., Pandey A.: The ontology of next generation smart classrooms. In: Uskov, V.L., Howlett, R., Jain, L. (eds.) Smart Education and Smart e-Learning. Smart Innovation, Systems and Technologies, vol. 41, pp. 3–14. Springer, Cham (2015) 20. Glukhova, L.V., Syrotyuk, S.D., Sherstobitova, A.A., Gudkova, S.A.: Identification of key factors for a development of smart organization. In: Smart Innovation, Systems and Technologies, vol. 144, pp. 595–607 (2019)

Chapter 4

Impact of Offline and Online Lecture Formats on Student Satisfaction with the University Takumi Kato

Abstract The COVID-19 pandemic is driving digitization at an unprecedented rate, making online lectures the standard format of lecture delivery within universities around the world. Many studies have investigated the effects of online lectures. However, they do not compare online lectures with traditional offline formats. To fill this research gap, the current study compares the impact of offline and online lectures on student satisfaction with universities. A propensity score was applied to an online survey of university students in the Tokyo metropolitan area of Japan, and causal effects were estimated. The results indicated that satisfaction with offline lectures did not have a significant effect on students’ satisfaction with the university; however, satisfaction with online lectures showed a positive impact on satisfaction with the university. In addition, the results confirmed that smaller and rural universities tended to be slower to respond to online lectures. Market principles have been adopted by universities, and competition for growth has intensified on a global scale. Without an organization-wide strategy and effective budget allocation, universities may struggle to compete. As the results demonstrate, universities should prioritize online lectures over traditional face-to-face lectures in the future. While both lecture formats have unique advantages and disadvantages, failing to invest in the new type of lecture format could lead to lower student satisfaction with universities.

4.1 Introduction While striving toward sustainable economic development, many governments have focused on universities and encouraged independence and competition [1]. As a result, the higher education sector has witnessed the adoption of market forces and an intense competition for growth on a global scale [2]. Business schools were the frontrunners in this competition [3]. However, today it is difficult to compete in the T. Kato (B) School of Commerce, Meiji University, Tokyo, Japan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_4

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absence of an organization-wide strategy and an effective budget allocation [4–6]. Various costly activities such as advertising, investment in infrastructure, recruitment of academic stars, and discounts on tuition are aimed at attracting students [7]. Universities engage in performance-based funding races to secure expansive budgets [8]. Hence, an understanding of factors affecting student satisfaction is essential for universities to successfully strengthen their brands [9]. The COVID-19 pandemic has driven digitization at an unprecedented rate [10]. As such, universities need to consider and invest in the new service of online lectures. Despite some negative reactions, online lectures have become a standard service for universities around the world [11]. This service includes both lectures and seminar formats with a small number of students [12]. Many studies have already been conducted on the effects of online lectures [13–22]. However, the existing literature contains few comparisons of the online and traditional offline formats. To fill this research gap, this study compares the impact of offline and online lectures on university satisfaction. The results clarify which formats universities should prioritize to ensure appropriate resource allocation.

4.2 Related Work and Hypothesis Development Some previous studies have drawn negative conclusions about online lectures during the pandemic. These studies have warned that online lectures lead to a decline in student motivation and understanding of teaching materials, as well as a sense of isolation due to inadequate communication with teachers [13, 14]. However, the context for these studies was the unprecedented social situation of lockdown and lack of preparedness among teachers and students for transition to the online mode of learning [15]. Once the students and faculty became accustomed to the pandemic and the faculty’s online lecture skills improved [16], students responded positively to the online format for both lectures and seminars [17]. Several requirements have been established for increasing the effectiveness of online lectures. These include improving the perceived ease of using online lectures [18], securing an adequate degree of communication [19], creating an environment close to reality [20], effectively blending offline and online workload [21], and letting students set target values in advance [22]. Survey results suggest that when these requirements are met, students prefer online to the offline format [12]. However, a comparison of the impact of online and offline lectures on university satisfaction has not yet been demonstrated. Based on the existing literature [12], the following hypotheses were proposed: H1: Satisfaction with offline lectures positively influences satisfaction with the university. H2: Satisfaction with online lectures positively influences satisfaction with the university.

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H3: Satisfaction with online lectures, rather than satisfaction with offline lectures, has a greater impact on satisfaction with the university.

4.3 Method From October 1 to 5, 2022, an online survey of university students (age: mean = 19.917, standard deviation = 1.366, min = 18, max = 24) in the Tokyo metropolitan area was conducted. The target location was the metropolitan area (Tokyo and neighboring prefectures), which has the largest number of universities in Japan. Given that the last state of emergency in Japan, due to COVID-19, was declared in the summer of 2021, it can be assumed that at the time of the survey, these students had experienced typical university life for more than a year. Specifically, their university experience did not coincide with the initial stages of the online lecture reform, or the period of isolation and lockdown. The survey was administered to the target age and region of residence from the survey collaborators database owned by Cross Marketing Group Inc., a Japanese research company. A total of 300 people were asked to respond. Data from 278 respondents, excluding students from junior colleges and vocational schools, were used for verification. Respondent attributes are shown in Table 4.1. The survey included questions about five attributes: gender, age, form of school (university, junior college, or vocational school), university location (prefecture), and university type (national/public or private). Next, respondents were asked to evaluate their university on various factors. As shown in Table 4.2, the survey included three questions related to satisfaction, and following the approach in existing literature, five questions each on lectures, campus, and facilities [23–26]. All questions were presented with a five-point Likert scale (Definitely disagree = 1; Definitely agree = 5). Thus, the survey included a total of 23 questions, combining five attribute questions. Table 4.1 Respondent attributes

Item

Content

Number of respondents

Ratio (%)

Gender

Male

141

50.7

Female

137

49.3

Age

18

46

16.5

19

58

20.9

20

97

34.9

21

46

16.5

22

18

6.5

23

7

2.5

24

6

2.2

37

13.3

241

86.7

University type

National/Public Private

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Table 4.2 Variable list No.

Category

Variable

Question

1

Satisfaction

Satisfaction

I am satisfied with the university 3.500

1.146

2

Offline

I am satisfied with face-to-face lectures

3.385

1.140

3

Online

I am satisfied with online lectures (real time/on-demand)

3.083

1.167

LEC_Faculty

The university has many competent professors

3.532

1.084

5

LEC_English

The university has a lot of lectures in English

3.072

1.147

6

LEC_Discussion

The university is rich in opportunities for group discussion

3.284

1.089

7

LEC_Fieldwork

The university is rich in 3.032 fieldwork and research activities

1.096

8

LEC_Paper

The university is rich in opportunities for presentations

3.295

1.051

CAM_Beauty

The university has a beautiful campus

3.428

1.226

10

CAM_Location

The university has good access to the campus

3.155

1.292

11

CAM_Size

The university has a large campus

3.367

1.200

12

CAM_Nature

The university has a lot of nature 3.442 on its campus

1.220

13

CAM_Shops

The university has many shops and restaurants in and near the campus

3.018

1.212

FAC_Library

The university has a well-equipped library

3.809

1.066

15

FAC_Study

The university is well-equipped for study and research

3.619

1.091

16

FAC_IT

The university is well-equipped with IT facilities (PCs, Wi-Fi)

3.532

1.136

17

FAC_Field

The university has plenty of playgrounds and places for club activities

3.176

1.163

18

FAC_Relax

The university has plenty of places for students to relax

3.353

1.142

4

9

14

Lectures

Campuses

Facilities

Note SE means standard error

Mean

SE

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For hypothesis verification, this observational study used propensity score matching [27], which is a typical method for estimating effects when randomization is impossible. Multiple covariates were aggregated into one variable called the “propensity score,” which was used to correct covariates in the treatment and control groups. For H1 verification, students who were satisfied with offline lectures formed the treatment group, and students who were not satisfied with offline lectures constituted the control group. As regards the question on the degree of satisfaction with offline lectures (No. 2 in Table 4.2), those who selected the top two items on the 5-point Likert scale were defined as satisfied, and the rest were defined as dissatisfied. Similarly, for the verification of H2, students who were satisfied with online lectures made up the treatment group, and students who were not satisfied with online lectures formed the control group. When the true value of the propensity score of each subject is unknown, logistic regression model is commonly used for estimation. The objective variable here was offline satisfaction dummy (H1)/online satisfaction dummy (H2), and the explanatory variables were evaluation variables for university services (Nos. 4–18 in Table 4.2). The stepwise method was then adopted to select valid variables. Accordingly, the effects were estimated by inverse probability weighting (IPW), which represents the reciprocal weight of the propensity score. Assuming that the propensity score of individual i was psi , the expected value of the treatment group (result Y1 [No. 1]), and the control group (result Y0 [No. 1]), could be obtained using Eqs. (4.1) and (4.2). The average treatment effect (ATE) could be calculated using E(Y1 ) − E(Y0 ). When ATE was positive and the 95% confidence interval did not include zero, the effect was judged to be significant at the 5% level. As such, the hypothesis was supported. In the following equations, N represents the sample size, z = 1 represents the treatment, and z = 0 represents the control. The statistical analysis software R was used to perform the analysis. N N  z i Y1  z i / psi i=1 psi i=1

(4.1)

N N  (1 − z i )Y1  1 − z i / 1 − psi 1 − psi i=1 i=1

(4.2)

E(Y1 ) =

E(Y0 ) =

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4.4 Results and Implications 4.4.1 Results Table 4.3 shows the results of the logistic regression model for estimating propensity scores. In the offline format, competent professors, abundance of discussion opportunities, and well-equipped libraries contributed to lecture satisfaction. In the online format, IT facilities and campus size had a positive effect on lecture satisfaction, while the abundance of nature had a negative effect. A large campus size indicates that the university is large and has the capacity to invest in new technologies. A nature-rich campus is likely to be located in the countryside, indicating a slower response to new initiatives as compared to the universities in the city center. Both McFadden’s Pseudo R-squared value and Adjusted McFadden’s Pseudo R-squared value, confirmed the validity of these models. Using the estimated propensity scores, ATE was calculated to be 0.226 for offline lectures (Table 4.4). However, no significant effect was detected because the 95% confidence interval included zero; as such, H1 was unsupported. In the online lectures, ATE was 0.337. A significant effect was obtained at the 5% level, and H2 was Table 4.3 Results of logistic regression models for estimating propensity scores Variable

Offline lectures

Online lectures

Odds ratio

SE

p-value

Odds ratio

SE

p-value

Intercept

0.000

1.110

0.000

LEC_ Faculty

2.597

0.191

0.000

***

0.002

0.925

0.000

***

***

1.406

0.169

0.043

*

1.416

0.149

0.020

*

1.818

0.173

0.001

***

CAM_Size

1.845

0.165

0.000

***

CAM_ Nature

0.621

0.154

0.002

**

1.713

0.173

0.002

**

LEC_ English LEC_ Discussion

1.607

0.183

0.009

CAM_ Location

0.755

0.152

0.063

FAC_ Library

1.857

0.220

0.005

FAC_Study

1.486

0.232

0.087

FAC_IT

**

**

McFadden

0.360

0.233

Adjusted McFadden

0.319

0.184

Note SE means standard error; ***p < 0.001; **p < 0.01; *p < 0.05

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Table 4.4 Estimation results of average treatment effect (ATE) Target

E(Y 1 )

E(Y 0 )

ATE

95% confidence interval

Offline lectures

3.684

3.458

0.226

− 0.060

–

0.511

Online lectures

3.774

3.437

0.337*

0.090

–

0.585

Note ***p < 0.001; **p < 0.01; *p < 0.05

supported. H3 was also supported, as the data indicated that online lectures had a greater effect on university satisfaction than offline lectures.

4.4.2 Theoretical Implications Many publications have indicated the efficacy of online lectures, which have become the standard service at universities after COVID-19 [13–22]. However, previous research has not compared online and offline formats. As such, current knowledge is limited and segmented; it does not provide comprehensive information about university lectures. This study fills the existing research gap and extends the current understanding of online lectures.

4.4.3 Practical Implications This study has two practical implications. First, the need for online lectures is greater than that for offline lectures for university students. Hence, universities should give higher priority to online lectures than traditional face-to-face lectures. As both formats have their own set of advantages and disadvantages, universities cannot restrict themselves to one format. However, failing to invest in the new format could lead to lower student satisfaction with universities. Second, evaluation of student satisfaction should cover multiple services. Given the limited university resources, organization-wide strategies and effective budget allocation are essential for effectively increasing student satisfaction [4–6]. Therefore, it is important to look at the whole picture, compare the effects of online and offline formats, and prioritize accordingly.

4.4.4 Limitations and Future Work This study has four main limitations. First, the results are limited to universities in the metropolitan area of Tokyo, which may hinder generalization. Second, the present study reflects only the current students. To strengthen their brands, universities must

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also appeal to others. Specifically, alumni are of great value to higher education institutions because of the time and money they can spend on their alma mater [28]. In recent times, reskilling has become popular among business executives [29]; university education can play an important role here. Third, it is not possible to judge the difference between the effect of real-time and of on-demand forms of online lectures on satisfaction. Fourth, it is not possible to judge the suitability of this study’s results for online-centered universities. For example, Minerva University, which uses small-group online education on a global scale without having its own campus, is growing rapidly [30]. In Japan, there are similar online-centered universities or correspondence courses at traditional universities. As such, future research should consider how universities can expand, both in area and in the capacity to reach out to groups in addition to current students. More research is needed on the segmentation of the format of online lectures, as well as on the new style of online-centered universities. Acknowledgements This work was supported by Nomura School of Advanced Management (A004).

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28. Snijders, I., Wijnia, L., Rikers, R.M., Loyens, S.M.: Alumni loyalty drivers in higher education. Soc. Psychol. Educ. 22(3), 607–627 (2019). https://doi.org/10.1007/s11218-019-09488-4 29. Kato, T., Yamamoto, S., Miyaji, A., Katsuki, S., Kataoka, C.: Which is more effective for job satisfaction, expert knowledge or career status through training? Differences between large enterprises and SMEs. J. Appl. Soc. Sci. 17(1), 74–91 (2022). https://doi.org/10.1177/193672 44221128553 30. Yoshimi, S.: Online university, pandemics and the long history of globalization. Inter-Asia Cult. Stud. 21(4), 636–644 (2020). https://doi.org/10.1080/14649373.2020.1832306

Chapter 5

Possibilities of Using Multimedia Technologies in the Learning Process for Children with Special Needs Natalya Prokofyeva, Sabina Katalnikova, Viktorija Ziborova, and Andrejs Semrjakovs

Abstract This study is devoted to issues related to one of the priority areas in education—the introduction of smart information technologies into educational process and search for new learning methods for different types of trainees. This article contains three sections: the first one describes multimedia technologies used in the learning process, their advantages, and disadvantages; the second section describes the problem of educating children with special needs, their rehabilitation and interaction with special pedagogues; the third section presents the results of a survey of children participating in the experiment, as well as the opinions of special pedagogues. In this paper, the authors analyzed literature on creation and development of educational media solutions for children and considered recommendations for developing applications for both ordinary children and children with various problems. To conduct the study, the authors developed an application that includes three groups of tasks for the child: identifying an object (what object is on the screen, what kind of object it is); name the object (as the development of speech); repeat the action (as part of the awareness of the capabilities of your body). The developed system was tested in a specialized institution of preschool education for children with special needs.

5.1 Introduction At the present stage, one of the topical issues is the search for new methods in the educational process. One of the priority areas in education is the introduction of various media technologies into the educational process. The use of new technologies in education makes it possible to create and develop special skills in children with different abilities [1]. The use of media technologies allows you to make lessons more N. Prokofyeva (B) · S. Katalnikova · V. Ziborova · A. Semrjakovs Riga Technical University, 6A Kipsalas Street, Riga 1048, Latvia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_5

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visual and effective in terms of learning. For example, the use of media technologies allows the pedagogue to visually show the movement of objects and their interaction; this helps children learn the material much better [2]. This paper considers the possibility of replacing various objects that are now used by special pedagogues of a preschool institution to draw the child’s attention to the learning and rehabilitation process with media analogs that can keep the child’s attention to the learning and rehabilitation process longer. Smart technologies in education for children with special needs have the potential to revolutionize the way that these children learn and interact with their educational environment. These technologies, which include a wide range of digital tools and platforms, can provide children with special needs with a level of accessibility and support that may not be possible with traditional teaching methods. The authors believe that the use of media technologies in the process of education and rehabilitation will be able to interest children with special needs. Attracting the attention of children will help them better master the material, motivate them to the learning process, as well as attracting the attention of children will simplify the work of special pedagogues of a preschool institution and make the rehabilitation process simpler and more interesting for the child. There are many different smart technologies that are being used in education for children with special needs, and the field is constantly evolving as new tools and platforms are developed. The key is to find the technologies that are most suitable for the individual needs and abilities of each student, and to use them in combination with other teaching strategies and supports to create a comprehensive and effective learning experience. This article contains three sections. The first describes multimedia technologies used in the learning process, their advantages and disadvantages. The second section describes the problem of educating children with special needs, their rehabilitation and interaction with special pedagogues. The third section presents the results of a survey of children participating in the experiment, as well as special pedagogues. At the end, conclusions on the results of the study and plans for further work are presented.

5.2 Multimedia Technologies in the Learning Process There are many sources related to the use of multimedia technologies in the educational process. Multimedia technologies use text, graphics, animation, and sound to convey information to the user. For example, a phone, computer, and tablet can use text, sound, video, and other media elements to convey information. Also gaining popularity applications in augmented reality uses various media technologies [3] to “complement our world”. These smart technologies can be used to create immersive learning experiences that can be tailored to the needs and abilities of individual children. Separately, it is worth mentioning educational media. They refer to communication channels that carry messages for educational purposes [4]. Educational media is

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a tool that can be used by the pedagogue to simplify the learning process [5], helping trainees to stay motivated while learning new material, as well as better assimilate the material covered. It should be noted that the development of educational media is a complex process and requires the attention of many specialists.

5.2.1 Analysis of Related Research from the Technical and Pedagogical Side The literature on the creation and development of educational media solutions for children was analyzed. Recommendations for the development of educational applications are considered, both for ordinary children and for children with special needs, as well as various technologies that have been used to implement educational applications. In the study [6], the authors suggest using gamification techniques to develop a video game for children from 4 to 5 years old to learn English. The authors hypothesized that by focusing on app-assisted education, the trainees are motivated to learn in a fun way, which will improve their performance. The VAK learning model— visual, auditory and kinesthetic [6] was used in this work. The authors believe that this approach makes it possible to carry out the process of studying the material in a fun way and keep the child’s attention. Twenty-four children took part in the experiment. The results of the assessment of children’s knowledge before and after using the application were obtained. As a result, it was recorded that before using the application, the average grade in children was 6.5, and after applying the proposed solution, the average grade became 8.5 [6]. In another study by Veronica and Calvano [7], a more complex topic is raised, the use of media technologies to explain to children the problem of sea pollution. The study demonstrates the use of media technology to raise awareness of sea litter, one of the major threats to sea species. As a media technology, this article deals with educational video and educational game. The video, based on animated graphics, allows children to imagine the problem of sea litter. The content of the video was carefully chosen by the authors to make it easier for children to understand the problem. The game introduced a simple concept where kids help a fish. The paper separately notes that game design is a complex process that requires the participation of different specialists [7]. To develop the product faster, it was decided to use the prototype approach. The authors note that the development of a prototype allows you to determine the requirements for the product at an early stage and helps to get the user’s reaction earlier. As part of the experiment, a group of fourth-grade children was formed. At first, the children were shown a video about the problem with garbage in the sea, then this topic was discussed in the class, and after that, the children used the proposed game on computers at school for an hour. As a result of the work, the authors noted the positive impact of media technologies on children. It was interesting for children to get new knowledge in video and game format. The

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authors also noted that pedagogues showed interest in a new method of knowledge transfer [7]. The paper [5] considers the possibility of using media technologies and the gamification approach for teaching children with Special Educational Needs (SEN) and especially for Deaf and Hard of Hearing (DHH) children. The authors used the gamification approach as the use of game design elements in non-game contexts [8]. The work raised two main questions. First, what are the main difficulties that trainees face? The second question is how does the multimedia environment of learning and gamification affect the level of understanding and trainees’ achievement? To solve the first question, interviews were conducted with pupils, teachers, and parents to assess the difficulties on the part of each participant. The answer to the second question was obtained as a result of interviews after experiments and observation of pupils. As a result of the study, the authors note the positive impact of educational games on children with special educational needs. Also, the best result was achieved with the “Special educator and learning games” approach. A special needs educator is a worker who can help children with disabilities in the process of learning and mastering new skills [5]. From this, it can be concluded that it is worth developing and considering an application for children with special educational needs, as an auxiliary material for a special pedagogue, and not its replacement. In work [9], the authors note that information technology today is not a luxury, but a necessity. New technologies are already a part of our life, we must apply technologies in the education system. In this paper, the authors consider three applications— Soundmatch, Sequences for Kids and Patterns. To evaluate these applications, a group of pedagogues and children was formed. Participants interacted with the proposed applications for a while. At the end of the experiment, the group was offered a survey, which consisted of four groups of questions [9]: • • • •

Application Design Contents Interaction with the Application Communicative Level of the Application.

As a result of the work, the authors also noted a positive assessment of the programs under consideration, both from pedagogues and from children. The authors believe that the use of mobile applications as an additional tool has a positive effect on the education process, provided that the application is used correctly, and the child can take the opportunity to contact the pedagogue. In work [10], the issue of educating children in mathematics is considered. The authors hypothesized that the use of multimedia technologies will help children better understand mathematics. As a solution to the problem, it was decided to use the gamification approach. To evaluate this approach, the authors evaluated the developed solution for computers. The test group consisted of twenty elementary school pupils. The test group was evaluated before and after applying the proposed solutions. As a result of the work, the authors note the positive impact of the proposed solutions. So, the average score before using the application was fifty-eight, and after using the

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application, the average score increased to seventy-nine [10]. The paper also noted that in the learning process, pupils enthusiastically study the material, as they could do this in the process of interacting with the game. The work [1] turned out to be quite interesting and useful. The paper describes the process of developing an application for children at risk of learning disabilities. A solution is proposed based on digital multimedia that can be accessed online, which will improve the reading and writing skills of children before they experience underachievement in school [1]. Also, the work [11] was additionally considered, in which useful requirements can be noted when developing a solution for people with special needs. The authors divided the requirements proposed for consideration into four levels: • • • •

Graphical layout Structure and navigation User Language.

The suggested guidelines for developing mobile applications and websites can be adapted to other technologies and used to create a useful media-based learning application.

5.2.2 Choice of Development Technology Various solutions for the organization of education and the technologies used in them were considered, both for ordinary children and for children with special needs. As a result, a review of the literature on this topic was conducted and, on its basis, a diagram was created (Fig. 5.1), which shows the separation of technologies. To determine the right technology to implement the system (prototype), it was decided to add some technical questions to interviews with special preschool pedagogues. Based on the results of the interviews and on the reviewed studies, the authors plan to select the appropriate technology to implement a prototype for children with developmental disabilities.

5.3 System Development Process for Children with Special Needs The process of creating the system was based on the rapid application development (RAD) model, that is, based on the minimum requirements, a prototype solution is created and based on feedback, the necessary changes are made to the prototype [12] (Fig. 5.2).

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Fig. 5.1 Results of the review of literature

Fig. 5.2 Rapid application development model

The authors, while creating a system for each game, demonstrated prototypes to special pedagogues of a preschool educational institution. If necessary, changes were made to the prototypes of the games according to the comments and remarks of special pedagogues.

5.3.1 Analysis of the Interview of Special Pedagogues In order to learn more about the problem area and to obtain information about the tasks for children that a special pedagogue can offer a child, the authors conducted

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interviews with special pedagogues of a preschool educational institution. The interview contained questions related to children’s problems in the learning process, tasks for children, and technical questions were added to the interview questions. For example, about the experience of interacting with various technologies and the availability of these technologies for special pedagogues. People with developmental disabilities can learn new skills just like everyone else, but they require more time and repetition to do so. To increase the time of work with the child and achieve the concentration of the child on the assigned tasks, a special pedagogue uses various items that he has available. It can be pictures, pencils, objects that make different sounds. Separately, it was noted that special pedagogues try to present each task as some kind of game. This helps the child to stay focused on the task for longer and helps the child improve their skills. Based on this information, the solution to the problem can be achieved by creating educational video games that the child can interact with. Educational video games are made up of many components: audio effects, video, images, and more that fall under the definition of media technology. Thus, the authors are considering the possibility of creating simple video games with goals that will coincide with the goals of assignments from a special pedagogue. Based on the results of the interview, a scheme of interaction with the application of the child and the special pedagogue was developed (Fig. 5.3). The application should motivate the child to complete tasks, and a special pedagogue, in this system, should be an instructor and assistant. It is expected that the child, using the application, will receive new knowledge and repeat the material covered. From a special pedagogue, in this system, is expected to help the child understand how to interact with the application and help him in the process of learning and repeating the knowledge gained. For this study, three groups of tasks for the child were selected, in which media technologies can be applied: 1. Definition of the object (what object is on the screen, what kind of object is it). 2. Name the object (as the development of speech). 3. Repeat the action (as part of the awareness of the capabilities of your body).

5.3.2 Prototype Development At the first stage of developing the application (prototype), the requirements obtained from the interview were determined: • The prototype must implement three tasks—game processes: – “Find the Object” is a simple game where the child has to choose the correct object from N given objects. – “Name the object”—the child must name the object shown to him. – “Repeat the action”—the child must repeat the action or pose shown to him.

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Fig. 5.3 Scheme of interaction with the application of the child and the special pedagogue

• The task should end when the child has repeated several actions or poses. • All tasks are expected to display the result. • If the task is completed correctly, show a window with a positive result. The window with the result should be green and contain positive text. Example: “Well done!”. • If the task is not completed correctly, show a window with a negative result. The window with the result should be red. The window with the result should have text with an error and ask about repeating the task. Example: “The answer is not correct, let’s try again?”. • Application design should be simple and clear. • Clickable areas should be highlighted on hover or interaction. • Pictures can be drawings, photographs, symbolic images, must be understandable, must not fade into the background. • Avoid background sounds, moving text, flashing images. • Navigation should be consistent and similar on every page/section. • Text should be simple and precise. Minimum number of cuts.

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Fig. 5.4 Example of proposed video game “Find the object”

• The proposed solution should support different languages. The developed prototype consists of three video games. The first game is “Find the Object”, where the child must choose the correct object from N proposed objects (Fig. 5.4). During the discussion, animals were chosen as objects for this game. Special pedagogues asked to implement animals, because according to the observation of special pedagogues, animals attract the most attention of children. Also, pedagogues asked to make some changes to the prototype, in particular, there was a request to add the ability to listen to the “animal sound”, which was done using the Audio() constructor of the JavaScript language. The second game is “Name the object”, where the child needs to name the object shown to him (Fig. 5.5). The big question was how to write down the words of the child and compare them with the name of the object.

Fig. 5.5 Example of proposed video game “Name the object”

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Fig. 5.6 Test queries for the word “dog” Fig. 5.7 Example of proposed video game “Repeat the action”

Authors have implemented this feature using the Web Speech API, which allows web application developers to include speech recognition and synthesis in their web pages [13]. For example, test queries for the word “dog” can be seen in the figures (Fig. 5.6). According to pedagogues, this game can be a good method of motivation for the development of speech in children with developmental disabilities. The third video game is “Repeat the action”, where the child needs to repeat the action or pose shown to him. By decision of the authors and special pedagogues, five simple poses were chosen—HandsDown, HandsUp, HandsOnChest, HandsToMid, HandsToSide (Fig. 5.7). To analyze the actions of the child, the authors considered various ready-made solutions and chose the “Teachable Machine” technology [14]. It is a free platform for working with taught models. Authors chose this technology because it is very simple and, if the program is useful, then special pedagogues will be able to add new poses or actions to the “Repeat Action” game on their own. As training data, at least sixty examples were recorded for each pose. The user interface can be seen in Fig. 5.8. On the left side, the user sees the pose that he should take, and on the right side, the user sees what his camera shoots, with markers for the algorithm. Fig. 5.8 Example of the user interface

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Fig. 5.9 Example of messages

It was decided to leave the markers, as they will help put the user in the correct position before starting the task. The trained “Teachable Machine” model returns data as a probability for each pose in JSON format. If the name of the pose with the maximum probability matches the name of the pose that is currently shown in the picture, and the probability of matching is greater than 80%, then the pose is considered repeated and the function is called to request a new pose, where the counter of correct repetitions of poses is incremented and a new pose is requested. Authors paid great attention to issuing comments on the answer or action of the child. If the task is completed correctly, show a window with a positive result. The window should be green and contain positive text. If the task is not completed correctly, show a window with a negative result in red. The window with the result should have text with an error and ask about repeating the task. Also, an additional message if the child made a mistake three times, but this comment is also similar to a positive message, in order to evoke a positive reaction in the child and motivate him to continue the task (Fig. 5.9). The developed system was tested in a specialized institution of preschool education. The authors asked special pedagogues to observe the children in the classroom and describe how the children adopted the new technology. The pedagogue showed the child how to interact with the proposed tasks (Fig. 5.3), completed the tasks with the child several times, and then let the child practice using the proposed solution. Figure 5.10 shows how the children complete the assigned tasks using the proposed system.

Fig. 5.10 Example of children interacting with the proposed system

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As it can be seen, the developed system uses media technologies: images, audio, video, and the system is also available as an Internet application, that is, it can be run both on a computer and on a mobile device.

5.4 Survey Results To test the developed solutions, special pedagogues agreed to conduct several sessions with children and evaluate the impact of the developed solution. The experiment involved 20 children with different levels of development (High level—7, Medium level—7, Low level—6). Special pedagogues helped the children understand how to interact with the developed games, asked the children about their well-being and followed the progress of the assignments. After eight classes, special pedagogues transferred the received information to the authors of the work. It was noted that not in all games, the full amount of children could take part due to their abilities. On Fig. 5.11 results of the survey of children after 8 sessions can be seen—did the children like the proposed game and did they want to continue the session with this game. As it can be seen from the diagram, most children answered positively. The game “Repeat the action” stands out separately. Special pedagogues reported that it was difficult for children to understand how the game works, as it is a non-standard game where the child has to repeat actions on his own, and not use the screen of the device or buttons. The authors of the work also received comments from special pedagogues. They have noticed a positive impact of using the proposed solution for some children.

Fig. 5.11 Results of the survey

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At the first lesson, it was difficult for children to understand what they need to do, however, after several lessons, some children could independently interact with the solution and correctly completed the task. As can be seen from Fig. 5.11, some children began to help their friends to understand how the game works and what to do. Special pedagogues pointed out that each child is unique and for some children eight lessons is not enough. So, children with a high and medium level of development, after eight lessons, had a positive attitude toward games and agreed to continue to complete the task. In turn, children with a low level of development in most cases did not show interest in games.

5.5 Conclusion and Future Work Overall, smart technologies will continue to play a central role in education for children with special needs in the future. These technologies have the potential to provide personalized and adaptive learning experiences, as well as greater accessibility and support to students with a wide range of needs and abilities. During the experiment, a positive impact on the development of children was revealed. Obviously, it would be useful to continue the experiment to find out how to modify the game in order to interest children with a low level of development. To do this, it is necessary to increase the number of children participating in the classes, and the number of classes itself. Then, after processing the results, determine the functional and non-functional requirements for the program and make the necessary changes. After that, the experiment should be continued. The authors hope that their work will help children improve their level of development.

References 1. Toki, E., Drosos, K., Simitzi, D.: Development of digital multimedia resources to support early intervention for young children at risk for learning disabilities. Pedagogy—Theory & Praxis 5, 129–142 (2012). Accessed April 2022 from https://www.researchgate.net/public ation/262336551_Development_of_digital_multimedia_resources_to_support_early_interve ntion_for_young_children_at_risk_for_learning_disabilities 2. Lei, X., Zhang, A., Wang, B., Rau, P.-L.: Can virtual reality help children learn mathematics better? The application of VR headset in children’s discipline education, pp. 60–69 (2018) 3. Rahman, N., Mailok, R., Husain, N.: Mobile augmented reality learning application for students with learning disabilities. Int. J. Acad. Res. Bus. Soc. Sci. 10 (2020) 4. Ritakumari, S.: Educational media in teaching learning process. 8, 8 (2019). Accessed March 2022 5. http://www.gangainstituteofeducation.com/documents/Singh-Ritakumari.pdf 6. Chan, G.L., Santally, M.I., Whitehead, J.: Gamification as technology enabler in SEN and DHH education. Educ Inf. Technol. (2022)

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7. Toasa, R., Burbano, E., Constante, A., Hidalgo, L., Morales, F.: A custom and dynamic game using gamification techniques to children from 4 to 5 years old. In: 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–5 (2019) 8. Veronica, R., Calvano, G.: Promoting sustainable behavior using serious games: SeAdventure for ocean literacy. IEEE Access 8, 196931–196939 (2020) 9. Deterding, S., Dixon, D., Khaled, R., Nacke, L.: From game design elements to gamefulness: defining “gamification”. In: Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments, New York, NY, USA, pp. 9–15 (2011) 10. Ramírez Moreno, H.B., Ramírez, M.R., Rojas, E.M., del Consuelo Salgado Soto, M.: Digital education using apps for today’s children. In: 2018 13th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–6 (2018) 11. Rohendi, D.: Game-based multimedia for horizontal numeracy learning. Int. J. Emerg. Technol. Learn. (iJET) 14(15), Art. No. 15 (2019) 12. Dattolo, A., Luccio, F.L.: A review of websites and mobile applications for people with autism spectrum disorders: towards shared guidelines. In: Gaggi, O., Manzoni, P., Palazzi, C., Bujari, A., Marquez-Barja, J.M. (eds.) Smart Objects and Technologies for Social Good, vol. 195, pp. 264–273. Springer International Publishing, Cham (2017) 13. Rapid Application Development (RAD) Model: An Ultimate Guide For App Developers in 2022, Low-code, Sep. 05, 2021. Accessed March 2022 from https://kissflow.com/low-code/ rad/rapid-application-development/ 14. Web Speech AP. Mozilla Corporation. Accessed April 2022 from https://developer.mozilla. org/en-US/docs/Web/API/Web_Speech_API 15. Teachable Machine. Google Corp. Accessed April 2022 from https://teachablemachine.withgo ogle.com/

Part II

Smart e-Learning

Chapter 6

Online Ed.D. Program Development: A Program Level Perspective Erik A. Dalmasso and Jeffrey P. Bakken

Abstract The online Doctor of Education (Ed.D.) in Educational Leadership at Bradley University launched in the fall semester 2019 with a concentration in Higher Education Administration and Leadership (HEAL). In the fall of 2022, program administration surveyed both active students and program alumni regarding the overall student experience. Using questions guided by the Community of Inquiry (CoI) framework, students provided feedback as it relates to their individual social, cognitive, and teaching/learning needs. This paper will discuss considerations for online graduate program administrators interested in how technology applies to programmatic decisions, access, institutional relationships, communication, and overall program advocacy.

6.1 Introduction Online graduate programs are continuing to emerge and define a growing educational space for students who desire asynchronous flexibility, professional mobility and educational “fit” that might not be available within a relatively close proximity of home or work [1]. The evolution of online delivery models, in concert with new online technologies, such as more robust Learning Management Systems (LMS), has provided opportunities for more faculty and student interaction [2]. Much like adult students enrolled in residential graduate programs, online students expect a supportive curriculum, faculty, and staff, as well as an engaging, interactive experience. As more graduate programming, both at the master’s and doctoral level, moves to various online platform(s), university officials have become more engaged in building rigorous content, with similar support and interaction as found on a residential campus [2]. E. A. Dalmasso (B) · J. P. Bakken Department of Education, Counseling and Leadership, Bradley University, Peoria, USA e-mail: [email protected] J. P. Bakken e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_6

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Program administrators are often faced with complex decisions regarding course offerings, institutional support (technology, student affairs, and academic affairs), institutional policies, instructional design, enrollment management, and various dayto-day program management [3]. These equal, yet shifting priorities can cause delay in long-term planning and focus while taking care of immediate issues [3]. The feedback shared by current students, and program alumni can provide the necessary focus on long-term, high-level priorities as a foundation for planning, program policy, institutional relationships, and communication [4]. Smartness Levels The smartness levels of sensing, inferring, self-learning, adaptation, anticipation, and self-optimization [5–7] are an integral component of the Ed.D. Online Program in educational leadership. We are constantly collecting data about our new and current students (sensing). We take this data and use it to inform our processes with students (inferring). We evaluate this data to obtain information about our existing curriculum and for curriculum revisions (self-learning). Based on this obtained data, we use it to inform our teaching practices and how we might optimize the student learning experience (adaptation). This new knowledge is used to prepare for future events in our program and to alleviate any program problems or discrepancies (anticipation). Finally, we look at the overall structure of the program and investigate if changes need to be made based on the data (self-optimization). Incorporating these components is essential for the success of this online program in educational leadership. This iterative process is essential to program growth and improvement. As an emerging program, we not only look to best practices, but are guided by the impact and structure feedback we receive from our students and our faculty to improve our development processes in instructional design, scheduling of course offerings, policy creation, and communication structure. Viewing the program through the lens of smartness levels also guides our advocacy within the institution. Online graduate level education, while growing rapidly in interest, stands alongside traditional graduate level programs on our residential campus. Program administrators are charged with bringing the online student and faculty voice, successes, needs, and proven barriers to a heightened level of awareness among decision-makers on campus. In addition, it is imperative that faculty feel open to discovery and improvement through all smartness levels as well. Communication through committee work helps to inform strong pedological decisions regarding program consistency, authentic relevancy of literature and assessment, and overall connection between our core, concentration and scholarly action research coursework. This constant focus on improvement through communication, deliberation, reflection, and intentional action helps to ground the overall experience of our adult, professional students, as well as our practiced faculty.

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6.2 Research Project Goals (A) To find out what programmatic components in an online Ed.D. Program are important to the success of students from a student perspective quantitatively. (B) To find out what programmatic components in an online Ed.D. Program are important to the success of students from a student perspective qualitatively.

6.3 Method At the end of the fall 2022 semester, an anonymous response survey was sent to all current Bradley University online Ed.D. students, and program alumni. This survey, guided by the Community of Inquiry (CoI) framework [8], included questions focused on three levels: programmatic characteristics of the program, faculty interaction with students and support, and the student/learner experience. In total, the survey was distributed, via an anonymous email link, to 66 students. The survey was accessible for 30 days, directly after the conclusion of the fall semester. Researchers evaluated 26 survey submissions with an overall response rate of 39.4%. The goal of this survey was to better understand the state of the of Bradley University online Ed.D. Program, and the impact of programmatic, faculty and student learner decisions as they apply to multiple levels of the online student learning experience. For the purposes of this paper, the researchers identified survey questions focused at the programmatic level for focused analysis and synthesis of student feedback.

6.4 Research Project Outcomes A total of seven quantitative and seven qualitative questions from the survey addressed the online Ed.D Program. Students responded to each question that had a quantitative response using either a 2-point, 3-point, or a 4-point Likert scale depending on the question. Each question with a quantitative response also had a qualitative question that accompanied it asking the students if they had any additional thoughts regarding the question. Results of the quantitative analysis are listed below in Table 6.1.

6.5 Survey Answers Figures 6.1 and 6.2 address questions 2 and 3 above. Results of the qualitative analysis are listed below in Table 6.2.

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Table 6.1 Quantitative results about the program (1) Access to high speed internet is essential for the transmission Yes: 100% of educational content. Video, live lecture sessions, large files, and video conferencing software require a sustainable, high speed connection. Do you have access to reliable broadband/high speed internet access at your home or work setting? (2) How do you access course material, live sessions, discussions, Laptop/Desk Computer: 15 etc.? Smart Tablet: 1 Combination: 10 (3) Earning an online graduate degree requires some comfort with Extremely Comfortable: 15 the use of technology, Canvas LMS, websites, online academic Moderately Comfortable: 10 search engines, audio/visual equipment, etc. How comfortable are Slightly Comfortable: 1 you with the technology demands of this program? (4) How easy or difficult is it to obtain the resources that you need Extremely Easy: 9 from the Bradley University (BU) library online? Moderately Easy: 12 Somewhat Easy: 3 Not Easy: 2 (5) Group advisement sessions are offered throughout the program (noted on your plan of study—Welcome, Internship and Graduation). Do you attend group advisement sessions when offered live?

Yes: 22 No: 3

(6) Group advisement is a technique used in online programs to share important, broad programmatic messages to multiple students at one time. How helpful are group advisement sessions for major program milestones (Welcome Overview, Internship, Graduation)?

Extremely Helpful: 16 Moderately Helpful: 10

(7) Do you watch Group Advisement presentation recordings when provided to the broader program audience?

Yes: 18 No: 7

Fig. 6.1 How do you access course materials

How do you Access Course Materials?

Laptop/DeskComputer Fig. 6.2 How comfortable are you using technology

Smart Tablet

Combinaon

How Comfortable Are You Using Technology?

Extremely Comfortable

Moderately Comfortable

Slightly Comfortable

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Table 6.2 Qualitative results about the program (1) Access to high speed internet is essential for the transmission of educational content. Video, live lecture sessions, large files, and video conferencing software require a sustainable, high speed connection. Do you have access to reliable broadband/high speed internet access at your home or work setting?

Reliable broadband is extremely important High speed internet is important Programs are a challenge when there are technical issues

(2) How do you access course material, live sessions, discussions, etc.?

A computer or laptop is best Harder to access information on phones

(3) Earning an online graduate degree requires Listing technological expectations would be some comfort with the use of technology, helpful Canvas LMS, websites, online academic It helps to be proficient with technology use search engines, audio/visual equipment, etc. How comfortable are you with the technology demands of this program? (4) How easy or difficult is it to obtain the resources that you need from the Bradley University (BU) library online?

Sometimes hard to obtain articles Librarians are very helpful

(5) Group advisement sessions are offered Yes, they are very helpful throughout the program (noted on your plan of Very nice to meet and see faculty and other study—Welcome, Internship and Graduation). students in the program Do you attend group advisement sessions when offered live? (6) Group advisement is a technique used in Multiple sessions would be nice and closer to online programs to share important, broad when classes actually begin would be helpful programmatic messages to multiple students at one time. How helpful are group advisement sessions for major program milestones (Welcome Overview, Internship, Graduation)? (7) Do you watch Group Advisement presentation recordings when provided to the broader program audience?

Nice to have this option when I can’t make the live session

6.6 Discussion Overall, students found it necessary and extremely important to have high-speed internet to access the Ed.D. Program and be able to participate in a meaningful way. Most students used a laptop or desktop computer (phones were not preferred) and students who were already comfortable with the use of technology had a better experience in the program. Students also mentioned that they felt it was relatively easy to find resources from the library online and that a librarian being available was an added bonus to help them. Lastly, group advisement sessions were a very good technique to obtain important content about the program, but it was suggested that more sessions are needed and offering them closer to when classes begin would be helpful.

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Viewing the feedback through the program level lens is helpful for many reasons. Within our program, many designated with program level responsibilities also teach a significant course load with varying areas of expertise. The ability to articulate program needs, while simultaneously understanding and voicing the complexity faculty work in the online environment is beneficial. This evaluative process provided meaningful feedback for many levels of our program development. Our faculty gained insight into our pedological successes and barriers, preferred student communication styles, and the overall connection of our coursework to the authentic professional practice of our students. Program administrators benefited as well. Viewing the feedback from the program level perspective, several themes emerged broadly, informing future programmatic decision-making with intentionality.

6.6.1 Access Students need access through their preferred mode(s) of technology. Throughout the literature, students indicate the need for access, not only for their coursework, but to all levels of the student experience. From a program administration lens, students need reliable access to common to the residential student. Institutional tech support, student affairs, academic affairs, library, and bookstore were areas of particular concern for online students [9].

6.6.2 Campus Connection Because access to needed campus resources is of great consideration for online students, it is incumbent on the program to make meaningful connections with integral student support personnel on the residential campus. Campus administrators and personnel must understand the needs of online students, in concert with the needs of residential students [9, 10]. Planning through the unique strategies, communication and office representation for online graduate students can prove challenging across campus, as resources are stretched to meet the needs of the institution [6].

6.6.3 Communication Online students want a clear, consistent, and concise messages. Because of the asynchronous nature of online degree work, it is important that students feel confident of the path to an answer, while they are working in the virtual space [11]. Viewing communication through a structural lens is important when considering a program

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decision structure, grievances, student support, program policy, and faculty/student accountability [11].

6.6.4 Advocacy Program administrators are not only advocates for their students, but also the program as a whole. For online programs that exist on a traditional, residential campus, it is important to advocate for the needs of online students. The relationships built with college and institutional personnel is an essential component of program advocacy. Does the current technology infrastructure support online learning? Do barriers exist for online students in comparison to residential students? Who is speaking to the concerns of online students?

6.7 Future Directions Program administrators face an exciting opportunity as the popularity of online graduate/doctoral programs continues to increase. This unique skill set provides a unique space within higher education administration to be an on-campus representative of the program’s students and faculty. From basic student and faculty needs to complex discussions of technology infrastructure, support, communication management and the overall student experience, program administrators are the point person(s) responsible for comprehensive movement forward. More research is needed in several areas of program evaluation. Additional inquiry into the student experience with relation to institutional technology infrastructure, instructional design techniques, pace and scope of coursework relating in the professional setting, persistence barriers, and motivation and engagement is needed for continual program development and overall student success. This paper represents a small, yet important window, into the lived experiences of our online doctoral students, and our overall program development and reflection process. While not intended to be exhaustive in scope and position, we believe the overall dialogue is helpful toward the growing discourse of online learning, specifically as it applies to adult learners working toward a terminal degree, while maintaining complex, full-time employment.

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References 1. Shepherd, J., Nelson, B.: Balancing act: a phenomenological study of female adult learners who successfully persisted in graduate studies. TQR (2015). https://doi.org/10.46743/21603715/2012.1770 2. Schroeder, S.M., Terras, K.L.: Advising experiences and needs of online, cohort, and classroom adult graduate learners. NACADA J. 35, 42–55 (2015). https://doi.org/10.12930/NACADA13-044 3. Franklin, C., Lightfoot, E., Zimmerman, S.: Practices and policies for doctoral education and leadership. J. Soc. Work. Educ. 54, 724–726 (2018). https://doi.org/10.1080/10437797.2018. 1511202 4. Yang, D., Baldwin, S., Snelson, C.: Persistence factors revealed: students’ reflections on completing a fully online program. Distance Educ. 38, 23–36 (2017). https://doi.org/10.1080/ 01587919.2017.1299561 5. Uskov, V., Bakken, J., Gayke, K., Jose, D., Uskova, M., Devaguptapu, S.: Smart University: a validation of “smartness features - main components” matrix by real-world examples and best practices from universities world wide. In: Smart Education and e-Learning 2019. Springer Nature Singapore (2019) 6. Uskov, V., Bakken, J., Pandey, A., Singh, U.: Smart University taxonomy: features, components, systems. In: Smart Education and e-Learning, pp. 3–16. Springer International, New York (2016) 7. Uskov, V., Bakken, J., Pandey, A.: The oncology of next generation smart classrooms. In: Smart education and e-learning, pp. 1–15. Springer, New York (2015) 8. Garrison, D.R., Anderson, T., Archer, W.: Critical thinking, cognitive presence, and computer conferencing in distance education. Am. J. Distance Educ. 15, 7–23 (2001). https://doi.org/10. 1080/08923640109527071 9. Leontyeva, I.A.: Modern distance learning technologies in higher education: introduction problems. Eurasia J. Math. Sci. T. 14 (2018). https://doi.org/10.29333/ejmste/92284 10. Rovai, A.P., Jordan, H.: Blended learning and sense of community: a comparative analysis with traditional and fully online graduate courses. IRRODL 5 (2004). https://doi.org/10.19173/irr odl.v5i2.192 11. Waight, E., Giordano, A.: Doctoral students’ access to non-academic support for mental health. J. High. Educ. Policy Manag. 40, 390–412 (2018). https://doi.org/10.1080/1360080X.2018.147 8613

Chapter 7

Using CAI to Provide Early Literacy Instruction for All Learners Haya Shamir, Erik Yoder, and David Pocklington

Abstract Identifying effective means of ameliorating achievement gaps and addressing literacy deficits is essential. Computer-assisted instruction (CAI) is one avenue for providing this effective instruction to the students that can most benefit from it. In the current study, kindergarten students in a public school district in Illinois received supplemental computer-adaptive reading instruction. Performance was assessed at the beginning and end of the kindergarten school year, and scores for students who had used the program to fidelity, at least 1500 min, were compared to the scores of students with less than 800 min of usage. Students that used the program to fidelity scored significantly higher than comparison students. Demographics in terms of special education status and ethnicity were examined, and no significant effects were found, indicating that all groups assessed were benefiting from the use of the program in comparable terms.

7.1 Introduction Research has shown that early childhood education plays a major role in a student’s later academic success [1]. Differences in student level performance, as well as stressors and environmental factors that unequally burden students from different socioeconomic and ethnic backgrounds, are identifiable early in an educational career [2]. In order to ensure all students are equally capable of achieving their greatest potential, it is important to focus on education at a young age. The past decade has seen a dramatic increase in the number of studies pertaining to educational research H. Shamir (B) · E. Yoder · D. Pocklington Waterford Institute, Taylorsville, UT, USA e-mail: [email protected] E. Yoder e-mail: [email protected] D. Pocklington e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_7

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[3] and specifically research in early literacy instruction [4]. These studies can help educators and policy-makers identify high-quality interventions intended to increase universal literacy.

7.2 Literature Review Computer-assisted instruction (CAI) involves the use of technology to facilitate education to at an individual student level [5]. CAI can be designed specifically with young learners in mind, making use of age-appropriate content and multimedia activities to foster engagement. As each student use the software, it measures their personal progress and mastery of the concepts being taught to determine whether they should be given additional instruction and scaffolding for a particular lesson. This allows for individualized lesson plans for each student, to keep them on track with learning content at their level. It also provides students and educators with direct feedback on their progress in real time, shedding light on where the students are struggling most so they can be given extra attention in those areas. CAI has been demonstrated to be an effective tool in early childhood education. A meta-analysis from the past twenty years found an average of medium effect sizes for student learning achievement when the use of technology was incorporated in their education, and personalized learning software significantly moderated these effect sizes [6]. Research has also demonstrated that using CAI as a supplement to a student’s education can be especially impactful for young learners [7]. A study of a blended learning program, in which elementary school students completed digital instructional activities alongside face-to-face traditional classroom instruction, found that students who progressed further through the individualized digital lessons during the school year experienced higher growth in reading performance by the end of the school year [8]. The most prominent gains were observed in students from kindergarten through second grade, suggesting this age range benefited the most from using the supplementary CAI. Research has also explored the use of CAI to specifically address the achievement gap between early learners. Students with regular access to the use of computers and software are able to develop a familiarity with using these tools, allowing them to take advantage of the instructional benefits they can provide [9]. Correspondingly, students without access to technology at a young age, such as those from marginalized communities, may miss an important potential source of instruction and be at an academic disadvantage to their more digitally savvy peers [10]. However, merely having access to technology is not always enough to impact a student’s learning, as not all activities that are done on a computer will lead to a specific learning outcome. One study of primarily African American students in an urban school district found an increase in reading assessment scores after students used a CAI literacy intervention [11]. Preliminary research also suggests that computer-assisted instruction may be effective for addressing achievement gaps for young African American students [12]. In a small sample study examining first-grade, African American students

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receiving supplemental CAI literacy instruction, the majority of students improved their comprehension scores and reading rates. Another population that may be able to benefit from CAI are students in special education classes. Although much of the research behind instructional methods for students with disabilities is qualitative [13], studies can provide insight about using technology to instruct these students. Special education can support students at a variety of age ranges; however, students with special education experience the most growth in reading skills at a young age [14]. Instructional multimedia activities that students experience as games can help young learners with disabilities stay engaged with the content [13]. However, some studies have suggested that CAI is not necessarily better than traditional instruction; a case study of elementary students with autism found no difference in outcomes between teaching sight words through teacher-lead instruction compared to using CAI, and these students preferred working directly with the teachers rather than the software [15]. Further research is needed to explore the use of CAI as universally beneficial tool for all students.

7.3 Research Goal The goal of the current study is to evaluate the efficacy of Waterford Reading Academy (WRA), a CAI program to teach early literacy skills to kindergarten students. It was hypothesized that students who used the program for a meaningful amount of time during the school year would score higher on a literacy assessment at the end of the year compared to students who did not use WRA as much.

7.4 Research Methodology 7.4.1 Participants This study consisted of kindergarten students (N = 606) enrolled in a public school district in Illinois during the 2021–2022 school year. The sample included 49% female, 62% African American, 22% Caucasian, and 17% received special education. The experimental group (n = 540) consisted of students who used the computeradaptive instruction program for more than 1500 min. The control group (n = 66) consisted of students who used the computer-adaptive instruction program for less than 800 min.

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7.4.2 Materials Waterford Reading Academy (WRA). A CAI reading curriculum for early learners. The software utilizes a broad spectrum of educational media in a sequence customized to each student’s specific ability and pace of development. Northwest Evaluation Association (NWEA) Measures of Academic Progress (MAP) Growth. The MAP Growth is a valid assessment intended to measure individual achievement and growth. The skills considered relevant to kindergarten students for this study are the Overall RIT Reading, Foundational Skills, Language and Writing, Literature and Informational Text, and Vocabulary Use and Functions.

7.4.3 Procedure Students were expected to use WRA for fifteen minutes per day, five days per week. Usage was tracked within the program and total minutes of the adaptive program usage was calculated. The MAP Growth assessment was administered to all students at the beginning and end of the school year to assess literacy.

7.5 Research Outcomes 7.5.1 Baseline Equivalence Using Independent Samples t-tests Independent samples t-tests were conducted to determine baseline equivalence of pretest scores at the beginning of the school year between experimental and control groups. Across all subskills, baseline scores were not significantly different between the students in the experimental and control groups (see Table 7.1). Table 7.1 MAP growth reading beginning of year scores by subskill Subskill

Experimental

Control

N

M

N

M

t

p

Overall RIT reading

511

134.57

34

134.50

−0.03

0.977

Foundational skills

509

131.33

34

131.44

0.06

0.954

Language and writing 511

135.29

34

137.44

0.86

0.393

Literature and informational text

510

136.76

34

135.21

−0.78

0.435

Vocabulary use and functions

511

134.99

34

133.91

−0.42

0.679

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7.5.2 Post-test Group Differences Using Independent Samples t-tests

MAP Growth Reading Score

Independent samples t-tests were conducted to examine group differences in end of year scores between experimental and control groups (see Fig. 7.1). Overall RIT Reading. Analysis of Overall RIT Reading end of year scores revealed a significant difference between groups, t(1, 604) = −4.67, p < 0.001, due to higher end of year scores made by experimental students (M = 151.37) than by control students (M = 143.92). Effect size (d = 0.61). Foundational Skills. Analysis of Foundational Skills end of year scores revealed a significant difference between groups, t(1, 604) = −3.98, p < 0.001, due to higher end of year scores made by experimental students (M = 151.15) than by control students (M = 144.17). Effect size (d = 0.52). Language and Writing. Analysis of Language and Writing end of year scores revealed a significant difference between groups, t(1, 604) = −4.31, p < 0.001, due to higher end of year scores made by experimental students (M = 151.33) than by control students (M = 144.20). Effect size (d = 0.56). Literature and Informational Text. Analysis of Literature and Informational Text end of year scores revealed a significant difference between groups, t(1, 603) = −3.82, p < 0.001, due to higher end of year scores made by experimental students (M = 150.99) than by control students (M = 143.97). Effect size (d = 0.50). Vocabulary Use and Functions. Analysis of Vocabulary Use and Functions end of year scores revealed a significant difference between groups, t(1, 604) = −4.16, p < 0.001, due to higher end of year scores made by experimental students (M = 151.95) than by control students (M = 143.62). Effect size (d = 0.54). 154 152 150 148 146 144 142 140 138 Overall RIT Reading

Foundaonal Language and Literature and Vocabulary Skills Wring Informaonal Use and Text Funcons Control

Experimental

Fig. 7.1 MAP growth reading end of year scores by subskill

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MAP Growth Reading Score

84 160 155 150 145 140 135 130 African American

Caucasian

Ethnicity Control

Special Educaon

No Special Educaon

Special Educaon Status Experimental

Fig. 7.2 Overall RIT reading end of year scores by demographics

7.5.3 Post-test Group Differences Using ANOVAs

Map Growth Reading Score

Overall RIT Reading. Two separate two-way ANOVAs were conducted to examine the effects of WRA and demographics on Overall RIT Reading end of year scores (see Fig. 7.2). Ethnicity. There was no significant interaction between the effects of ethnicity and WRA on Overall RIT Reading end of year scores, F(3, 596) = 1.29, p = 0.278. Simple effects analysis showed that for African American and Caucasian students, students in the experimental group significantly outperformed students in the control group. Special Education Status. There was no significant interaction between the effects of special education status and WRA on Overall RIT Reading end of year scores, F(1, 602) = 1.94, p = 0.164. Simple effects analysis showed that for students with special education and students without special education, students in the experimental group significantly outperformed students in the control group. Foundational Skills. Two separate two-way ANOVAs were conducted to examine the effects of WRA and demographics on Foundational Skills end of year scores (see Fig. 7.3). 160 155 150 145 140 135 130 African American

Caucasian

Ethnicity Control

Special Educaon

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Special Educaon Status Experimental

Fig. 7.3 Foundational skills end of year scores by demographics

MAP Growth Reading Score

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160 155 150 145 140 135 130 African American

Caucasian

Ethnicity Control

Special Educaon

No Special Educaon

Special Educaon Status Experimental

Fig. 7.4 Language and writing end of year scores by demographics

Ethnicity. There was no significant interaction between the effects of ethnicity and WRA on Foundational Skills end of year scores, F(3, 596) = 2.14, p = 0.094. Simple effects analysis showed that for African American and Caucasian students, students in the experimental group significantly outperformed students in the control group. Special Education Status. There was no significant interaction between the effects of special education status and WRA on Foundational Skills end of year scores, F(1, 602) = 1.74, p = 0.188. Simple effects analysis showed that for students with special education and students without special education, students in the experimental group significantly outperformed students in the control group. Language and Writing. Two separate two-way ANOVAs were conducted to examine the effects of WRA and demographics on Language and Writing end of year scores (see Fig. 7.4). Ethnicity. There was no significant interaction between the effects of ethnicity and WRA on Language and Writing end of year scores, F(3, 596) = 1.06, p = 0.367. Simple effects analysis showed that for African American and Caucasian students, students in the experimental group significantly outperformed students in the control group. Special Education Status. There was no significant interaction between the effects of special education status and WRA on Language and Writing end of year scores, F(1, 602) = 3.82, p = 0.051. Simple effects analysis showed that for students with special education and students without special education, students in the experimental group significantly outperformed students in the control group. Literature and Informational Text. Two separate two-way ANOVAs were conducted to examine the effects of WRA and demographics on Literature and Informational Text end of year scores (see Fig. 7.5). Ethnicity. There was no significant interaction between the effects of ethnicity and WRA on Literature and Informational Text end of year scores, F(3, 595) = 0.73, p = 0.536. Simple effects analysis showed that for African American and Caucasian students, students in the experimental group significantly outperformed students in the control group.

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MAP Growth Reading Score

86 160 155 150 145 140 135 130 African American

Caucasian

Ethnicity Control

Special Educaon

No Special Educaon

Special Educaon Status Experimental

Fig. 7.5 Literature and informational text end of year scores by demographics

MAP Growth Reading Score

Special Education Status. There was no significant interaction between the effects of special education status and WRA on Literature and Informational Text end of year scores, F(1, 601) = 0.01, p = 0.905. Simple effects analysis showed that students without special education in the experimental group significantly outperformed students in the control group. Students with special education in the experimental group scored slightly higher than the control group, but the difference was not significant. Vocabulary Use and Functions. Two separate two-way ANOVAs were conducted to examine the effects of WRA and demographics on Vocabulary Use and Functions end of year scores (see Fig. 7.6). Ethnicity. There was no significant interaction between the effects of ethnicity and WRA on Vocabulary Use and Functions end of year scores, F(3, 596) = 1.29, p = 0.276. Simple effects analysis showed that for African American and Caucasian students, students in the experimental group significantly outperformed students in the control group. Special Education Status. There was no significant interaction between the effects of special education status and WRA on Vocabulary Use and Functions end of year scores, F(1, 602) = 1.34, p = 0.247. Simple effects analysis showed that for 170 160 150 140 130 120 African American

Caucasian

Ethnicity Control

Special Educaon

No Special Educaon

Special Educaon Status Experimental

Fig. 7.6 Vocabulary use and functions end of year scores by demographics

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students with special education and students without special education, students in the experimental group significantly outperformed students in the control group.

7.6 Discussion The results of the current study are consistent with previous findings that CAI can be an effective resource to supplement young students’ learning [6]. While students had similar literacy scores on average at the beginning of the year, students in the experimental group who used the supplemental CAI software to fidelity throughout the school year achieved significantly higher literacy scores when assessed at the end of the school year compared to their control group counterparts. Overall effect sizes ranged from 0.50 to 0.61, indicating a medium effect across each of the measured subskills. These results demonstrate that the use of CAI as a supplement to traditional classroom learning can have a significant impact on students’ literacy outcomes.

7.7 Conclusions Among the most important considerations when implementing a CAI program is that it benefits all students [10]. Analysis by the available demographic data showed no interaction effect between group assignment and demographics, indicating that all students in the experimental group scored higher on the end of year literacy assessment on average, regardless of ethnicity or special education status. African American students as well as Caucasian students within the experimental group significantly outperformed their control group counterparts across all literacy subskills. Results by special education status were also strong, as scores for students with and without special education were significantly higher in the experimental group than the control group for all subskills, with the exception of Literature and Informational Text, where experimental group students with special education slightly outperformed the control group, but the results were not significant. Particularly noteworthy is that across all subskills, students with special education in the experimental group scored similarly to students without special education in the control group, essentially catching up to their peers that were not part of special education after one year of using WRA. Overall, this indicates that WRA provided a meaningful impact in early literacy skills across all of the students in the sample.

7.8 Next Steps One of the limitations of the current study is that students were only observed over the course of a single school year. Future research would benefit from following up

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with students one or more years after using WRA, measuring the degree to which this type of early literacy instruction can impact students’ later academic success.

References 1. McCoy, D.C., Yoshikawa, H., Zio-Guest, K.M., Duncan, G.J., Schindler, H.S., Magnuson, K., Yang, R., Koepp, A., Shonkoff, J.P.: Impacts of early childhood education on medium and long-term educational outcomes. Educ. Res. 46(8), 474–487 (2017) 2. Merolla, D.M., Jackson, O.: Structural racism as the fundamental cause of the academic achievement gap. Sociol. Compass 13(6), e12696 (2019) 3. Gopalan, M., Rosinger, K., Ahn, J.B.: Use of quasi-experimental research designs in education research: growth, promise, and challenges. Rev. Res. Educ. 44(1), 218–243 (2020) 4. Hoffman, E.B., Whittingham, C.E., Teale, W.H.: A decade of early literacy research: who did what, where, and to what end? Read. Teach. 72(3), 283–288 (2018) 5. Jacob, B., Berger, D., Hart, C., Loeb, S.: Can technology help promote equality of educational opportunities? RSF Russell Sage Found. J. Soc. Sci. 2(5), 242–271 (2016) 6. Zheng, L., Long, M., Zhong, L., Gyasi, J.F.: The effectiveness of technology-facilitated personalized learning on learning achievements and learning perceptions: a meta-analysis. Educ. Inf. Technol. 27, 1–24 (2022) 7. Wilkes, S., Kazakoff, E. R., Prescott, J. E., Bundschuh, K., Hook, P. E., Wolf, R., Macaruso, P, et al.: Measuring the impact of a blended learning model on early literacy growth. J. Comput. Assist. Learn. 36(5), 595–609 (2020) 8. Prescott, J.E., Bundschuh, K., Kazakoff, E.R., Macaruso, P.: Elementary school–wide implementation of a blended learning program for reading intervention. J. Educ. Res. 111(4), 497–506 (2018) 9. Israel, M., Pearson, J.N., Tapia, T., Wherfel, Q.M., Reese, G.: Supporting all learners in schoolwide computational thinking: a cross-case qualitative analysis. Comput. Educ. 82, 263–279 (2015) 10. Rowsell, J., Morrell, E., Alvermann, D.E.: Confronting the digital divide: debunking brave new world discourses. Read. Teach. 71(2), 157–165 (2017) 11. Council, M.R., III., Gardner, R., III., Cartledge, G., Telesman, A.O.: Improving reading within an urban elementary school: computerized intervention and paraprofessional factors. Preventing School Fail. Altern. Educ. Child. Youth 63(2), 162–174 (2019) 12. Gibson, L., Cartledge, G., Keyes, S.E.: A preliminary investigation of supplemental computerassisted reading instruction on the oral reading fluency and comprehension of first-grade African American urban students. J. Behav. Educ. 20(4), 260–282 (2011) 13. Koh, C.: A qualitative meta-analysis on the use of serious games to support learners with intellectual and developmental disabilities: What we know, what we need to know and what we can do. Int. J. Disabil. Dev. Educ. 69(3), 919–950 (2022) 14. Mize, M.K., Park, Y., Moore, T.: Computer-assisted vocabulary instruction for students with disabilities: evidence from an effect size analysis of single-subject experimental design studies. J. Comput. Assist. Learn. 34(6), 641–651 (2018) 15. Johnson, A.M., Nikolaros, J.: Problem based learning in a special needs environment. Clute Int. Conf. San Francisco Proc. 2018, 186–190 (2018)

Chapter 8

Online Doctoral Faculty Engagement: Building Connections Through Authenticity Erik A. Dalmasso and Jeffrey P. Bakken

Abstract Adult learners enrolled in online doctoral programs face nuanced challenges unique to them. Overwhelmingly, students enrolled in Bradley University’s (BU) online Doctor of Education (Ed.D.) in Educational Leadership, with a concentration in Higher Education Administration and Leadership (HEAL) are fulltime working professionals, with focused time to commit toward a terminal degree. In the fall of 2022, program administration surveyed both active BU HEAL students and program alumni regarding the overall student experience. This survey, guided by the Community of Inquiry (CoI) framework, provided important feedback for individual and programmatic student successes but also noted important barriers that exist in the virtual environment. This paper will discuss student observations related to faculty’s use of technology to provide a more holistic learning environment that provides rigor, curiosity, compassion, and authenticity.

8.1 Introduction During the COVID pandemic and subsequent campus shutdowns, faculty on a global level experienced the migration of curriculum and instructional design to a completely online format [1]. This global emergency provided an unmistakable glimpse at the challenges and opportunities of online teaching and learning, interactive instructional design, virtual community, feedback, and communication [1]. Faculty members who had established teaching acumen in the online space already knew that the planning and execution of online coursework and classroom community were different than that of a residential, in-class experience, but with platforms changing in totality, many institutions provided professional development opportunities for a more seamless transition, campus-wide [1]. E. A. Dalmasso (B) · J. P. Bakken Department of Education, Counseling and Leadership, Bradley University, Peoria, USA e-mail: [email protected] J. P. Bakken e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_8

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As newer, more robust technology became available, institutions ramped up capacity, on an interim basis, to provide access for students, faculty, and staff to perform their work with students and campus colleagues. This immediate change also provided an opportunity for a progressive dialog regarding the needed technology and personnel infrastructure, institutional online student support, and online instructional design expertise [2]. For online faculty members, course development and dissemination of course materials continue to be an area of intense learning. Additionally, online faculty members can also provide a singular virtual connection to the institutional and program missions, campus community, and support resources [3]. Further, the nuances of online adult learning motivation, support, and subject mastery often require an engaged virtual presence, both in class (synchronous and asynchronous), and during office hours [4, 5]. Dual Agency Relationship Drawing on the work of Burlea and Burdescu [6], the work of faculty and students in an online environment is unique in comparison with traditional and residential on campus learning. The function of the learning management system (LMS) plays a critical role in the establishment of effective discourse, motivation, and engagement. Different from a non-virtual classroom, the LMS is the main bridge between content and delivery. Burlea and Burdescu [6] reference the promotion of entrepreneurial spirit for students, encouraging unique contributions from students within the capacity of the LMS. Utilizing built in technology, such as group spaces, file sharing, video sharing, and feedback, the LMS creates an interactive learning environment where unique and productive ways to promote an interaction between students and faculty are enhanced. Promotion of new technological tools available also builds a more robust dual agency relationship between students and faculty. If not intentional about the use of the LMS, and not exploring new functions and capabilities, the faculty can regress into more of a moderator of course content, rather an active learning participant. Additionally, providing timely, relevant feedback, either in written form or short video, provided within the LMS, can establish an ongoing dialog about subject matter, associated learning, and spark new ideas for course integration and additional perspectives. Smartness Levels The smartness levels of sensing, inferring, self-learning, adaptation, anticipation, and self-optimization [7–9] are an integral component of the cyclic nature of development of our program curriculum. Course faculty collects data during every iteration of a course offering and stores an active log of potential curricular ideas and student feedback for redevelopment (sensing). Developing faculty then gather the collected data within the course log from multiple course sections to provide a more comprehensive view of challenges and opportunities (inferring). Faculty, with the aid of institutional instructional designers, evaluate this data to obtain information about our existing curriculum and for curriculum revisions (self-learning). Based on this obtained data,

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program faculty uses it to inform teaching practices and to optimize authentic relevance for students (adaptation). This new knowledge is used to prepare for future offerings of individual coursework in the program and to alleviate any curricular or technology infrastructure problems or discrepancies (anticipation). Finally, as a faculty committee, we look at the overall structure of the curriculum and investigate if changes need to be made based on the data (self-optimization). Incorporating these components is essential for the continued and future success of this online program in educational leadership. This process is essential to the maturation, relevance, and overall development of our program curriculum. Our curriculum development cycle is performed on an annual basis and has led to meaningful updates, intentional deletions, refocused course scope, and more purposeful connectivity between each course in the program. Students take notice when curriculum changes are made based on the data provided and are overall very positive about their contributions to our collective curriculum development efforts. In addition, new ideas have been implemented with respect to our technological capacities within our LMS. Each redevelopment cycle we continue to improve our use of features of the LMS and outside vendor products. The data provided by students, coupled with our faculty’s investment in providing a more robust, authentic learning experience, while minimizing barriers, leads to continual improvement.

8.2 Research Project Goals (A) To find out what faculty components in an online Ed.D. program are important to the success of students from a student perspective quantitatively. (B) To find out what faculty components in an online Ed.D. program are important to the success of students from a student perspective qualitatively.

8.3 Method At the end of the fall 2022 semester, an anonymous response survey was sent to all current Bradley University online Ed.D. students and program alumni. This survey, guided by the Community of Inquiry (CoI) framework [10], included questions focused on three levels: programmatic characteristics of the program, faculty interaction with students and support, and the student/learner experience. In total, the survey was distributed, via an anonymous email link, to 66 students. The survey was accessible for 30 days, directly after the conclusion of the fall semester. Researchers evaluated 26 survey submissions with an overall response rate of 39.4%. The goal of this survey was to better understand the state of the of Bradley University online Ed.D. program, and the impact of programmatic, faculty, and student

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learner decisions as they apply to multiple levels of the online student learning experience. For the purposes of this paper, the researchers identified survey questions focused at the faculty level for focused analysis and synthesis of student feedback.

8.4 Research Project Outcomes A total of eight quantitative and seven qualitative questions from the survey addressed the online Ed.D. program. Students responded to each question that had a quantitative response using either a 2-point, 3-point, or a 4-point Likert scale depending on the question. Each question with a quantitative response also had a qualitative question that accompanied it asking the students if they had any additional thoughts regarding the question. Results of the quantitative analysis are listed below in Table 8.1.

8.5 Survey Answers Figures 8.1, 8.2, 8.3 and 8.4 address questions 4, 5, 6, and 8 above. Results of the qualitative analysis are listed below in Table 8.2.

8.6 Discussion Students reported that the role of their faculty advisor needs to be described better, and their preference for communicating with them is through Zoom where dialog can happen. Zoom tends to be more personable, conversations can be had about important topics, and this helps to engage the student with the content and professor. Most students enjoy group work, but it can be difficult with different time zones and if all group members do not fully participate. Students also rated participating in live sessions with the instructor and other classmates as extremely useful as they were able to get their concerns immediately addressed. They also reported that others sharing their own personal experiences regarding class topic helped them to understand the material better. Lastly, the use of video received positive ratings as students enjoyed putting a face to a name, and the format was extremely helpful. From the faculty perspective, some root themes revealed a framework of thinking as we move forward program wide.

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Table 8.1 Quantitative results about faculty (1) Each student with the Bradley University Ed.D. program is Email: 19 assigned a faculty advisor to advise with issues related to individual Video conference: 7 plans of study, CUHSR submission/documentation, internship site approval, and academic mentoring. How do you prefer to correspond with your faculty advisor? (2) Often, faculty advisors and course faculty encourage communication and interaction with their students, during office hours or through email. This dialog can promote program engagement while completing an online program. Do you feel engaged when meeting with Ed.D. faculty using technology, such as Zoom, email?

Extremely engaged: 22 Moderately engaged: 4

(3) Do online group work/projects promote strong curricular and professional discourse among classmates?

Yes: 18 No: 7

(4) Online group work is an authentic reality in today’s workplace environment. Does online group project engagement enhance your learning experience?

Extremely enhanced: 6 Moderately enhanced: 11 Slightly enhanced: 6 Not enhanced: 3

(5) How useful are synchronous live sessions within a course?

Extremely useful: 17 Moderately useful: 4 Slightly useful: 4 Not useful: 1

(6) How useful are recordings of live sessions, if not able to attend live?

Extremely useful: 14 Moderately useful: 8 Slightly useful: 2 Not useful: 2

(7) Many of our courses use online discussion forums as a means of Yes: 22 robust dialog and diverse feedback among the class over specific No: 4 topics and issues. Have you experienced strong engagement with your peers and/or faculty during online discussions? (8) Overall, many of our courses use video submission as a part of online class discussions and presentations. Is this format helpful to you as an adult learner?

Fig. 8.1 Student feedback about online group work

Extremely helpful: 16 Moderately helpful: 7 Slightly helpful: 1 Not helpful: 1

Does Online Group Work Enhance Learning?

Extremely or Moderately Enhanced

Slightly or Not Enhanced

8.6.1 Being Live (Virtually) Students need to see and hear from their faculty members. Many appreciate the use of live streaming, video recording, and video conferencing to witness not only

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Are Synchronous Live Sessions Useful?

Extremely or Moderately Useful

Fig. 8.3 Student feedback about recording of live sessions

Are Recordings of Live Sessions Useful?

Extremely or Moderately Useful

Fig. 8.4 Student feedback about video submissions for discussions

Slightly or Not Useful

Slightly or Not Useful

Are Video Submissions for Discussions Useful?

Extremely or Moderately Helpful

Slightly or Not Helpful

course content, but also faculty advisor meetings, research discussions, and private meetings. In addition, virtual live introductions to campus academic and student resources (i.e., academic librarians; counseling services; writing support) within the course content, or private meeting, as appropriate, provides a broader community for students in need [11].

8.6.2 Access Using a centralized learning management system (LMS) provides a one-stop portal for virtual learning. When developing coursework for the complete online environment, it is important to remember that students are relying on all elements of the portal to work correctly, as indicated by the course syllabus. Dead links, paywalls, invalid third-party and vendor products (to name a few), place barriers on the learning experience, and delay the overall progress of the online student community. It is expected that barriers happen from time to time, however, it is incumbent on the faculty member to communicate a plan to navigate barriers, and find alternatives, if necessary [11].

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Table 8.2 Qualitative results about faculty (1) Each student with the Bradley University Ed.D. program is assigned a faculty advisor to advise with issues related to individual plans of study, CUHSR submission/documentation, internship site approval, and academic mentoring. How do you prefer to correspond with your faculty advisor?

The role of the faculty advisor needs to be better communicated to students The mode of communication depends on the questions being asked: sometimes, a Zoom call is preferred rather than email

(2) Often, faculty advisors and course faculty Yes, Zoom is preferred as it is very personable encourage communication and interaction with and easier to discuss important topics where their students, during office hours or through you need to have dialog with a professor email. This dialog can promote program engagement while completing an online program. Do you feel engaged when meeting with Ed.D. faculty using technology, such as Zoom, email? (3) Do online group work/projects promote strong curricular and professional discourse among classmates?

Yes and no It can be difficult as we are all in different time zones Some students do not do their fair share of the work

(4) Online group work is an authentic reality in today’s workplace environment. Does online group project engagement enhance your learning experience?

Yes, overall it is enhanced It depends on the group you are assigned Can be difficult because of work and times to meet other classmates

(5) How useful are synchronous live sessions within a course?

Very useful for those who attend Very important in our research courses All courses should have them

(6) Many of our courses use online discussion Yes, I am most engaged when others use real forums as a means of robust dialog and diverse examples from work feedback among the class over specific topics Feels like busy work, but you can learn and issues. Have you experienced strong engagement with your peers and/or faculty during online discussions? (7) Overall, many of our courses use video submission as a part of online class discussions and presentations. Is this format helpful to you as an adult learner?

I like the use of video, but the technology can be challenging Helps put a face to other classmates and their ideas

8.6.3 Development Students expect curriculum and research expertise from their faculty members, but they also want to see a willingness to reach beyond common virtual delivery practices. Students noted that while individual adult learners should be responsible for continued growth of technology acumen, faculty should be as well. Faculty comfort, or lack thereof, with LMS structure and capabilities, virtual conferencing, video

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editing/recording, academic/scholarship search, and screen share were noted areas of potential instructional barriers [11].

8.6.4 Professional Responsibilities Because students enrolled in an online doctoral program are adult learners, it is important to consider their professional and life responsibilities outside of their coursework. Topics such as polling availability for synchronous live sessions, providing recordings for easy download, group work availability across multiple time zones, and time zone complexity for assignment submission, individual meetings and course requirements were all noted areas of importance [12].

8.6.5 Authentic Learning Students not only want an authentic relationship with their faculty, and they also want their learning to be authentic as well. Students noted that courses and assignments with learning objectives tied to their professional practice were of great value. Several noted the strength of applying individual course learning and research to their work and mentoring relationships as important factors of motivation toward degree completion [12].

8.7 Future Directions Continued technological progress and scholarship focused the development of faculty in the virtual environment have provided tools for more positive outcomes. Online faculty is the conduit to a campus connection, but also professional experiences in the field. A progressive comfort with the virtual learning space comes in various, and sometimes complex forms for online faculty of adult learners. Several foundational considerations emerged from student reflection—they want to know that their faculty care about their success, and design experiences that reflect that commitment through quality instructional design, authentic presence and purpose in the virtual classroom, and quality connection to the broader learning community. As new concentrations come on within our program, new elements of this research will continue to emerge. Considering the limited scope of this study, this paper does not serve as an exhaustive representation of global faculty development for online programs. Rather, this paper is a reflection of a singular program, student experiences, and program faculty development. The themes, however, have the potential to resonate more broadly.

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References 1. Williamson, B., Bayne, S., Shay, S.: The datafication of teaching in higher education: critical issues and perspectives. Teach. High. Educ. 25, 351–365 (2020). https://doi.org/10.1080/135 62517.2020.1748811 2. Adedoyin, O.B., Soykan, E.: Covid-19 pandemic and online learning: the challenges and opportunities. Interact Learn Environ 1–13 (2020). https://doi.org/10.1080/10494820.2020. 1813180 3. Kebritchi, M., Lipschuetz, A., Santiague, L.: Issues and challenges for teaching successful online courses in higher education: a literature review. J. Educ. Technol. Syst. 46, 4–29 (2017). https://doi.org/10.1177/0047239516661713 4. Taylor, J.E.: Motivational Immediacy: Fostering Engagement in Adult Learners. Stylus Publishing, LLC, Sterling, Virginia (2022) 5. Martin, F., Budhrani, K., Kumar, S., Ritzhaupt, A.: Award-Winning faculty online teaching practices: roles and competencies. OLJ 23 (2019). https://doi.org/10.24059/olj.v23i1.1329 6. Burlea, A.S., Burdescu, D.D.: An integrative approach of e-Learning: from consumer to prosumer. In: Smart Education and e-Learning, pp. 269–279. Springer International (2016) 7. Uskov, V., Bakken, J., Gayke, K., Jose, D., Uskova, M., Devaguptapu, S.: Smart University: a validation of “smartness features - main components” matrix by real-world examples and best practices from universities world wide. In: Smart Education and e-Learning 2019. Springer Nature Singapore (2019) 8. Uskov, V., Bakken, J., Pandey, A., Singh, U.: Smart University taxonomy: features, components, systems. In: Smart Education and e-Learning, pp. 3–16. Springer International, New York (2016) 9. Uskov, V., Bakken, J., Pandey, A.: The oncology of next generation smart classrooms. In: Smart Education and e-Learning, pp. 1–15. Springer, New York (2015) 10. Garrison, D.R., Anderson, T., Archer, W.: Critical thinking, cognitive presence, and computer conferencing in distance education. Am. J. Distan. Educ. 15, 7–23 (2001). https://doi.org/10. 1080/08923640109527071 11. Leontyeva, I.A.: Modern distance learning technologies in higher education: introduction problems. Eurasia J. Math. Sci. T. 14 (2018). https://doi.org/10.29333/ejmste/92284 12. Fahara, M.F., Castro, A.L.: Teaching strategies to promote immediacy in online graduate courses. Open Praxis. 7, 363 (2015). https://doi.org/10.5944/openpraxis.7.4.228

Chapter 9

Reliability Issues with At-Home Assessment During the COVID-19 Pandemic Haya Shamir, Erik Yoder, and David Pocklington

Abstract The COVID-19 pandemic dramatically changed the research landscape, forcing an increased adoption of in-home assessment and presenting researchers with new obstacles. The current study examined the issues posed to the validity and reliability of assessments conducted during these circumstances. Scores for prekindergarten students in four states were assessed on a literacy assessment at the start and end of the 2022 school year and compared across testing environments, either proctored in person or assessed at home. Scores for students that were assessed in an unsupervised environment were consistently higher than scores for students assessed in person. Differences in demographics were also assessed in terms of ethnicity, socioeconomic status, primary language, and prior exposure to other preschool programs. Notably, testing in an unregulated setting masked the variance of SES and prior exposure, hiding what had been significant relationships when tested in more controlled settings. This research highlights the need for an increased focus on reliability and methodology in post-pandemic education.

9.1 Introduction It is hard to overstate the detrimental effect that the COVID-19 pandemic is likely to have on education. Widespread school closures during the pandemic are plausibly going to have effects on student development for years to come. Specifically for young students’ developing literacy skills, it has been estimated that, based on known patterns of learning loss, young students who were in kindergarten during the crisis H. Shamir (B) · E. Yoder · D. Pocklington Waterford Institute, Taylorsville, UT, USA e-mail: [email protected] E. Yoder e-mail: [email protected] D. Pocklington e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_9

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may end up losing out on roughly two thirds of early critical literacy instruction [1]. The pandemic created an urgent need for quality remote learning directed toward young students [2].

9.2 Literature Review The pandemic seriously impeded ongoing research, both specifically in education and more broadly, either forcing the adoption of models that did not require in person assessment or outright suspending research which could not be done remotely [3]. As in person assessment ceased to be a viable option for measuring outcomes of education research studies, this resulted in an increased adoption of online testing. However, the infrastructure that facilitated this approach was not equally distributed or available in all communities. Even prior to the pandemic, people living in rural communities were disproportionately less likely to participate in studies compared to individuals living near population centers [4]. These same communities, living in areas without established Internet or communication infrastructure, were also at the most risk for being left behind in the push toward primarily online research; in 2016 as few as 63% of adults in the United States living in rural areas reported that they had access to a high-speed Internet service [5]. This potential for less prosperous communities to be underrepresented, reducing overall generalizability, was noted as a concern for research conducted during this time [6]. Remote research is not in itself a perfect solution and offers a number of unique challenges and issues. A fully remote randomized trial of fourth grade students receiving language intervention conducted during the pandemic noted high attrition, difficulty controlling for participant demographics, and in particular, validity and reliability concerns that could result from a lack of control over the in-home testing environment [7]. It is difficult to perfectly replicate a classroom learning environment in a home setting. Even without the complications introduced by the pandemic, educational research is difficult to conduct. A recent meta-analysis examining 141 large-scale educational randomized controlled trials conducted in the United States, and the United Kingdom found a mean effect size of 0.06, indicating small and uninformative effects for most of the studies assessed [8]. The authors would go on to recommend taking all possible steps to ensure the reliability of the data being reported in future studies. As much as these issues with assessment and reaching underserved communities were exacerbated by the pandemic, including individuals from highly rural or underserved communities in education research is not necessarily a new concern. Waterford Upstart is an example of a program aimed specifically at addressing this issue [9]. Upstart was originally developed as a means of bringing prekindergarten education to the most rural and isolated regions of Utah through the use of computerassisted instruction while simultaneously ensuring that households had access to the necessary hardware. Upstart has expanded to a nationwide program and plays a role in early education in 28 states.

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Research has suggested differences in-home learning environments which have noticeable impacts on the learning trajectories for young students. A recent study which explored various cognitive and environmental predictors for early literacy found that foundational skills, including letter knowledge and vocabulary, were linked to exposure to books in the home [10]. On a more causal note, research has shown that changes in-home environments can result in corresponding changes in literacy outcomes. A recent meta-analysis collated results from studies examining the efficacy of book giveaway programs intended to facilitate early exposure to literacy found that children that had benefited from these programs scored higher on measures of literacy skill at the start of their schooling [11]. It should be acknowledged that when talking about environmental influences, it can be difficult to parse more specific cause and effect relationships. It is complicated, for instance, to distinguish between the impact of exposure to literacy through the availability of books in a household and the impact of an involved caregiver willing to read to a child or the child’s own innate ability. Some research has indicated that when parental reading habits are accounted for, the number of books available in a house does scale positively with a child’s literacy [11]. Additionally, some research has pointed to home environment having a mediating effect between socioeconomic status and expressive vocabulary for young learners [12]. A recent study examining the impacts of the pandemic revealed an interesting potential wrinkle in at-home assessment; teachers were concerned that students were receiving too much assistance from their caregivers [13]. Teachers reported work being completed at suspicious times or with perfect spelling and punctuation, raising the prospect that parents were risking the validity of the assignments. It is necessary to demonstrate, with confidence, that approaches that are being recommended to caregivers are actually effective.

9.3 Research Goal The goal of the current study was to compare assessment results for students conducted during the pandemic, in at-home environments, to data from comparable students collected in controlled environments.

9.4 Research Methodology 9.4.1 Participants The sample for this study (N = 5123) consisted of prekindergarten students in North Dakota, Ohio, Oklahoma, and Utah during the 2021–2022 school year. In all districts assessed, students were assessed both in person in proctored environments (n =

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4321) and at-home (n = 802). The sample included 49% female, 76% Caucasian, 29% below 185% of the federal poverty guideline, and predominantly (91%) used English as their primary language.

9.4.2 Materials Upstart. A computer-adaptive kindergarten readiness curriculum for early learners. The software utilizes a broad spectrum of educational media in a sequence customized to each student’s specific ability and pace of development. Updates and messages are used regularly to encourage parental engagement in the education process. Upstart also makes use of coaching to help families get the most out of the program [14]. Waterford Assessments of Core Skills (WACS). WACS is an adaptive assessment designed to assess 11 key pre-literacy and reading skills. Initial content validity for WACS was established against state and national standards for the 11 subtests. All items were then calibrated for item response theory to determine item difficulty. To establish concurrent validity and predictive validity, student performance on WACS was compared to performance on five commonly used standardized tests also measuring early reading skills; all correlations between tests are significant, ranging from r = 0.41 to r = 0.78 (median r = 0.63). Additional analyzes indicate that WACS is internally consistent and has strong test–retest reliability (r = 0.90). Of the skills assessed, the segmenting strand is not included in the adaptive sequence for students in prekindergarten. As a result, segmenting is not assessed in this study. WACS is scored on a scale from 1000–7000, with grade equivalent ranges every 1000 points, with prekindergarten equivalent scores falling between 1000–2000, kindergarten falling between 2001–3000, and so on [15].

9.4.3 Procedure Students that participated in the program were expected to use Upstart for 15 min a day, five days per week. The program ran for 39 weeks from September 2021 to May 2022. Usage was tracked within the program and monitored weekly, and the total minutes of the adaptive curriculum usage for the duration of the program per group ware calculated. WACS was administered at the beginning and end of the program to assess the literacy skills of the students before and after their participation in the program. At the beginning of the year, testing was done exclusively online, with students at-home in uncontrolled testing environments. At the end of the year, the majority of students (n = 4321) were assessed in person in controlled testing environments, while a subset of students (n = 802) were still assessed at home, allowing for a comparison between students who were roughly equivalent to each other apart from the environment in which they were assessed.

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9.5 Research Outcomes 9.5.1 Group Differences Using Independent Samples t-tests. Independent samples t-tests were conducted to examine group differences in end of year WACS scores between students who were tested at home and in person (see Fig. 9.1). Analysis of beginning of year WACS scores revealed no significant difference between groups, t(1, 4843) = −1.33, p = 0.183. At the start of the year, scores between students that would later be assessed at home (M = 1972) and students that would later be assessed in person (M = 1990) were comparable to each other, falling in the prekindergarten advanced range. Analysis of end of year WACS scores revealed a significant difference between groups, t(1, 5121) = 9.02, p < 0.001, due to higher end of year scores made by students who were tested at home (M = 2764.12) than by students who were tested in person (M = 2607.39). Effect size (d = 0.35). Students assessed at home, in uncontrolled environments, were scoring in the kindergarten advanced range, while students tested in more controlled environments were testing in kindergarten intermediate range. Ten further independent samples t-tests were conducted to examine group differences in strand scores in end of year WACS scores between different testing environments (see Table 9.1 and Fig. 9.2). Analysis of WACS scores revealed that for all ten strands assessed, scores made by students who were tested at home were significantly higher than scores made by students who were tested in person. Means for students who were tested at home ranged from 2079 (letter recognition) to 3270 (non-words). Means for students who were tested in person ranged from 2049 (letter recognition) to 3119 (non-words). Effect sizes were small to medium, from d = 0.11 (letter sound) to d = 0.47 (reading comprehension). 2900

WACS Scores

2700 2500 2300 2100 1900 1700 1500 Overall Beginning of Year At Home

Fig. 9.1 End of year overall WACS scores

Overall End of Year In Person

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Table 9.1 End of year WACS scores by testing environment At home

In person

N

M

N

M

p

d

Overall

802

2764.12

4321

2607.39

0.00**

0.35

Blending

802

2908.31

4313

2791.66

0.00**

0.16

Initial sound

802

2616.86

4318

2552.48

0.00**

0.19

Letter recognition

802

2078.71

4321

2048.95

0.00**

0.16

Letter sound

802

2576.01

4321

2514.93

0.01*

0.11

Listening comprehension

483

2927.31

2951

2623.83

0.00**

0.23

Non-words

372

3270.29

1794

3119.11

0.00**

0.17

Reading comprehension

369

2984.45

1778

2366.75

0.00**

0.47

Real words

379

3231.71

1858

3112.28

0.00**

0.18

Sight words

379

3097.13

1857

2953.30

0.00**

0.26

Vocabulary

802

3008.79

4321

2789.82

0.00**

0.26

At Home

Vocabulary

Sight Words

Real Words

Reading Comprehension

Non Words

Listening Comprehension

Leer Sound

Leer Recognion

Inial Sound

3400 3200 3000 2800 2600 2400 2200 2000 1800 Blending

WACS Scores

*p < 0.05, **p < 0.001

In Person

Fig. 9.2 End of year WACS scores by strand

9.5.2 Group Differences Using ANCOVA An ANCOVA was conducted to examine group differences in end of year WACS scores between students who were tested at home and in person, while controlling for beginning of year scores (see Fig. 9.3). Analysis indicated a significant effect for testing environment on end of year scores while controlling for beginning of year scores, F(1, 4842) = 112.09, p < 0.001, due to the higher end of year scores made by students who were tested at home (M = 2792.78) than by students tested in person (M = 2615.07). Effect size (d = 0.39). Students assessed at home, in uncontrolled environments, were scoring in the kindergarten advanced range, while students assessed in more controlled environments were scoring in kindergarten intermediate range.

WACS Scores

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2850 2800 2750 2700 2650 2600 2550 2500 At Home

In Person

Fig. 9.3 End of year overall WACS scores controlling for beginning of year scores

Ten further ANCOVAs were conducted to examine group differences in strand scores in end of year WACS scores, while controlling for beginning of year scores, between different testing environments (see Table 9.2 and Fig. 9.4). Analysis of WACS scores revealed that for eight of the ten strands, assessed scores made by students who were tested at home were significantly higher than scores made by students who were tested in person. Effect sizes were small to medium, from d = 0.14 (letter sound) to d = 0.61 (reading comprehension). For the non-words and real words strands, scores made by students who were tested at home were higher than scores made by students tested in person, but the differences were not significant. Table 9.2 End of year WACS scores by testing environment controlling for beginning of year scores At home

In person

N

M

N

M

p

d

Overall

733

2792.78

4112

2615.07

0.00**

0.39

Blending

731

2932.12

4089

2806.33

0.00**

0.17

Initial sound

736

2628.18

4101

2557.78

0.00**

0.19

Letter recognition

736

2081.53

4112

2051.60

0.00**

0.15

Letter sound

736

2603.39

4111

2522.44

0.00**

0.14

Listening comprehension

405

2947.49

2502

2659.35

0.00**

0.21

Non-words

76

3506.38

401

3352.01

0.14

–

Reading comprehension

72

3480.37

388

2694.65

0.00**

0.61

Real words

83

3455.80

428

3312.04

0.08

–

Sight words

80

3307.45

428

3110.56

0.00**

0.37

Vocabulary

736

3033.59

4110

2791.95

0.00**

0.32

*p < 0.05, **p < 0.001

H. Shamir et al. 3800 3300 2800

At Home

Vocabulary

Sight Words

Real Words

Reading Comprehensi…

Non Words

Leer Sound

Leer Recognion

Inial Sound

1800

Listening Comprehensi…

2300 Blending

WACS Scores

106

In Person

Fig. 9.4 End of year WACS scores by strand controlling for beginning of year scores

9.5.3 Group Differences Using ANCOVA—Demographics Four separate ANCOVAs were conducted to examine the effect of testing environment and demographics on end of year while controlling for beginning of year scores (See Table 9.3). Ethnicity. Analysis revealed no significant interaction between the effects of testing environment and ethnicity on end of year scores while controlling for beginning of year scores, F(1, 4717) = 0.00, p = 0.961. Simple effects analysis indicated that for Caucasian students and students of other ethnicities, scores for students who were tested at home were significantly higher than scores for students who were tested in person. Economic Disadvantage. Analysis revealed a significant interaction between the effects of testing environment and socioeconomic status on end of year scores while controlling for beginning of year scores, F(1, 4422) = 11.52, p < 0.01. Post hoc analysis indicated that when students were tested at-home scores for students Table 9.3 End of year WACS scores by testing environment and demographics controlling for beginning of year scores At home

In person

N

M

N

M

114

2630.34

845

2467.38

Ethnicity

Other Caucasian

597

2818.39

3166

2653.23

Economic disadvantage

Over 185%

491

2802.24

2570

2674.65

Under 185%

176

2736.19

1190

2476.27

No other preschool

620

2786.84

3574

2623.87

Other preschool

113

2825.34

538

2556.64

English

689

2803.64

3743

2637.29

Non-English

13

2675.96

56

2501.80

Other preschool Primary language

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above, and below 185% poverty were not significantly different (p = 0.068). When tested in person, scores for students who were above 185% of the poverty line were significantly higher than those of students who were below 185% of the poverty line (p < 0.01). Other Preschool. Analysis revealed a significant interaction between the effects of testing environment and attendance of another preschool on end of year scores while controlling for beginning of year scores, F(1, 4840) = 5.08, p < 0.05. Post hoc analysis indicated that when students were tested at-home scores for students who had previously attended another preschool and students who had not attended another preschool were not significantly different (p = 0.368). When tested in person, scores for students who had not previously attended another preschool program were significantly higher than those of students who had previously attended another preschool program (p < 0.01). Primary Language. Analysis revealed no significant interaction between the effects of testing environment and primary language on end of year scores while controlling for beginning of year scores, F(1, 4496) = 0.00, p = 0.951. Simple effects analysis indicated that for students whose primary language was English, scores for students who were tested at home were significantly higher than scores for students who were tested in person. Scores for students whose primary language was not English who were tested at home were slightly higher than in the control group, but the difference was not significant.

9.6 Discussion The current study compares literacy assessment results for students in different testing environments, measured during the COVID-19 pandemic. Results indicated that scores for students who were assessed at home were significantly higher than scores for students who were assessed in a proctored environment. This expands on prior research on the technical difficulties of moving to a remote design model, particularly issues in reliability and validity that can come from an uncontrolled testing environment [7].

9.7 Conclusions When looking at specific demographics, the move to home assessment seems to have masked the variance from socioeconomic status and the previous attendance of another preschool. When tested in a proctored environment, students below 185% of the poverty line did worse than their peers, as did students who had attended some form of other preschool program prior to the pandemic. These differences vanish when considering children who were assessed at home. Pointing to a specific causal relationship for this effect is difficult. Alongside the overall higher performance

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of students tested at home, it is possible to see this as arguing that students were receiving support on the assessment from caregivers. This interpretation would be consistent with concerns previously raised by educators that parents were possibly too active in their children’s education during the pandemic [13]. It is also reasonable to conclude that students may have performed better because they were in an environment where they were more comfortable, this would also be consistent with prior research, which has indicated that home environment can play a moderating role between socioeconomic status and performance on literacy assessments [12].

9.8 Next Steps To acknowledge a fundamental weakness of the current study, it involved assessment in a difficult to control environment in the middle of an unprecedented disaster. The current study was also not able to isolate the effect of the pandemic itself from other environmental factors. The specific circumstances of the study may prove to be difficult to replicate going forward.

References 1. Bao, X., Qu, H., Zhang, R., Hogan, T.P.: Modeling reading ability gain in kindergarten children during COVID-19 school closures. Int. J. Environ. Res. Public Health 17(17), 6371 (2020) 2. Nugroho, D., Lin, H. C., Borisova, I., Nieto, A., Ntekim, M.: COVID-19: Trends, promising practices and gaps in remote learning for pre-primary education (2021) 3. Noonan, D., Simmons, L.A.: Navigating nonessential research trials during COVID19: the push we needed for using digital technology to increase access for rural participants? J. Rural Health: Offic. J. Am. Rural Health Assoc. Nat. Rural Health Care Assoc. 37(1), 185 (2021) 4. Virani, S., Burke, L., Remick, S.C., Abraham, J.: Barriers to recruitment of rural patients in cancer clinical trials. J. Oncol. Pract. 7(3), 172–177 (2011) 5. Vogels, E.A.: Some digital divides persist between rural, urban and suburban America (2021) 6. Lourenco, S.F., Tasimi, A.: No participant left behind: conducting science during COVID-19. Trends Cogn. Sci. 24(8), 583–584 (2020) 7. Ozernov-Palchik, O., Olson, H., Arechiga, X., Kentala, H., Solorio-Fielder, J.L., Wang, K. L., Gabrieli, J, et al.: Implementing Remote Developmental Research: A Case Study of an RCT Language Intervention During COVID-19 (2021) 8. Lortie-Forgues, H., Inglis, M.: Rigorous large-scale educational RCTs are often uninformative: should we be concerned? Educ. Res. 48(3), 158–166 (2019) 9. Shamir, H., Miner, C., Izzo, A., Feehan, K., Yoder, E., Pocklington, D.: Improving early literacy skills using technology at home. Int. J. Learn. Teach. 5 (2019) 10. de Bondt, M., Willenberg, I.A., Bus, A.G.: Do book giveaway programs promote the home literacy environment and children’s literacy-related behavior and skills? Rev. Educ. Res. 90(3), 349–375 (2020) 11. van Bergen, E., van Zuijen, T., Bishop, D., de Jong, P.F.: Why are home literacy environment and children’s reading skills associated? What parental skills reveal. Read. Res. Q. 52(2), 147–160 (2017)

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12. Lohndorf, R.T., Vermeer, H.J., Cárcamo, R.A., Mesman, J.: Preschoolers’ vocabulary acquisition in Chile: the roles of socioeconomic status and quality of home environment. J. Child Lang. 45(3), 559–580 (2018) 13. Timmons, K., Cooper, A., Bozek, E., Braund, H.: The impacts of COVID-19 on early childhood education: capturing the unique challenges associated with remote teaching and learning in K-2. Early Childhood Educ. J. 49(5), 887–901 (2021) 14. Heuston, D.H., Parkinson, J.W.: The Third Source: A Message of Hope for Education. Waterford Institute (2011) 15. Shamir, H.: Assessing reading in young learners: using a computerized adaptive reading test for pre-kindergarten through 2nd grade. J. Educ. Multimed. Hypermed. 27(4), 507–527 (2018)

Chapter 10

Student Support in an Online Environment: Doctoral Student Feedback Erik A. Dalmasso and Jeffrey P. Bakken

Abstract Bradley University (Peoria, IL, USA) offers an online Ed.D. in Educational Leadership, with a concentration in Higher Education Administration and Leadership (HEAL), with additional concentrations coming online in the near future. Online doctoral programs continue to emerge to meet the needs of working professionals seeking a terminal degree. Many factors weigh the online doctoral student experience and potential barriers to completion. This paper originates from doctoral student survey feedback from current students, as well as program alumni given in the fall of 2022. The survey was guided by the Community of Inquiry (CoI) framework and provides meaningful reflection from adult learners engaged in rigorous online terminal degree coursework, in conjunction with their professional and personal responsibilities. This paper will discuss the virtual adult learner feedback as it applies to the use and comfort of technology, potential barriers in the virtual space, and the potential value added in virtual relationships.

10.1 Introduction Online adult learners, choosing to return to graduate study toward a terminal degree, enter the virtual classroom with individual and sometimes competing motivations [1–3]. Whether to bolster a professional career, develop expertise in a specific topic area, or produce and publish substantive research, many students are completing rigorous doctoral level coursework and writing, while employed in highly experienced professional positions [1–3]. Over the past decade, many higher education institutions, globally, have introduced online doctoral programs marketed to, and designed for, working professionals—Bradley University (BU) is no exception. E. A. Dalmasso (B) · J. P. Bakken Department of Education, Counseling and Leadership, Bradley University, Peoria, USA e-mail: [email protected] J. P. Bakken e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_10

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Doctoral students enrolled in BU’s online Ed.D. in Educational Leadership, with a concentration in Higher Education Administration and Leadership (HEAL), bring incredible professional experience to their virtual discussions, academic writing, and authored scholarship. They have made a choice to pursue a terminal degree, while incorporating coursework, research, and writing into their professional practice. BU’s program enrolls students from multiple regions of the United States, representing senior-level professional acumen at all levels of higher education— community college; public and private institutions, state higher education agencies; and corporate education. Online doctoral programs have the opportunity to provide working professionals the time and autonomy to be fully invested in complex professional work, while remaining highly engaged in curriculum and research [4]. Student feedback regarding barriers and opportunities prescient to his/her individual student experience is a valuable tool [4]. Smartness Levels The smartness levels of sensing, inferring, self-learning, adaptation, anticipation, and self-optimization [5–7] are key to students being successful in the Ed.D. online program in educational leadership. Students are constantly collecting data about themselves, from other students, and from professors trying to figure out how they can be successful in this online program (sensing). They take this data and use it to inform themselves on what they are doing positively as a student and what information they can glean to help improve themselves (inferring). They then evaluate this data to obtain information about how successful or not successful they were and process that information to be more productive in their future online coursework (self-learning). Based on this obtained data, they then modify or change their behaviors to be a more successful student in an online platform (adaptation). This new knowledge is used to prepare for future events in the program and to alleviate any program problems or discrepancies for them as a student (anticipation). Finally, they look at the overall structure of the program and investigate how they can modify or change their behaviors to be more successful based on the data they have obtained (selfoptimization). Incorporating these components is essential for the success of these students in this online program in educational leadership. Human Factors in e-Learning According to Burlea [8], several complex human factors exist as potential barriers to adult student success in an online environment. Factors of time, perception of individual technological competency, motivation and interest, and perception of overall course relevance to professional careers were noted as possible barriers within the student experience and can contribute to an overall lack of completion of a course, or program overall. Burlea [8] noted that tight timelines attached to assignments or group projects proved difficult, as affected students lacked the personal time, and potentially, the access to a high-speed connection, or computer hardware throughout their day.

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These concerns resonate within our program as well. While our students have chosen the doctoral program because of the concentration focus, it is a goal of our program that our coursework incorporates authentic literature, discussions, and written or presentation assignments that show a direct connection to the student’s professional work. Creating some built-in autonomy for students within assigned projects, and discussions, allow them to make direct connections to their work. The individual circumstances surrounding a student’s access to reliable broadband or up to date computer hardware can be difficult to gage on a regular basis, but an open line of communication with the faculty member is critical for engagement. Creating development opportunities for student comfort surrounding the use of the learning management system (LMS) including particular features necessary to participate at a high level is a meaningful takeaway from Burlea’s work. Within a cohort model, there is a potential for a community response to aide a student that is struggling with specific aspects of online course management. In addition, university technical support, with wide availability and specific LMS knowledge, stands as a resource for struggling students as well.

10.2 Research Project Goals (A) To find out what student perspective components in an online Ed.D. program are important to the success of students from a student perspective quantitatively. (B) To find out what student perspective components in an online Ed.D. program are important to the success of students from a student perspective qualitatively.

10.3 Method At the end of the fall 2022 semester, an anonymous response survey was sent to all current Bradley University online Ed.D. students and program alumni. This survey, guided by the Community of Inquiry framework [9], included questions focused on three levels: programmatic characteristics of the program, faculty interaction with students and support, and the student/learner experience. In total, the survey was distributed, via an anonymous email link, to 66 students. The survey was accessible for 30 days, directly after the conclusion of the fall semester. Researchers evaluated 26 survey submissions with an overall response rate of 39.4%. The goal of this survey was to better understand the state of the of Bradley University online Ed.D. program, and the impact of programmatic, faculty, and student learner decisions as they apply to multiple levels of the online student learning experience. For the purposes of this paper, the researchers identified survey questions focused at the student perspective level for focused analysis and synthesis of student feedback.

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Table 10.1 Quantitative results about the student experience (1) Even though your connection to Bradley’s campus and community is virtual, do you feel connected to the BU student experience?

Extremely connected: 6 Moderately connected: 5 Slightly connected: 13 No connection: 2

(2) While in an online learning environment, do you feel a connection between your coursework/research, and your professional responsibilities?

Extremely connected: 19 Moderately connected: 4 Slightly connected: 2 No connection: 1

(3) How connected do you feel with your cohort classmates?

Extremely connected: 13 Moderately connected: 12 No connection: 1

(4) Does your online connection to your cohort provide motivation and support to complete the doctoral program?

Yes: 20 No: 5

(5) This doctoral program puts an emphasis on authentic, practical Yes: 25 learning experiences, within all coursework, faculty interaction, and internship experiences. From your perspective, have you found this to be true?

10.4 Research Project Outcomes A total of five quantitative and six qualitative questions from the survey addressed the online Ed.D. program. Students responded to each question that had a quantitative response using either a 2-point, 3-point, or a 4-point Likert scale depending on the question. Each question with a quantitative response also had a qualitative question that accompanied it asking the students if they had any additional thoughts regarding the question. Results of the quantitative analysis are listed below in Table 10.1.

10.5 Survey Answers Figures 10.1, 10.2, 10.3 and 10.4 address questions 1–4 above. Results of the qualitative analysis are listed below in Table 10.2. Fig. 10.1 Survey outcomes about connection to Bradley University campus

Connecon to Bradley Campus

Extremely or Moderately Connected

Slightly or No Connecon

10 Student Support in an Online Environment: Doctoral Student Feedback Fig. 10.2 Survey outcomes about connection to coursework

Connecon to Coursework

Extremely or Moderately Connected

Fig. 10.3 Survey outcomes about connection to cohort classmates

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Slightly or No Connecon

Connecon to Cohort Classmates

Extremely or Moderately Connected

Fig. 10.4 Survey outcomes about connection to cohort to provide motivation to complete program

No Connecon

Connecon to Cohort Provide Movaon to Complete Program?

Yes

No

10.6 Discussion In regards to students, most students only felt slightly connected to campus, but they did feel very connected to faculty. A suggestion was to incorporate more live sessions. Student did feel extremely connected in regards to their coursework and their professional responsibilities and really liked the action research component as it related directly to their professional growth. Overall, students felt either extremely or moderately connected to their classmates. They also mentioned that connecting with others in their cohort was beneficial in regards to keeping them on track, for support, and to examine class assignments and activities. Lastly, students felt an emphasis of the program was on authentic, practical learning experiences, and again, the action research project was mentioned as it directly impacted their work environment. Student feedback focused on the student experience providing meaningful context for program administration and faculty. Thinking more broadly about the data in concert with the established scholarship, some core themes emerged that deserve further examination.

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Table 10.2 Qualitative results about the student experience (1) Even though your connection to Bradley’s campus and community is virtual, do you feel connected to the BU student experience?

Overall, I feel connected to classmates, but not Bradley I feel connected to the faculty More live sessions would help

(2) While in an online learning environment, do Yes, I do feel a connection is present you feel a connection between your Love the action research as that directly coursework/research, and your professional applies to me responsibilities? (3) How connected do you feel with your cohort Very close classmates? We communicate a lot outside of class We help each other out (4) Does your online connection to your cohort provide motivation and support to complete the doctoral program?

Yes, the outside support from classmates is very important Don’t feel connected to students who don’t attend live sessions

(5) This doctoral program puts an emphasis on authentic, practical learning experiences, within all coursework, faculty interaction, and internship experiences. From your perspective, have you found this to be true?

Yes, especially regarding the action research project Faculty and students sharing real-life experiences related to content make are authentic

10.6.1 Access Online students need access. The use of technology (i.e., computer, digital tablet, or smartphone) is the only connection to the institution, therefore, appropriate access to campus support networks (i.e., student affairs, financial aid, and IT) and academic resources (i.e., library) is critical. As online learning becomes more inundated with more sophisticated LMS capability and specialized vendor products, it is important to consider how these advancements integrate with institutional legacy systems [9].

10.6.2 Interaction Many students appreciate their connection to their online learning community. Even though most coursework experiences are asynchronous, students indicate that synchronous interactions help build supportive connections throughout the program. Further, connections made with faculty, and classmates can provide layers of support and mentorship [1].

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10.6.3 Balance Online doctoral programs are rigorous in nature. One of the main reasons students choose an online program is because of the asynchronous nature of the coursework, and how it parallels with their other professional and personal/familial commitments. Providing recordings of in-class live sessions and programmatic group advising sessions is one example of allowing students to be active but also be present for life/professional commitments [10].

10.7 Future Directions As BU continues to grow within the online graduate education space, we will continue to learn as well. Student feedback is an essential component when considering strategic program planning, instructional design, policy creation, advisement, and mentorship. Overwhelmingly, students work very hard to navigate the rigors of online doctoral work in concert with their professional and personal lives. They respect and appreciate the opportunity for personal connection within the virtual space. Eliminating unnecessary barriers, providing essential technology access, and an overall awareness of their online learning experiences to appropriate administration are fundamental. Because this study and paper are limited in scope, it is not intended to provide an exhaustive understanding of barriers for online adult graduate students. However, the overall themes explored do provide some common human context, in addition to the established content scholarship. There are several directions; our program can and should explore for additional development and research, including cohort/community development, evaluation of rigor and time needed to complete coursework, strategies for balance within a student’s professional schedule, and a continual focus on impactful pedological strategies aligned with the career aspirations of our students.

References 1. Martin, F., Stamper, B., Flowers, C.: Examining student perception of readiness for online learning: importance and confidence. OLJ 24 (2020). https://doi.org/10.24059/olj.v24i2.2053 2. Yang, D., Baldwin, S., Snelson, C.: Persistence factors revealed: students’ reflections on completing a fully online program. Distance Educ. 38, 23–36 (2017). https://doi.org/10.1080/ 01587919.2017.1299561 3. Rovai, A.P., Jordan, H.: Blended learning and sense of community: a comparative analysis with traditional and fully online graduate courses. IRRODL 5 (2004). https://doi.org/10.19173/irr odl.v5i2.192 4. Taylor, J.E.: Motivational Immediacy: Fostering Engagement in Adult Learners. Stylus Publishing, LLC, Sterling, Virginia (2022) 5. Uskov, V., Bakken, J., Gayke, K., Jose, D., Uskova, M., Devaguptapu, S.: Smart University: a validation of “smartness features—main components” matrix by real-world examples and best

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E. A. Dalmasso and J. P. Bakken practices from universities world wide. In: Smart Education and e-Learning 2019. Springer Nature Singapore (2019) Uskov, V., Bakken, J., Pandey, A., Singh, U.: Smart University taxonomy: features, components, systems. In: Smart Education and e-Learning, pp. 3–16. Springer International, New York (2016) Uskov, V., Bakken, J., Pandey, A.: The oncology of next generation smart classrooms. In: Smart education and e-learning, pp. 1–15. Springer, New York (2015) Burlea, A.S.: The complexity of an e-Learning system: a paradigm for the human factor. Inf. Syst. Dev.: Chall. Pract. Theory Educ. 2, 867–878 (2008) Garrison, D.R., Anderson, T., Archer, W.: Critical thinking, cognitive presence, and computer conferencing in distance education. Am. J. Dist. Educ. 15, 7–23 (2001). https://doi.org/10. 1080/08923640109527071 Columbaro, N.L.: e-Mentoring possibilities for online doctoral students: a literature review. Adult Learn. 20, 9–15 (2009). https://doi.org/10.1177/104515950902000305

Part III

Smart University

Chapter 11

Organizing the University 4.0: New Goals and Insights to Promote the Digital Transformation of Higher Education Institutions to Succeed Next E-learning Era Eleonora Veglianti, Elisabetta Magnaghi, Nunzio Casalino, Alessandro Gennaro, and Marco De Marco Abstract In response to the negative economic impacts for EU citizens of COVID19 and Ukraine war, in the EU educational market, it is urgent to support digital transformation plans at all levels by effective activities and customized online services. It is also required to support new inclusive smart digital pedagogical methods and advanced skills for a resilient free adoption of digital tools for professors (digital integrated learning, advanced multimedia simulations, but also interactive 3D, VR, holographic tools, AR, gamification, virtual simulations, 3D and 360° immersive environments, etc.). New effective learning paths have to be considered also for students, including participants with fewer opportunities: physically impaired (deaf, blind, paralytic), cognitive impaired, and low-income learners. This with the aim to create the condition of a more EU integrated strategy to imply a real digital revolution in educational processes and learning skills and to prepare advices and proposals for several organizations in the field such as: EU Council on the enabling factors for successful digital education by 2024, OECD and UN, the EU Future Government 2030+ , the EU’s strategy for e-Skills in the 21st Century, KES international network, E. Veglianti · E. Magnaghi Université Catholique de Lille, Lille, France e-mail: [email protected] E. Magnaghi e-mail: [email protected] N. Casalino (B) · A. Gennaro Department of Economics and Management, Università Degli Studi Guglielmo Marconi, Rome, Italy e-mail: [email protected] A. Gennaro e-mail: [email protected] M. De Marco Università Telematica Internazionale UniNettuno, Rome, Italy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_11

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and the FOME—Future of Management Education international scientific alliance. So digital technologies, used properly, can make this transition possible: surely, there had been several e-learning platforms for some years now, but their role has only in the last months gained massive relevance concurrently with the pandemic global situation. At the moment, only online universities are already ready for a smart betterquality distance learning, with innovative tools for courses’ management, including live sessions, interactive teaching, and collaborative activities. Most EU universities had to cope with the new trends of e-learning (for the authors identified as University 4.0) that is requiring for universities a quick organizational and cultural rearrangement and a change of strategies that perhaps represented the most difficult step for professors and administrative staff.

11.1 Introduction Academic organizations have been undergoing extensive changes during the last years that are driven by globalization, social mobility, and mostly new technologies (Moran et al. 2018). The innovation and technological tools in digital learning are increasingly spreading and new challenges and opportunities emerged. A large number of universities in different countries improve or implement new services and platforms to reach a wider presence both at national and international level. Many activities and many organizations became totally online through collaborative internet platforms and information and communications technologies (ICTs). Therefore, globalization, networked organization, and communication technologies allow universities to develop new models of collaboration in teaching, in research, in common programs design, in teachers and students exchange also in a virtual way. In other words, nowadays, it is much clearer than in the past that the current society needs a smarter, more flexible, and resilient education system approach based on new digital elements and on new services and practices. This is an important aspect both for public and private entities which provides the necessity to rethink about how these institutions are organized. Therefore, the concept of University 4.0 and Education 4.0 in general can be considered the next step in education, where digital technologies and personalized data create the appropriate environment for an education that is student-centered, allowing flexible, adaptive, and dynamic learning pathways (Gueye and Exposito 2020). Consequently, the aim of this research is to carefully study this phenomenon in a narrative approach that occurs with the data collected. The paper is structured as follows: Sect. 11.2 explores the background and related work; Sect. 11.3 provides the results and the discussion part; Sect. 11.4 describes in detail an effective and innovative project considered a best practice in the e-learning research field. Finally, Sect. 11.5 presents the conclusion, analyzing the limits of the paper and suggesting further works.

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11.2 Background and Related Work E-learning has evolved in the past years. With the emergence of more and more advanced technologies such as Web services, development of lightweight, flexible, and inclusive e-learning systems, it becomes a reality. So, organizations, in general, and universities, in particular, have to propose a new architecture for e-learning systems. The latter could be based, for example, on Web services and on two-way interactive agents. Therefore, possible outcomes of the assessment of institutional capacity and readiness for online learning are as follows: • The institution has the internal capacity to develop or expand its online learning program and is ready to do so. • The institution has substantial internal capacity but is lacking one or more essential components or functions which need to be added or enhanced before it is ready to fully develop or expand its online learning. • The institution lacks the internal capacity to develop or expand its online learning program and is not ready to do so at the present time. Furthermore, the implementation of the assessment findings was determined according to the institutional mission, the goals, and priorities of the institution’s leadership and whether the overall culture of the institution supported the development or expansion of online learning (Miller et al. 2014). In line with this, it is important to enhance the internal capacity through investments in personnel, infrastructure, or technology. For instance, a university that wishes to expand its operations and offerings to include fully online programs for students outside its geographical area will need to invest in an infrastructure of student services and technical support to accommodate the unique needs of learners who may never set foot on campus. An alternative to performing all operations, services and support completely inhouse, is to outsource one or more functions to a third–party e-learning vendor, such as Learning House or Pearson Embanet, or to a local consulting or solutions firm, such as an instructional design group or I.T. solutions provider. Following this, several universities use third-party vendors to, in essence, run their online education programs, as the vendors handle program and course development, marketing, student recruiting, technical support, and reporting. The typical model for this type of arrangement involves revenue sharing, with the vendor receiving a percentage of tuition and/or fees paid by the students. An advantage of this approach is a relatively quick start-up time, as the vendor supplies the personnel, courses, materials, and expertise needed to establish and operate the program. Leaders and faculty at a higher education institution may be wary of turning over so much control (and revenue) to third-party vendors and may have concerns that their online programs might be merely copies of programs that the vendors are providing for other colleges and universities (Riter 2017). There may also be the fear that if the partnership with the full-service vendor was ever to be discontinued, the vendor could “pack up the program and go”, leaving the institution and its learners without any program at all. A less invasive alternative is to use a third-party vendor

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Fig. 11.1 E-learning process model

to provide a more limited number of services in areas for which the institution does not have sufficient capacity, such as providing instructional design support for course development, after-hours help desk/technical support, admission and retention services, or data analytics reporting. This hybrid approach (i.e., mostly in-house with outsourcing only in areas that the institution lacks capacity) has the advantage of giving the institution the time to build up its internal capacity, while it can still offer its own online programs rather than someone else’s to its learners. Given the above-mentioned scenario, it seems that online learning will continue to increase in ubiquity across the education field. Universities that do not currently offer online courses will likely do so in the not-too-distant future. Institutions that now offer only online courses will undoubtedly expand to online degrees. Moreover, in an era of declining higher education enrolments, most of the universities are looking for strategies to increase the size of their student bodies. Thus, several models emerged to perform a systematic assessment of the internal capacity of this kind of organization to be ready to establish or to expand online learning (Fig. 11.1). Obviously, most standardization efforts focus on data integration but not on smart application integration. Complying with these standards, the interchange of educational content between servers or peers is still a problem which has not been solved satisfactorily. It is necessary to develop a new e-learning system architecture which can integrate both data and applications. The next generation systems should have the following characteristics: open architecture and interfaces; integration; loose coupling; flexibility; reusability; maintainability; compatibility; effective personalization (Fig. 11.2). Generally, there are several groups of people involved in an e-learning system, namely authors, learners, administrators, and trainers. Authors and learners are main players, in a traditional e-learning system, all the functional modules needed by users are resided in the server. In our proposed architecture, only a set of key functional modules will reside in the learning management server. Other assisting functional modules will be distributed on the Internet and can be invoked on demand via standard interfaces. All the functional modules are implemented as Web services that can be easily integrated and reused. In detail as identified in the E-learning process model of Khanb Badrul (2012), it is fundamental to distinguish two phases: • Phase I related the content development. This process means that e-learning materials are designed and produced or developed following a plan that must

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Fig. 11.2 E-learning content development and delivery phases

incorporate instructional design principles for meaningful learning. It consists of the following stages: (1) planning, (2) analysis, (3) design, (4) development, (5) evaluation. • Phase II related the content delivery. This process means that e-learning materials are made available to the users. It consists of the following stages: (6) delivery of learning environment, (7) administration of learning environment, (8) promotion of learning offerings, (9) maintenance of learning environment. Therefore, the e-learning systems with this architecture are highly interoperable, flexible, and lightweight and can be extended by choosing required functionality from elearning related Web services remotely residing on the Internet according to the user’s requirements.

11.3 Results and Discussion In the current era, it is clear that universities should think about the e-learning approach. The latter, as discussed above in the background section, implies an important digital transformation. Specifically, courses in the e-learning method should be provided with different teaching methodologies and different technological solutions, depending on the specificity of the contents to be valued and targets to achieve. Of course, the most important characteristic that is detected is adopted in production as well as the highest attention to detail and to the aspects of communication, both in graphic parts and in the use of video, audio, and connected effects. The teaching methods adopted are, of course, always aimed at making learning paths that are life-centered, i.e., close to the personal experience of end-users, task-centered, i.e., framed with respect to the conduct of their operational tasks and problemcentered, based on the resolution of the problems. The learning path is progressive, and the user can move freely, or in any case in a sequential manner depending on the educational lines defined together to the learner, displaying and viewing even more

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insight environments of insights (regulations, glossaries, cards, etc.), and participating actively in their own learning process through specific moments and interactive assessment/self-assessment that allow to: • observe the learning path in total, shedding light on the possible aspects that require further analysis; • support the entire learning path, providing moments of reflection, and also testing with respect to the topics covered by a specific learning object; • improve their own learning processes. The technologies used allow to submit the contents by combining the smart simultaneous use of multiple media depending on the characteristics of the different courses that you are going to define with the customer in the context of planning (movies, written text, charts, and images that summaries the topics, animations made in flash, audio contributions, etc.) always in full compliance with the requirements required by industry standards (W3C, AdlScorm, ISO/IEC 9241 and ISO/IEC 13,407, etc.) and by existing legislation (Law no. 4 dated 9 January 2004) and those required in the notice, including full integration in Moodle platform, fruition from work stations equipped with Windows, Linux, Mac OS X, and fruition from tablet based on iOS and Android in addition to Scorm standards. In particular: Usability. The course should adopt methods for the design and development of interactive training products by integrating the user-centered approach in the entire production cycle and calibrating the processes of learning about real needs and characteristics of learners. The first key element in the design is undoubtedly the usability understood as the effectiveness, the efficiency, and the satisfaction with which specified users achieve specific goals in particular environments. The concept of usability (definition contained in the standard ISO 9241-11:1998) is born within traditional ergonomics, particularly in the framework of studies to improve the usability of software products by analyzing the way in which users build a mental model of the product that they are using, creating certain expectations on its functioning; task of usability studies and try to match as much as possible the mental model of who designs the software (design model), with the mental model of its operation that the end-user builds (user model) in such a way as to make the interaction easy and rewarding. Interactiveness. The simple fact that students have at their disposal the means of communication to interact among themselves is not sufficient to ensure that these interactions take place and that, above all, the communication exchanges activated from learners produce useful results from the point of view of learning and are appreciated by the students. There are, in fact, a multiplicity of factors that can affect the quantity and quality of the interactions in an online course, which should be taken into consideration in the design and distribution of courses to create a learning environment in which students can establish a constructive dialog with the teachers and be of support to each other. Among the factors that most influence the interactions, there are the level of structuring of courses; the magnitude of the classes; the feedback; the familiarity that learners have with technologies. Promoting interactions therefore means taking into consideration all of these elements, not limited to putting at the

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disposal of the students the tools to communicate but encouraging them to use them and involving them in activities that facilitate socialization. The solution consists in the realization of an e-learning product based fundamentally on the methodological approach of the extended e-learning. The educational model puts the focus on interactivity and the strong involvement of the learner, diversifying the times for the enjoyment and use of the learning tools. The fundamental element is represented by the modular organization of the content into self-consistent educational units (learning object) that can be used in contexts and different paths, ensuring the ability to vary the path structure in time by adding or reconfiguring toward further learning objects. An important aspect is represented by the different nature of the learning object developed that vary both in terms of multimedia and interactivity. The structure of the learning path. Digital technologies, used properly, can make this transition possible: Surely, there have been several e-learning platforms for some years, but their role gained massive relevance concurrently with the pandemic global situation. Now, online universities are already ready for effective quality distance learning, with tools for course management, including live sessions, interactive teaching, and collaborative activities. Most EU universities had to cope with the emergency situation that called for a quick organizational and cultural rearrangement and a change of strategies, which perhaps represented the most difficult step for professors and administrative staff. The contents of the learning path should be structured in modules and teaching units. Introduction and development of educational objectives. This section reveals the objectives of the instructional unit in the form of questions so as to capture the attention of the learner and to motivate in an attempt to find the answer to the questions posed initially. Then, it performs the fruition of the sequential narrative/interactive animations, commented by an external sound. During the exposure are inserted interactive moments that require a strong educational involvement of the learner acquainting himself with the proposed contents. At the end of the exhibition, the summary is conducted of the concepts covered in such a way as to display the path taken. The issues exposed in the screens are accompanied by links to materials for in-depth study. The terms of greater importance or complexity are connected to the glossary. Self-assessment. In this stage, the acquisition of content is delivered via a test composed of multiple-choice questions. The aim is not quantitatively to evaluate the acquired knowledge, but to stimulate personal reflection of the learner on the concepts treated, also indicating any deficiencies through the reintroduction of the content in which these weaknesses have emerged during the test.

11.4 EDU-GATE: Target Groups and Outcomes It is important to consider also some initiatives that are trying to enhance the educational impact, the innovation, and the quality of online universities courses. For example, we considered the results achieved in the project EDucational University

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GATeway to enhance innovative e-learning capabilities, resilience, and new best practices (EDU-GATE) are a project funded by the Erasmus + Program—project ID: 2020-1-IT02-KA226-HE-095538. It is leaded and coordinated by Prof. Nunzio Casalino, and the partnership is composed of 7 beneficiaries: 4 Universities and 3 SMEs (all of them highly skilled in the digital training sector): Luiss (Italy) (Coordinator), University of Macedonia (Greece), Rigas Tehniska Universitate (Latvia), Burgaski Svoboden Universitet (Bulgaria), Linfa Digital (Italy), Symplexis (Greece), European Center for Quality (Bulgaria). For this reason, the EDU-GATE project is providing a detailed European multisectoral analysis indicating the comparison of the current situation of teaching digitalization (as-is and to-be analysis), together with a transversal needs’ analysis. These in-depth multi-countries research was carried out for the determination of innovative approaches and concrete supporting tools for post-graduate and master learning needs for educational institutions. A detailed analysis of digital educational requirements was also produced, providing cutting-edge tools to foster innovation and value creation in educational activities and academic communities building. After the already done preliminary academic partners needs’ analyzes, a common study was carried out for determination of new requirements, determining new key and transversal competences, reflecting them to concrete and open resilient educational programs, and sharing at European level effective tools in an integrated platform infrastructure. The identified target groups are professors, adjunct/associate teachers, researchers, students, associations and consortium of universities, and Ministries of Education, who want to develop an effective knowledge in digitalization and innovative learning activities for more resilient educational contexts. Starting from preliminary studies and experiences of the 7 partners, it provides detailed advice for the preparation of the EDU-GATE methodology, learning path, and multimedia tools. The main outcomes are the follow: 1. Analysis of the impact and diffusion of teaching digitalization in European universities. Impact and diffusion of digital education in Higher Education Institutions: research on innovative solutions, methods, skills, and best practices to enhance the next EU Digital Education Action Plan; 2. Online training curriculum to identify a learning contents, experiential knowledge, and skills assessment tools: methodology of development and delivering; 3. Integrated online platform for digital integrated learning, multi-level cooperation, and resources sharing. Curriculum design by innovative solutions, methods, skills, and best practices, to identify learning contents, experiential knowledge, and skills assessment tools. Learning contents, knowledge sharing area, and 3D, VR, holographic tools. The main aim is related to EDU-GATE modules and the implemented innovative multimedia products. 4. Learning contents, classroom guidelines, and skills assessment tools. Also, an innovative curriculum, including the most advanced educational design and delivering techniques, was created for the development of teachers’ skills, and

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a certificate of attendance can be released to participants. This also includes an advanced e-learning platform, integrated in EDU-GATE Website, that was designed by the 7 involved expert organizations (https://edugate-project.eu). Almost 12 innovative multimedia e-modules have been designed, permitting effective learning activities and the sharing of best practices. It allows the necessary knowledge to give the possibility to teachers and students to increase their competences and give more opportunities, inclusion and effectiveness, by the digitalization of learning flows, innovative learning tools, and new procedural applications in educational institutions. Some of the planned e-modules have been specifically devised to address special needs students in order to allow them to gain the basic competences required by their personal curriculum. Students with special needs almost 12–3 for each country were also involved in the preparation and testing of some learning units. Also, during the planned national multiplier events, physically impaired (deaf, blind, paralytic), cognitive impaired, and low-income students were invited and involved in next testing activities to create the EDU-GATE integrated gateway in order to be effectively inclusive, that is, in order to allow any person with any physical impairment to do them. Pre-requisites of professor activities are already defined by professional standards. E-education and e-teaching are based on some technological standards of teachers’ professional dealing standards. Some standards (Technology standards for All Illinois Teacher; UNESCO 2008) already describe as the general teachers’ competence in the application of ICT, and some standards are described as specific e-competencies for special e-education system modeling (e-learning, e-teaching, etc.). Both tasks pose problems to teachers who have been used to following more traditional teaching methods so far. Therefore, innovative teachers and e-teachers should be able to organize different types of e-learning and e-teaching scenarios. Some students with special needs and a good number of students with socially, economically, and culturally disadvantaged backgrounds have been involved in the activities performed to design and validate all the project products. All students were informed about the possibility to be part of the target group of this Erasmus + initiative and students with impairments, and/or disadvantages were encouraged to join. Teachers and researchers from EU universities and other educational institutions were also involved in the learning sessions in order to identify the best ways to share expertise and help other teachers to understand how to address special needs in the most appropriate ways. Specific events with institutions, educational associations, and public and private teaching organizations were held for this purpose. Concretely, the participants from each country attended the multimedia online EDUGATE modules, and then, they had the chance to verify their digital learning skills and knowledge by the available e-tests. The project activities have been shared with local target groups and stakeholders in each country. All the intellectual outputs were translated into the languages of the Partners’ countries (EN, IT, BG, GR, LV).

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11.5 Conclusions Still today, most traditional universities are not prepared to adopt innovative technologies and multimedia advanced contents. These technologies may provide quality online/hybrid education at a lower cost than that of conventional education. Therefore, technologies coupled by the changes in organizations and in overall society are creating new paradigms in the education field. This scenario requires important learning environments supported by appropriate resources (Reigeluth and Khan 1994). To be competitive, today, educational organizations should develop efficient and effective learning systems based on specific contents to provide a better e-learning process. As seen in this paper, creating an innovative smart e-learning course does not depend only on the usage of overwhelming technology; but it means to work on the contents, on the production process as well as on the tools. Universities 4.0. should keep in mind that “e-learning is the fastest-growing and most promising market in the education industry” (Hall 2001). It is necessary to consider specificities and to involve in the development of new advanced contents (AR, VR, holograms, gamification, virtual simulations, 3D and 360° immersive environments, etc.) even those with fewer opportunities, such as those with physical or cognitive disabilities and young people with low income: In this, technology offers itself as a facilitator of the process. In addition, EDU-GATE will urge a more integrated and strategic dialog with the states of the Union in order to prepare a proposal that can influence the European Council on the enabling elements necessary to make digital education more effective in the coming years. The described innovative initiative is also aimed at researchers, professors, staff, and stakeholders such as companies, social economy organizations, authorities, student associations, and graduates who are enrolled in post-graduate courses at universities and European organizations involved in collaborative activities with realities academic.

References ˙ 1. Zuchowski, I., Casalino, N., Murat, B.: Experience of academic staff in mentoring programs. Int. J. Manage. Econom. 58(2), 23–41 (2022) 2. Ally, M.: Foundations of educational theory for online learning (2004) 3. Casalino, N., Cavallari, M., De Marco, M., Ferrara, M., Gatti, M., Rossignoli, C.: Performance management and innovative human resource learning through flexible production systems aimed at enhancing the competitiveness of SMEs. IUP J. Knowl. Manage. 4, 29–42 (2015) 4. Bailey, G.D.: A road map for understanding integrated learning systems. Educ. Technol. 32(9), 3–5 (1992) 5. Uskov, V., Casalino, N.: New means of organizational governance to reduce the effects of European economic crisis and improve the competitiveness of SMEs. Law Econom. Yearly Rev. J. Queen Mary Uni. London UK 1(1), 149–179 (2012) 6. Wenger, E.C.: Community of Practice: Learning. Cambridge University Press, Cambridge, Meaning and Identity (1999) 7. Davenport, T.H.: Process Innovation: Reengineering Work Through Information Technology. Harvard Business Press (1993)

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8. Baskerville, R., Capriglione, F., Casalino, N.: Impacts, challenges and trends of digital transformation in the banking sector. Law Econom. Yearly Rev. J. LEYR, Queen Mary Univ. UK 9(2), 341–362 (2020) 9. ICEF Monitor Report.: Study Projects Dramatic Growth for Global Higher Education Through 2040, Germany (2020) 10. Ken42.: Advantages of innovative solutions in e-learning, Ken42.com, Ken42 EdTech Limited (2021) 11. Hinings, B., Gegenhuber, T., Greenwood, R.: Digital innovation and transformation: an institutional perspective. Inf. Organ. 28(1), 52–61 (2018) 12. Yoo, Y., Boland, R.J., Lyytinen, K., Majchrzak, A.: Organizing for innovation in the digitized world. Organ. Sci. 23(5), 1398–1408 (2012) 13. Fitzgerald, M., Kruschwitz, N., Bonnet, D., Welch, M.: Embracing digital technology: a new strategic imperative. MIT Sloan Manag. Rev. 55, 1 (2014) 14. Pellegrini, M., Uskov, V., Casalino, N.: Reimagining and re-designing the post-Covid-19 higher education organizations to address new challenges and responses for safe and effective teaching activities. Law Econom. Yearly Rev. J. LEYR, Queen Mary Univ. London, UK 9(1), 219–248 (2020). 15. Project website: https://edugate-project.eu/ 16. E-learning platform: https://edugate.linfa.it/

Chapter 12

Systematic Approach to Project Management at Smart University Leyla F. Berdnikova, Svetlana I. Sotskova, Irina V. Kalashnikova, Elizaveta I. Gnatishina, Ekaterina A. Afonichkina, and Elena A. Khramova

Abstract In modern conditions, a smart university is a synthesis of scientific, educational, and innovative activities. Using advanced and latest technologies in their functioning and development, smart universities are centers for the emergence of innovations and promising projects. Project activity is the most important direction in the development of smart universities. To increase competitive advantages and achieve high performance, smart universities are implementing various projects. Thus, the growth of the interconnection between different areas of activity of smart universities, the emergence of new demands from the external environment and employers, and the intensification of internal and external relations have caused the need to apply a systematic approach to project management. Within the framework of this approach, a smart organization is considered as a set of various related activities and elements that are in contradictory unity and in relationship with the external environment. The article reveals the concept of the project and presents its main properties for a smart university. Considerable attention is paid to the classification of projects. The result of the study is the proposed model for applying a systematic approach to project management in a smart university. The results obtained were tested in project management in the university’s smart division on the example of the department. L. F. Berdnikova (B) Togliatti State University, Togliatti, Russia e-mail: [email protected] S. I. Sotskova Samara State University of Economics, Samara, Russia I. V. Kalashnikova Pacific National University, Khabarovsk, Russia E. I. Gnatishina Volga State University of Service, Togliatti, Russia E. A. Afonichkina Peter the Great St.Petersburg Polytechnic University, St. Petersburg, Russia E. A. Khramova National Research Mordovia State University, Saransc, Russia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_12

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12.1 Introduction For a long time, project management has been underestimated in entrepreneurial activity. This is due to the fact that entrepreneurs approached its implementation in a rather formalized manner. However, practice shows that project management based on a systematic approach allows you to most effectively build a business and develop various areas of a company’s activities. Currently, one of the important areas in a smart university is the implementation of projects within the framework of project activities. In modern conditions, a wide study of the activities and development prospects of smart universities is being carried out, in particular in the works of scientists: Uskov et al. [1–3], Serdyukova et al. [3–6], Hinings et al. [7]. The study of the directions of strategic development is devoted to the works of authors such as Weerakkody et al. [8], Berdnikova et al. [9], Tikhomirov and Dneprovskaya [10]. The issues of project activities in smart universities are widely considered in scientific papers Tokuç et al. [11], Mitrofanova et al. [12]. In the process of project management, it is necessary to rely on a systematic approach, taking into account the specifics of the activities of a smart university. Project management contributes to the growth of the competitive advantages of a smart organization, strengthening its position in the market environment, and allows efficient use of opportunities and resources. This is facilitated by the use of innovative management methods and technologies, delegation of authority, allocation of resources, consolidation of responsibility, as well as the establishment of priority areas for development. Any organization, including a smart university, should be considered as a system that includes various related activities. A systematic approach helps to identify the influence of factors that affect such a system and increases attention to the relationships between its elements. According to the system approach, managerial actions not only functionally follow from each other but directly and indirectly influence each other. In this regard, the changes that occur in a separate division of a smart university cause changes in other divisions, as well as in the results of its work. Each manager in the process of developing and implementing management decisions should take into account their impact on the outcome of the work of a smart organization. The main task of managing a smart university should be focused on the integration of its elements and the development of ways to preserve its integrity. The main purpose of this article is to develop a model for applying a systematic approach to project management, taking into account the specifics of a smart university. The scientific novelty of the research consists in clarifying the conceptual apparatus in the field of project management and developing a model for applying a systematic approach to project management, taking into account the specifics of the smart university.

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12.2 Statement of the Problem in General Form and Its Connection with Important Scientific and Practical Tasks In today’s dynamically developing environment, project management should focus on continuous development. Project management problems are not new but still remain relevant. The complication of economic relations requires the improvement of management approaches and methods. The need to apply a systematic approach to management has been proved for a long time. But nevertheless, at each stage of economic development, new needs arise in the development of models for its application. In this regard, the main goal of this article is to develop a model for applying a systematic approach to project management, taking into account the specifics of a smart university. One of the identified problems should also include the lack of an unambiguous definition of the concept of “project” among specialists. Consider its main interpretations. A project is a task with established inputs and ways to achieve it. The project is an idea, an idea, an image embodied in the form of a description, justification, calculations, drawings that reveal the content of the idea and the possibility of its practical application. A project is a set of elements and their interrelationships that contributes to the achievement of the set goals. The project can also be characterized as a system of interrelated elements of the internal environment, such as managers, personnel, resources, deadlines, goals, and methods that interact with each other and with the external environment. The conducted research revealed the main approaches to the definition of the project: system and activity. In our opinion, from the standpoint of a systematic approach, the project should be considered as a system of temporary actions that contribute to obtaining a certain result. The accelerating pace of socio-economic change has triggered a new wave of research in the field of smart university management and improving the effectiveness of innovative, scientific, and educational projects. Based on the study, we have identified the characteristics of the project through the prism of a systematic approach (Fig. 12.1). Let us consider each selected characteristic. One-off implies that projects are a kind of one-time phenomenon. Projects can come and go but still leave concrete results. Uniqueness implies that each project is individual and inimitable. Innovation is manifested in the fact that the implementation of the project creates new products, goods, services, and technologies. The time limit indicates that each project in its implementation is limited in time. Each project has a deadline for its implementation. The effectiveness of the project involves obtaining certain results from its implementation. Each project is aimed at obtaining the desired goals and results.

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One-time

Uniqueness

Efficiency

Innovation

Time limit

Fig. 12.1 Characteristics of the project through the prism of a systematic approach

We believe that from the standpoint of a systematic approach, the project in the activities of a smart university has certain properties (Fig. 12.2). The main difficulty in implementing a project in a smart university as a system is observed in the number of its elements, in the interaction of participants, the speed of developing solutions, and the difference in the interests of participants. Therefore, the key task of implementing a project in a smart university is to reduce multitasking for its participants, to bring private interests closer to common ones.

structure complexity

dynamic processes

interconnection of elements

integrity of the project as a system

multifunctionality of the project positive impact of the project on scientific and educational activities

Fig. 12.2 Properties of the project in the activities of a smart university from the standpoint of a systematic approach

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12.3 Presentation of the Main Research Material with Full Justification of the Obtained Scientific Results 12.3.1 Classification of Projects in the Activities of a Smart University Effective project management in a smart university is largely determined by the correctness of their classification. Modern scientific and practical literature presents a broad classification of projects. However, not enough research has been carried out in the field of classifying projects that take into account the specifics of the activities of a smart university. Based on the study, we propose a classification of projects that are typical for the activities of smart universities (Fig. 12.3).

Depending on the place of execution

Depending on the nature and subject area

Depending on the structure, composition of the project

Depending on belonging to technical areas

Depending on the level of participants

Depending on the duration of the project

• external projects implemented outside the smart university • internal projects implemented within a smart university • innovative projects • investment projects • educational projects • research projects • mixed projects • monoproject • multiproject • megaproject • technical projects • non-technical projects

• international projects • federal projects • regional projects • local projects • short term projects • medium term projects • long-term projects

• defect-free projects According to quality requirements • modular projects and ways to ensure it • standard projects

In terms of resources spent and profit received, projects

• commercial projects aimed at making a profit • social projects involving the achievement of social goals

Fig. 12.3 Classification of projects typical for the activities of smart universities

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In a broad sense, the project can be considered as a creative, reasonable, activity of the subject, aimed at achieving a specific goal. Each smart university project is implemented through project activities. The key elements of the project activities of a smart organization include • • • • •

subject of design; design object; purpose of design; design technology; means, methods, and conditions of design.

The subjects of design can be both people and enterprises, teams that set themselves the goal of transforming reality. Design objects include objects of material nature; intangible properties and relationships; advertising campaigns; technologies; services; goods; bills, etc. Each design object is individual and endowed with specific features. Considering the project as a system contributes to the most efficient use of opportunities and resources, allows you to take into account the influence of external and internal factors on its implementation.

12.3.2 Development of a Model of a Systematic Approach to Project Management in a Smart University The project as an object of system management is described by a set of characteristics, such as the purpose of the project, the cost of the project, the quality of the project, the timing of the project, the risks of the project, and others. The subject area of the project is the content of the project and includes the purpose and objectives of the project, the amount of work and resources that are necessary for its implementation. The goal of the project is focused on obtaining the desired result of the activity, which can be achieved in a timely manner. The project strategy is the central link in the development, evaluation, and implementation of the project. The strategy of any project can be characterized as a set of goals and principles that allow allocating the necessary resources for the duration of the project. Project management represents the methodology, organization, planning, management, coordination of labor, financial, material, and technical resources throughout the entire project cycle, aimed at achieving its goals. The project management structure forms the basis for defining how a particular project will be managed. It consists of the following sections: (1) content of project management; (2) project management process.

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The content of project management describes the environment in which it is implemented and reflects the period of its life cycle. The project management process is aimed at revealing the overall picture of the interaction of project management processes and includes certain subsystems. Such subsystems reflect the management: • • • •

design and integration of the project, time, finances, and quality of the project, team and communications, risks and provision of the project.

The term “system” is used to characterize the object under study as a whole, complex, and unified. The system is characterized by orderliness, integrity, and patterns. Due to the fact that smart universities have certain specifics in their activities, a systematic approach to managing smart university projects should take it into account. In Fig. 12.4, we present our proposed model for applying a systematic approach to project management, taking into account the specifics of the activities of a smart university. Unlike the existing ones, the proposed model for applying a systematic approach to project management focuses on the specifics of the smart university and takes into account the demands of the external and internal environment. An important link in this approach is the regular assessment of customer satisfaction as a result of the project. This model has been implemented in project management as part of the project activities of a structural unit of a smart university. With its help, project needs were quickly identified, and the project itself was considered as an integral system integrated into the scientific and educational activities of the smart university. In particular, projects were identified that meet the requirements of the external and internal environment of a smart university. Project management was based on the systemic interconnection of all elements of project management. At the stages of project implementation, their relevance, satisfaction with consumer requests in these projects, and efficiency were revealed. The introduction of the proposed systematic approach made it possible at the preliminary design stage to identify projects that are losing their relevance and do not meet the needs of consumers. This contributed to the reduction of unjustified: • labor costs by 30%; • financial costs by 40%; • time costs by 30%. Thus, the application of the proposed model for applying a systematic approach to project management contributes to the effectiveness of the implementation of project activities in a smart university.

• System analysis of external and internal environment requests

The content of project management in a smart university

The process of project management in a smart university

Resource management

Evaluating the effectiveness of a project in a smart university

• Assessment of satisfaction with the requests of the external and internal environment

• INPUT

• EXIT Products, service, technology

Satisfaction with the demands of the external and internal environment, consumers

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Requests of the external and internal environment, consumers and their requirements

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Fig. 12.4 Model for applying a systematic approach to project management, taking into account the specifics of a smart university

12.4 Conclusions of the Research and Prospects for Further Research in this Direction Conclusions. The study showed that it is necessary to apply a systematic approach in the management of smart university projects. 1. On the basis of the study, the characteristics of the project were identified through the prism of a systematic approach. 2. The study made it possible to identify the main properties of the project in the activities of a smart university from the standpoint of a systematic approach. 3. In the course of the study, a classification of projects was established that is typical for the activities of smart universities. 4. The study made it possible to develop a model for applying a systematic approach to project management, taking into account the specifics of a smart university. The proposed model for applying a systematic approach can be used both at the level of a separate structural smart unit, and in general in a smart university.

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Next steps. To develop a systematic approach to project management, it is necessary to develop mechanisms for applying systems theory and system analysis in project management, taking into account all the main processes of a smart university.

References 1. Uskov, V.L., Bakken, J.P., Gayke, K., Jose, D., Uskova, M.F., Devaguptapu, S.S.: Smart university: a validation of “smartness features—main components” matrix by real-world examples and best practices from universities worldwide. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning, SIST, vol. 144, pp. 3–17. Springer, Singapore (2019) 2. Uskov, V.L., Bakken, J.P., Howlett, R.J., Jain, L.C. (eds.): Smart Universities: Concepts, Systems, and Technologies, p. 421. Springer (2018). ISBN 978-3-319-59453-8 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). ISBN 9783319396897. https://doi.org/10.1007/978-3-319-39690-3 4. 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). 5. 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, p. 101 6. Glukhova, L.V., Syrotyuk, S.D., Sherstobitova, A.A., Pavlova, S.V.: Smart university development evaluation models. In: Smart Education and Smart e-Learning, Smart Innovation, Systems and Technologies, vol. 144, pp. 539–551, Springer, Cham (2019). 7. Hinings, B., Gegenhuber, T., Greenwood, R.: Digital innovation and transformation: an institutional perspective. Inf. Organ. 28(1), 52–61 (2018) 8. Weerakkody, V., Janssen, M., Dwivedi, Y.K.: Transformational change and business process reengineering (BPR): Lessons from the British and Dutch public sectors. Gov. Inf. Q. 28(3), 320–328 (2011) 9. Berdnikova, L.F., Sergeeva, I.G.., Safronova, S.A., Smagina, A.Y., Ianitckii, A.I.: Strategic management of smart university development, In: Smart Education and Smart e-Learning, Smart Innovation, Systems and Tehnologies, vol. 188. pp. 293–305. Springer, Cham (2020) 10. Tikhomirov, V., Dneprovskaya, N.: Development of Strategy for Smart University, 2015 Open Education Global International Conference, pp. 22–24. Banff, Canada (2015) 11. Tokuç, A., Uran, Z., Tekin, A.: Management of big data projects. In: Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution, pp. 279–293 (2019) 12. Mitrofanova, Y.S., Chehri, A., Tukshumskaya, A.V., Vereshchak, S.B., Popova, T.N.: Project management of smart university development: models and tools. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning, Smart Innovation, Systems and Tehnologies, vol. 240, pp. 339–350. Springer, Singapore (2021)

Chapter 13

The Concept of New-Generation Lecturers for Smart Universities: Case Study and Trends Svetlana A. Gudkova , Inga V. Treshina , Marina V. Malashchenko , Tatiana S. Yakusheva , and Marina V. Dayneko Abstract The issues of modern educational environment require revision of approaches and methods used by educational management for the concept of newgeneration scholars and lecturers for universities. According to the authors vision, new-generation faculty members should be masters of both hard skills and soft skills including their mastery in science and creation of training courses for the latest elearning platforms on the base of key components of smart pedagogy. The effective management for higher institutions is achieved through a reasonable combination of the scholars’ soft and hard skills and methods of systems approach. The practical significance includes the proposed concept based on the management apparatus, represented by the smart pedagogy procedural framework, education management simulation and SCORM standard 2004. The assessment and case study are considered in the patented system of distant education described in the study.

13.1 Introduction Nowadays higher education management is based on the latest e-learning platforms and components of smart pedagogy dealing with the knowledge transfer in the process of educational activities. The relevance of the chosen topic of the research is justified by the need to find tools for managing the scholars’ training and educational processes in order to be effective and competitive in the international educational markets. This is the reason why more and more universities all over the world choose the model of the open university based on the promoting educational contents through distant S. A. Gudkova (B) · T. S. Yakusheva · M. V. Dayneko Togliatti State University, Togliatti, Russia e-mail: [email protected] I. V. Treshina Moscow Pedagogical State University, Moscow, Russia M. V. Malashchenko Southern Federal University, Rostov-On-Don, Russia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_13

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education. For making it effectively, the basics of the theory of constraints in relation to educational activities are studied and interpreted in the application to the processes of development of human resources capacity of the university. Currently, education management is carried out through the innovations and transformations affecting the mission and strategy of becoming a smart university from three key sides: science, education and society.

13.1.1 The Problem of Research The problem of the study is to consider and define the integrative methodology for effective HR management in education based on innovative approaches.

13.1.2 The Purpose and Targets The purpose of the study is to review the approaches of smart pedagogy development in integration with the management theory and the theory of systems constraints by E. Goldratt. The Targets 1. To outline the core competencies and the role of the new type of scholars and lecturers for higher education system and society. 2. To identify basic managerial concepts for further development of higher education institutions.

13.2 Theory for Research The peculiarity of this study is the practical application of the conclusions obtained by integrating the conceptual foundations of management [1] with the theory of constraints (proposed in 1980 by Dr. E. Goldratt) [2], the conceptual foundations of smart university development [3–5], the need for human resource capacity simulation [6] to meet the demands of society [7, 8] and advanced training [9] in the conditions of digital transformation and SCORM systems use [10]. It is the SCORM standard to make e-learning visible and understandable, so we also consider this approach in training at the university, with the subsequent transformation of skills in the learning and educational process, which is demanded by the evolutionary diversity of business communities [10]. Nowadays, the human capital is considered to be both the base for innovative transformations of higher education and the main or key directions of economic and social development. For example, the use of crowdsourcing management model [6]

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is aimed at the rapid development of knowledge export processes when teaching international students.

13.3 Research Hypothesis The hypothesis of the study is that training in higher education needs to be carried out simultaneously in three key directions: (a) active implementation of new intelligent and digital technologies and new digital learning platforms; (b) upgrading the qualifications of university teaching staff in accordance with the requirements of society and digital communications; (c) training human resources for society and economy through the use of current digital trends and external environment demands and the use of various online e-learning courses. To achieve the objectives, four basic components were chosen: Goldratt’s theory of constraints, SCORM standard 2004, crowdsourcing model and knowledge export.

13.4 Rationale for the Choice of the Basic Components for the Solution of the Tasks The first basic component is the theory of constraints [1], which can be used for education HR management too because its key motto The strength of any chain depends on its weakest point and conclusions allow different institutions solving the identified problems and contradictions in time: • between the required deadlines and available resources for the implementation of the tasks; • between the quality of transferred knowledge and the cost of its acquisition [2, 6]. The second basic component is SCORM standard because it allows teaching staff to develop new educational resources for distance learning and to apply the acquired knowledge not only for higher education systems but also for business systems [9, 10]. The third basic component crowdsourcing model of implementing team interaction in implementing smart pedagogy principles and technologies is chosen as the implementing team activity relies on the collaboration of many intellectual resources engaged on a voluntary basis [4, 5, 10]. The fourth basic component—knowledge export—is chosen as it allows the university to remain competitive in the face of rapidly changing demands of the external environment [1, 7, 8].

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13.5 Our Results 13.5.1 The Model for Managing New Knowledge Creation Processes Based on SCORM 2004 Nowadays, a lot of experts consider that the system of distant education is one of the elements of open education and forms the basis of the information society. A lot of modern universities are using integrative models for traditional and distant education because this is the path making the university more competitive in the educational market. Table 13.1 presents a comparative analysis of two state educational projects (Rosdistant and NewGenUniv), which provide an opportunity to obtain higher professional education in a distance form. Both projects are implemented on the basis of TSU. Unlike most mass education systems such as Coursera, in the projects presented, we do not study individual courses, but professional educational programs. Evaluation and expertise of the content is done by the stakeholders. The authors in this paper have modeled the key points of using SCORM in modern universities. An Example of SCORM Standard Implementation on MOODLE Table 13.2 presents the relevance and advantages of SCORM technology for modern higher education on the example of tested universities: Togliatti State University (TSU), Moscow Pedagogical State University (MPSU) and Southern Federal University (Rostov-on-Don, SFU). The implementation of e-learning in SFU on the basis of SCORM 2004 standard is based on MOODLE 1.9. The analysis of internet resources showed a rather high level of SCORM technology application: TSU—87.3%, MPU—79.2%, and SFU—88.5%. Only learning opportunities were assessed and disclosed in the article. It should be noted that only foreign language learning opportunities were assessed. Completeness of coverage was analyzed on the basis of curriculum research by the authors of the survey. English Language Learning Content Based on the Hybrid Approach: Case Study Togliatti State University has developed English and English for Special Purposes course content for teaching Foreign Language discipline based on SCORM standards. Table 13.1 Results of the analysis Name of project

Targeted market/language

Skills for scholars

Rosdistant

Russia, CIS countries/Russian

Design, development and application of educational content in accordance with the professional curriculum

NewGenUniv

International, Asia/English

Design, development and application of educational content in English (CLIL, crowdsourcing, EdTech)

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Table 13.2 Analysis of the benefits of the SCORM standard Advantages

Description

Application for the university

Clear content structure

The content developer defines the structure of the course and sets the algorithm for the transition to the following sections of the content

Rosdistant and NewGenUniv projects—higher education online for all bachelor’s and master’s degree programs offered at the university

Modularity

The content for a course is presented in several modules, and it is possible for the course developer to transfer any module into a new course

The course structure contains an introductory lecture by the teacher explaining the aims and objectives of the course, followed by modules (M1-M4). Each module consists of the following parts: theory, practice, a test, a final test

Platform compatibility

Adaptable to any distance learning platform

Implemented, commercialization of the tested programs

Visual database for students’ work progress

Students can complete the course modules at any convenient time, with positive results retained even in the case of technical failures

Implemented in the Rosdistant system

Feedback

Ability to assess the learner’s achievements both automatically through a set of algorithm and presented speech clichés as well as through consultation with the coach

Implemented in the Rosdistant system

A special feature of the proposed online training is the availability of sustained feedback, allowing the effectiveness of new knowledge formation to be monitored and assessed. Figures 13.1 and 13.2 represent different stages for students’ activity while studying the content and getting the necessary number of grades for the subject according to the curriculum. Figure 13.3 shows the listening options that are implemented in the author’s content. Thus, the use of the new platform is aimed at improvement of the skills in the design, development and application of educational content in English. A more detailed algorithm of applying CLIL, crowdsourcing and EdTech technologies and techniques is shown below.

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Fig. 13.2 Online training: comprehensive reading

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Fig. 13.3 Online training: listening

13.5.2 CLIL and EdTech Training for New-Generation Lecturers: The Algorithm The authors consider that the following steps can be useful while organizing hard skills and soft skills development for new-generation lecturers at smart universities: STEP 1. Preliminary testing of teachers of technical training areas to identify their level of foreign language proficiency. For effective work of CLIL, the teacher at the initial stage should have at least B1 level (CEFR standard). STEP 2. Theoretical block explaining the concept of CLIL and the peculiarities of its implementation in the Russian system of education. STEP 3. Analysis of the existing educational and methodological base of EdTech, based on analysis of the website with CLIL developments. The link to it opens a working window in which you could form your own CLIL lessons using the suggested template. STEP 4. Practical part, teamwork. Organizational, managerial and educationalmethodological, scientific creativity of the project group. Accumulation of linguistic competences and thesaurus compilation in the teacher’s subject area. For example, mechanical engineering, chemical industry and information technology. The result of this stage is the preparation for the development of intellectual maps of the CLIL lesson in their discipline.

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STEP 5. Development of intellectual maps based on the content design hybrid approach. STEP 6. Realization of feedback. Discussion of the developed mind maps and the developed content, refinement of comments and correction of the linguistic, stylistic or semantic errors in the presented content. STEP 7. Assessment of the level for the required competencies. External assessment, expertise and report. The algorithm described is based on hybrid approach taken both from CLIL and EdTech methodologies. It has been already implemented in the educational process, but it is constantly being improved due to the feedback process.

13.6 Conclusion and Trends Conclusions 1. At present, new requirements are imposed on modern Russian universities, one of which is the development of foreign language professional competence of teaching staff to create and promote authoring courses and educational projects in the international market. The article shows the possibility of implementing two projects, implemented on the Rosdistant platform. 2. The practical value lies in providing an opportunity for universities to increase the attractiveness of educational programs for foreign students and increase the competitiveness of the university in the international educational market through educational collaborations. 3. The authors believe that the effectiveness of knowledge export programs implementation is possible only in conditions of cooperation and application of SCORM standards in integration with modern methods of education management and digital competencies formed in the process of CLIL and EdTech application. Future Trends As part of further research on the topic under study, it is planned to organize a collection of feedback from students in order to improve the content of the content and organize professional development courses for teachers of colleges and universities.

References 1. Sherstobitova, A.A., Gudkova, S.A., Kazieva, B.V., Kaziev, K.V., Kaziev, V.M., Yakusheva, T.S.: University innovative networking in digital age: theory and simulation Smart Innovation, Systems and Technologies, T. 240. C. 293–303 (2021) 2. Goldratt, E.M., Cox, J.: The Goal: a process of ongoing improvement. J. Bus. Strategy 15(6), 26–27 (1994)

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3. 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 4. Uskov, V.L., Bakken, J.P., Aluri, L.: Crowdsourcing-based learning: the effective smart pedagogy for STEM education. In: Proceedings of 2019 IEEE Global Engineering Education Conference EDUCON, April 9–11, 2019, Dubai, UAE, IEEE (in print) 5. Glukhova, L., Yakusheva, T.S., Korneeva, E.N., Burenkova, D.Y., Treshina, I.V.: Modern approach for strategic development of smart universities: Digitalization and the knowledge export. In: Smart Innovation, Systems and Technologies, pp. 327–338 (2021) 6. Gudkova, S.A., Glukhova, L.V., Sherstobitova, A.A., Nemtcev, A.D., Tsvetkov, A.A., Bechelova, A.R.: Human capital of smart university: trajectories of innovative development. In: Smart Innovation, Systems and Technologies, T. 305 SIST. C. 259–268 (2022) 7. Gudkova, S.A., Glukhova, L.V., Kuznetsova, O.A., Yakusheva, T.S., Treshina, I.V., Malashchenko, M.V.: Digital humanities and smart pedagogy for managing intellectual development in higher education. In: Smart Innovation, Systems and Technologies, vol. 305 SIST, pp. 365–374 (2022) 8. Gudkova, S.A., Glukhova, L.V., Syrotyuk, S.D., Krayneva, R., Filippova, O.A.: Validating development indicators for smart university: quality function deployment. In: Smart Innovation, Systems and Technologies, vol. 240, pp. 241–252 (2021) 9. Gudkova, S.A., Dayneko, M.V., Yashchenko, N.V., Burenkova, D.Y., Treshina, I.V.: Manageral approach for foreign language learning and forestimg in a smart university environment. In: Smart Innovation, Systems and Technologies, vol. 240, pp. 395–405 (2021) 10. Argotte, L., Arroyo, G., Noguez, J.: Intelligent learning system based on SCORM learning objects. In: Lecture Notes in Computer Science, T. 7094 LNAI, № Part 1. C. 222–231 (2011)

Chapter 14

Organizational and Methodological Support of the Strategic Analysis of the Resource Potential of Smart University Leyla F. Berdnikova, Natalya A. Igoshina, Andrei S. Vasilchuk, Lyubov K. Shamina, Iuliia A. Anisimova, and Anastasia Yu. Malyarovskaya Abstract Modern conditions of digitalization significantly affect the development of technologies, the transformation of educational organizations. This is facilitated by the spread of digital technologies, the emergence of innovative opportunities and the development of information and telecommunications links. Such conditions contribute to the emergence of a new digital generation of people focused more on smart education. The conditions of digitalization significantly affect the activities of smart universities, opening up additional opportunities and causing certain risks for the development of resource potential. Like any other organization, a smart university operates in a changing external environment. Such changes can both positively and negatively affect the achievement of its strategy and the use of resources. This calls for a systematic strategic analysis of the resource potential. The article reveals the concept of strategic analysis of the resource potential of a smart university. An organizational model for a step-by-step strategic analysis of the resource potential in a smart university in the context of digitalization is proposed. As a result of the study, a model of methodological support for the strategic analysis of the resource potential of a smart university was also developed. The results obtained were tested when conducting a strategic analysis of the resource potential in the smart division of the university on the example of the department.

L. F. Berdnikova (B) · I. A. Anisimova · A. Yu. Malyarovskaya Togliatti State University, Togliatti, Russia e-mail: [email protected] N. A. Igoshina Samara State University of Economics, Samara, Russia A. S. Vasilchuk ANOO VO Centrosoyuz RF “Russian University of Cooperation”, Mytishchi, Russia Lyubov K. Shamina Baltic State Technical University “VOENMEH” Named After D.F. Ustinov, St. Petersburg, Russia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_14

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14.1 Introduction At present, the conditions of digitalization form new requirements for the development of educational organizations, including smart universities. This also applies to the need for more efficient use of all types of corporate resources in order to achieve strategic goals and objectives. Digital transformation involves the introduction of advanced technologies into the business processes of a smart university. Such a transformation is focused not only on the acquisition and use of technological equipment, modern software products, but also on changes in approaches to the perception of business processes, management methods and communication relationships. All of the above proves the need for the constant development of the resource potential of a smart university. It should be noted that at present, the works of authors such as: Basuony, Biazzo, Garengo, Malgwi, Dahiru, Lawrie, Cobbold and other [1–5]. The conditions of digitalization provide new opportunities for improving both the educational process and scientific activities at a smart university. The functioning of smart universities is covered by the works of scientists such as: Uskov et al. [6–8], Serdyukova et al. [9–11]. Issues of strategic management and development, as well as analysis of the resources of organizations and smart universities are disclosed in the works of authors such as: Berdnikova et al. [12–14], Tikhomirov and Dneprovskaya [15], Rampersad and Hussain [16]. Digital communication channels, robotization, artificial intelligence, chat bots are becoming common elements of everyday life. It is digitalization that makes it possible to increase the productivity of personnel and the profitability of an enterprise, reduce the time for carrying out certain operations and optimize the functionality of personnel. This confirms the need to improve the organizational and methodological support for the strategic analysis of the resource potential of a smart university. The main purpose of this article is to develop organizational and methodological support for the strategic analysis of the resource potential of a smart university. The scientific novelty of the study lies in the refinement of the definition of the strategic analysis of the resource potential of a smart university, the development of an organizational model of step-by-step implementation and a model of methodological support for the strategic analysis of the resource potential of a smart university.

14.2 Statement of the Problem in General Form and its Connection with Important Scientific and Practical Tasks The choice of strategy should be accompanied by sufficient information about the opportunities, risks and resource potential. It requires a qualitative analysis focused

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on finding directions for the strategic development of a smart organization in the long term. At present, for the successful development of each smart organization, it is necessary not only to adapt to the external environment by adapting its internal structure and behavior, but also to actively diagnose the external conditions of its activities, regularly identifying threats and opportunities in the environment. With high uncertainty, this is very relevant and provides the basis for competent management and choice of strategy. The increasingly urgent modern problems of limited resources require research and regular strategic analysis of the resource potential of a smart organization. The efficiency and rationality of its use predetermines the success of development in the future and allows not only to maintain and consolidate positions in a competitive environment, but also to enter new promising markets. The role of strategic analysis is increasing every year. At the same time, its theoretical foundations and methodological tools are expanding, new types, techniques and methods are emerging that contribute to a comprehensive study of socio-economic phenomena and processes. At present, the strategic analysis of the resource potential of a smart university is an actual direction. Of course, unlike other types of analysis, strategic analysis is focused on determining the directions for the long-term development of a smart organization, establishing its strategy. In our opinion, the strategic analysis of the resource potential of a smart university is a set of techniques and methods that contribute to the study of the state, movement and efficiency of using the resources of a smart university, as well as the influence of the external environment on them to identify opportunities and activate its potential in order to determine the strategy and directions for effective development in the long term. The strategic analysis of the resource potential of a smart university is aimed at researching each type of resource and helps to establish the effectiveness of their use, identify opportunities and reserves to improve performance and achieve a strategy. The main problem of conducting a strategic analysis of the resource potential is its insufficient adaptation to the activities of smart universities. The organization of a strategic analysis of the resource potential and its tools should take into account the specifics of the activities of a smart university, the availability of those unique resources that it owns. This situation justifies the need for the development of organizational and methodological support for the strategic analysis of the resource potential, focused on the work of smart universities.

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14.3 Presentation of the Main Research Material with Full Justification of the Obtained Scientific Results 14.3.1 Organizational Basis for Strategic Analysis of the Resource Potential of a Smart University For the effective functioning of a smart university in the current period and in the future, it is necessary to have information about changes and trends in the external and internal environment, quickly identify opportunities and risks that affect the resource potential and achieve the strategy. With this information, the management of a smart organization will be able to measure opportunities against needs, objectively assess the situation and make the right informed management decisions. Figure 14.1 shows the organizational model proposed by us for a step-by-step strategic analysis of the resource potential of a smart university in the context of digitalization. The first step involves collecting the required information to conduct a strategic analysis of the resource potential of a smart university. Important at this stage is the verification of information, checking it for quality, reliability. The results of the analysis will depend on how objective and relevant the information is. The second step involves the choice of methods, methods and techniques for conducting a strategic analysis of the resource potential of a smart university. Such a choice is determined by the goals, objectives of the analysis, as well as the availability of the necessary information. The third step is focused on the strategic analysis of the external environment. It involves an assessment of external opportunities and risks that can directly affect the development and use of the resource potential of a smart university. The fourth step is aimed at a strategic comprehensive analysis of the internal environment of a smart university, identifying its internal capabilities and weaknesses. The fifth step involves a strategic analysis of smart university resources, their state, movement and use. At this stage, the current resource potential is assessed, and the vectors of its possible growth are established. The sixth step involves the systematization of the data obtained, the formulation of conclusions and recommendations. The seventh step involves determining the directions for the strategic development and growth of the smart university’s resource potential. The eighth step aims to identify the need to adjust the smart university strategy. The ninth step is the final one and provides for control over the implementation of the strategy and strategic development of the smart university. It is important to form a database based on the results of each step of the strategic analysis of the resource potential of a smart university. The database is necessary for the subsequent assessment of indicators in dynamics and identification of the main trends. Regular strategic analysis will allow timely

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Step 2. Choice of methods, methods and techniques

Step 3. Strategic analysis of the external environment

Step 4. Strategic due diligence of the internal environment

Step 5. Strategic resource analysis

Step 6. Systematization of data, formulation of conclusions and recommendations

Step 7. Determination of directions for strategic development and growth of the resource potential of a smart university

Step 8. Determine the need for adjustment of the smart university strategy

Formation of a database of results of a strategic analysis of the resource potential of a smart university

Organizational model of a step-by-step strategic analysis of the resource potential of a smart university

Step 1. Information gathering

Step 9. Monitoring the implementation of the strategy and strategic development of the smart university

Fig. 14.1 Organizational model of a step-by-step strategic analysis of the resource potential of a smart university in the context of digitalization

identification of both opportunities and risks that affect the state and use of resource potential in achieving strategic goals.

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14.3.2 Development of a Model of Methodological Support for the Strategic Analysis of the Resource Potential of a Smart University In the strategic analysis of the resource potential, it is important to study not only the current state of resources, but the search for growth reserves for their effective use in the future. In addition, it is important to determine the impact of changes in resource potential on the strategic development of a smart university, taking into account the opportunities and risks of the external environment. Figure 14.2 shows the model of methodological support developed by us for the strategic analysis of the resource potential of a smart university. The proposed model of methodological support for the strategic analysis of the resource potential of a smart university makes it possible to analyze the degree of availability of resources, their state, movement and efficiency of use. The developed model of methodological support for the strategic analysis of resource potential takes into account the unique resources that are typical for a smart university. In addition, the model pays significant attention to assessing the current state of the resource potential and its forecast, taking into account the influence of environmental factors. It is important to analyze the impact of changes in the resource potential on the achievement of the strategy. Based on the results of using this model, the smart university development strategy can be adjusted. Currently, this model is being tested in the activities of the university’s smart division. The introduction of the proposed model allows you to quickly identify areas to improve the efficiency of resource use. In particular, based on the results of its application in the activities of the university’s smart division, reserves for increasing the efficiency of the use of human resources were identified. In particular, in order to fulfill the key strategic performance indicators of the smart division of the university, measures were developed in a timely manner, which made it possible to increase by 18% the indicators in terms of the volume of research work and 25% of the publication activity of the teaching staff.

14.4 Conclusions of the Research and Prospects for Further Research in this Direction Conclusions. The study showed that a strategic analysis of the resource potential of a smart university should take into account the specifics of its activities and the availability of unique resources that significantly affect the achievement of the strategy. 1. Based on the study, the author’s definition of the strategic analysis of the resource potential of a smart university is proposed.

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Directions of strategic analysis of resource potential

Human resources

Intellectual resources

Material and technical resources

Analysis of resource endowment of a smart university

Analysis of the state and movement of resources

smart technologies

Financial resources

Informational resources

Resource efficiency Analysis

Assessment of the current resource potential

Innovation Resources

Time resources

Assessment of the external environment for resource potential

Forecast of resource potential in the future

Analysis of the impact of changes in resource potential on the achievement of the strategy

Fig. 14.2 Model of methodological support for the strategic analysis of the resource potential of a smart university

2. The study made it possible to develop an organizational model for a step-by-step strategic analysis of the resource potential of a smart university in the context of digitalization. 3. The result of the study is the proposed model of methodological support for the strategic analysis of the resource potential of a smart university, considering the special types of resources characteristic of these organizations. The proposed organizational model of step-by-step implementation and the model of methodological support for the strategic analysis of the resource potential of a

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smart university can be used both at the level of a separate structural smart unit, and in general in a smart university. Next steps. For the development of methodological tools for strategic analysis, it is necessary to develop a methodology for evaluating indicators of the efficiency of using the resource potential of a smart university, taking into account strategic guidelines.

References 1. Basuony, M.A.K.: The balanced scorecard in large firms and smes: a critique of the nature, value and application. Acc. Financ. Res. 3(2), 14–22 (2014) 2. Biazzo, S., Garengo, P.: Performance Measurement with the Balanced Scorecard. A Practical Approach to Implementation within SMEs, p. 133. Springer-Verlag, Berlin Heidelberg 3. Morard, B., Stancu, A., Jeannette, C.: A comparison between two balanced scorecards: optimal vs. Kaplan and Norton model. J. Econ. Bus. Manag. 3(2), 302–308 (2015) 4. Malgwi, A.A., Dahiru, H.: Balanced scorecard financial measurement of organizational performance: A review. J. Econ. Financ. 4(6), 1–10 (2014) 5. Lawrie, G., Cobbold, I.: Development of the 3rd Generation Balanced Scorecard (Working Paper). In: 2GC Active Management, 25 March 2015, 19 p. http://2gc.eu/assets/files/resour ces/Papers/2GC-WP-201403-Evolution_of_the_BSC.pdf 6. Uskov, V.L., Bakken, J.P., Gayke, K., Jose, D., Uskova, M.F., Devaguptapu, S.S.: Smart university: a validation of “smartness features—main components” matrix by real-world examples and best practices from universities worldwide. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning 2019. SIST, vol. 144, pp. 3–17. Springer, Singapore (2019) 7. Uskov, V.L., Bakken, J.P., Howlett, R.J., Jain, L.C. (eds.): Smart Universities: Concepts, Systems, and Technologies, 421 p. Springer (2018). ISBN 978-3-319-59453-8 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, pp. 3–14, Springer, Cham (2016). ISBN 9783319396897. https://doi.org/10.1007/978-3-319-39690-3 9. 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) 10. 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, p. 101 11. Glukhova, L.V., Syrotyuk, S.D., Sherstobitova, A.A., Pavlova, S.V.: Smart university development evaluation models. In: Smart Education and Smart e-Learning, Smart Innovation, Systems and Technologies, vol. 144, pp. 539–551. Springer, Cham (2019) 12. Berdnikova, L.F., Sergeeva, I.G., Safronova, S.A., Smagina, A.Y., Ianitckii, A.I.: Strategic management of smart university development. In: Smart Education and Smart e-Learning, Smart Innovation, Systems and Tehnologies, vol. 188, pp. 293–305. Springer, Cham (2020) 13. Berdnikova, L.F., Igoshina N.A., Gerasimova E.A.: Conceptual aspects of strategic human resources management in the context of digitalization. In: Lecture Notes Networks and Systems, 161 LNNS, pp. 544–551 (2021) 14. Berdnikova, L.F., Sherstobitona, A.A., Schnaider, 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 Tehnologies, vol. 144. pp. 583–593 (2019)

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15. Tikhomirov, V., Dneprovskaya, N.: Development of Strategy for Smart University. Open Education Global International Conference, Banff, Canada, April, pp. 22–24 (2015) 16. Rampersad, H., Hussain, S.: Authentic Governance: Aligning Personal Governance with Corporate Governance, Management for Professionals, Chapter 8 Corporate Balanced Scorecard. Springer International Publishing Switzerland, pp. 65–78 (2014)

Chapter 15

Decision-Making Training for Students and Managers Using Data Science and Smart Platforms Anna A. Sherstobitova, Bella V. Kazieva, Lyudmila V. Glukhova, Valery M. Kaziev, and Elena I. Koroleva Abstract The purpose of the work is to conduct a systematic analysis of the goals and opportunities for learning adaptive management and decision-making on personnel in the conditions of a multifactorial internal and external environment. The problem of adaptive training of graduate students to the realities of digital business is also touched upon. The methods of system analysis were used, and the relevance of the systems of the classes “Virtual Classroom”, “Open Mass Training”, “Learning on Demand”, “Flexible Learning”, “Mobile Learning” and “Flipped Classroom” was noted. A logistic model of migration in the human resources environment was proposed. The systems of “marketplace” remote and distributed training are studied, considering the efficiency and flexibility of staff training solutions. For example, supported by “meeting rooms”, virtual desktops, machine learning and gamification. The chain of data science management is given, the model (profile) of the client is investigated, and the indexes of his consumer choice are identified.

15.1 Introduction The digital economy puts forward “digital requirements” for the organization of work, personnel and the content of training programs. The competencies of managers and specialists formed by the traditional educational system are very often insufficient for the evolution of a new, digital infrastructure, economy and corporate culture.

A. A. Sherstobitova (B) · L. V. Glukhova Togliatti State University, Togliatti, Russia e-mail: [email protected] B. V. Kazieva · V. M. Kaziev Kabardino-Balkarian State University Named After H.M. Berbekov, Nalchik, Russia E. I. Koroleva Samara State Technical University, Samara, Russia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_15

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Large-size and mid-size companies need high-quality personnel management— Human Resource (HR)—and relationships with government authorities—Government Relations (GR). The GR manager of the company asks and seeks answers to the following types of questions: 1) How do the authorities treat the key business processes of the company, its management, product (service)? 2) What impact do the decisions of the authorities have on the performance of the company? 3) What role does the company play in solving the problems and challenges facing the authorities? 4) What is the consumer policy, image, partnerships of the company? 5) How does the company “capitalize” contacts with government officials? 6) What are the legal ways to protect the interests of the company when making decisions by the authorities?

15.2 Literature Review Relationships are impossible without effective training and recruiting, virtual (VR, virtual reality) and augmented (AR, augmented reality) realities. They overlay VR elements and relationships onto the representation of the real world, connecting them into the user’s unique digital environment. GR includes establishing and maintaining relationships with government authorities, as with the stakeholders of a business company [1]. Digital HR transformations have intensified recently [2, 3]. Machine learning, innovative technologies for digital integration of highly qualified personnel into the business environment are also widely used [4]. Modern education takes this into account by using VR technologies [5, 6] and the digital infrastructure of smart universities [7]. The work systematically analyzes the possibilities and technologies for teaching students of applied specialties (not only managers, but also “applied workers”— mathematicians, computer scientists and economists) with a focus on digital platforms of the HR, VR and smart classroom.

15.3 Integration and Reliance on Data (Data Analytics) and “Soft Skills” The sphere of personnel training, education at universities and schools has passed the first stage of mass and emergency (“quarantine”) digitalization. Accumulated practical experience in teaching is under conditions of uncertainty and multi-criteria. The limited experience available is still difficult to analyze, but such an analysis is

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already underway. Some of the results of this analysis allow us to adapt the following tools for personnel training: 1) 2) 3) 4) 5) 6) 7) 8)

Video television systems “Virtual Class” (Zoom, VVV, etc.); Massive Open Online Course (MOOC); “Learning on demand” (Inquiry-Based Learning, IBL); “Flexible, adaptive learning” (Flexible Learning); “Mobile learning” (m-Learning); “Blended, hybrid learning” (Blended Learning); “Flipped Classroom” (Flipped Classroom); “Gamification” (Gamification), etc.

It is necessary to pay more attention to “soft skills”—the competencies of an employee of a social, labor and motivational orientation. For example, the ability to negotiate, plan strategically, etc. Without infringing in any way on the competencies of the “hard skills” class—the basis of vocational training. “Soft skills” is a term that combines a wide range of competencies, more precisely, skills for the future: organize teamwork, negotiate, put forward ideas creatively, learn adaptively, etc. The competencies of the modern world in any field become obsolete quickly. But they are also quickly acquired. “Soft skills” are cross-cutting functional skills required regardless of the field of activity. The speed of development of ecosystems of labor markets and education also affects the profession [8]. Therefore, the skills required regardless of the industry and the area of their application are important. These are complex, systemic skills that span multiple areas. Soft skills are aimed at stability and relevance in a dynamic situation. The Moscow Economic Forum proposed the following: 1) cognitive flexibility; 2) result orientation; 3) the ability to think systematically and critically, perceiving a complex environment; 4) find connections between systems and adaptation mechanisms, etc. It is difficult to automate them (often impossible), but it is necessary and possible to intellectualize them. For example, soft skills programs of many companies are focused on situational decision-making and safety, effectiveness of the result. This is subject to personnel policy, project activities (from setting tasks to support), motivation and personal success, KPI, etc. The main key abilities are as follows: 1. Creativity. Many already have it—they found themselves in difficult situations, made difficult decisions. For example, in the context of the spread of COVID19, this ability could increase, stimulating prompt and creative problem solving. Experts believe that outside the comfort zone, this ability manifests itself better and faster.

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2. Communication. Communication, connections, their various channels and formats are an important part of the competencies, both professional and everyday, others. During the COVID-19 period, you should develop the skills of remote communication, learn how to do this and try to benefit from communications for yourself. 3. Emotional intelligence. This includes the ability of self-control, self-motivation and empathy—conscious empathy with the emotional state of others. Such abilities increase the demand for an employee or employer in the labor market. Both colleagues and subordinates need to control their emotions, everyone who is focused on results. 4. Adaptability. Adaptability is manifested in the appropriateness, efficiency, proportionality of response to control actions. Experts consider it an important skill to be able to overcome the fear of the unknown. 5. Teamwork. It is based on the ability to speak, listen, understand and make the right decisions by a group of like-minded people, perhaps even admitting they were wrong. You need to realize that you cannot do everything yourself, but everyone in the team is “important”. 6. The ability to convince. This is the ability to convey, justify one’s point of view and achieve its acceptance, division. It is important to choose arguments, to encourage the team to act in the interests of the project itself. We develop confidence—in ourselves, in the effectiveness of the project. 7. Time management. Remote work requires organizational and communication resources and efforts. Time management skills will allow you to prioritize competently, save time and other resources.

15.4 Model for the Evolution of HR Capabilities An analysis of technologies and systems, and most importantly, personnel training methods, makes it possible to identify primary trends, for example, an increase in search queries: “staff training systems” (more than 29.6 million pages in Google search), “staff training” (more than 39.9 million) and others HR technologies (HRT) are beginning to “migrate” from large sectors B2C, B2G, e-Commerce [9], etc. There is a “hype cycle” (Gartner agency), according to which migration in HR technologies from other areas occurs when entering “productivity plateau” of the logistical evolutionary process of the digital economy [10]. If y(t)—is the HR technological result at the moment t, α > 0—scaling factor, k min > 0—bottom line, a k max > 0—upper limit of change range y(t) or HR opportunities, then the process of infrastructure evolution is described by a logistic type model: y  (t) = α(y − kmin )(kmax − y).

(15.1)

Function y(t)—monotonically increasing in D(y). Therefore, the condition must be met:

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α(y − kmin )(kmax − y) > 0

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The solution of the equation is defined in the form: y(t) = kmin + ⎛

(kmax − kmin )Q(t) , Q(t) + b

Q(t) = exp⎝(kmax − kmin )

t

(15.3) ⎞

f (z)dz ⎠.

(15.4)

t0

Function f (z) determines the pace of the process, b—initial setting. In the digital economy, marketplaces (Amazon and others) have passed the “hype cycle” and falling interest; now distance learning systems have the qualities of a typical marketplace, “product”. HRT also has such a cycle: development from learning experience platform to employee experience technology. At the same time, in the context of geometric growth of data volumes used to manage business processes, these data should be abstracted, formalized and presented, regardless of the area under study. Large companies (Yandex, Mail, Uber, etc.) already use data mining, social mining, BI [11, 12] in business processes to make decisions and manage processes and data flows. Data drive technology is gaining popularity, and data is becoming a key factor in making not only business decisions, but also educational (especially business training).

15.5 Meeting Room Technology The responsiveness, interactivity and flexibility (“volatility” in remote employees) of staff training decisions require universal, integrated approaches, specialized APIs and scalable enterprise functionality. For example, companies need meeting rooms with an interactive widescreen monitor computer, a video system for streaming parallel video conferences. An equipped media center is needed for meetings with employees, partners and brainstorming. The distinctive features of such centers are: 1) 2) 3) 4)

dynamic widescreen multimedia presentations of high visual quality; the ability to update personal gadgets using BYOD wireless technology; the ability to conduct WWW surfing and dialogue contact (Skype, Zoom, etc.); the ability to update (e.g.„ through the functions of the Plug-and-Play class, “Touchscreen”) and save data, applications from an enterprise cloud resource in paperless and multi-user mode (used, for example, iCloud, Dropbox, Google Drive); 5) Virtual desktop infrastructure;

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6) the ability to safely transfer to the client computer the necessary applications of various manufacturers (e.g., Microsoft Office, GoToMeeting); 7) accessibility and profitability, etc. These features made it possible to introduce another understanding of the abbreviation HRT—Huddle Room Technology (“Talking Room Technology”) or wireless interactive, universal and standardized updating of information [13]. For example, the Huddle Hub Enterprise (HHE) cloud solution with 1–6 relay rooms for 10–100 users on average allows you to create virtual rooms for visitors where they can enter and interact with partners and colleagues (BYOD technology) from a device connected to intranet networks.

15.6 Intellectualization and Reliance on Machine Learning The development of artificial intelligence systems (IS, intelligence system) occurs under the influence of data technologies and also develops the latter. This affects business processes, the market for educational and intelligent systems. For example, there is a demand for T-shaped personnel who have high competence in the subject area, but also evolve in related fields. It has cross-functionality [14]—the ability to easily function in the specifics of related departments, for example, a financier with knowledge of blockchain, hashing. According to expert data (Headhunter, Yandex, Workshop), the demand for data science professionals is growing—it is higher than for IT professionals. Moreover, these are not only mathematicians, but even humanities who are able to manage in an instrumental environment, “make a profit” to business. Due to their disadvantage, some companies are loyal to experience in this area and consider “simply” training experience. Therefore, there is growing interest in competencies in the field of data science, SII. Although it is impossible to get such a specialty at a university, the corresponding online courses are already popular (e.g., in “Netology”). The essential support of data science, intelligence systems creates machine learning, for example: 1) with a teacher (supervised learning), focused on representative training samples and correct answers (object-response pairs), variations in parameters until the computer produces acceptable (in terms of accuracy, time, cost) results; 2) without a teacher (unsupervised learning) and correct answers, identification of hidden correlations, relationships of variables; 3) hybrid, with the training of SII in part of the data and the use of a combining algorithm for the rest; 4) with reinforcement (reinforcement learning), when the intelligence systems interact with the environment during the performance of the task, for example, by self-learning, supporting the knowledge base and output rules, decision-making skills;

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5) deep learning—with or without a teacher, mimicking the neural networks of people, their structure and signal transfer, activity in learning. Analysts McKinsey note a growing shortage of managers of data science, machine learning, which in 2020 amounted to over 1.9 million.

15.7 What is the Popularity of Managers and Their Need for Russia? In Russia, the pace is different, but the trend is the same: managers are in demand everywhere—from retail to the production sector, from marketing to game development (Fig. 15.1). Especially HR, GR managers [15, 16]. Competencies in the field of knowledge engineering and the possession of its tools become a strong advantage of many specialists, allowing you to solve various tasks yourself, that is, to extract and update, segment, visualize data, extract hidden business process connections from them, including those that cannot be extracted in traditional ways. Although state investment in digitalization 3 years ago lagged behind business investment in a number of developed countries (Figs. 15.2 and 15.3), in Russia this gap is now narrowing. The issues of digital management, digital business without HR technologies are effectively difficult to solve.

Data Science Management

Problem

Infrastructure

Company's goals, resources and needs for the decision to be made

Decision-making tools and technologies

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Data Analyst, Business Analyst

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Solution compliance with specifications, quality requirements

Fig. 15.1 Data science and decision chain

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Fig. 15.3 Volume of the Russian market HRM systems (rubles, billion)

For example, in Russia such professions are in demand: 1) Data marketer (marketing analyst), he must know the data to the analyzed problem (“unloading,” interpretation, integration, modification, etc.) and endto-end analytics necessary to forecast demand and promote the product. 2) Product analyst (“product” analyst, manager) who knows the principles of artificial intelligence systems (machine learning), collects and analyzes data, prototypes algorithms and generates ideas about the evolution of the company and personnel.

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3) GameDay analyst (“game” analyst, manager of the gaming industry), he according to analytical data improves user experience, holds the client and predicts the trend of the product. 4) A data journalist who not only extracts insights from data, but also explains them to (visually, audibly) readers and puts forward practically verifiable hypotheses. Exotic professions include writers of artificial intelligence systems, recyclers of “digital garbage” in big data, digital neuro-marketers, etc. The specialties already familiar to large business companies include data mining (data engineering) and social mining engineers, data scientist, data and BI analytics, experts (in quality, verification, reliability, elasticity, ethics, etc.), machine learning engineers, designers (UX, graphic), etc. Following the trends in the scaling of the activities of marketplaces and digital logistics services, an increase in the volume and requests for unskilled categories of workers is predicted (especially taxi drivers, delivery workers, recruits, recruiters and others who became popular professions during the COVID-19 period).

15.8 Conclusion New innovative technologies decentralize the product chain (services, goods), and the need for highly qualified personnel and new approaches to its motivation is growing. Companies should develop personnel, a system for improving their competencies. Business structures that pay sufficient attention to personnel will be competitive. The struggle is also for medium-skilled personnel, a more mobile group. Decisions affecting the evolutionary potential of companies and the influence of power structures on the adoption of such decisions and the development of business partnership with the administration became important. Data integration, end-to-end and streaming analytics affect the departments of marketing, sales, design, etc. The customer model (profile, portrait) is generated, and indexes (parameters) defining the product selection are identified.

References 1. Mack C.S. Business, Politics, and the Practice of Government Relations, Westport (2016) 2. Kruglov, D.V., Kruglova, O.D.: Features of personnel support in the conditions of digitalization. Leadership Manag. 6, 479–486 (2019). https://doi.org/10.18334/lim.6.4.41299 3. Nagibina, N.I., Shchukina, A.A.: HR-Digital: digital technologies in human resource management. Internet J Sci 1, 1–17 (2017) 4. Nonka, A.Y., Borisova, A.A.: Personnel support: finding and attracting personnel based on artificial intelligence technologies. Labor Econ. 2, 959–970 (2019). https://doi.org/10.18334/ et.6.2.40559

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5. Kavanagh, S., Luxton-Reilly, A., Wuensche, B., Plimmer, B.: A systematic review of Virtual Reality in education. Themes Sci. Technol. Educ. 2(10), 85–119 (2017). https://www.learnt echlib.org/p/182115/. Retrieved 4 Jan 2022 6. Hu-Au, E., Lee, J.J.: Virtual reality in education: a tool for learning in the experience age. Int. J. Innov. Educ. 4, 215–226 (2017). https://doi.org/10.1504/IJIIE.2017.091481 7. Sherstobitova, A., Kaziev, V., Kazieva, B., Glukhova, L., Gudkova, S., Yakusheva, T.: Challenges of digitalization: Smart pedagogy for smart university. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning-2021, Smart Innovation, Systems and Technologies, vol. 240, pp. 363–369 8. Serdyukova, N., Serdyukov, V.: Algebraic Formalization of Smart Systems. Theory and Practice. Springer Nature, Switzerland (2018) 9. Gromov, A.Y., Kezhvatova, A.T., Levshin, D.A.: Study of the influence of inaccurate information in intelligent personnel selection systems. Izvestia TulSU Ser. Tech. Sci. 2, 131–136 (2018) 10. Arkhipova, M.Y., Nizhegorodtsev, R.M., Goridko, N.P., Afonina, E.V., Karev, A.V.: Management of scientific and technical development: horizons of the digital economy. Sunrise-A, Moscow (2020) 11. Big data in computational social sciences and humanities, In: Big Data in Computational Social Science and Humanities, pp. 1–25 (2018). https://doi.org/10.1007/978-3-319-95465-3_1 12. Tribyasheva, A.G.: Big Data. What results can be achieved with big data analytics. In: Corporate financial statements. International Standards, 1 (2019). https://www.datanomics.ru/artciles/pra kticheskie-aspekty-vnedreniya-innovatsij-v-kompanii 13. Shchelkunova, S.A., Metlin, S.V., Bykova, M.S.: New approaches to staff training. Moscow Econ. J. 1, 472–480 (2021). https://doi.org/10.24412/2413-046Kh-2021-10043 14. Salnikova, N.I.: Human resources management as an essential component of the company’s development strategy. Pers. Dev. Manag. 3, 21 (2012) 15. Mukhaev, R.T.: GR-management: science or art of effective public communication? Knowl. Underst. Skill 1, 114–126 (2018) 16. Uskov, V.L., Bakken, J.P., Penumatsa, A., Heinemann, C., Rachakonda, R.: Smart pedagogy for smart universities. In: Smart Innovation, Systems and Technologies, pp. 3–16 (2018). ISBN 978-3-319-59450-7. https://doi.org/10.1007/978-3-319-59451-4

Chapter 16

Algorithm for Strategic Management of Project Activities in Smart University Leyla F. Berdnikova, Olga S. Aksinina, Elena V. Shchepotkina-Marinina, Elena A. Borgardt, Oksana A. Lugovkina, and Pavel A. Kabanov

Abstract At present, the sustainable development of a smart university is determined by the right choice of strategic guidelines that allow efficient use of available resources and opportunities. The strategy acts as a fundamental basis in managing in a market environment, ensuring the economic growth of a smart organization, increasing its competitiveness. In modern conditions, a smart university is expanding its capabilities and activities. At the same time, the implementation of project activities becomes an integral part of it. Project activities can be implemented as part of the educational process. At the same time, students act as the main innovators and generators of projects. Also, in a smart university, project activities can be implemented by the teaching staff as part of research activities. One way or another, a modern smart university is a kind of center for the emergence of innovative promising projects. Like any other activity, project activity needs competent strategic management. The article reveals the concepts of project strategy and strategic management of project activities of a smart university. The main tasks of strategic management of project activities in a smart organization are established. The result of the study is the proposed algorithm for the strategic management of project activities, taking into account educational and research activities. The results obtained were tested in the strategic management of project activities in the smart division of the university on the example of the department.

L. F. Berdnikova (B) · Elena A. Borgardt · O. A. Lugovkina · P. A. Kabanov Togliatti State University, Togliatti, Russia e-mail: [email protected] O. S. Aksinina Samara State University of Economics, Samara, Russia E. V. Shchepotkina-Marinina ANOO VO Centrosoyuz RF “Russian University of Cooperation”, Mytishchi, Russia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_16

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16.1 Introduction A modern smart university is a center of scientific, educational, innovative and project activities, where promising ideas, groundwork and projects are born. For a long period of time, universities were associated only with scientific and educational activities. However, today there are new demands from the external environment for innovative technologies, products and services. Smart universities, having ample opportunities and intellectual resources, are transforming into progressive innovative organizations capable of responding to such requests. Currently, the activities of the smart university are studied in the works of scientists: Uskov et al. [1–3], Serdyukova et al. [3–6], Hinings et al. [7]. Issues of strategic development and management are revealed in the works of authors such as: Weerakkody et al. [8], Berdnikova et al. [9], Tikhomirov and Dneprovskaya [10]. The following scientific works are devoted to the study of projects in smart universities: Tokuç et al. [11], Mitrofanova et al. [12]. Strategic management is an important part of management, which is aimed at the successful, effective development of a smart university in the future, taking into account the rapidly changing conditions of the competitive environment. Strategy development is one of the key functions of the management system. At the same time, the manager must understand how the strategy can affect the well-being of the smart university, its competitive advantages. It is important for a manager to choose the right business approaches, methods and actions to implement the strategy in order to achieve the intended results. We believe that the strategic management of project activities in market conditions is a promising and necessary direction, which is of great importance and is actively used as the basis for conducting management processes of a smart university in market conditions. This direction has received recognition throughout the business world. By studying strategic management, managers will be able to work better in a rapidly changing environment, correctly assess the situation and take the steps necessary for a smart university. The main purpose of this article is to develop an algorithm for the strategic management of project activities in a smart university. The scientific novelty of the research consists in highlighting the main goals of project activities for a smart university and developing an algorithm for the strategic management of project activities of a smart university, taking into account educational and research activities.

16.2 Statement of the Problem in General Form and its Connection with Important Scientific and Practical Tasks The project strategy is a way to achieve it, taking into account the mission and vision of the project.

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When developing a project strategy, key tasks related to: • establishing the mission and strategic vision of the project; • development of the project goal; • formation of functional strategies to achieve the goals of the project. The project must ensure the uniqueness of solutions—this is its mission. Strategic vision is a long-term desired image of the future, that is, the desire to achieve goals. The vision of the project reflects the image of the future as it is presented by the owners and participants. Practice shows that there are situations when participants have a different vision of the project. This raises the need to find consensus and develop a single vision statement for the project. In a smart university, when developing a project vision, it is necessary to focus on the following areas: • the environment for which the project will be implemented; • ways to carry out the project and work; • the main resources required for the implementation of the project. The project goals represent the specific results that the project initiator wants to receive, taking into account the requests of the stakeholders. In the strategic management of project activities, a system and hierarchy of project goals are used. The goals can be different, for example, market, innovative, financial and others. At the same time, goals of different levels are distinguished, taking into account the hierarchy. The main problem in the implementation of project activities in a smart university is the search for directions and ways to combine the possibilities of developing and implementing projects within the framework of educational and research activities for their subsequent commercialization and scaling. At the same time, it is important to choose the right ways to strategically manage project activities that meet not only the needs of the external environment, but also the interests of the smart university.

16.3 Presentation of the Main Research Material with Full Justification of the Obtained Scientific Results 16.3.1 Project Activity Strategies and Principles of Strategic Project Management in a Smart University Strategic management represents the management of a smart organization, focused on consumer needs, able to respond flexibly to changes occurring in the organization, respond to challenges from the external environment and develop competitive advantages.

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Effective strategic management contributes not only to the stable operation of a smart university in the current period, but also to its development in the long term, as well as the achievement of key goals. In some scientific studies, strategic management is considered as a shift in emphasis in managing an organization from tactics to strategy. It is believed that strategic management is carried out in an organization when strategic goals are set first and then tactical ones. That is, strategic goals prevail over tactical ones. Every smart organization will thrive with well-defined strategic and tactical goals. The strategy is aimed at the formation of global attitudes, and tactics are the method of achieving them. As noted earlier, project activities play a significant role in the activities of a smart university. Strategic management of project activities should be based on the overall strategy and mission of a smart university. In the process of developing management decisions, it is necessary to take into account their influence on general strategic and tactical goals. An effective tactical step ensures the implementation of the strategic plan. At the same time, tactical mistakes made can devalue successful strategic ideas. The implementation of project activities in a smart university should contribute to the achievement of specific strategic guidelines, such as a promising increase in competitiveness, profitability and profitability. The implementation of project activities may include various functional strategies that are interconnected with the overall development strategy of a smart organization. Based on the study, we have identified the basic strategies for the project activities of a smart university (Fig. 16.1). The business strategy is characterized by the strategy of business units, which sets the vectors of actions that provide the competitive advantages of the smart university. A functional strategy is characteristic of a diversified smart organization. It allows you to set the area of development of a separate unit within a smart university. An important role of functional strategy is to support the overall business strategy and competitiveness of a smart organization. In addition, the functional strategy is Fig. 16.1 Basic strategies for project activities of a smart university

Business strategy

Functional strategy Operational strategy

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aimed at developing management guidelines that contribute to the achievement of the desired functional goals. Business and functional strategies are interconnected. On the one hand, the coordination of functional strategies is being implemented, which does not allow focusing only on narrow goals. On the other hand, a narrower functional strategy, in comparison with a business strategy, details its individual provisions. The development of a functional strategy helps to determine the optimal behavior that allows efficient use of resources in the performance of specific functions. The operational strategy complements the business and functional strategies and ensures the completeness of the strategic planning system. It is characterized by specific activities in the process of implementing individual functional areas. The operational strategy interacts with the work of the main operational units that solve daily operational tasks of great strategic importance. Thus, when implementing project activities in a smart university, strategic settings are primarily determined, and subsequently their tactical solutions are developed. In other words, the general directions for the development of a smart university dictate the ways of their implementation. Strategic project management is based on key principles. We have identified the main principles of strategic management of the project activities of a smart university. They are shown in Fig. 16.2. Thus, the development of a strategy for managing the project activities of a smart university involves determining the required resources, technologies and qualified

principle of scientific and analytical foresight and strategy development

the principle of taking into account and coordinating external and internal factors in the development of a smart university the principle of matching the strategy and management tactics of a smart university

principle of priority of the human factor

principle of certainty of strategy and smart university of strategic accounting and control the principle of compliance of the project management strategy of the smart university with the available resources, technologies and consumer needs

strategy effectiveness principle

Fig. 16.2 Basic principles of strategic management of project activities of a smart university

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Planning

Organization

Motivation

Project execution control

personnel. Without taking into account these parameters, as well as the influence of factors of the external and internal environment, the implementation of the project will be impossible.

16.3.2 Allocation of Functions and Development of an Algorithm for the Strategic Management of Project Activities in a Smart University The project as an object of system management is described by a set of characteristics, such as: the purpose and cost of the project, the quality and timing of the project and the risks of the project. Strategic project management performs certain functions. We have identified the main functions of the strategic management of project activities, characteristic of a smart university (Fig. 16.3). All of the above functions are interconnected. Let us reveal their content. Planning contributes to the development of plans for the implementation of project activities, with given parameters, focused on achieving the goals and objectives of the project. The organization is a strategic management function aimed at ensuring the implementation of project activities and the achievement of the project strategy. Motivation is a process that encourages project participants to take action to implement it according to the developed plan. Control is a complex function of strategic project management. The effectiveness of control is achieved when it is strategic, economical, timely and focused on achieving specific results. The strategic management of the project activities of a smart university should focus on the overall strategy, as well as take into account the leading areas of activity, such as: educational and research. Based on the study, we have proposed an algorithm for the strategic management of the project activities of a smart university (Fig. 16.4).

General strategy smart university

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Exit

Fig. 16.4 Algorithm for strategic management of project activities of a smart university

The proposed algorithm is focused on the development of project activities carried out in the educational process. In addition, the algorithm takes into account the possibility of implementing projects that scientists and students of the smart university are working on. Approbation of this algorithm was carried out as part of the project activities of the smart division of the university. At the project selection stage, projects were evaluated for compliance with the strategies. Projects that did not meet the goals were rejected. At the stage of determining the possibility of implementing the project within the framework of the project activities of the smart university, projects were selected that could subsequently be implemented in the process of the project activities of the smart university. It should be noted that the introduction of the proposed algorithm made it possible to increase the efficiency of the implementation of project

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activities by 18%, to reduce the costs of implementing projects that do not correspond to the strategies of a smart university by 27%. As a result of the implementation of the proposed strategic management algorithm, promising projects developed by students and researchers were identified, which were subsequently implemented in the project activities of the smart university.

16.4 Conclusions of the Research and Prospects for Further Research in this Direction Conclusions. The study showed that the strategic management of the project activities of a smart university should be based on certain principles and consumer needs and take into account its own capabilities and resources. At the same time, the strategic management of the project activities of a smart university should combine educational and research activities. 1. Based on the study, the main tasks of the strategic management of project activities in a smart organization are established. 2. The study made it possible to identify the main strategies for the project activities of a smart university. 3. In the process of research, the main principles and functions of the strategic management of the project activities of a smart university are highlighted. 4. The result of the study is the proposed algorithm for the strategic management of project activities, taking into account educational and research activities. The proposed algorithm for the strategic management of project activities can be used both at the level of a separate structural smart unit and in general in a smart university. Next steps. For the development of strategic management of project activities, it is necessary to develop a system of indicators to assess its effectiveness and degree of influence on the overall strategy of a smart university.

References 1. Uskov, V.L., Bakken, J.P., Gayke, K., Jose, D., Uskova, M.F., Devaguptapu, S.S.: Smart university: a validation of “smartness features—main components” matrix by real-world examples and best practices from universities worldwide. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning 2019. SIST, vol. 144, pp. 3–17. Springer, Singapore (2019) 2. Uskov, V.L., Bakken, J.P., Howlett, R.J., Jain, L.C. (Eds). Smart Universities: Concepts, Systems, and Technologies, p. 421. Springer (2018). ISBN 978-3-319-59453-8 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). ISBN 9783319396897. https://doi.org/10.1007/978-3-319-39690-3 4. 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,

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Smart Education and Smart e-Learning, Smart Innovation, Systems and Technologies, vol. 59, pp. 83–96. Springer, Cham (2016) 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, p. 101 Glukhova, L.V., Syrotyuk, S.D., Sherstobitova, A.A., Pavlova, S.V.: Smart university development evaluation models. In: Smart Education and Smart e-Learning, Smart Innovation, Systems and Technologies, vol. 144, pp. 539–551. Springer, Cham (2019) Hinings, B., Gegenhuber, T., Greenwood, R.: Digital innovation and transformation: an institutional perspective. Inf. Organ. 28(1), 52–61 (2018) Weerakkody, V., Janssen, M., Dwivedi, Y.K.: Transformational change and business process reengineering (BPR): lessons from the British and Dutch public sectors. Gov. Inf. Q. 28(3), 320–328 (2011) Berdnikova, L.F., Sergeeva, I.G., Safronova, S.A., Smagina, A.Y., Ianitckii, A.I.: Strategic management of smart university development. In: Smart Education and Smart e-Learning, Smart Innovation, Systems and Tehnologies, vol. 188, pp. 293–305. Springer, Cham (2020) Tikhomirov, V., Dneprovskaya, N.: Development of Strategy for Smart University, 2015 Open Education Global International Conference, pp. 22–24. Banff, Canada (2015) Tokuç, A., Uran, Z., Tekin, A.: Management of big data projects. In: Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution, pp. 279– 293 (2019) Mitrofanova, Y.S., Chehri, A., Tukshumskaya, A.V., Vereshchak, S.B., Popova, T.N.: Project management of smart university development: models and tools. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning, Smart Innovation, Systems and Tehnologies, vol. 240, pp. 339–350. Springer, Singapore (2021)

Chapter 17

Ecosystems for Higher Education and Society Svetlana A. Gudkova , Elena N. Korneeva , Raisa K. Krayneva , Irina V. Khristoforova , and Aizhan Omarova

Abstract The aim of the work is a systematic study of the learning ecosystem, formation and development of intellectual capital of the ecosystem in the future. Systemic and complex approaches and methods have been used: system analysis, synthesis, identification, aggregation and others, as well as methods of mathematical modeling and statistics. The main results of the article are the following: (1) a systematic analysis of intellectual capital of the organization taking into account the evolution of the ecosystem; (2) the possibility of applying indicators like D. Tobin, Social Mining type indicators for educational ecosystem; (3) quality assessment of intellectual capital of organizations. The achieved results will make it possible to develop new mechanisms for managing “educational ecosystems”, as well as to increase the capitalization of competencies through their contribution to the formation of intellectual capital.

17.1 Introduction A modern model of education that meets the technological, social and environmental challenges of the twenty-first century, meets the requirements of a changing economy and society, helps to form “skills of the future”–this is flexible personalized lifelong learning. Such a model calls for new ways of organizing, including moving toward digital platforms and networks of educational opportunities, as well as new approaches

S. A. Gudkova (B) · E. N. Korneeva Togliatty State University, Togliatty, Russia e-mail: [email protected] E. N. Korneeva · R. K. Krayneva · I. V. Khristoforova Financial University Under the Government of the Russian Federation, Moscow, Russia A. Omarova Yessenov University, Aktau, Kazakhstan © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_17

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to regulation based on the involvement of all stakeholders instead of centralized decision-making. Digital learning environments, learning ecosystems (Learning Ecosystem–LEco) develop similarly to natural ecosystems, in which they are complex and selforganizing synergetic systems [1], where organisms interact in autonomous or collective struggle for common resources. Here, the complexity of the organization lies in combining the productive interaction between students and teachers, parents and administrators, the external environment and the internal component of the staff. They interact through innovative products and resources (technologies, methods, learning materials, etc.) according to the conditions and needs of education (consumer, normative, public, governmental). Today, the LEco-category can easily include not only facilities and the relationships developing within them, innovative technologies and methods of their application, but also modern smart campuses, smart libraries and various web-communities. Important components are access points for distance learning communication, web technologies, interactive tools and digital media repositories, as well as methods for implementing personalized learning and achieving goals [2]. This paper considers LEco-problems in interaction with an environment in which there are ever-evolving situations of uncertainty and risk. Today it is necessary to pay special attention to the system of interaction of educational structures with the external environment interested in the development of intellectual potential of the country as a whole. This is a new category involving research, planning, establishment and maintenance of relations with state structures, as well as with business partners or stakeholders interested in the result of integration of educational and commercial activity of the company and sphere of influence. In the U.S., the European Union, Canada, an appropriate system of interaction between business and the authorities has been formed [3]. In Russia they are still at the stage of formation. There are obstacles, in particular, negative attitudes toward lobbyism in general, reluctance to recognize it, as well as extremes–pressure on the authorities, weak adaptability to political and market changes. All our research is related to the third category–“intellectual capital” or the set of competencies, technologies that contribute to the achievement of the company’s strategic objectives.

17.2 Literature Review and Theory The concept of LEco attracts many managers and scientists today (for example, [3–5]. For example, E2E systems are also considered ecosystems in computer science [6, 7]. Many scholars and scientists believe that LEco will become interdisciplinary, combining new educational connections and achievements in all areas of study, especially in their IT infrastructure [8]. Nowadays intellectual capital is the key to success in any field and its development takes place in the system of higher education. In Russia, as shown in Fig. 17.1, the

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Fig. 17.1 Dynamics for universities’ ranking in Russia

number of universities is declining. The authors’ vision is based on the analysis of works [4, 7]. In Russia, LEco today begins to acquire a professional character, becoming an important regulator of market relations, allowing business structures to effectively solve strategic problems. The concept of “intellectual capital” is multifaceted [9, 10] and it includes the following elements: 1. human capital as a symbiosis of competence and quality of knowledge; 2. structural capital as the presence within the organization of patents for inventions, know-how, licenses, new software components; 3. customer equity or relationships with target customers, distributors, suppliers and partners [11]. According to key ideas of some scholars [12, 13] intellectual capital includes competencies, qualifications, heuristics of employees, any intangible assets (software, trademarks, databases, management procedures and training features) that contribute to the growth of income, competitiveness and sustainability of the company. They also mentioned that capital may be morally “worn out”, outdated, but knowledge is dynamically modified.

17.3 Structure Analysis for Intellectual Capital in LEco Intellectual capital is a type of long-term capital. By receiving quality education that meets the needs of stakeholders, training professional competences and improving skills, it facilitates mobility in the labor and education markets. A distinctive feature

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Simulation of personal competence development Diversificasion of business structure

Knowlege Economy

Diversification of production

Simulation of technology development

Diversification of Business processes

Organization development

Fig. 17.2 The author’s vision: the structural transformation of knowledge capital

of intellectual capital is the need for its continuous reproduction through investment. It is worth noting that a lot of successful corporations invest more in intellectual capital than in equipment because the return on investment in the development of intellectual capital is five times greater than investment in material resources, or in production. Such investment is possible through leveraged, proprietary, governmental budget, regional and municipal or local budgets and the use of modern methods. Studies show that a 10% increase in staff education leads to an equivalent 10% increase in productivity. A similar increase in fixed assets leads to an increase in labor productivity of only 3.5% [10, 12]. The practice of knowledge capitalization is carried out according to the following principle: knowledge economy–stimulation of technology development–stimulation of personnel competence development–diversification of production–diversification of business processes–diversification of business structures–organizational development (Fig. 17.2). An important feature of intellectual capital is its dynamism. Therefore, in our work we applied methods of system and simulation, as well as mathematical tools that allow making certain conclusions based on the indices of D. Tobin, R. Eccles and others. We have the idea of following approaches taken from some modern studies [4, 14–16]. It’s advisable to measure the capital of knowledge and its dynamism taking into account the influence of control parameters. The issue of human capital forecasting is a poorly formalized and structured multifaceted problem. It’s also poorly studied. But a forecast is possible if you use relevant, identifiable models. The relevance of forecasting the dynamics of intellectual capital increases with the complexity of information systems and flows affecting corporate parameters: 1. employment by intellectual (automated) labor (to a thousand employees); 2. indices (share of income, ratio) of intellectual and industrial production; 3. human potential index, etc. Capital is transformed into intellectual capabilities, for example, copyrights can be transferred and sold. Companies are interested in the growth of their intellectual capital, manageability and development.

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Staff knowledge is the basis of the ability to predict the evolution of the company, its status and the ability to make creative decisions on time. It’s necessary to invest in use, the possession IT, support systems of a solution (SPPR) and artificial intelligence (CII). Many large companies already have staff knowledge and training managers, knowledge analytics and time managers, project testing and verification specialists, Data Mining.

17.4 Our Results 17.4.1 Modeling Intellectual Capital Development Management Opportunities in LEco We will use the results of the research in [3, 10, 16] and apply their findings to our research. Multiplicative valuations that consider the effects of collaborative actions are necessary to identify the “cost” of a particular employee. The Pearson’s statistics are applicable (1): χ2 =

n  ( f i − pi n)2 , pi n i=1

(17.1)

where n is the “length” of the sample, pi is the probability of the i-th state (predictive), f i is its empirical (heuristic) frequency. It’s possible to offer the Weibull distribution (2):  −λt α+1 , V (t) = 1 − exp α+1 

(17.2)

where λ defines the scale, is the asymmetry of the data stream; by changing these values, approximate descriptions of the law can be obtained (3)–(4).  T =

∞

F(t)dt,

(17.3)

0

 D=2 0

∞

 t F(t)dt −

2

∞

F(t)dt

.

(17.4)

0

The forecast should be based on Big Data, Data Mining, Social Mining, Elastic Data, etc. Knowledge, data management and analysis systems provide analysts, designers and models with the following capabilities:

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1. making appropriate predictions and conclusions: apply decision algorithms, for example, neurolinguistic programming, a multi-agent approach and neural systems; 2. identifying links of facts (events): to draw knowledge, to classify by community; 3. using cognitive maps: visualize, compare maps (combine, complement, highlight, scale, etc.); 4. analyzing: reorder time series, detect trends, filter and consider stochastic behavior; 5. using situational modeling of development: to produce competitive and factor analysis. The problem of objectivity in the control of intellectual capital, competencies implies the independence and objectivity of the evaluating subject. The objectivity of the control of the competence of the individual should take into account the following: 1. 2. 3. 4. 5. 6. 7.

the personal factor of the person being assessed; a base of indicators of the level of knowledge and skills; skills in applying technologies; criteria for measuring materials; methodology for the analysis and evaluation of intellectual capital; decision-making methods (objectivity, relevance, completeness of information); a mechanism for adapting the existing intellectual capital to emerging situations.

The significance of indicators of intellectual capital should be measured by the Shannon–Weaver index (5) [3]: H =−

n p   pi j ij log , S S i=1

(17.5)

where pi j is the intellectual capital of the i-th division (department) of the company. It’s not possible to estimate intellectual assets according to the same methodology, the existing methodologies are not relevant to the fair price of intellectual assets in the region, reflecting the value of only components, only approximately. According to Stuart [15], it’s not necessary to complicate an excessively initial task, trying to measure “non-strategic” parameters, it’s necessary to measure necessarily the main factors, on which intellectual capital is significantly dependent.

17.4.2 Russian Educational System and the Accumulation of Intellectual Capital The introduction of such models of collaborations is accompanied by written appeals to the authorities and public organizations, the media, financial support (affecting the

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decision), participation in commissions and working groups, parliamentary requests, mass actions with the participation of the population (demonstrations, rallies). The Togliatty State University has analyzed the level of competencies. Some proposed competences of LEco specialist are listed below, noting three competence levels: advanced (A); basic (B), threshold (C), see Table 17.1. Below we show the results of LEco specialist’s competencies formation in higher education institutions (Fig. 17.3). A measurement scale (Table 17.2) was derived to assess the significance of the results obtained. In it, the indicators of the boundaries of variation were determined by expert judgment (Fig. 17.4). Below we show the results of LEco specialist’s competencies formation in higher education institutions (Fig. 17.5). Table 17.1 Competencies of LEco specialist (Manager) (2022) №

Competence

Level TSU

1

Power structure, capabilities and limitations

A

2

Business Correspondence

B

3

ICT (LAN, Internet, Intranet)

B

4

Office Document Management (Records Management)

B

5

Computer Office (MS Office)

B

6

Communication Ethics

B

7

Productive communication with authorities

B

8

Links with authorities

B

9

Intelligent Decision Support Systems

B

10

Security of the State and its institutions, structures

B

11

Working with Media

A

12

Strategic Management

B

13

Time Management

C

14

Risk Management

B

15

Human Resources Management

B

16

Events Management

B

17

Image Management

A

18

Political Literacy

A

19

Media Literacy

A

20

Industry issues

A

21

Web-based analytical tools

C

22

Cognitive Flexibility

B

23

Logic-traditimic thinking

B

24

Self-study, self-development

A

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100 90 80 70 60 50 40 30 20 10 0 Power ICT (LAN, Computer Productive Intelligent Working with Time Human Image Media LiteracyWeb-basedLogic-traditimic structure, Internet, Office (MScommunication Decision Media Management Resources Management analytical tools thinking capabilities and Intranet) Office) with authorities Support Management limitations Systems

Fig17.3 LEco specialist’s competence model

70,00

2022

2023

60,00 62,50

50,00 40,00 30,00

54,17

41,67 29,17

20,00 8,33

10,00 0,00

A

B

4,17

C

Fig. 17.4 Model assessment for measurement scale

Table 17.2 The measuring scale

Level

Indicator

Criteria value

Advanced

A

A ≥ 76.1

Basic

B

51 ≤ B ≤ 76

Threshold

C

C < 50

Figure 17.5 shows the results obtained by the authors through various types of assessments in the management groups. It is evident that each of the universities obtained high indicators of LEco competence development. Such work will continue in the future. The intention is to identify each level of competence acquisition and the impact of its importance on the growth of intellectual capital.

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Self-study, selfdevelopment

2022

Industry issues

Media Literacy

Political Literacy

Image Management

Strategic Management

Working with Media

Office Document Management (Records

ICT (LAN, Internet, Intranet)

2021

Power structure, capabilities and

100 90 80 70 60 50 40 30 20 10 0

191

Fig. 17.5 Assessment model for the tested period

17.5 Conclusion Intellectual capital affects the image of the company and is reflected in intellectual property (brand, patent, trademark, software, competence of management and employees, corporate spirit, etc.). Although human capital is not the property of the company, organizational, innovative and process-oriented capital (adding value to products) is its asset. Intellectual capital of the corporation, its efficiency depends on competences, training and investments in personnel training. There are also results–professionalism, productivity growth, income after training. Intellectual and material aspects are united. They are united by participation in the capitalization of knowledge, exposure to both types of wear and tear. Intellectual capital determines the competitiveness of a corporation, and its ability to effectively raise and apply capital is investment attractiveness. Dependence of intellectual capital and efficiency of companies in the market (not only in the labor market) in the digital economy increases market interest in competences. This also applies to corporate governance, reducing asymmetries in competences. In particular, the capitalization and Tobin measure are important. Systemic analysis of the problem situation in the work shows that the responsibility of companies to the society in accumulation and capitalization of competences is increasing. Consequently, the need to capitalize on the competencies and intellectual potential of companies is increasing. The study will allow to implement effective mechanisms of management for companies’ innovations, as well as to improve capitalization of their intellectual infrastructure and capabilities. Future Trends Within the framework of this study, it is planned to develop knowledge bases and master new platforms and technologies by all participants in the collaboration process.

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It’s necessary to approach LEco comprehensively, from the standpoint of system analysis and a multi-agent approach in the network business, highlighting and investigating the processes of monitoring, control, Data Mining, Social Mining, ICT support, legal and political support, modeling (forecasting) situations and solutions, establishing contacts and negotiation processes, impact management through media and the public, etc. It’s customary to interpret LEco as development state power relations with business, trade unions, public and other organizations by monitoring and analyzing the activities of the authorities, parties and organizations of society.

References 1. Brodo, J.A.: Today’s ecosystem of e-learning [El. Res.]. Trainer Talk 3(4) (2006). http://ene wsbuilder.net/salesmarketing/e_article000615779.cfm 2. Pirie, C.: E-Learning ecosystems: The future of learning technology [El. Res.]. Chief Learning Officer: Solutions for Enterprise Productivity. http://clomedia.com/articles/view/e_learning_e cosystems_the_future_of_learning_technology 3. Sherstobitova, A.A., Glukhova, L.V., Kaziev, V.M., Palferova, S., Rachenko, T.A., Gudkova, S.A.: Educational ecosystem and government relations based on the company’s intellectual capital. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and E-Learning - Smart Pedagogy. SEEL-22 2022. Smart Innovation, Systems and Technologies, vol. 305. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-3112-3_34 4. Melikyan, A.V.: Cluster analysis of Russian universities based on the dynamics of their performance indicators. Voprosy statistiki. (In Russ.) https://doi.org/10.34023/2313-6383-2021-285-58-68 5. Kaziev, V.M.: Introduction to analysis, synthesis and modeling of systems. M Binom. Knowledge Lab, p. 422. Intuit.ru (2007) 6. Kaziev, V., Kazieva, B., Kaziev, K., Kudaeva, F.: The Self-organizational Potential of SMART University and Its Evolution / SLET-2020, Innovative Approaches to the Application of Digital Technology in Education, pp. 70–79. Stavropol, Russia (2020) 7. Sherstobitova, A., Gudkova, S., Kazieva, B., Kaziev, K., Kaziev, V., Yakusheva, T.: University innovative networking in digital age: Theory and simulation. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and E-Learning-2021: Smart Innovation, Systems and Technologies, vol. 240, pp. 293–299 (2021) 8. Fagadar, C.F., Trip, D.T., Badulescu, D.: Entrepreneurial competencies and higher education institutions: A bibliometric study. J. E-Learn. High. Educ. Article ID 804268 (2021). https:// doi.org/10.5171/2021.804268 9. Machlup, F.: Knowledge, Its Creation, Distribution and Economic Significance, vol. III. Princeton University Press, Princeton, NJ (1984) 10. Strielkowski, W., Guliyeva, A., Rzayeva, U., Korneeva, E., Sherstobitova, A.: Mathematical modeling of intellectual capital and business efficiency of small and medium enterprises. Mathematics (9), 1–21 (2021). https://doi.org/10.3390/math9182305 11. Fadeeva, T.A.: Development of ideas about intellectual capital and its main characteristics. Int. Sci. J. “Symbol of Science” (3), 89–95 (2016) 12. Edalova, E.S., Hanina, A.V.: Peculiarities of investment in intellectual capital. Mod. Sci. Res. Innov. [El. Res.] (4) (2017). http://web.snauka.ru/issues/2017/04/81460 13. Andriessen, D., Stam, C.: The intellectual capital of the European Union 2008: Measuring the Lisbon strategy for growth and jobs. Elec. J. Knowl. Manag. 7(4), 489–500 (2008) 14. Ruus, J., Pike, S., Fernström, L.: Intellectual capital: Management practice. SPb.: Higher School of Management. p. 436 (2010)

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15. Stuart, T.: Intellectual capital. A new source of wealth of organizations. In: Stewart, T. (ed.) Pen with English V.A. Nosdrina. M.: Generation, p. 368 (2007) 16. Sirazetdinov, R.M., Ustinova, L.N.: Application of a method of J. Tobin to a research of the intellectual capital at innovatively active enterprises. Russ. Bus. 16(19), 3285–3294 (2015). https://doi.org/10.18334/rp.16.19.1983

Part IV

Smart Education: Case Studies and Research

Chapter 18

Mathematical Simulation for Quality Management in State System of Higher Education Lyudmila V. Glukhova, Svetlana D. Syrotyuk, Svetlana A. Gudkova, Anna A. Sherstobitova, Sabina Sh. Palferova, and Anton A. Gudkov

Abstract The aim of this paper is to consider management tools for sound planning, control and regulation of students’ competence level based on mathematical simulation methods. Innovative ideas include experimental planning methods applied for minimizing differences of the final results in educational systems in response to uncontrollable factors, while maximizing the possibility of obtaining a guaranteed and targeted result. The proposed model has been tested in the conditions of smart education in the process of learning on the basis of remote technology for distant education “Rosdistant”.

18.1 Introduction The relevance of the topic is justified by the need to form such a level of knowledge and competences of university graduates, which would be relevant and meaningful for future employers. According to PMBOK standard, special attention is paid to the requirements of stakeholders and constantly changing demands of the external environment in order to assess the level of competence formation. Therefore, the authors consider the quality of competences to be the level of professional and other complementary skills in a particular subject area that meets and satisfies stakeholder requirements. The importance of implementing this approach is evidenced in numerous publications by many researchers and experts. Currently, there are various [1] studies dealing with adaptive and smart economy, smart environment and smart universities. Some authors consider a new generation of universities implementing innovative L. V. Glukhova (B) · S. D. Syrotyuk · A. A. Gudkov Volga Region State University of Service, Togliatti, Russia e-mail: [email protected] L. V. Glukhova · S. A. Gudkova · A. A. Sherstobitova · S. Sh. Palferova Togliatti State University, Togliatti, Russia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_19

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approaches based on project management very effectively. The authors of the studies consider the possibilities of PMBOK standards implementation in the process of knowledge bases designing for students and apply agile technologies to manage the process of their accumulation [2]. At the same time, an important indicator of innovativeness and smartness of higher education institutions is organizing the key factors of success and development for new generation educational institutions [3]. Taking into the consideration the fact that the project approach is an important tool for shaping higher education graduates into internal and external labor markets [4], the authors of the research emphasize the need to create a set of assessable graduate competencies of the specific quality level. From the management point of view, these indicators and their quality level should be planned at the initial stage of training process and then monitored in the process of their simulation and development. Therefore, in this paper the authors propose a mathematical apparatus that allows obtaining a dynamic assessment of knowledge and skills improvement, reflecting the quality level of the competences. This justifies the choice of the research topic and its relevance.

18.1.1 Problem Statement The research problem in the presented study can be formulated as follows: insufficiency of methodological basis for assessing the quality level of university graduates’ training for stakeholders’ requirements. In contrast to other studies and approaches, the authors of this research consider the possibility of forecasting quality indicators of competencies being formed and propose tools to manage the process of their formation and diagnose the compliance of achieved values with the planned indicators.

18.1.2 Purpose and Tasks The aim of the study is to simulate a dynamic model for managing the quality of the graduates’ competencies based on a multivariate normal distribution. Research Objectives: 1. To analyze the existing approaches to the possibility of constructing a model of competence development based on process management and quality assessment of the obtained results. 2. To simulate and develop the dynamic model of quality management for competences. 3. To conduct an experiment and assess the dynamics of the obtained results.

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18.1.3 Research Hypothesis The hypothesis of the study is the assumption that a dynamic model for monitoring learning quality indicators will reduce management risks in higher education and increase the sustainability of the knowledge transfer process. The quality management is represented in the study as the management process dealing with the planning for the required level of quality and monitoring processes to ensure the achievement of the planned indicators of the training quality, as well as, preventing, identifying and eliminating the causes that could make the decrease in the achieved level of quality.

18.2 Basic Objective for the Experiment The statement of task for the experiment is as follows: to assess the level of competence formation for a toolmaking manager based on the basic components defined as “6 competence attributes”: (1) “knowledge”; (2) “understanding”; (3) “application”; (4) “analysis”; (5) “synthesis”; (6) “evaluation”. This set of characteristics for formed competences was determined by expert confusion based on the analysis of employers’ requirements of large industrial enterprises. In this paper, the method of expert assessment of the choice of attributes for the competence to be formed was not considered. The assessment of the formed set of attributes was based on the Bloom’s taxonomy [5].

18.2.1 Theoretical and Methodological Basis of the Study The following main positions of this study can be highlighted. 1. Smart universities are rapidly developing all over the world. Their formation is based on the ideas of application of intellectual technologies, mobile devices, digital and computer technologies and Internet facilities. All this makes it possible to apply modern e-learning technologies, which even in the conditions of COVID19 pandemic allowed organizing the learning process at a high level. At present, the higher education system implements the policy of applying PMBOK standard based on the formation of a set of knowledge and skills within each particular specialty [6]. 2. The authors of many works point out that this development has been made possible by globalization, digitalization and informatization, which has allowed the creation of collaborations of many agents of the educational market to

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implement export education projects [7], or for the digital transformation of the business community [8]. Also, many modern methods, such as the automated control system [9], statistical modeling [10] and the robust approach method presented in [11], have made it possible to assess stakeholder requirements [12] and to apply risk management methods in managing the processes of accumulating certain skills and experience, diagnosing the level of competence required, as basic management tools to reduce the uncertainty of external influences. Management tools based on mathematical simulation apparatus such as probabilistic models or system and process approaches are interesting and practically significant, allowing the use of systematization, classification and identification of external users [13] when dealing with diverse information. 3. The competence approach has been used by many Russian and foreign experts. The most significant is the quality characteristic considered in the modern studies [5, 7, 12]. For example, the research paper by L.V. Glukhova and S.D. Syrotyuk applied methods of experiment planning in integration with the robust approach. A complete factor experiment was used to substantiate the performance of the assessed indicators as well as their level of quality [12]. Another research paper by the same authors [13] evaluated the company learning tools for assessing content quality indicators. This toolkit is in demand in the era of transition to distance education. The probabilistic approach is one of the basic ones together with multivariate analysis methods [13] and taxonomy methods [5]. The methods of statistical processing of experimental results are practically significant. For example, in the work of the authors, O.A. Kuznetsova, S. Sh. Palferova and A.A. Sherstobitova [10] demonstrated in practice statistical analysis and synthesis tools for assessing the level of competence formation in higher school graduates. The authors of this study have chosen this particular set of management tools that allow them to achieve the goal of the study.

18.3 Mathematical Simulation Analysis of the possibilities for formalization and mathematical justification of process activities, as well as the personal experience of the authors, suggested that the Hotelling value T 2 could be used. T 2 = N (x − μ)T S −1 (x − μ),

(18.1)

where x—the mean value vector and S—the covariance matrix for the extension sample N.

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According to the economics, the Hotelling model can be used to predict changes in the controllable parameters, depending on different situations. For example, the dynamics of price change given the available supply and demand. The authors will use the findings of (3) in order to be able to assess the dynamics of changes in the competences of graduates (cost), depending on stakeholders’ demands (demand) and the quality of educational services provided by the university (supply).

18.4 Our Results 18.4.1 Quality Management Model for the University Graduates Competence The authors believe that the main attributes or factors of competence can be assessed using Bloom’s taxonomy [5], according to which each competence can be assessed on six attributes: knowledge (A1), understanding (A2), application (A3), analysis (A4), synthesis (A5) and evaluation (A6). Currently, in accordance with the standards of higher education for the bachelors in Russia, the graduate is to have the following competences, namely general cultural (GC), general professional (GP), professional (PR), and professionally applied (PAC) competencies. The authors of the article consider the corresponding set of competencies as an n-dimensional vector. X = (X 1 , X 2 , ...X n ), and get four competence vectors X GC , X G P , X P R , X P AC . According to Bloom’s taxonomy, each component of the competence vector will have six estimated values. Each attribute can be assigned a weight coefficient and a generalized indicator of competence assessment as a component of the corresponding competence vector can be calculated. We introduce conditional notation. Two competencies were evaluated as components of the vector: The ability to evaluate the results of the application of mathematical and natural science knowledge in professional activities, we will conditionally designate it as “PC-2”; the ability to apply fundamental mathematical and natural science knowledge in professional activities, conditionally “PC-3”. Thus, the vector is considered as a two-dimensional vector of competence, the formation and assessment of the level of which was carried out in the study of the discipline “Mathematical Analysis” in the first year of study. Each competency was assessed against Bloom’s taxonomy. The following diagnostic tools were used: achievement tests to assess levels of knowledge and understanding; presentation and defense of the final work on the discipline. When using this diagnostic tool, the levels of application, analysis, synthesis and evaluation were evaluated. The results of competence diagnostics in a group of 25 students are presented in Table 18.1. The table fragmentarily reflects the process of diagnosing a specific competence in the context of six assessed features.

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Table 18.1 Results for professional competencies in % Student number in the group list

Competence

A1

A2

A3

A4

A5

A6

1

PC-2

61

61

58

23

20

17

2

PC-3

63

60

59

25

20

20

3

PC-2

64

60

59

25

23

21

4

PC-3

64

64

60

26

23

20

5

PC-2

53

50

43

18

19

16

6

PC-3

57

52

45

17

19

17

23

…

…

24

PC-2

100

100

99

66

64

61

25

PC-3

96

91

85

44

45

39

To obtain generalized indicators of the level of competencies, weighting coefficients were used: α1 = 0,05, α2 = 0,05, α3 = 0,1, α4 = 0,2, α5 = 0,2, α6 = 0,4 . The results of calculating the generalized indicators for each competence (GIPC) are given in Table 18.2. The analysis of the obtained results showed that the average value for each of the assessed competencies reflects the indicator for the group as a whole. If we evaluate each student, we can conclude that there is a significant variability in the process of formation of such competence as “the ability to evaluate the application of mathematical and natural science knowledge in professional activities”. On average, this competence is formed by 27,3%. The competence as “the ability to apply fundamental mathematical and natural science knowledge in professional activities” was formed by 25,9%. Table 18.2 Generalized indicators of professional competencies in%

Student number in the group list

GIPC-2 (x1 )

GIPC-3 (x2 )

1

23,3

25,05

2

25,5

25,6

3

19,45

20,15

4

22,65

22,85

5

39,95

38,5

6

25,05

24,75

57,5

42,25

… 25

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18.4.2 Experimental Processing of the Research Results The authors determined estimates of mathematical expectations, variance and correlation coefficient, confidence region for the vector of mathematical expectations with reliability γ = 0,95. The level of reliability is chosen by expert way. Vector X PC is determined by such values as μ1 and μ2 . The point estimates for μ1 and μ2 –arithmetic mean value x 1 and x 2 are equal: x1 = x2 =

1 (23, 3 + 25, 5 + ... + 57, 5) = 36, 19 25

1 (25, 05 + 25, 6 + ... + 42, 25) = 33, 62 25

To determine the covariance matrix S of the sample, the authors used centered random variables u i j = xi j − x j : 

 −12, 87 −10, 69 . . . 21, 31 U = −8, 574 −8, 024 . . . 8, 626 ⎛ ⎞ −12, 87 −8, 574   ⎟ −12, 87 −10, 69 . . . 21, 31 ⎜ ⎜ −10, 69 −8, 024 ⎟ = UTU = −8, 574 −8, 024 . . . 8, 626 ⎝ · · · ··· ⎠ T

 =

7667, 28 5035, 168 5035, 168 4640, 30



21, 31

8, 626

The unbiased matrix estimation —covariance matrix and S sample are equal.     1 7667, 28 5035, 168 1 319, 47 209, 80 T U U= = S= 209, 80 193, 35 N −1 24 5035, 168 4640, 30 Thus, the unbiased estimates of variances and standard deviations are as follows: s12 = 319, 47; s1 = 17, 87; s22 = 193, 35; s2 = 13, 91 The authors calculated the sample correlation. ρˆi j =

209, 80 = 0, 84 17, 87 · 13, 91

The inverse matrix to S is the following.

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S

−1

 =

0, 011 −0, 012 −0, 012 0, 018



Using formula (2), the authors found the value of the Hotelling statistics

T = 25 · 36, 19 − μ1 33, 62 − μ2 2





0, 011 −0, 012 −0, 012 0, 018



36, 19 − μ1 33, 62 − μ2

 =

0, 275(36, 19 − μ1 )2 − 0, 6(36, 19 − μ1 )(33, 62 − μ2 ) + 0, 45(33, 62 − μ2 )2 . At the level of significance α = 1 − γ = 0, 05 and a range of discretion ν1 = 2 and ν2 = 23, the authors defined the point F-distribution—F(0, 05; 2; 23) = 3, 42. Then according to formula (5), 0, 275 · (36, 19 − μ1 )2 − 0, 6 · (36, 19 − μ1 ) · (33, 62 − μ2 ) + 0, 45 · (33, 62 − μ2 )2 ≤ 7, 137

The math expression. 0, 275 · (36, 19 − μ1 )2 − 0, 6 · (36, 19 − μ1 ) · (33, 62 − μ2 ) + 0, 45 · (33, 62 − μ2 )2 ≤ 7, 137 (18.4)

defines the confidence region for the vector of means. Since the formation of a twodimensional competence vector X PC depends on the indicators μ1 i μ2, , expression (18.4) is an objective function tending to an extremum (7.137). The further goal of the study is to determine the values of the indicators μ1 and μ2, at which this extremum is reached.

18.4.3 Model of Quality Management of Formed Competences Table 18.3 shows the assessment of the quality of μ1 i μ2 , for which the objective function (4) quantifies the degree of fulfillment of the requirements of the process of organizing a two-dimensional competence vector X PC . Table 18.3 Evaluation of the quality of indicators μ1 and μ2 Indicator

Variable values

Quality assessment

μ1

28, 81 ≤ μ1 ≤ 43, 57

High, as it is in the range of acceptable values

μ2

27, 91 ≤ μ2 ≤ 39, 33

High, as it is in the range of acceptable values

18 Mathematical Simulation for Quality Management in State System … Table 18.4 Quality indicators for the signs of the competencies

Competence features

Limits of variation

Knowledge (X1) 70 ≤ x1 ≤ 85, 9

205

Quality indicator High level

Understanding (X2)

61 ≤ x2 ≤ 69, 6

Well-defined level

Ability to use (X3)

70 ≤ x3 ≤ 85, 9

Well-defined level

Analysis (X4)

61 ≤ x4 ≤ 69, 6

Well-defined level

Synthesis (X5)

40 ≤ x5 ≤ 60, 9

Minimum expected level

Assessment

61 ≤ x6 ≤ 69, 6

Well-defined level

Table 18.4 shows the average values for each of the signs of the formed competencies (fragment). At the same time, the authors propose to evaluate each sign of competence on the following five-point scale: insufficient level (24, 3 ≤ xi ≤ 39, 9); level of understanding (minimum expected) (40 ≤ xi ≤ 60, 9); baseline (well defined) (61 ≤ xi ≤ 69, 9); high level (70 ≤ xi ≤ 85, 9); strong level (excellent) (86 ≤ xi ≤ 100). It should be noted that the assessment of the indicators level was obtained in dynamics over three periods (three semesters of study) using the example of assessing residual knowledge.

18.5 Conclusion 1. The paper described a proposed probabilistic approach in conjunction with the methods of multivariate analysis to assess the formation of indicators of competence according to Bloom: (1) “knowledge”; (2) “understanding”; (3) “application”; (4) “analysis”; (5) “synthesis”; (6) “assessment”. 2. The obtained results made it possible to determine the intervals of variation for the controlled parameters. 3. Experimental processing of the level of knowledge was carried out on the basis of the ideas of Bloom’s taxonomy and a reasonably chosen mathematical apparatus. 4. For the experiment, two groups of students were selected, and the levels of competency indicators were assessed over (in dynamics) three periods. 5. The following control functions were applied: • Goal setting: The goals of the experiment and the conditions for its implementation were described; • Planning: In order to plan the target values of indicators that allow assessing the quality, indicators of confidence intervals were calculated, when falling into which the objective function achieved a given quantitative assessment

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of the degree of fulfillment of the requirements for the process of forming a two-dimensional competence vector; • Control: The function was applied repeatedly; the results were analyzed and corrected; possible changes were studied; • Analysis: The achieved indicators were analyzed, and the obtained values were compared with the planned (required) ones. Next Steps Currently, the study is ongoing, a methodology for applying the described approaches is being developed, and a set of rules is being accumulated that allows experts to carry out the processes of assessing the knowledge level, skills and their development into new qualities.

References 1. Uskov, V.L., Bakken, J., Aluri, L., Rachakonda, R., Rayala, N., Uskova, M.: Smart pedagogy: Innovative teaching and learning strategies in engineering education. In: II IEEE World Engineering Education Conference—EDUNINE 2018., Buenos Aires, IEEE, 11–14 March, 2018. https://ieeexplore.ieee.org/document/8450962 2. Sherstobitova, A.A., Glukhova, L.V., Khozova, E.V., Krayneva, R.K.: Integration of agile methodology and PMBOK standards for educational activities at higher school. In: Uskov, V., Howlett, R., Jain, L. (eds.) Smart Education and e-Learning 2020. Smart Innovation, Systems and Technologies, vol. 188. Springer, Singapore (2020). https://doi.org/10.1007/978-981-155584-8_29 3. Identification of key factors for a development of smart organization. In: Glukhova, L.V., Syrotyuk, S.D., Sherstobitova, A.A., Gudkova, S.A. (eds.) Smart Innovation, Systems and Technologies, vol. 144. pp. 595–607 (2019) 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) 5. Bloom, B.S. (ed.): Taxonomy of Educational Goals: Classification of Educational Goals: Handbook I, the Cognitive Domain. Longman, New York (1956) 6. Gudkova, S.A., Yakusheva, T.S., Vasilieva, E.A., Rachenko, T.A., Korotenkova, E.A.: Concepts of educational collaborations and inovative directions for university development: knowledge export educational programs. In: Smart Innovation, Systems and Technologies, vol. 188, pp. 305–315 (2020) 7. Abdolshah, M., Moradi, M.: Fuzzy quality function deployment: an analytical literature review. J. Ind. Eng. 2013, 1–11 (2013). https://doi.org/10.1155/2013/682532 8. Glukhova, L.V., Syrotyuk, S.D., Gudkova, S.A., Aleksandrov, A.Y.: Model-based analysis for smart university development. In: Smart Innovation, Systems and Technologies, vol. 188, pp. 455–465 (2020) 9. Glukhova, L.V.: Constructing an automatic information system for quality control in the training of engineer managers. In: Proceedings of Higher Education Institutions. Textile Industry Technology2(283), pp. 124–128 (2005) 10. Kuznetsova, O.A., Palferova, S.Sh., Sherstobitova, A.A.: Application of multivariate statistical methods for assessment of educational competencies. In: Smart Innovation, Systems and Technologies, vol. 144, pp. 609–618 (2019) 11. Hua, C., Zhang, L., Guan, X.: Robust Control for Nonlinear Time-Delay Systems, p. 300. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-5131-9 12. Gudkova, S.A., Glukhova, L.V., Syrotyuk, S.D., Krayneva, R.K., Filippova, O.A.: Validating development indicators for smart university: quality function deployment. In: Uskov, V.L.,

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Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning 2021. KES-SEEL 2021. Smart Innovation, Systems and Technologies, vol. 240. Springer, Singapore (2021). https://doi.org/ 10.1007/978-981-16-2834-4_20 13. Sherstobitova, A.A., Kazieva, B.V., Kaziev, V.M., Filippova, O.A., Koroleva, E.I., Glukhova, L.V.: Modeling the adaptive stability and competitiveness of the university during digital changes. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning— Smart Pedagogy. SEEL-22 2022. Smart Innovation, Systems and Technologies, vol. 305. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-3112-3_40

Chapter 19

Quasi-Fractal Logic Usage in Risk Management Models of Smart Systems Natalia A. Serdyukova, Vladimir I. Serdyukov, Olga A. Kusminova, and Svetlana I. Shishkina

Abstract These days, algebra and logic are the key points in main areas of digital transformation research. These areas can be considered as the most important mechanisms in enabling strategic, fundamental advances in digital transformations of almost all spheres, which are provided and connected the social society functioning. One of the most difficult and interesting issues in the field of digital transformation is the study of the emergence of a system from a chaotic state. The paper introduces the concept of a presystem. It presents the following obtained results: (1) description of a metric error on a chaotic presystem, (2) the existence an element in a chaotic closed associative presystem at which any connections are impossible to set, (3) the existence a level in a chaotic closed associative quasi-fractal presystem at which connections are impossible to set, and (4) practical applications of the developed theory to (a) assess risks of smart systems and (b) explain possible causes of forecasting errors in smart systems, in smart university ranking systems, in financial-economic systems.

19.1 Introduction: Background and Research Methodology We start with outlining background and research methodology of algebraic quasifractal systems in investigation of complex smart systems. It runs as follows.

N. A. Serdyukova (B) Plekhanov Russian University of Economics, Moscow, Russia e-mail: [email protected] N. A. Serdyukova · O. A. Kusminova Russian Customs Academy, Moscow, Russia e-mail: [email protected] V. I. Serdyukov · S. I. Shishkina Bauman Moscow State Technical University, Moscow, Russia V. I. Serdyukov Institute of Education Management, Russian Academy of Education, Moscow, Russia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_20

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A concept of a quasi-fractal algebraic system [1], which is based (a) on a concept of an algebraic system, proposed and developed by A.I. Malt’sev [2], (b) on a model of a system in the form of an algebraic system [3], and (c) on a notion of a fractal, created by Benoit Mandelbrot, was introduced in [4], as it was mentioned in [5]. In the same paper, we outlined some stages of research methodology, and we used to investigate the problem of connections between qualitative and quantitative indicators of smart systems functioning. They run as follows. In [3], on the basis of works [6–16], methods of algebraic formalization of smart systems, which make it possible to connect qualitative and quantitative indicators that assess the state, structure, connections, and functioning of smart systems, were proposed. In [1], the concept of a quasi-fractal algebraic system was introduced, and on this basis a methodology that allows to investigate a smart system in more detail, adding new factors to the model of factors determining the system that describe previously indivisible elements of the initial model of factors determining the system, that is an attempt to use zoom, was developed. Works [1, 3] are in the field of digitalization of smart systems. In [17], the digitalization of propositional algebra and NPC was investigated, and on this base, various ways of numerical estimating the truth values of propositional algebra and NPC closed formulas were considered. Ibidem, problem of the information reliability and its decision were given. In [18], we used the theory of algebraic systems and the notion of quasi-fractal algebraic system, [1], to temporal logic. On this base, the expressive properties of temporal logic were investigated. Also, logic in terms of different algebraic systems was considered. That allows us to investigate risk sustainable subsystems of a smart system. In [19], we showed how to apply temporal quasi-fractal logic to decisions of practical problems. We have considered here applications of temporal quasi-fractal logic to risk analysis of smart planning systems and to smart control systems and constructed algorithms for them, enabling to define the time of achievement the goals of planning and control. In [20], we described the course “algebraic methods in digitalization of smart systems” developed by the authors on the basis of works on algebraic formalization and identification of smart systems carried out in the period from 1998 to the present time. Almost simultaneously with the development, fragments of the course were tested at a seminar on financial mathematics at the Academy of Budget and Treasury of the Ministry of Finance of the Russian Federation and were discussed at mathematical seminars at Bauman Moscow State Technical University, Plekhanov Russian University of Economics. In this paper, we continue to investigate the applying of quasi-fractal logic’s mechanisms to digital transformation of economic and educational systems, more precisely to the cases in which the full information about connections of investigated system is unknown. The goal of our research is to formulate some new results based on algebraic quasi-fractal logic and give a scheme of their proofs. Methodology of the theory of algebraic systems, introduced by A.I. Malt’sev, [2], and methodology of the theory of fractals, introduced by Benoit Mandelbrot, [4], are used.

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In the present paper, we shall basically use the modeling of complex systems using quasi-fractal groups of factors that determine the system. Besides it, we shall stay on a practice usage of main results received in this paper: the assessment of smart systems’ risks and explanation forecasting errors in smart university ranking systems.

19.2 Problem Statement Problem 1. The main research problem of this paper concerns the question about the description of chaotic structure. In these directions, the following questions are considered: • Description of a metric error on a chaotic presystem. • The existence of an element in a chaotic closed associative presystem at which any connections are impossible to set. • What connections are impossible to set? Problem 2. The next research problem is the question of application the results received on Problem 1 to practice. We consider two such applications in this paper. Problem 1 is related to risk assessment and, in particular, to risk assessment of smart systems. There are two points of view to chaos description. The approach from the internal side of the system describes chaos as chaos, because it does not see connections between elements of the system. We want to remind here Kurt Gödel’s theorem about incompleteness: “The logical completeness (or incompleteness) of any system of axioms cannot be proved within the framework of this system. To prove or disprove it, additional axioms (strengthening of the system) are required. «So, from the internal point of view, that is from the position of a system we are describing, we get chaos structure as we could not know all connections between the elements of the system. The term “chaos structure” is actively used in scientific literature. Another approach to the description of chaos—from the external side of the system, when all connections that were invisible from inside the system, become visible gives us an opportunity to consider the investigated system as the system. Comparison of these two approaches gives an assessment of risks, moreover, a quantitative one. This approach can be used in economics and finance and in learning theory. Problem 2 concerns forecasting errors, in particular, forecasting errors in smart university ranking system and forecasting errors in economics. We shall use here the technique of modeling complex systems using quasi-fractal groups of factors that determine the system. Usually while modeling the system, the investigator does not know all connections between the factors with the help of which he describes the system. To clear up the stated problem—Problem 2—we use the notions of an amenable group, amenable and paradoxical systems, in order to understand if it is possible to construct measure on the groups of factors with the help of which the investigated system is modeling.

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19.3 Main Results 19.3.1 Main Notions and Definitions According to Prigogine’s works, chaos is characterized by the absence of connections between elements. By a presystem, we mean a closed set of chaotic particles and their interactions. In this regard, the model of a closed set of chaotic particles and their interactions can be a free non-Abelian group Fωγ of a rank ωγ , where ωγ is an ordinal. Let Fωγ be a free non-Abelian group with free generators {ai |i < ωγ }. So, F ωγ is     written in the alphabet ai |i < ωγ ∪ ai |ai−1 i < ωγ ∪{∅}, F ωγ =< Fωγ , ,−1 , e >.   α F ωγ =< Fωγ , ,−1 , e >∝ Also we can consider quasi-fractal model Q Fk 0) or (∀[x]ker ϕ ∈ Fωγ /kerϕ)(∀y ∈ Fωγ /kerϕ)([x]ker ϕ = [y]ker ϕ → d([x]ker ϕ, [y]ker ϕ) > 0) 3. (∀x ∈ Fωγ )(∀y ∈ Fωγ )(d([x]ker ϕ, [y]ker ϕ) = d([y]ker ϕ, [x]ker ϕ)) or (∀x ∈ Fωγ /kerϕ)(∀y ∈ Fωγ /kerϕ)(d([x]ker ϕ, [y]ker ϕ) = d([y]ker ϕ, [x]ker ϕ))      d([x]ker ϕ, z) ≤ d([x]ker ϕ, [y]ker ϕ)  or 4. ∀x ∈ Fωγ ∀y ∈ Fωγ ∀z ∈ Fωγ +d([y]ker ϕ, z)     ∀x ∈ Fωγ /kerϕ ∀y ∈ Fωγ /kerϕ ∀z ∈ Fωγ /kerϕ   d([x]ker ϕ, z) ≤ d([x]ker ϕ, [y]ker ϕ) +d([y]ker ϕ, z) Now we can define a metric error on a chaotic presystem S modeled with the group of factors Fωγ . Definition 3.2. The kernel ker ϕ is called a metric error on a chaotic presystem S modeled with the group of factors Fωγ . Now we need the notion of an amenability introduced by John von Neumann, as it was mentioned [in Kapovich, Drutu, p.396], studied properties of group actions that make paradoxical decompositions possible (like for the action of the group of isometries of R n for n ≥ 3) or, on the contrary forbid them (like for the action of the group of isometries of R 2 ). He defined the notion of amenable group, based on the existence of a mean of a finitely additive measure invariant under the action of the group and equivalent to the nonexistence of paradoxical decompositions for any space on which the group acts. The needed definitions run as follows.  Definition 3.3 [22]. A discrete group G = G, ,−1 , e is called amenable, if it admits a G-invariant probabilistic measure, i.e., such a mapping μ : P(G) → [0, 1] defined on the set P(G) of all subsets ofG, that     1. μ A B = μ(A)+μ(B),∀A, B ⊆ G. Here A B designates a disjoint union that isA ∩ B = ∅. This is a property of a finite additivity. 2. μ(g A) = μ( A), ∀ ∈ G, ∀A ⊆ G. Here g A = {ga|a ∈ A}. This is a property of a G invariance. 3. μ(G) = 1. This is a property of a normalization.

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 Example 3.4 Every finite group G = G, ·,−1 , e is an amenable one. In fact,μ( A) = |A| . |G| Here |A| is the number of elements of A. Example 3.5 NonAbelian free group F 2 of a rank 2 is not an amenable one. For proof, see [22, p.232]. Also, a concept of a dynamic system was considered in [22, p.232]. This concept is in tight connection with a concept of a group. Definition 3.6 A dynamic system is called a triple G, X, a , where G is a group, X is a space, a : G  g → αg ∈ BT (X ) is the action of a group G by bijective transformations of the space, and BT (X ) is the set of bijective transformations of X. The concept of an amenable (paradoxical) metric space is a natural generalization of the concept of an amenable paradoxical group [22, p.238]. Let us remind that a metric space is said to be discrete if for all x ∈ X and for every n ∈ N , the cardinality |B{x, n)| of a ball of radius n and center x is finite. Definition 3.7. Let (X, d) be a discrete metric space. A bounded perturbation of unity is a mapping γ : S ↔ T , where S and T are such subsets of a space (X, d) such that ||γ || = sup x∈S d(x, γ (x)) is a finite number. The sets α(γ ) = S and ω(γ ) = T are called the domain and rank for γ , respectively. Denote by W (X ) = {γ |γ : S ↔ T ; S, T ⊆ X, ||γ || < ∞} the set of all bounded bijective perturbations of the identity of the metric space (X, d). Definition 3.8 A discrete metric space (X, d) is said to be amenable if there exists a W (X )− invariant probability measure on X, i.e., the mapping μ : P(X ) → [0, 1] such that.     1. μ A B = μ(A)+μ(B),∀A, B ⊆ X . Here A B designates a disjoint union, that is, A ∩ B = ∅. This is a property of a finite additivity. 2. μ(∝ (γ )) = μ(ω(γ )) , ∀ γ ∈ W (X ). This is a property of a γ invariance. 3. μ(X ) = 1. This is a property of a normalization. Definition 3.9 A dynamic system G, X, a is called an amenable one if the space X admits α invariant probability measure, that is, such a map μ : P(X ) → [0, 1] that the following conditions take place:    1. μ A B = μ(A) + μ(B),∀A, B ⊆ X. This is a property of a finite additivity. 2. μ ∝g (A) = μ(A) for all g ∈ G, A ⊆ X . This is a property of ∝ invariance. 3. μ(X ) = 1. This is a property of a normalization. If the group G = G, ·, −1 , e is taken as a space X and the action λ is given by left shifts, that is,λg (h) = gh, then the amenability of the group G = G, ·, −1 , e is equivalent to the amenability of the dynamic systemsG, G, λ . The next definition from [22] is the key one for the notion of an amenability.

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Definition 3.10 [22]. The dynamic system G, X, a is said to be paradoxical or equivalently, X admits a paradoxical expansion if there exist such subsets A1 , A2,..., An, B1 , B2,..., Bm, of X that. ⎧       · · · An B1 B2 · · · Bm , ⎨ A1 A2    X= αg1 (A1 ) αg2 (A2 ) · · · αgn (An ),    ⎩ αh 1 (B1 ) αh 2 (B2 ) · · · αh m (Bm ). If the system G, G, λ is a paradoxical one, then the group G = G, •, −1 , e is paradoxical too. A paradoxical dynamic system G, X, a cannot be an amenable one; this is a consequence from Tarski theorem. Theorem 3.11 [22]. Let G, X, a be a dynamic system. Then the following two mutually exclusive cases are possible: 1. G, X, a is an amenable one, and 2. X is a paradoxical space. Now we can formulate the following results. Theorem 3.12. In a chaotic closed associative presystemCh = F, ∗, −1 , e , where F is a countable free non-Abelian group, there exists an element (or a vertex of the Cayley graph of a free non-Abelian group F that we used to model chaotic closed associative presystemCh) at which any connections are impossible to set. Proof The proof is based on the notion of an amenable group, and the fact that non-Abelian free group is not an amenable one. Theorem 3.13. In a chaotic closed associative quasi-fractal presystem C h = −1 , e = A1, , there exists a level at which connections are imposF ∝ n∝ , ∗, −1 −1 , e re F ∝ , e is a countable free non-Abelian sible to set. Here F ∝ n ∝ , ∗, n∝ , ∗, group,∝< ωγ . Proof The proof is based on the notion of an amenable group, and the fact that non-Abelian free group is not an amenable one. In [17], we defined a digitalization function μ with the help of which one can get a numerical estimation of closed formulas of narrow predicate calculus (NPC). Besides it, the digitalization function is a straight generalization of a notion of a probability measure, so we get. Theorem 3.14. There does not exist a digitalization function μ on a model in the form of a group of a chaotic closed system. That is, it is impossible to construct numerical assessments of statements which are true in the model in the form of a group of a chaotic closed system. Proof The proof is based on the notion of an amenable group, and the fact that non-Abelian free group is not an amenable one. Besides it, the digitalization function μ, is a straight generalization of a probability measure [17].

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Theorem 3.15. Let a system S be modeled by a group of factorsG S = G S , •, −1 , e , and {a, b, ai , |i ∈ I } be a set of generators of the group G S in such a way that there are no connections betweena, b. Then, 1. S contains a chaotic pre-subsystem S1 modeled by a free non-Abelian group G S1 = G S1 , •, −1 , e with free generatorsa, b. 2. There does not exist a probabilistic measure on a system S if S is modeled by a group of factors G S = G S , •, −1 , e . Proof The theorem is the consequence of Theorems 3.12 and 3.13

19.3.2 Practice Application Now we shall show how the main results from Sect. 3 can be used in practice.

19.3.2.1

Risk Assessment of Smart Systems

Issues related to risk assessment and, in particular, to risk assessment of smart systems are in deep connections with (a) chaotic structures and (b) risks. As to concern chaos, its description depends on a describer. The internal approach from the side of the system describes chaos as chaos, because it does not see connections, and considers situation as chaos. We get this if the describer is within the system. An external approach to the description of chaos—from the side out of the system, from which all connections that are invisible from inside the system can be seen. Comparison of these two approaches gives an assessment of risks, moreover, a quantitative one. This approach can be used in economics and finance and in learning theory.

19.3.2.2

Forecasting Errors in Smart University Ranking Systems

The above-mentioned Theorems 3.12 and 3.13 have the following applications. In the ranking systems of smart universities, in financial and economic smart systems, probabilistic models are often used in which there is no connection between at least one pair of factors determining the model. In the case of closedness and associativity of the model, this means the presence of a free non-Abelian group in the group of factors that model the system under study. Since a free non-Abelian group of rank 2 is not amenable, the methods of probability theory are inapplicable in this case, that is, their use leads to errors in forecasting.

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19.4 Conclusions: Future Steps We have considered the following two main problems in this paper: (1) a description of chaotic structure and (2) applications of Problem 1 solutions to practice. The following results have been obtained as solution of Problem 1: • A description of a metric error on a chaotic presystem (see Definition 3.2 above). • An existence an element in a chaotic closed associative system Ch at which any connections are impossible to set (see Theorem 3.12 above). • An existence of a level in a chaotic closed associative quasi-fractal presystem −1 C h = F ∝ , e = A1, at which connections are impossible to set (se n∝ , ∗, Theorem 3.13 above). We considered the following two applications in this paper: (1) the first one deals with risk assessment of smart systems, and (2) the second one concerns forecasting errors, in particular, forecasting errors in smart university ranking system and forecasting errors in economics. The next research problem is the existence of the giant component in a chaotic presystem. Erd˝os–Rényi’s algorithm is inapplicable to free non-Abelian groups, quasi-fractal free non-Abelian groups, to groups containing a free non-Abelian group of rank 2, to quasi-fractal groups containing a free non-Abelian group of rank 2, since these constructions are non-amenable and it is impossible to construct a probability measure on them. Thus, it turns out that there is no giant component in chaotic presystem [1, 23].

References 1. Serdyukova, N.A., Serdyukov, V.I.: Algebraic Identification of Smart Systems. Theory and Practice, Intelligent Systems Reference Library, vol. 191. Springer, Switzerland (2021) 2. Malt’sev, A.I: Algebraic Systems. Nauka, Moscow (1970). (In Russian) 3. Serdyukova, N.A., Serdyukov, V.I.: Algebraic Formalization of Smart Systems. Theory and Practice, Smart Innovation, Systems and Technologies, vol. 91. Springer, Switzerland (2018) 4. Mandelbrot, B.: The Fractal Geometry of Nature. San Francisco: W.H. Freeman. ISBN 978– 0–7167–1186–5 (1983) 5. Serdyukova, N.A., Serdyukov, V.I., Shishkina, S.I.: Mechanisms of Digital Transformation: Algebraic Quasi-fractal Logic, IV International Scientific Forum on Computer and Energy Science (WFCES II 2022). https://youtu.be/Tr25-qUP6aU 6. Shafarevich, I.R.: Basic Concepts of Algebra. Izhevsk Republican Printing House, Izhevsk (1999). (In Russian) 7. Karpenko, A.C.: Logic at the turn of the millennium. Log Investig 7, 7–60 (2000). (In Russian) 8. Mesarovich, M., Takahara, Y.: General System Theory: Mathematical Foundations. Academic Press, New York (1975) 9. Gilbert, D., Ackerman, V.: Fundamentals of Theoretical Logic. State Publishing, House of Foreign Literature, Moscow (1947). (In Russian) 10. Gabbay, D.M.: Labelled Deductive Systems, vol. 1. Clarendon Press, Oxford (1996) 11. Gabbay, D.M.: What Is a Logical System? Clarendon Press, Oxford (1994)

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12. Wansing, H.: Displaying Modal Logic. Kluwer Academic Publishers, Dordrecht (1998) 13. Vereshchagin, N.K., Shen, A.V.: Languages and Calculus, Lectures on Mathematical Logic and Theory of Algorithms. Part 2. Language and Calculus. 4th ed. (Rev). MCNMO, Moscow (2012). (In Russian) 14. Gentzen, G.: Investigations of Logical Inferences, the Mathematical Theory of Logical Inference. Moscow, pp. 9–74 (1967). (In Russian) 15. Whitehead, A., Russell, B.: Principia Mathematica. Cambridge University Press, England (1910–1913) 16. Halmos, P., Givant, S.: Logic as Algebra, Dolciani Mathematical Expositions, No. 21. The Mathematical Association of America, Washington (1998) 17. Serdyukova, N.A., Serdyukov, V.I.: Digitalization of Propositional Algebra and NPC, Procedia Computer Science 00 (2019) 18. Serdyukova, N.A., Serdyukov, V.I.: Mechanisms of temporal Quasi—Fractal logic in smart systems. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning— Smart Pedagogy, vol. 305, pp. 456–464 (2022) 19. Serdyukova, N.A., Serdyukov, V.I., Kusminova, O.A., Kusnetsov, A.N., Shishkina, S.I.: Temporal logic usage in control and planning models of smart systems. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning—Smart Pedagogy, vol. 305, pp. 427–435 (2022) 20. Serdyukova, N.A., Serdyukov, V.I., Shishkina, S.I.: Innovative “algebraic methods in digitalization of smart systems” course. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning, vol. 240, pp. 305–313 (2021) 21. Verbitsky, M.: Amenable Groups, Lecture 1, Summer Mathematical School “Algebra and Geometry”, August 1–7, Yaroslavl, Russia. 22. Ceccherini-Zilberstein, T.G.: Around the Amenability, Itogi nauki i tekhniki. Ser. Modern mat. and her app. Subject. review, vol. 69, pp. 229–259 (1999) 23. Serdyukova, N.A., Serdyukov, V.I.: Algebraic Quasi—fractal Logic, preprint (2022)

Chapter 20

Research and Simulation of Cryptocurrency Market as an Innovative Use Case for Emerging Smart Education Anna A. Sherstobitova, Bella V. Kazieva, Olga A. Kusnetsova, and Tatiana V. Polteva

Abstract The purpose of this work is to study the processes in the cryptocurrency market from the point of into account uncertainty, diversity, and ensuring interoperability. Research hypotheses: uncertainty, “white noise” in the cryptocurrency market is reduced by adaptive diversity regulation. Methods of system analysis and modeling, differential equations, random processes, etc., were used. The main result of the article is a study of mathematical models of cryptocurrency market diversity taking into account volatility and their purchasing power. Examples, conclusions, and approaches to model identification are given. In practice, you can use it, for example, to test payment systems.

20.1 Introduction In the cryptocurrency market, there is variability, many different cryptocurrencies. This leads to uncertainties that prevent transactions from increasing and reduce information and financial entropy. The evolutionary potential of the market is determined by the diversity and dynamics of cryptocurrency interactions with the environment, stability, and competitiveness. As a result of innovations, trade in the virtual foreign exchange market in the world and in Russia increased. Consumers, large business enterprises, and financial institutions are now more actively involved in the exchange of cryptocurrencies, payments, and ICOs (initial coin offer, investment portfolio). A. A. Sherstobitova (B) · O. A. Kusnetsova · T. V. Polteva Togliatti State University, Togliatti, Russia e-mail: [email protected] B. V. Kazieva Kabardino-Balkarian State University Named After H.M. Berbekov, Nalchik, Russia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_21

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The main systemic hypotheses of our studies are 1. blockchain stimulates self-improvement of the market; 2. medium-term legal, technological, and social changes can influence the dynamics of market processes; 3. diversity and uncertainty in the market (“white noise”) are reduced with adaptive control. Analysts consider it best to launch an investment portfolio to buy new cryptocurrencies (FLSK, VET, HT, etc.). Investors are waiting for their sharp jump in 2023, an uptrend. Many tokens claim to be another BTC in long-term investing.

20.2 Literary Review Analysis of emerging trends in higher education in 2022–2023 [1] showed that currently the integration of digital technology is a must-have in 2023 and in subsequent years to optimize the hybrid learning environment, and it becomes part of the university of the future. An analysis of multiple scientific sources showed that the main technological trends in higher education include artificial intelligence (AI), virtual reality (VR), augmented reality (AR), digital twins, Metaverse (including digital avatars and NFT art for use in Metaverse and other Web3-based virtual environments), Internet of Things (IoT), blockchain, cloud, gamification, and chatbots. These technologies will enhance the digital transformation of higher education in the future. In this study, the authors are focusing on blockchain technology. The cryptocurrency market is incomparable with the traditional financial market in terms of liquidity, for example, the total amount of money in circulation and in accounts. But its testing is actively ongoing, and the blockchain methodology is developing [1, 2], being introduced in many industries, for example, education [3–5] and policy [6]. Analysts and researchers (see, for example, [7] predict by 2022 an investment in blockchain in the amount of $12.4 billion). Financiers model financial processes in markets statistically, on collections of comparable objects (quantities) with relatively simple and formalized interactions. Developmental models have also been studied, for example, the Hurst model [8]. Unlike similar models e.g., [9]), we consider models that allow us to take into account the diversity and probability of destabilization of financial markets, fluctuations, oscillatory modes—generators of cryptocurrency diversity growth. This is important for practical (financial) applications. Theoretically, blockchain allows you to solve problems of information asymmetry and authenticity of transactions. All transactions will be algorithmically verified. The blockchain methodology provides data processing protection and access for each verified customer in the system [10]. The purchasing power of cryptocurrencies depends on the readiness of buyersellers using the currency and the strength of the response of states to the requirements of the modern market. In Russia, it is planned to introduce a “digital ruble” with great

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stability, security, and, possibly, as part of a hybrid model. Along with the traditional (fiduciary) ruble, the digital ruble will become a new means of payment issued by the Central Bank of the Russian Federation. It will supplement the payment capabilities of Russia. The work simulates crypto-financial processes using statistical sets (ensembles) and simple formalized relationships. The interspecific principle of species coexistence in the natural ecosystem is used. Trends, gradients (pace) of processes, and adaptability of the cryptocurrency market to fluctuations in the market, for example, spatial or temporal, are taken into account. Previously introduced and investigated by one of the authors for various systems, the class of equations with a saturation effect is applied to the simulation of the cryptocurrency market. The species diversity of the cryptocurrency market—as in biological communities [8]—is influenced by key processes: competition; absorption; variability; rate (gradients) and trends in processes, etc. Factors are also random, for example, rumors and “impulses”. With a protracted and sluggish global economic crisis, it is necessary to effectively track finance, investments, indicators (indexes), technologies, and risk situations, including in the cryptocurrency market. Many countries, such as Japan, have long progressed in digital assets and on exchanges. They have strengthened their cryptocurrency positions and controlled about half of the world cryptocurrency trade. Despite Japan’s positive example, most other countries are reluctant to support digital asset market rules. Some do not believe that the market is large or important enough to worry about it; others are concerned about money laundering and the risks of fraudulent and even terrorist use of assets. In countries with complex exchange rates, regulators are tired of introducing a new regulatory framework for crypto-transactions. Based on the analysis of the conclusions and data obtained from some modern studies [11–14], the authors of the article have applied methods of mathematical simulation to study the dynamics of the cryptocurrency market.

20.3 Results 20.3.1 Blockchain and the Evolution of the Cryptocurrency Market The development of the digital economy intensifies digital assets in many countries, although crypto-investors sometimes lose large. For example, in 2014, one of the world’s largest bitcoin exchanges (MtGox) is filed for bankruptcy after stealing more than $460 million in bitcoins. Investors are losing so much money that the emerging digital asset market is at risk. Many see this as a sign that their trade should be prohibited.

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Thanks to innovations that constantly improve the user interface and blockchain access services, the evolution in the redistribution of wealth will develop and gain momentum every day. Blockchain economics is a new approach to distributed decentralized management, a class of secure technologies. This is the foundation of a decentralized autonomous organization (DAO) that facilitates the application resistant to intervention, transactions, and procedures. Lack of regulation is often encouraged by the digital asset market itself. For example, bitcoin was a counteraction to the 2008 financial crisis. Regulation makes this tool part of a system that they are trying to “overthrow”. When there are no rules, certain groups can benefit from deterministic chaos. Cryptotrading believes in working with regulators within the existing financial system and uses a combination of blockchain and asset to make international payments faster, cheaper, and more efficient. Such regulation helps in the development of innovative solutions and protects investors and consumers who use these services. For example, Ripple uses Ledger and XRP, has long advocated a three-way approach to managing specific cases, and aimed at eliminating consumer risks and advising banks on the use of digital assets. A progressive and thoughtful approach can work effectively. Blockchain liberalization leads to the democratization of the entire financial system and vice versa. This creates favorable conditions for the transition from electronic (remote) transactions, payments to digital blockchain transactions based on a distributed system for storing transaction data and the currencies themselves, and “parallel transactions” of the token-transaction type. Actors in the peer-to-peer network connect tokens through blockchain technology, and the result is formed—a chain of payments. Modeling relationships in the cryptocurrency market is an evolutionary direction. With it, you can activate blockchain relationships, chains at available rendezvous points with the user. You can activate smart contracts, which protect against “highperformance” exchange speculation with a simulated, programmable protection strategy.

20.3.2 Cryptocurrency Market Species Diversity Model The adaptation of cryptocurrency (ICO) in the blockchain is systemically demanding to the protective capabilities of altcoins. Therefore, the adaptation mechanism of the cryptocurrency market is complicated by fluctuations in the market, in the environment, causing structural changes. Spatial heterogeneity of transactions also affects. Fluctuations in market parameters of an external nature, an increase in their frequency or amplitude, are especially “influential”. If the market initially sets the initial demand vectors for cryptocurrencies

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s0 = (s10 , s20 , . . . , sk0 ) and cryptocurrency rates (capitalization) x0 = (x10 , x20 , . . . , xn0 ), then over the period of time (0,T ) a state characterized by vectors s1 = (s11 , s21 , . . . , sk1 ), x1 = (x11 , x21 , . . . , xn1 ) is achieved. A certain share of cryptocurrencies from the market is withdrawn (cashing, loss of wallets, purchase of goods, etc.) and the vector changes to x2 = (x12 , x22 , . . . , xn2 ). The cycle repeats again. Then we can describe the evolutionary process by the system: 



xi (t) = ai (t)xi (t), s (t) = −a1 (t)x1 (t) − a2 (t)x2 (t) − . . . − an (t)xn (t), t ∈ [0, T1 ], T1 < Tk = T, (20.1) 

Here i = 1, 2, . . . , n, ai (t)—the specific rate of increase of the i-th cryptocurrency and t—the process time. We do not take into account the cost of acquisition-consumption, we consider the same. Integrating from 0 to T1 the first equation of the system, we get 

T1

xi (T1 ) = xi0 ex p

 ai (t)dt .

(20.2)

0

For s(t), we have 



T1

s (t) = −a1 x10 ex p





T1

a1 (z)dz − . . . − an xn0 ex p

0

 an (z)dz .

(20.3)

0

If denoted 

t

Ai (t) = xi0 ex p 0

then

 ai (z)dz ,

(20.4)

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s (t) = −

n 

ai Ai (T1 ).

(20.5)

i=1

From here we find a function in the interval [0; T1 ] s(t) = −

n 



t

L i (T1 )

ai (z)dz + s0 .

(20.6)

0

i=1

For the second stage [T1 ; T2 ], similarly, we obtain 

T2

xi (T2 ) = xi (T1 )ex p

 ai (t)dt

(20.7)

T1

or, considering xi (T1 ),  xi (T2 ) = xi0 ex p

T2

 ai (t)dt .

(20.8)

0

Continuing iterations, we get  xi (T ) = xi (Tk ) = xi0 ex p

Tk

 ai (t)dt ,

(20.9)

0

The transition to a discrete–continuous model was carried out in order to be able to adaptively take into account the dynamics of each cryptocurrency (changes in the functions ai (t), i = 1, 2, . . . , n after the next iteration). Diversity on the exchange provides the potential for diversification of the cryptocurrency virtual portfolio and the feasibility of investment strategies with low risks. As a measure of diversity, you can take an information and entropy measure [9], because the information received is a measure of uncertainty, for example, diversifying the portfolio. This measure can be measured (scale diversity) with Shannon–Weaver, Margalef, Simpson, etc.

20.3.3 Cryptocurrency Market Activity Model We set cryptocurrency sampling with vector x(t) = {x1 (t), x2 (t), . . . , xn (t)}, t ∈ [0; T ]—time point, xi (t), i = 1, 2, . . . , n—“phase” variable, for example, x1 (t)—currency exchange rate, x2 (t)—its capitalization, x3 (t)—volatility, etc. The attention of traders at time t to “traded” cryptocurrencies y(x, t) depends on their demand, competitiveness, and market volatility. We submit this market evolution to the Fokker–Planck–Kolmogorov equation, which can be solved using the Monte

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Carlo method [10]: yt =

n n  

n 

(ai j y)xi x j −

i=1 j=1

(bi y)xi + dz − cy,

(20.10)

i=1

where ai j , bi , d, c are the coefficients (exchange, demolition, emission, and mining) determined by the activity of the cryptocurrency and the market, z(x, t) is the mining (internal emission) function of the cryptocurrency. Consider the model situation for the identification task 0 < t < 1, ai j = 0, z = 0, d = 0,

(20.11)

and bi coefficients are given, considering the possibility of their approximation, with any degree of accuracy in the form 

 bt 2 bi = 1 − exp 2 , t −1

(20.12)

where b is the average value; in more mathematically justified dependence—in the form of [11]

exp

bi = 1 −

bυ 2 (t) υ 2 (t)−1

0,

 , υ(t) < 1; υ(t) ≥ 1.

(20.13)

You can connect equations for type parameters to the Fokker–Planck–Kolmogorov system (equation): 

xi (t) = bi (x, t) + ξi ,

(20.14)

where ξi is the “white noise” introduced by the factor xi , i = 1, 2, . . . n. Evolutionary economics and evolutionary business require evolutionary modeling of business processes and situations. But there is a barrier to the relevant study of situations and systems—stochasticity, fluctuation, and complexity. In the economic physics of processes, the carriers of these qualities may be the value of shares [12], stock indices, currency (cryptocurrency) rates, etc.

20.3.4 Cryptocurrency Purchasing Power Model Cryptocurrency market mechanisms, processes, algorithms, and models are different from traditional ones. For example, for traditional currency, the course is defined by a ratio of price levels in the countries

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k(t) =

p(t) , p0 (t)

(20.15)

where p(t), p0 (t) are price levels in the country and abroad in time point of t. In the cryptocurrency market, there is no country “responsible” for inflation (both internal and external) and for issue. We will estimate a course regression dependence. The function k(t), in our opinion, is possible to take, for example [13], as function k(t) =

1

1+e

 − a+ pp(t) b (t)

(20.16)

0

or 

p(t) k(t) = a p0 (t)

b ,

(20.17)

where a, b are parameters identified by the maximum likelihood method. Probability of success of a result for a variable of y is equal p((x = 1)|t ) = k(t),

(20.18)

p((x = 0)t) = 1 − k(t).

(20.19)

If samples are independent, then logarithmic function of credibility can be written down L((a, b)|x) =

n 

(xi ln(k(ti ) + (1 − xi ) ln (1 − k(ti ))).

(20.20)

i=1

The normal system for a, b has the form ⎧ ⎪ ⎪ ⎨

n 

(1 − ti )ea+bti =

i=1

n  ⎪ ⎪ ⎩ ti (1 − xi )ea+bti = i=1

n  i=1 n 

ti , ti xi .

(20.21)

i=1

Values a, b are as the solution of this system. At t = 1, we get a chance k(1) = ea+b . 1 − k(1) At t = 0, the chance is equal

(20.22)

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k(0) = ea . 1 − k(0)

229

(20.23)

Therefore, the relation of chances is equal to eb . As for purchase power of currency, it in traditional option is defined as k(t) = k(0)

I (t) , I0 (t)

(20.24)

where I (T ), I 0 (t), respectively, are the price indexes within the country and abroad. If M(t), M0 (t), respectively, are national and foreign money supply, then k(t) =

M0 (0)K (0) M(t) . M(0) M0 (t)

(20.25)

A cryptocurrency economy may use the equation of exchange P X = V M,

(20.26)

where X—the volume of goods supply in national economy (the prices are invariable) and V —the rate of the money. Considering that P0 X 0 = V0 M0 ,

(20.27)

we receive the main equality of parity of purchase power: V − X = V0 − X 0 .

(20.28)

Entering μ0 —cryptocurrency mining volume, the ratio for k(t) can be written down in a look: 1 dM 1 dk = − μ0 . k dt M dt

(20.29)

Practically such situation is improbable: It does not consider “white noise”. Build no equilibrium models, for example: 1 dp 1 dk = − μ0 . k dt p dt

(20.30)

Also, other hypotheses relatively μ0 are possible. In particular, the logistic dependence can appear more pragmatically. In practice, the trajectory of behavior of a cryptocurrency course has to be reflected by various basic dependences in various sites of a trajectory.

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Each cryptocurrency has its own evolutionary potential and its own activation of blockchain relations. Cryptocurrency market changes investment process, trying to reduce uncertainties.

20.4 Discussion Let us give some situational examples and test experiments for the models developed above. Example 1. When considering the hypothetical mono-currency market (e.g., bitcoin), you can record the balance ratio (31): x(t) + s(t) = x0 + s0 .

(20.31)

This is a test example, theoretical and hypothetical. Experiment 1. If you set the hypothetical demand for bitcoin on the 24.11.2021 on the crypto market in the amount of 50,000 tokens, then using the graph (Fig. 20.1) and the bitcoin rate on the 17.12.2021, you can predict at the end of the year the demand for bitcoin at the level of 45,951 tokens. This is also a hypothetical (test) value. The consideration of “life” examples and experiments does not present mathematical difficulties. The difficulty is only in targeted monitoring of market data. Example 2. In the case of one factor.

Fig. 20.1 Bitcoin exchange rate schedule ($) in October–November 2021

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x1 = x, b1 = b, ξ1 = ξ,

(20.32)

we get 

t

x(t) = x0 + (1 + ξ )t − 0



 bz 2 exp 2 dz. z −1

(20.33)

So, the process in the simplest, hypothetical, mono-cryptocurrency (e.g., “bitcoin”) market dynamically develops according to the laws 

 bv 2 exp 2 x(t) = x0 + (1 + ξ )t − dv v −1 0 ⎛ ⎞ −c(1 + ξ − I )t (t)

2  ⎠, y(t) = y0 ex p ⎝ ξ + 1 − 2 exp t 2bt−1    t bv 2 exp 2 I (t) = dv. v −1 0 

t

(20.34)

(20.35)

(20.36)

Experiment 2. Obviously, at time t, the condition for random noise must be met 

bt 2 ξ = 2 exp 2 t −1

 − 1.

(20.37)

At a course bitcoin on 17.12.2021 of equal 42,907 dollars, we will receive for December 31 the expected course in 43,962 dollars. If we consider the Poisson process in the market, μ is an indicator of the waiting time for a purchase sale, λ is the density of transactions, then the probability of preferring this token at the next trading moment is determined similarly [13]: P0 =

 n  k=0

m  m! m! αk + αk k−n k!(m − k)! n n!(m − k)! k=n+1

−1 ,

(20.38)

where m is the number of activated cryptocurrencies, n is the power of the considered variety of tokens, and α = λ/μ the normalization condition is fulfilled (the sum of all pk is 1).

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20.5 Conclusions 1. The conducted study of technological trends in the system of higher education for 2022–2023 showed that one of the technologies that require careful development in the educational process is the blockchain technology. The study’s analysis revealed that blockchain technology provides secure storage for student records and other important documentation. Blockchain will remain a trend in EdTech in 2023. It is expected to grow 85.9% from 2022 to 2030. 2. The evolution of economic relations requires digital interactions aimed at adapting to a species-diverse environment. It is important to have an up-todate understanding of the state of the cryptocurrency market, its trends, and sources of growth. Without mathematical and information-logical forecasting of processes on the cryptocurrency market, adaptability and stability are impossible. Comprehensive assessment and situational modeling of such processes will reveal risk points and zones, as well as situational, competitive behavior (prices, rates, portfolios, etc.). 3. The results obtained in the paper are useful both for theoretical aspects of the research (volatility, stability, etc.) and for practical applications including analysis of cyclicality, risks, situations, etc. The theory of cryptocurrency relations and transactions is still little studied, and this issue is to be considered and developed. Therefore, the models presented in the authors’ work with all their simplifications (and possibly complexities) can be effectively used to test various processes including capitalization, purchasing power, financial “bubbles”, etc. 4. The application of blockchain technology to the education field is in its infancy. Therefore, an analysis of the state-of-the-art blockchain research in the field of education was conducted. Trends In the field of education, the blockchain technology: 1. could be effectively used for the following tasks: issue and verify academic certificates, share students’ competencies and learning achievements, and evaluate their professional ability. However, a wide range of other applications are emerging rapidly; 2. could bring significant benefits to education including providing a secure platform to share students’ data, lowering cost, and enhancing trust and transparency; 3. is not without challenges; managers and policymakers should consider challenges related to security, privacy, cost, scalability, and availability before adopting this technology. Finally, the educational areas, in which blockchain technology was applied so far, are still limited. Therefore, the potential for blockchain technology is still unexploited. In the area of cryptocurrency market development, it can include classification of cryptocurrencies, development of cryptocurrency derivatives, and smart contracts.

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Service crypto-assets that are exchanged for other products (services), such as cryptocurrencies with floating market capitalization, will be developed.

References 1. Technology Trends in Higher Education: https://www.fierceeducation.com/technology/techno logy-trends-higher-education-2023 (2023) 2. Chen, G., Xu, B., Lu, M., Chen, N.-S.: Exploring blockchain technology and its potential applications for education. Smart Learn. Environ. 5(1) (2018) 3. Tapscott, D., Tapscott, A.: Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World, p. 324. Penguin, New York (2016) 4. Sherstobitova, A.A., Iskoskov, M.O., Kaziev, V.M., Selivanova, M.A., Korneeva, E.N.: University financial sustainability assessment models. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Innovation, Systems and Technologies, vol. 188, pp. 467–477. Springer (2020). https:// doi.org/10.1007/978-981-15-5584-8 5. Sherstobitova, A.A., Kaziev, V.M., Krayneva, R.K., Gudkova, S.A., Filippova, O.A., Gudkov, A.A.: Blockchain methodology for smart academic environment in Russia. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning-2021, Smart Innovation, Systems and Technologies, vol. 240, pp. 429–440 (2021). https://doi.org/10.1007/978-98116-2834-4 6. Park, S., Specter, M., Narula, N., Rivest, R.: Going from Bad to Worse: From Internet Voting to Blockchain Voting, No.6 (DRAFT) (2020). https://people.csail.mit.edu/rivest/pubs/PSNR20. pdf (Accessed on 29 November 2021) 7. Ertz, M., Boily, E.: The rise of the digital economy: Thoughts on blockchain technology and cryptocurrencies for the collaborative economy. Int. J. Innov. Stud. 3(4), 84–93 (2019). https:/ /doi.org/10.1016/j.ijis.2019.12.002 8. Mikhailov, A. Yu.: Cryptocurrency market development: Hearst method. Finance: Theory and practice 24(3), 81–91 (2020). https://doi.org/10.26794/2587-5671-2020-24-3-81-91 9. Alammary, A., Alhazmi, S., Almasri, M., Gillani, S.: Blockchain-based applications in education: a systematic review. Appl. Sci. 9, 2400 (2019). https://doi.org/10.3390/app9122400 10. Bandara, I.B., Ioras, F., Arraiza, M.P.: The emerging trend of blockchain for validating degree apprenticeship certification in cybersecurity education. In: Proceedings of the 12th Annual International Technology, Education and Development Conference, pp. 7677–7683. Valencia, Spain, 5–7 March 2018 11. Kiryakin, K.S.: The Monte Carlo method for solving the Fokker-Planck-Kolmogorov equation. Collection of Scientific Works of NSTU 3(49), 41–46 (2007) 12. Kaziev, V.M., Glukhova, L.V., Kazieva, B.V., Kaziev, K.V., Sherstobitova, A.A.: Digital transformations and evolutionary diversity of the business community. Bulletin of Volzhsky University named after V.N. Tatishchev, vol. 1(2), pp. 45–52 (2021) 13. Balatsky, E.V.: Factors of exchange rate formation: Pluralism of models, theories and concepts. World Economy and International Relations 1, 46–58 (2003) 14. Kaziev, V.M., Kazieva, B.V.: Modeling of the cryptocurrency market in the context of growing diversity and uncertainty on it/Modern problems of applied mathematics, computer science and mechanics. In: International Scientific Conference, vol. 1, pp. 38–39. Publishing House of KBSU, Nalchik (2022)

Chapter 21

Literary Tourism: A Case Study ˇ Miloslava Cerná , Anna Borkovcová , and Petra Poulová

Abstract Background: Mastering academic writing skills should be a matter of course for university students. Smart technologies are ubiquitous. Research assistants, grammar and plagiarism checkers, and citation generators have found their rightful place in academic writing. However, for many undergraduate students, the bachelor’s thesis is their first major scientific work written in the final year of their bachelor’s studies. We present a technique for taking students through the first steps in science on the example of literary tourism topic, which is a trendy heritage issue but neglected topic in the Management of Tourism curriculum. Methods: The case study is based on a mixed method approach. A brief survey, thematic analysis, review, heuristic method, a follow-up discussion, and presentation are employed. Results: Students work within the blended learning frame. They work out their research in progress, present and justify their work. Students’ outputs are visualized and placed into the chapter of the e-course accompanying the guide activity in English language subject. Conclusion: The smart approach utilizing technology enhance learning helps to increase awareness and access to science in students of not only in bachelor’s study programs but is useful in students of doctoral studies who face eternal difficulties in mastering research methods.

21.1 Introduction Mastering academic writing skills should be a matter of course for university students. Smart technologies are ubiquitous. Research assistants, grammar and plagiarism checkers, and citation generators have found their rightful place in academic writing. However, for many undergraduate students, the bachelor’s thesis is their first major scientific work written in the final year of their bachelor’s studies. Students of the ˇ M. Cerná (B) · A. Borkovcová · P. Poulová University of Hradec Králové, Hradec Králové, Czech Republic e-mail: [email protected] A. Borkovcová e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_22

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Management of Tourism bachelor’s study program, University of Hradec Králové can enroll in the methodological seminar that is an e-course located in the university learning management system (LMS) Blackboard https://oliva.uhk.cz/. This course was designed for all university students; moreover, students can also enroll into the elective, one-semester academic writing e-course [1] on the same LMS platform. The aim of the methodological seminar e-course is to provide students with study material on how to write an academic text, e.g., seminar work and final bachelor’s or master’s thesis, including how to cite and comply with the formal requirements of the work and research methodology. The other core study area in the content of the e-course is work with information, where to search, how to evaluate and process information, and consequently, how to present findings. The e-course is of a high standard in terms of content, but students lack motivation and drive for practical training. There is the issue of a decreasing enrollment in the management of tourism bachelor’s degree, as well as the long-term trend of the lowering level of study competences in students entering this study field [2]. Universities in the Czech Republic offer bachelor’s, master, and follow-up doctoral study programs. The largest share reaching three-fifths of all university students in 2020, was made up of bachelor’s students and the smallest share just about 7% represented students of doctoral programs. On a nation-wide scale, the decline in university enrollment is a serious issue that affects the entire tertiary level of education. The overall number of university students at Czech universities decreased by a quarter between 2010 and 2020. On the other hand, the number of international students grew by a third [3]. There are two critical factors that contributed to the problematic situation. A significant setback is the general poor state of the tourism sector brought on by the COVID-19 pandemic. A disincentive is the absence of a master’s degree after earning a bachelor’s degree in the management of tourism field. In addition to the objectively difficult situation, there is a perspective of students’ language competence, especially the problem with the students’ writing skills. Students believe that writing skills are the least important of all of language skills, according to the results of prior research [4]. Students do not find development of writing skills important because there is smart technology; there are writing assistants like Grammarly or QuillBot and neural machine translators that can do their work. A linguistic style is another challenging language problem in students. They are familiar with colloquial English but not with the professional English of academic texts. Students watch and understand movies, follow YouTubers and streamers [5]. As far as academic writing is concerned, they have rather limited experience with it. However, many believe they can get by because ‘everything’ is available on the Internet. This paper brings a kind of a scenario–a didactic tool on how to motivate and guide students of Management of Tourism bachelor’s program in their first steps in academic writing utilizing smart technology within the blended learning concept. Literary tourism was the selected topic of students’ academic research. Literary tourism has a centuries long history, but as a field of academic research, its history spans just over several decades [6].

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Literary tourism, generally based on national literature and its connection with certain places, is a distinctive type of tourism that gives a visitor an insight into the historical and cultural past of a country [7]. Literary tourism often distinguishes between real-life locations connected to writers’ lives (such as birthplaces, preferred residences, and gravesides) and imagined locations connected to literary works [8, 9]. Mintel [10] added two more forms of literary tourism—literary festivals and bookstores—to its most current market research study, in addition to those two already mentioned. Literary tourism provides numerous benefits to destinations when it is developed as a special interest tourism type [6, 11]. All attributes and characteristics of literary tourism fit the specialization of the students who represent the research sample in this study. Academic orientation in the issue of literary tourism is beneficial for their action in the practical, commercial, and working spheres. Thanks to the academic experience, they can gain a deeper insight into areas such as sustainability and destination management. The concept of the students’ academic research was to find an article on literary tourism, describe the way they conducted their search, introduce this academic article to the audience, and justify their choice. Since students represent Generation Z, we presumed that they would utilize advanced technology in researching, processing, and presenting their work [12].

21.2 Methodology The case study contributes into educational research. Blended learning is a form of education features smart learning as a technology-enhanced learning fitting the digital age. The smart approach includes the exploitation of smart devices and applications in face-to-face instruction at school and in home preparation for lessons [13]. An empirical enquiry is run within the real-life context [14] in the university setting. It gives a coherent insight into the educational issue of students’ actual academic steps while the holistic concept is preserved [15]. A brief survey, review, heuristic method, a follow-up discussion, and presentation are employed. Individual work and class work are combined within blended learning concept. The active engagement of students is of key importance. Researchers applied selfdetermination theory as they focused on both intrinsic and extrinsic motivation [16, 17]. As for the intrinsic motivation, researchers tried to raise interest in the exploration of a given topic, and as for the extrinsic motivation, they tried to guide students in mastering their writing skills so that they could successfully accomplish their bachelor’s thesis. Students proceed in an exploratory heuristic way [18], however, with the teacher’s assistance if help was needed in work with databases of bibliographic citations. A mixed method approach enabling contextualization was selected [19]. Because the research sample is small, it is not possible to generalize the findings. Qualitative

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and quantitative methods were coherently combined in the study. The explanatory sequential design was applied, where research began with data collection and analysis and was followed by a qualitative part. Methods fitting the qualitative approach were applied [20]: narrative methods [21] and literature review [22]. Findings were systematically contextualized.

21.2.1 Research Sample The research sample was made up of students of the bachelor’s degree program in tourism management. These sophomore students attended the course guide activity taught in English. In students of tourism management, there is a high percentage of drop-out students. There were two groups of students. The first group consisted of six students in present form of studies (full-time studies). For these students, guide activity was an elective subject. The other group consisted of seven students in combined form of study (part-time students). Students in the combined form of studies had a limited amount of face-to-face teaching/learning in the form of a 6-h block of sessions twice per semester. For this group in combined form of studies, the guide activity subject was compulsory. Participation in the research was on a voluntary basis in both groups. All students in both groups took an active part in the research. The reason for the participation on this task of all students in the combined form of study may be the fact that the active engagement will be reflected in the final evaluation. As for full-time students, their involvement could be expected, because of their interest. The subject was optional for them, about half of sophomore students got enrolled. Those were students who were interested in a content-intensive subject, including the history, geography, and culture of selected English-speaking countries, and students who would like to take a demanding language exam next year.

21.2.2 Didactic Scenario The didactic scenario on the involvement of students in academic work follows individual steps from mapping the situation, assigning the task, approaching the topic, working with sources, and presenting findings in a colloquium during lecture. The starting point was to find out how students were aware of broad terms such as “tourism” or “literature,” and subsequently, what they knew about the literary tourism phenomenon. We assumed literary tourism phenomenon would be known to them as a niche in cultural heritage tourism. The intended output was the acquisition of academic skills: how to work with resources encompassing activities like understanding texts, filtering resources, processing findings into a paper, usually in the form of a simplified review, how to grasp, paraphrase, and express ideas, or just how to find tips that will help them get engaged with appropriate sources [23, 24]. It was obvious that the technology of academic search engines and bibliographic databases

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and their selected functionalities were to be discussed. The other valuable output was a newly developed chapter in the guide activity in English Language e-course where students’ papers, presentations, and tips would be placed as an inspiration for students in coming years.

21.3 Results and Discussions This chapter provides a detailed description of work with students, students’ work, and specific outputs. When points for the discussion arise, they are often continuously inserted into the individual steps and commented on. The attempt is to consider and discuss potential issues immediately; however, it can feel less organized (Fig. 21.1). The initial phase was conducted as qualitative research. Writing questions to gauge students’ general conceptual comprehension was the first stage in determining the students’ level of familiarity with the issue. In this study, the students were not given explicit questions; instead, they were asked to write down 3–5 keywords for individual topics related to the basic studied topic of literary tourism. This way, a collection of data was gained. The first step was carried out as a survey, it might be viewed as a type of brainstorming. Students made a list of expressions they thought of when specific parts of the study topic were mentioned: tourism, literature, literary tourism, and the same keywords in the context of Australia and New Zealand. This gave researchers a better idea of what students of Management of Tourism already knew about these topics, what they associated with the individual areas. The collection of non-numerical data was analyzed and processed. Researchers conducted thematic analysis [25]. This is where qualitative research overlapped with quantitative research. A table was created showing the frequency of occurrence of individual terms–their interconnectedness or overlap. Findings were visualized in graphs. Themes within the data were located. Assigning the task was the next step. Students were encouraged to conduct their review based on the key words, find at least one scholarly publication, and paraphrase its main ideas and crucial findings. They were told that a quick training manual had been developed for them if they required further details on how to search for articles

Survey What do students already know?

Task Can you prepare a review of at least one academic arcle?

Fig. 21.1 Process of engaging students into research

Presenng Sharing the gained informaon

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or filter them. Then a section was created in the e-course where all students’ searches were shared if the student had agreed to that. All of the students agreed to share their contributions. The final challenging and highly motivating part was the possibility of extending this work with students into the following year. Students will have the opportunity to try working on a fictional academic paper, for which we will use the instructions for authors from the conference, which takes place every year at our faculty.

21.3.1 Survey as a Starting Point A survey of how students perceive general concepts such as tourism, literature, Australia, and New Zealand as well as awareness of literary tourism demonstrates both their enthusiasm and professional expertise of the students’ field of study. Based on the findings in the first step, it was possible to predict the direction that each student’s future progress in research would take and monitor or refute the interconnectedness of the initial research and later stages of the investigation. In accordance with the frequency of occurrence, clear explanatory graphs were made from acquired data. Abbreviations in the graphs mean: blue stands for full-time students and red stands for students of combined form of study (also called part-time students). Figure 21.2 shows the frequency of attributes related to the word “tourism.” Tourism was most often characterized by both groups of terms: “culture,” “history,” and “exploring.” The terms “traveling”, and “places of interest” occurred frequently among full-time students. For part-time students, it was the term “people.” When students were asked to write key words to Australia, all full-time students mentioned the iconic Opera House. For part-time students, nature, wildlife, and sea activities dominated, see Fig. 21.3. For both groups, the M¯aori theme followed by the movie theme were the most prevalent representations of New Zealand, see Fig. 21.4. Tourism Job, learning, personality growth Travelling Money, economic Holidays, Relax, Sleep Nature, Sea, Parks People 0

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Fig. 21.2 Top attributes related to the word “tourism”

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Australia Cuisine Uluru Aborigines Nature, wildlife Melbourne (F1) Great Barrier Reef 0

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Fig. 21.4 Top key-words related to the topic “New Zealand”

Full-time students associated literary tourism with concepts such as Guides or Travel books, whereas part-time students associated that with travelling to places linked to authors. Traveling through famous places from the books. Students in both groups mentioned libraries or bookshops, which is currently popular form of tourism. It was expected that some students will run their research in this area. See Fig. 21.5. Literary Tourism Guides/Travel books Libraries/bookshops Travelling to places linked to authors Travelling to film places Travelling through the books 0

Fig. 21.5 Top topics related to “Literary tourism”

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The following sub-chapter deals with the themes that individual students chose for their scholar paper review.

21.3.2 Main Points from Students’ Review The next step in involving students in the development of academic skills was the use of academic articles so that students could gain a more comprehensive knowledge of literary tourism phenomenon, retain that knowledge, and apply in their professional field. Students were encouraged to focus on keywords they used in the previous survey. However, they could be inspired by the topics that appealed to them during the follow-up discussion. Themes were storytelling, marketing, and competitive advantage; visiting places connected with author or literary setting, authenticity, genius loci, heritage preservation; fiction and reality in literary tourism; and interdisciplinarity and film induced industry. One contribution was based on the phenomenon of storytelling in travel tourism [26]. Two significant characteristics of the digital age and the promotion of the region in conjunction with economic development emerge. People always shared tales about places. It is natural to talk and to listen to stories about places. Telling and listening stories about place has an emotional charge; it can have a motivational effect, which is welcome when promoting a given place. Digital media is a powerful channel as it gives storytelling new dimension in innovations in communication. Place storytelling narratively presents itself to the market and achieve distinctive competitive advantage [27]. Several students grasped literary tourism as activities connected with visiting places of authors or literary settings. MacLeod’s scholar paper [28] highlights the importance of the genius loci of author’s places providing the literary visitor the experience of authenticity. The students liked the concept of the study, which was based on the analysis of Trip Advisor reviews. Squire [7] also defines literary tourism as tourism connected with some author; he emphasizes touristic experience and authenticity. The student who mentioned the Lake District in his survey selected this scholar who wrote about Beatrix Potter. Countryside and heritage preservation are valued. Quite a distinctive segment in literary tourism occupies scholar papers where fiction and reality intertwine. Literary tourists visit real sites associated with fictional characters. In van Es and Reijnders [29] study, there is a unifying theme of detective characters, authors analyze tourists experience. Literary tourism is diverse, and there are significant differences in how it is perceived in individual countries or regions. Somewhere, it has a long-standing tradition, and somewhere the tradition is just being created. But what is unifying is the category of age and the search for and satisfaction of nostalgia. Literary tourism is here for everyone [30] for generalist heritage visits as well as for highly motivated literary pilgrims [31].

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Literary tourism has its magic [32]. Students selected Gökhan’s study paper because he focused on the interdisciplinary position of literary tourism as a field of study comprising literature, history, and tourism [32]. The most widely researched topic was the film induced tourism in various spheres: in the sphere of the environment, the impact of tourism, [33, 34] its trends [35, 36]. It can be said that literary tourism is unique in its set of characteristics: possessing almost the same popularity as heritage tourism, it nonetheless comprises a much deeper spiritual connection between the visitor and the place. The distinctive difference that was determined between full-time and part-time students was in the number of articles students chose for their search. From the group of full-time students, three students chose three articles and two students chose two articles. In contrast, part-time students for which the guide activity subject is compulsory, the number of selected articles was one. To explain this phenomenon, it is possible apply self-determination theory [16], where full-time students in the present form of studies are more intrinsically motivated. Participation in the extra activity in an elective subject shows their genuine personal interest in the topic and research and therefore they paid more attention and time to the task and took an active lively involvement in the follow-up discussion. In contrast, students in the combined form of studies would have extrinsic motivation in such a case, as their main goal was only to complete a task in a compulsory subject, even though the task was voluntary. So, students chose the minimum number of articles, and the time and interest they put into creating the research was smaller than previous students; thus, we are not reducing the value of their work, we only state the finding.

21.3.3 Academic Search Engines and Bibliographic Databases Google Scholar was a popular tool with the research sample students. This tool can quickly find scholarly articles on selected topics. The problem is that it is more of a search engine than a virtual library, a database of accessible scholarly articles. It cannot provide a large number of requested articles because it cannot access them unless they are open source. Universities allow students access to their library, so they can use Google Scholar to find article titles and then use the university’s online library. Google Scholar ranks first in the top list of free academic search engines [37]. Gusenbauer [38] analyzed 12 academic search engines and bibliographic databases. Findings indicated that Google Scholar was underestimated and actually was the most comprehensive academic search engine. If students have a university account, they can access scientific network ResearchGate (https://www.researchgate.net). This commercial social networking site is another great source for research and academic writing. The second place in the ranking among our students belonged to this ResearchGate platform.

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244 Fig. 21.6 Databases used by students. Google scholar, Research gate a WOS or Scopus database

WOS and Scopus 13% ResearchGate 31%

Google Scholar 56%

Singh et al. [18] analyzed the two platforms to find out why there were found differences in publication and citation data for the same institutions, journals, and authors. Scopus and Web of Science occupy the first positions in the ranking of paid academic research databases. They are usually free for university students because academic institutions provide free access to them in their campus [39]. In terms of how students handled scientific databases, most of them used Google Scholar, which can be considered an entry into the world of scientific articles. Another widely utilized database was Research Gate. Some have even opened a WOS or Scopus database. Specifically, one full-time student used all four databases, five students used Research Gate, one student used Scopus and WOS, and the others used exclusively Google Scholar (Fig. 21.6).

21.3.4 Final Presenting of the Reviews The last part of this academic activity with students was the presentation of their findings to their classmates and a follow-up discussion with focus on presentation skills and argumentation skills. Some students wanted to include the articles they were most interested in in the newly created academic skills chapter into the e-course. We have warned them that our e-course should also be copyright compliant and that it is more appropriate to link only to the articles. Most of the articles were open access, those that were not, were available through the university network, where students could read them or download them for their own use. The course therefore had a section with links to all articles that students chose for their research and then searches for students who agreed to publish in the e-course.

21.3.5 Future Focus Feedback from the students was positive. They valued the practical opportunity to learn about scholar papers and search academic databases, particularly the opportunity to practice academic skills at the written and oral levels. They realized it was useful in developing their final thesis. As a result, we decided to develop the idea

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of mastering academic skills at a higher level. The next semester, students will be given the task of creating a fictional paper, i.e., seminar work, with the formal and content requirements of a professional text that fits the requirements of a real conference. Students will work with guidelines fitting the faculty’s annual conference HED [40]. Students will be given chance to try creating an academic paper, the best work of students participating in the specific research might be submitted to the conference itself. Students will be guided to adhere to the structure and format, to work properly with the sources with regard to the format of the citation and the relevant sources, and whether they will work out original research or a systematic review.

21.4 Conclusion We are outlining a scenario that could result in greater academic accomplishment and engagement among students. The students of the Management of Tourism bachelor’s program compiled current knowledge on the current demanding topic in their field of specialization. New trends are being formed, and some trends are fading. Changes accelerated by the introduction of the latest technologies are ubiquitous, and tourism is no exception. We would further introduce this type of optional activity for extra credit; it will involve the fictional preparation of a scientific article with the possibility of publishing one article that meets the required criteria, as part of the motivation of students for doctoral studies and preparation for scientific work. The utilization and acquisition of resources are often difficult for students. Our faculty is hosting an international conference, and doctoral students will also have the chance to experience what it’s like to write a scientific paper. If they are successful in submitting and having their paper accepted, they will also have the opportunity to present their work in front of an expert audience. Acknowledgements This paper is supported by the project SPEV 2023 at the Faculty of Informatics and Management of the University of Hradec Kralove, Czech Republic.

References 1. Klimova, B.: Assessment in the e-learning course on academic writing—a case study. In: Wu, T.-T., Gennari, R., Huang, Y.-M., Xie, H., Cao, Y. (eds.) Emerging Technologies for Education, pp. 733–738. Springer International Publishing, Cham (2017). https://doi.org/10.1007/978-3319-52836-6_79 ˇ 2. Cerná, M., Borkovcová, A.: Blended learning concept in selected tourism management ecourses with focus on content development including recommender system. In: Cheung, S.K.S., Kwok, L., Kubota, K., Lee, L.-K., Tokito, J. (eds.) Blended Learning. Enhancing Learning Success. pp. 175–187. Springer International Publishing, Cham (2018). https://doi.org/10. 1007/978-3-319-94505-7_14

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Part V

Smart Business/Company: Case Studies and Research

Chapter 22

A Model of Quality Management System for Digital Economy Anna A. Sherstobitova, Elena V. Kargina, Svetlana E. Vasilyeva, and Ekaterina N. Zolotareva

Abstract Nowadays, the issues quality management system simulation and development are considered to be relevant due to the strict competition and technological boom. The development of quality management system in the context of digitalization of the economy is connected with the rapid use of information, communication and digital technologies, providing effective activities between stakeholders as well as their participation in the business deals of the organization to create its value and image in the competitive market. Therefore, it is necessary to constantly focus on the changing approaches to quality management systems in the process of training in the university. In addition, the evaluation of the effectiveness of the quality management system should be carried out continuously.

22.1 Introduction and Theory Research The system of quality management defines the general trends in the competitiveness development for the businesses and organizations and which is intended for constant improvement of their activity [1, 2]. The issues of quality and competitiveness were considered by many researchers revealing some trends based on digitalization [3, 4]. The ongoing digital transformation of the economy is aimed at optimizing all business processes, including quality management systems, and providing support for production and product output with new technology and methods [5]. Although the issue of quality management system development is urgent, there are very few mechanisms for assessing this process in the conditions of digital transformation. The analysis of some works, in particular [6] showed the necessity of mastering new intellectual technologies in the education system in order to further transformation into professional activities. These conclusions are confirmed by the ideas of the authors in [7–10]. A. A. Sherstobitova (B) · E. V. Kargina · S. E. Vasilyeva · E. N. Zolotareva Togliatti State University, Togliatti, Russia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_23

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22.1.1 Problem Statement and Hypothesis The problem of the study is to find tools for the development of quality management system in a digital transformation. Hypothesis of the study: studying the features of digitalization in quality management systems for the learning process provides them being more effective for dealing with professional activities in the future.

22.1.2 Research Issues To solve the problems of organizing the improvement of tools for assessing and developing a quality management system in the context of the education and economy digitalization the following methods were used: • systematization–the collection and streamlining of information from scientific literature providing the authors idea about the importance and necessity of evaluating and developing a quality management system in the context of digital transformation. • dialectic method–research of the content of the concepts of “digital economy” and “quality management system” in modern market conditions, improving the capabilities of technical means and rapidly growing competition. • analysis and synthesis–data synthesis obtained in the frames of theoretical and practical research for the issue under study.

22.1.3 Purpose of the Study In accordance with the identified problem area, the research objectives are defined as the following: • present an argument and the author’s vision for the “digitalization”, “quality management system” content and concepts; • reveal the theoretical provisions, identify the specifics of the development for the quality management system, and the trends of its development in the context of digitalization of the economy; • represent the model of digitalization for the quality management system based on justification of the necessity to include in its key elements the HR training in higher educational institutions for improving the overall performance.

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22.2 Our Results 22.2.1 The Model of Quality Management System Structure in the Organization The essence of the quality management system is defined in the ISO 9000 Standards as a set of interacting and interrelated parts to develop goals and ways to achieve them, as well as management policies and tactics in the process of company management in relation to the quality of products and services. In accordance with the standard, as part of the quality management system, it is necessary: firstly, to pursue an active policy of accumulating company resources to create the quality of products or services that will be valuable to consumers; secondly, to constantly improve interaction with other external and internal stakeholders. The implementation of a quality management system is a complex process that should be considered within the framework of a single multi-element system subordinate to the ultimate goal of the company [2]. The key ideas of D. Hadian are represented below. The author’s approach to the company’s quality management system is shown in Fig. 22.1. The subjects of the company’s quality management system are the owners (shareholders or capital participants, depending on the chosen legal form), introducing a quality management system for the enterprise taking into account the specifics of the business. Also, the subjects of a quality management system can be managers responsible for the direct implementation of the main processes within the entire quality system. Quality management system tools are a set of technological tools through the use of which a quality management system is formed and implemented, namely: 1. Organizational tools are internal organizational documents (standards, development programs, instructions, regulations, and codes), developed in accordance with applicable law and which are the normative basis for the activities of entities implementing the quality management system. 2. Financial and economic instruments, which are reflected in the application of various ways to stimulate the effective work of personnel, which contribute to improving the quality of final products, goods, or services. The objects of a quality management system are products, processes, and relationships with parties that influence the main business processes. The subject of the quality management system specifies the object, highlighting the areas in which the work of the subjects of the formation and implementation of the quality management system is directed. Currently, the standards of the ISO 25000 series are more relevant to consider and study in higher education. For example, ISO 25000:2014. Systems and software engineering–Systems and software Quality Requirements and Evaluation (SQuaRE)–Guide to SQuaRE. The SQuaRE series of standards focuses on the quality of systems and software products. The quality management section of ISO/IEC 2500n deals with defining quality requirements for systems and software products, measuring and evaluating

254

A. A. Sherstobitova et al. QUALITY MANAGEMENT SYSTEM The subjects of the quality management system Business Owners - Creating a Quality System Managers - implementation of a quality management system Quality Management System Tools Organizational tools Financial and economic instruments Quality Management System Objects

Products, processes, relationships with stakeholders Quality Management System Model

Implementation of planning, control, quality assurance and quality improvement functions that require an appropriate organizational structure, resources, processes and monitoring Quality Management System Principles Customer focus; systems approach; staff involvement; continuous improvement of the company; factual decision making; mutually beneficial relationships with stakeholders The ultimate goal of a quality management system Increasing the profitability of the company, through the competitive positions and image improvement for direct consumers and the whole society

Fig. 22.1 Model for a company’s quality management system [2]

the quality of systems and software products, and does not deal with “quality management” of processes as defined in the ISO 9000 series. In mastering these standards during undergraduate education, the focus is on such issues as introducing a new common reference model; implementing product quality systems; implementing a data quality model; implementing quality measurement elements; and other processes related to digital transformations. The selection of dominant indicators allowed us to narrow the boundaries of the analysis and determine the parameters that can be used to systematically assess the

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Table 22.1 Quality management system performance indicators: methods and results Methods

Results

Expert justification for tool selection, %

TPS (Lean Production)

Minimizing all types of casualties

73.2

TPM (Total Productive Maintaining technical support for equipment Maintenance)

68.3

CRM (Customer Relationship Management System)

Mastering the principles of intellectualization, computerization and informatization on the example of the Information System. Competencies of optimizing marketing and improving customer service are developed. It is achieved through the preserving information about customers and the history of the relationship with them

62.5

BSC (Balanced Scorecard)

A management method that translates an organization’s strategic goals into specific actions through key performance indicators. Forecasting is done in four areas (financial processes; customer-related processes; internal business processes; training and growth)

60.4

effectiveness of the quality management system, including for modern computer and software systems. The threshold values of the indicators can be determined individually by the enterprise, as well as on the basis of rating assessments. Therefore, the authors of the article proposed the following methods of practical mastery of some quality tools in the university. They are shown in Table 22.1. The author’s vision is justified by the conclusions of experts from the production. The table shows that those tools and methods for training were selected that had received an expert rating of over 60%. Along with general organizational improvements, in order to achieve effectiveness, such a system focuses on the personnel component, where personnel should be focused on achieving the goals of the entire system, loyal, productive, easily trained, and highly qualified [8].

22.2.2 Model for Integration of the Tangible and Non-tangible Assets The main objectives of the quality management system are as follows: • follow the strategic goals of the company, contribute to their successful achievement, the staff should be well aware of their role in improving the effectiveness of the quality management system and the degree of remuneration for its provision;

256 Table 22.2 Performance indicators of the quality management system (the author vision)

A. A. Sherstobitova et al.

Indicator group

Characteristic indicator

The expert assessment

Indicators service quality

CC

23.7

DC

31.3

DT

26.2

IL

18.8

Financial development

FC

52.4

DT

47.6

HR development

CC

42.5

RL

36.8

IL

20.7

Technological development

DC

48.7

SC

51.3

Social indicators

SC

49.5

CC

50.5

• systematically analyze the quality of products of the resource, scientific, technological, and innovative potential; • constantly improve the quality of products / services, transmit a signal to all interested parties in the form of information about the organization’s capabilities to follow quality requirements. The novelty of the paper is the author’s vision of defining the indicators on the basis of competencies through studying and mastering digital tools (Table 22.2). The competencies that have been developed in working with ISO 25000-2014 reflect: • • • • • • •

a level of digital competency (DC); a level of intellectual literacy (IL); a level of digital trust (DT); a level of regulatory literacy (RL); a level of financial competency (FC); a level of communication competency (CC); a level of standardization (SC).

In the process of studying the quality management standards ISO 25000:2014 Standards in integration with the identified tools (TPS, TPM, CRM, and BSC) the following levels of hard and soft skills were achieved, including a set of obtained relevant competencies revealed in Table 22.2. The model of integration of tangible and intangible assets is shown in Fig. 22.2. This is the author’s vision for the possibility of improving the quality management system existing in the organization.

22 A Model of Quality Management System for Digital Economy

Methods to improve the organizational, financial and technological component of the Quality Management System

257

Competencies to be formed: intellectual, digital, communication, organizational, methodical, regulatory

ISO 25000:2014 ISO 9000

Fig. 22.2 Model for integration of the tangible and non-tangible assets (the authors vision)

22.2.3 Modeling the Development Rate for the Digital Society in Russia The initial stage forms a sub-index of the availability of information and communication technologies, the second stage characterizes the sub-index of their use, and the last stage reflects a sub-index of the ability of society to apply such technologies. Table 22.2 represents the estimated values of the speed of development of a digital society from the perspective of the use of information and communication technologies in a quality management system. According to the data presented, the first stage– Russia’s position in the access sub-index–is constantly improving, but remains very modest. The second stage is the transformation of countries into information societies, where intensity is measured by a sub-index of information technology use. Judging by the data in Table 22.3, Russia is gradually moving to the stage of intensive use of information and communication technologies in the quality management system. Table 22.3 is based on the author’s processing of the data from [10]. Calculations show that the digitalization level of companies goes at a rate of 10–12% per year, and the digitalization rate of service companies exceeds the digitalization rate of material production organizations by an average of 5–7%. Nevertheless, organizations in different fields pose different tasks from their use. In the service sector, CRM-, ERP-, and SCM-systems are widespread (for 2018—42.6% of companies), and the Internet is used to manage sales and purchases. In the field of material production, information and communication technologies are mainly used for managing automated production, for design and scientific research, as well as for staff training. In organizations in all areas of the Internet is actively used for reference purposes, to automate workflow, and making payments. As previously determined, digitalization factors can stimulate the development of a quality management system and contribute to the achievement of positive qualitative, economic, and social effects.

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Table 22.3 Digitalization trends Digitalization development areas

Units

Years 2018–2019

Internet use Using broadband internet

% of the total of organizations

Organizations having a Website

2020–2021

2022–2023

85

85

86

79

77

79

% of the total of Organizations using cloud services organizations

41

42

41

18

18

18

Organizations using CRM systems

% of the total of organizations

13

13

13

Organizations using ERP systems

% of the total of organizations

15

16

17

Organizations using the internet to buy resources

% of the total of organizations

17

18

19

Organizations using the Internet to sell goods, works, services

% of the total of organizations

12

12

12

Digitalization processes affect different areas of the quality management system, some change the form and nature of creating the quality of a service/product (organizational approach), others set directions for improving the system in a digital environment (behavioral approach). The organizational approach is based on the creation of quality in the framework of the network interaction of numerous participants in industrial or economic activities. As part of the quality management system of a digital company, IT has built internal processes and interaction with the consumer in such a way that its product gives customers a new, convenient experience.

22.2.4 The Model for Quality Assessment of the Participants Network Interaction in Economic Processes Awad et al. in [11] emphasize that digital companies design ecosystems that include a wide range of actors: customers, suppliers, partners, researchers. They also actively engage social networks and communities for various purposes, for example, to receive feedback from customers about a new product or service. Multiple studies of this issue indicate that the main task of companies in digital transformation is to gain leading positions in the market of goods and services by creating innovative advantages and improving the quality management system through the introduction of innovative technologies. Virtualization of business processes allows not only attracting interested parties to create value for the business, improving the quality of company services, but also saving transaction costs. In the context of digitalization, the development of a quality management system goes towards increasing the economic security of companies.

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The use of cloud technologies for data storage and transmission, the openness of external interactions creates the need to improve the protective mechanisms against cyberattacks. So, the main characteristics of digital companies with an efficiently built quality management system may be: ultimate centralization, or extreme centralization; ultimate automation; ultimate lean and flat organization; ultimate concentration (concentration on an extremely limited set of goods/services); ultimate simplicity. Based on the results of empirical studies, it can be stated that the accelerated development of the quality management system is due to the construction of a quality management system on the principles of consistency, flexibility, technology, security, and innovation in order to increase the value of the business and the competitiveness of the company. In the context of digitalization, the development of a quality management system is aimed at creating innovations that can improve the quality of manufactured goods and increase the efficiency of organization management. Figure 22.3 shows a digital transformation model of a quality management system. The advantages of using the model of digital transformation of the quality management system by the heads of enterprises and organizations include the following aspects: the model is the foundation for developing areas for improving the quality management system; the proposed model allows regulating the distribution of costs for quality (for technological innovations, investments in improving the quality of service, staff development and training, etc.), which will allow company owners to increase the transparency of the movement of financial resources; using the model, it is possible to rank the areas of the quality management system by the level of their influence on the performance indicators of the system itself, understand the significance, and structure the sequence of their improvement by the degree of impact on the resulting attribute.

The lagging development of a quality management system

Catching up with a quality management system

Progressive development of a quality management system

Orientation of the quality management system to achieve the goals of the company. The quality system does not contradict the goals and principles of the company.

The flexibility of a quality management system. The quality system can be quickly changed. All processes are debugged and flexible to change.

The manufacturability of the quality system. Develop a digital transformation strategy. The use of information and communication technologies

Improving the quality of the final product

Lower transaction costs

Security management system quality. Increasing the level of professional competencies by employees. Improving technology.

Return on investment

Advance development of a quality management system

The innovation of the quality management system. The use of innovations that can improve the quality of products Motivation. Staff turnover reduction

Fig. 22.3 Digitalization model of a quality management system (the author’s vision)

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22.3 Conclusion 1. Model of quality management system for digital economy should be empowered with a set of competences and skills among graduates 2. The standards to assess the quality of different processes in professional area is an essential component of the educational process. 3. The economy digitalization has changed the approach to standards assessing computerization, digital transformation, digital trust, and the results of their application. 4. According to the expert survey, the sets of competencies and skills for the labor market of industrial sector, on which the quality management system has already been simulated and which are to be adapted to the new conditions of society development were mastered and actualized. Future Trends 1. It is necessary to assess the vulnerability of the enterprise infrastructure and its quality management system during digital transformation 2. It is necessary to master knowledge, abilities, and both the graduates’ hard and soft skills to the specific requirements of the new standards in the quality management area. 3. A group of standards aimed at the simulation of information security profiles for industrial enterprises is being studied and mastered.

References 1. Hellman, P., Liu, Y.: Development of quality management systems: how have disruptive technological innovations in quality management affected organizations? Qual. Inno. Prosperity 17 (2013). https://doi.org/10.12776/qip.v17i1.154 2. Hadean, D.: The Development of Quality Management System ISO 9001. 4(4), 56–60 (2018) 3. Nikulcheva, O.S., Nazina, L.I., Khaustov, I.A., Tikhomirov, S.G.: Development of decision support system for quality management of university education. In: Proceedings of the Russian Conference on Digital Economy and Knowledge Management (RuDEcK 2020). Advances in Economics, Business and Management Research, Voronezh, 2020, pp. 500–505 4. Digitalization in Russian industry (2021). Available at: https://www.tadviser.ru/index.php/ 5. Durlach, P.J., Lesgold, A.M. (eds.): Adaptive Technologies for Training and Education. Cambridge University Press, Cambridge (2012). ISBN: 9780521769037. https://doi.org/10. 1017/CBO9781139049580 6. Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.): Smart Education and e-Learning 2020. SIST, vol. 188. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-5584-8 7. Sherstobitova, A., Ajupov, A., Syrotyuk, S., Karataev, A.: The risk-management theory in modern economic conditions. E3S Web Conf. 110, 02040 (2019) 8. Sherstobitova, A., Ajupov, A.A., Derzhavina, D.A., Torkhova, A.N., Kolesov, E.S.: Intellectual capital as a basis for the region economic development. J. Eng. Appl. Sci. 12(19), 4882–4887 (2017)

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9. Sherstobitova, A.A., Kazieva, B.V., Kaziev, V.M., Filippova, O.A., Koroleva, E.I., Glukhova, L.V.: Modeling the adaptive stability and competitiveness of the university during digital changes. In: Uskov, V.L., Howlett, R.J., Jain, L.C., (eds.) Smart Education and e-Learning— Smart Pedagogy. SEEL-22 2022. Smart Innovation, Systems and Technologies, vol. 305. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-3112-3_40 10. Abdrakhmanova, G., Vishnevskiy, K., Gokhberg, L., et al.: Digital economy indicators in the Russian Federation: 2021: data book. National Research University Higher School of Economics HSE, Moscow (2021). https://doi.org/10.17323/978-5-7598-2385-8 11. Awad, N., et al.: Why you should be talking about microservices? https://web-assets.bcg.com/ img-src/BCG-Why-You-Should-Be-Talking-About-Microservices-August-2019_tcm9-225 529.pdf

Chapter 23

A Model for Business System Infrastructure’s Vulnerability Assessment Lyudmila V. Glukhova , Olga A. Filippova , Svetlana D. Syrotyuk , Svetlana A. Gudkova , and Yuliya S. Munirova

Abstract The proposed intelligent management tool is related to the vulnerability assessment of business systems infrastructure. A three-stage infrastructure vulnerability assessment model is considered and proposed. A business system is defined as an organization operating in a competitive market environment. Infrastructure is disclosed as the business system environment that supports the core processes. The problem of accounting for and assessing infrastructure vulnerabilities is solved by developing an intelligent decision support information system. This work contributes to the creation of a learning development environment for mediated domain-specific knowledge acquisition. The knowledge base includes technologies related to information and communication systems for knowledge realization. The main idea is to integrate the requirements of information security audit standards and the concept of organizational performance management.

23.1 Introduction Personnel training development and courses in the digital economy are taking on a new status. The first priority is to match the competencies required by the market environment with the competencies requested from young professionals in the selection process for the position they are applying for. Therefore, the development of both the hard skills and soft skills of university graduates is an important aspect in higher education.

L. V. Glukhova (B) · O. A. Filippova · S. D. Syrotyuk · Y. S. Munirova Volga Region State University of Service, Togliatti, Russia e-mail: [email protected] S. A. Gudkova Togliatti State University, Togliatti, Russia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_24

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A new type of pedagogy called smart pedagogy (SmP) is being developed in the process where multidimensional and voluminous knowledge, including abstraction, generalization, and transformation, is in high demand. This multidimensional knowledge allows them making competent solutions to the problems they face in the process of work and research [1]. New challenges of information and digital society require graduates to have competences reflecting the presence of complex, critical and creative thinking, and the skills of data analysis and synthesis for decision-making in a particular subject area. In some studies, L. V. Glukhova, A. A. Gudkov consider some approaches to multistage training to build information protection profiles [2]. The introduction of the Federal Law dealing with the reliability of information used in organizational and production structures has actualized the issues of security for the operation of “critical information infrastructure entities” [1]. It is noted that the situation with the assessment of critical information infrastructure is problematic and arises in the multi-level passage of information in integrated corporate production structures, where the infrastructure plays an important role to ensure the effectiveness of the main production activities. In the scientific papers by L. V. Glukhova and Ya. S. Mitrofanova, it is proposed to solve this problem by means of standardization, which is reflected in [3, 4] for modeling the integrated system of information security of integrated production structure. Unfortunately, as shown by the results of repeatedly performed analysis of the effectiveness of the application of standards requirements to the processes of production structures, in particular to the processes of protection of information and other assets has shown that the practical use of the requirements of standardization is clearly insufficient.

23.1.1 The Problem of Research In the age of development of digital and information technology, the problem of intellectualization of knowledge and the formation of a database of personal indicators of skills and abilities is considered to be as very important and it acquires further experience in the direction of finding and blocking the vulnerabilities, possible failures, and risks of loss of important information assets from unauthorized access.

23.1.2 Research Goal and Objectives Research goal. The goal of this study is to find tools to manage the vulnerability assessment of business system infrastructure. In this regard, the article considers the peculiarities of developing information protection profiles in a production environment.

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Research Objectives. The objectives of this study are as follows: 1. Identification of prerequisites for the formation of an intelligent information system to build critical information protection profiles in the infrastructure; 2. Designing and developing an information security protection model based on regulatory documents; 3. Simulation of an algorithm for critical information protection profile creation

23.2 Literature Review The infrastructure of the enterprise includes a set of separate divisions and services, the main activity of which is aimed at ensuring the continuous and high-quality functioning of the main production and all areas of the enterprise [5, 6]. The authors consider the enterprise as a complex business system functioning under market conditions and fierce competition. One of the opportunities of a business system to remain competitive in a volatile external environment is innovative activity. In the process of design, production, and operation of new products, consideration of various production and non-production situations, which can initiate a leak of data on the innovation, which will negatively affect not only the timing of the innovation to market, but also the further development of the innovation itself, is becoming increasingly important [7–9]. A significant contribution to the development of methods for processing information, including secret information, is made by modern smart technology and systems [10, 11]. Therefore, it is important to consider both the main production of the business system and its engineering and enterprise infrastructure as multi-level communication channels that ensure the continuous and timely functioning of the main production. In this case, the security of critical information infrastructure should be studied [12–14]. This is possible with the formation of competence for the development of information security protection profiles. They are regulated by standards, namely GOST R 57,628-2017.

23.3 Research Methods The study was based on systemic, process, normative methods, and approaches. The following scientific approaches are used to achieve the expected results in this project. 1. Systemic approach. It enables the researchers to consider the enterprise infrastructure vulnerability management system as an integral, complex organizational and technical system of interconnected components, each of which is in certain cause-and-effect relationships between themselves and the system as a whole.

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2. Process approach. It enables the researchers to consider each of the processes of information protection provision (taken from Standards, namely GOST 57580.1) from four sides: input, output, control, and provision, because it allows to determine with a given probability the possibility of transition of one process to another and manage them. 3. Integrated approach. It allows to study the functional purpose of all components that make up the designed system of enterprise infrastructure nodes in the design of innovation and identify bottlenecks in the existing organizational and technical solution on a multilevel (individual, micro, and macro) aspect. 4. Cybernetic approach. It will be implemented in the preparation of applications for patents for field models and copyright certificate of computer programs and databases, simulating the activities of the intelligent system of accounting vulnerabilities of the enterprise design activity. The result of the utility model is aimed at improving the efficiency of management decision-making. 5. Robust approach. It provides an estimation for a steady innovative potential of the enterprise, and it defines the “noise factors” interfering with its development (on the basis of Taguchi methods, the relation “signal/noise” is estimated and the stability zone is defined), and also to allocate significant signals on the basis of methods of “experiment planning”. The problem is indirectly indicated in the publications of the authors for the last 10 years, but the targeted work devoted to the simulation and designing the intelligent information system or its individual components in this direction until recently has not been conducted. The issue remained open both at the conceptual level (the general paradigm of diagnosing the vulnerabilities of enterprise infrastructure in the organization of innovation activities) and at the level of identifying specific mechanisms for management decisions to improve the quality of innovation activities of enterprises and the formation of an index of their innovation potential.

23.4 The Rationale for Information Security Profiles in Business Systems Infrastructure A review of the regulatory documents presented in the article confirms the relevance. Standards for the formation of the terminology base of basic concepts related to the construction of competencies, containing a set of knowledge, skills, and abilities in the subject area of the information security and management process, have been introduced to the society recently. Table 23.1 represents the components of the initial level of knowledge base, which should be formed at the stage of studying the methodology described in the analyzed regulatory documents and standards, State Standards (GOST R 57,628-2017) [1]. Figure 23.1 represents the results of the survey of graduates in the direction “Organization of information protection” in 2022. The results of the diagram showed an

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Table 23.1 Prerequisites for the study for an information security protection profile at the enterprise Regulatory document/ Main content of extractable knowledge state standards

Skills and experience

GOST 15,408.1-2012

The methodology of information security protection profile formation (ISP)

Base of concepts used in information security assessment; operational management to refine information security requirements for a specific user; terminological vocabulary in the area of trust levels

STATE 15,408.2-2013 Class structuring of information security functional requirements in the enterprise

Techniques for developing a security task (SST)

STATE 15,408.3-2012 Composition and structure of functional components in terms of classes. Concept of information security audit in the context of stages and topics

The ability to build causal relationships to assess consistency between goals, problem, and security requirements

insufficient level of practical formation of the competence of building information protection profiles. The presumed reason is the low emphasis in the process of training in the university on mastering the regulatory framework of documents in practice. The overall analysis of the results reflects the fact that in the process of training in higher education institutions the basic concepts of the purpose of information security systems, the construction of Security Policy, the definition of threats and vulnerabilities are formed. Taken together, this determines the knowledge profile of training specialists in the field of information protection from unauthorized access Security policy 4,1

Threats and vulnerabilities: assessment and identification Security audits: methodology

14,4

11,7

Vulnerability identification practices 13,5

12,6

11,1

11,3 6,9

3,8 10,5

Functioning of an integrated information security system: regulatory requirements Design and development of complex information security system Creation of information security protection profile Description of a Security Assignment for a business system Identification of intruder model for infrastructure Identification of Information Security Assessment Objects

Fig. 23.1 Analysis of the results of training for the creation of information security profiles in the workplace

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and reflects the low level of formation of such concepts. as “security audit”, security assignments, and information security protection profile.

23.5 Our Results 23.5.1 Sensitivity Assessment Model for Critical Information Infrastructure Components The main objective of infrastructure vulnerability accounting is the “penetration testing” during an independent survey (audit), which allows to assess the current state of information security of corporate information systems, identify existing vulnerabilities, assess threats, plan further steps to minimize them, and develop recommendations to improve the level of security. The model for assessing the vulnerability of critical information infrastructure components is as following (Fig. 23.2). The algorithm consists of three components: 1. External penetration test (External Risk Analysis for Infrastructure) is a practical assessment of the current level of protection of information systems against threats associated with possible attacks by intruders via the Internet. Testing can be performed both without providing information about the target information system using the “black box” method, and with obtaining additional data and

External Risk Analysis for Infrastructure

Assessing the current level of protection of information systems against threats associated with possible attacks by intruders over the Internet.

Constructing a Model of the Intruder

Analysis of Internal Threats to Infrastructure

Evaluating the vulnerability of the internal structure of the system: vulnerability of software, access to servers, databases, network equipment, etc..

Construction of an Internal Threat Model

Assessment of Complex Infrastructure Security

Correctness assessment of settings for access to assessment objects and subjects of critical information

Building the Information Security Profile

Fig. 23.2 Sensitivity assessment model for critical information infrastructure components

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limited rights in the system using the “gray box” method. It is supposed that the main purpose of intruder is to get access to information system and critical information stored, processed, and transferred in it. Experts use methods and means of vulnerability detection and exploitation used by real attackers. 2. Internal penetration test (Analysis of Internal Threats to Infrastructure) is a set of measures to simulate insider actions, including collection of information about the customer’s local network, attempts to gain unauthorized access to servers, databases, users’ computers using vulnerabilities in software, network equipment, incorrect settings, and found accounts. 3. The third stage of the algorithm is for accumulating the results of the first and second stages. There is now a standardized procedure across all models. It is supposed to be tested in the process of a special business system. The collected material is systematized and summarized.

23.5.2 Algorithm for Step-By-Step Skills Development for Information Protection Profiles The relevance of the topic is justified by the policy of the Government of the Russian Federation, aimed at the rapid introduction of various tools of digitalization, and intellectualization in the production sphere. All these tools refer to innovations, which must be mastered, implemented, and designed on the basis of their implementation new knowledge and new labor products. One of the important conditions is the competence capacity of the personnel who are engaged in the implementation of innovations. Another important aspect is the search for tools to reduce the risk of introducing innovations and minimize them. Here, an important component is compliance with the requirements of regulatory documents. When training personnel at the university on the specialty “Organization of information protection” we propose to form practical skills of studying regulatory documents step by step according to the algorithm (Figs. 23.3 and 23.4). These figures suggest two stages: preparatory and main. Figure 23.3 represents fragments. The stated research aims at a comprehensive and multidimensional analysis of the enterprise’s activity in order to identify its functioning vulnerabilities, especially at the design stage of a new product, when there is a need to introduce significant financial resources. This stage is important because of the need to analyze big data coming from the external environment. It requires their identification, classification, and further processing in order to identify the existing level of information protection at the enterprise in its infrastructure.

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Preparatory Phase Study the requirements of GOST (15408.1, 15408.2, 15408.3) for simulating a Business System Security Policy. Glossary for technical terms, selection and analysis of examples of readymade technical decisions, etc.

Study the requirements of GOSTR 57628- 2017 for the construction of information protection profiles (IPP), information protection tasks (IPT), methods of evaluation of objects of assessment (OA), the audit procedure.

Examination of the existing document "Security Policy" for a particular business system and identification of inconsistencies with the requirements of GOSTR 5728-2017. Analysis of inconsistencies.

Fig. 23.3 Preparatory stage for skills to prepare for dealing with the safety profiles

23.6 Conclusion 1. The novelty of the proposed solutions lies in the integration of various approaches to improving the quality of the business performance management process in market conditions based on the ability to work with information security. 2. The practical value is evaluated and determined, since the proposed tool provides decision-making based on reducing the risk of information loss at each level of economic management (strategic, tactical, and operational) and thus reduces the vulnerability of the infrastructure and business environment.

23.7 Future Trends 1. The next steps are to compile a database of emerging vulnerabilities: infrastructure vulnerabilities are classified and identified. 2. For each of the vulnerability identifiers, a knowledge base will be compiled to correct and improve the situation. 3. An expert business infrastructure vulnerability management system will be designed and developed.

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Basic Algorithm Steps Analysis of security goals at the enterprise. Analysis of the existing level of trust. Identification of security problems, definition of formal and informal security requirements. assessment of existing risks and threats

Choice of methodology for threat analysis. Identification and specification of threats. Analysis of negative actions. Identification and specification of information security policies. Identification of security problems. Defining the Information Security Objectives.

Structuring of security threats and their classification by type. Identification of objects of assessment (OA). Grouping of all threats and policies by headings: a) access management (objects, attributes, operations, access rules); b) user management (types of users, identification, authentication); c) proprietary protection OA (fault detection, trusted recovery, etc.); d) secure interaction (connection establishment, connection properties, rules); e) auditing (audit logging, response, incident management, analysis); f) architecture requirements (required properties and limitations), and other

Fig. 23.4 Main stage for skills to deal with safety profiles

References 1. Information technology. Security techniques. Guide for the production of Protection Profiles and Security Targets (GOST R 57628-2017) 2. Application of GOST R 57628-2017 standard identification of threats to economic security of industrial business structures. In the collection: tatishchev readings: current problems of science and practice. In: Glukhova, L.V., Gudkov, A.A.(eds.) Materials of the XVIII International Scientific-Practical Conference. In three volumes. Togliatti, pp. 19–22 (2021) 3. Mitrofanova, Ya.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) 4. Mitrofanova Y.S., Glukhova L.V., Burenina V.I., Evstafeva O.A., Popova T.N.: Smart production: features of assesing the level of personsl digital readiness. In: Procedia Computer Science. 25. Cep. “25th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2021”, pp. 2962–2970 (2021) 5. González-Granadillo, G., González-Zarzosa, S., Diaz. R.: Security information and event management (SIEM): analysis, trends, and usage in critical infrastructures. Sensors 21(14) (2021)

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6. Granadillo, G.G., El-Barbori, M., Debar, H.: New types of alert correlation for security information and event management systems. In: Proceedings of the 8th International Conference on New Technologies, Mobility and Security, NTMS, Larnaca, Cyprus, 21–23 November 2016 7. GOST R ISO/MEK 15408-1-2013 Information technology. Security techniques. Evaluation criteria for IT security. Part 2. Security functional components. Moscow: Standartinform Publ., (2014) (in Russ) 8. Erokhin, S., Petukhov, A., Pilyugin, P.: Critical information infrastructures security modeling. In: 2019 24th Conference of Open Innovations Association (FRUCT), Moscow, Russia, pp. 82– 88 (2019) 9. Erokhin, S.D., Petukhov, A.N., Pilyugin, P.L.: Principles and tasks of asymptotic security management of critical information infrastructures. T-Comm. 13(12), 29135 (2019) (in Russ, EDN: BVZBIG) 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, Springer, Cham (2016) 11. 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 12. 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) 13. Validating development indicators for smart university: quality function deployment. In: Gudkova, S.A., Glukhova, L.V., Filippova, O.A., Syrotyuk, S.D., Krayneva, R.K. (eds.) Smart Innovation, Systems and Technologies, vol. 240, pp. 241–252 (2021) 14. Glukhova, L.V., Syrotyuk, S.D., Gudkova, S.A., Aleksandrov, A.Y.: Model-based analysis for smart university development. Smart Innov. Syst. Technol. 188, 455–465 (2020)

Chapter 24

Marketing Research for Regional Development Anna A. Sherstobitova, Elena V. Kargina, Slavyana O. Shanogina, and Natalya A. Nesmeyanova

Abstract The purpose of the article is to consider marketing methods of analysis for development of one of the regions in Russia for assessment the current state of automotive industry, which is the flagship for the economy of the tested region, and the impact of this assessment on the content for training the specialists and the graduates for the considered industry. The analysis of the organizational and marketing research enabled the authors to make a conclusion about the lack of marketing information about the automotive industry peculiarities. A forecast scenario for the development of the machine-building cluster until 2025, taking into account the specifics of functioning at the present time and the marketing data obtained in the study, is proposed. The obtained results can be used to optimize the production processes at businesses as well as the staff training for the car production and mechanical engineering.

24.1 Introduction Nowadays, the Russian machine-building industry is under the reorganization which consists of the corporate structure restructuring and territorial redistribution of capacities. Digital transformations as well as the introduction of smart technologies affect the manufacturing processes at the enterprises. Robotic complexes, automated control systems, and cloud data storage are widely used. Digital transformations of production influence the concept of smart industries and smart enterprises. These changes have a direct impact on the country’s economy, where a large number of machine-building enterprises working in the industrial and scientific spheres are concentrated.

A. A. Sherstobitova (B) · E. V. Kargina · S. O. Shanogina Togliatti State University, Togliatti, Russia e-mail: [email protected] N. A. Nesmeyanova Volga Region State University of Service, Togliatti, Russia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_25

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Today, in Russia, their activities are carried out in the following directions: • 337 enterprises are producing industrial equipment, components, and consumables (manufacturing equipment); • 79 enterprises producing agricultural machinery and equipment; • 73 manufacturing enterprises of the construction sector using machinery for the production of steel structures, complex fittings, and equipment; Within the framework of the territorial redistribution of capacities, the Government of the Russian Federation has formulated the task of creating new production sites. This actualizes the issues of bringing traditional machine-building complexes in line with new emerging requirements, including through the development of innovative trends, in order to ensure their sustainable functioning in the system of related industries.

24.2 Literature Review The new digital production paradigm and the concept of Industry 4.0 have led our society to the integration of the latest advances in production with modern information and communication technologies. Now more than ever, manufacturing companies need to adapt to changing customer needs, rising resource costs, and increasing uncertainty. Particular attention must be paid to marketing analysis. Enterprises are facing new customer requirements and global competition, leading to fundamental changes in today’s industry. Against this background, Industry 4.0 is now the main concept for solving these problems in manufacturing [1]. There are many new methods and technologies of digital transformation. For example, the concept of a digital double (DD) plays a key role in the future of smart manufacturing. The analysis of the possibility of developing the concept of digital double, its maturity and importance in the fourth industrial revolution is shown in some modern papers [2]. Guerra-Zubiaga et al. presented an approach to the development of the DD for manufacturing systems in order to optimize the process of planning and commissioning. The results of the study reflect the views of experts in various industries. Lugert et al. [3] proposed the development of value stream maps to support improvements in the manufacturing process. The authors suggest assessing the current state of the method from the user’s perspective and considering its future sustainability in the context of ongoing digitalization. To better assimilate new intelligent technologies and systems, several researchers suggested an application of innovative approaches such as smart pedagogy into higher education [4, 5]. Gudkova et al. [6] proposed to evaluate the quality of the processes of production activities on the basis of the methods of the robust approach. For this purpose, it is

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necessary to purposefully form target groups in the process of training, which could apply modern smart technologies in practice [7, 8].

24.3 Our Research 24.3.1 Problem Statement Modern concepts of industry development on the basis of partnership and cooperation make it possible to substantiate the expediency of industry functioning, reduce production costs, optimize the use of resources, increase profits, and achieve the required key performance indicators. Many modern authors are sure that marketing interaction of subjects in the B2B segment can guarantee a significant increase in the efficiency of machine-building enterprises through the introduction and use of new marketing trends and tools.

24.3.2 Research Issues Due to the necessity for further scientific and practical development of an innovative approach to the development of marketing activities at enterprises, the main areas of research in this article are as follows: • to study necessary competencies for employees at a smart enterprise in the automotive industry; • to simulate a possible forecast scenario of smart enterprise development for the near future.

24.3.3 Purpose of the Study According to the identified problem area, the following objectives of the study are defined: • to choose the methods and approaches used in the analysis to determine the market capacity, key characteristics and features, marketing information, providing the feasibility of optimization at enterprises of the automotive industry; • to give recommendations on the implementation and application of the results obtained in the course of the study in order to optimize the activity at the machinebuilding industry.

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24.4 Research Methods To solve the problems and issues for the improvement of marketing and sales activities at car manufacturing and machine-building enterprises the following methods were used: • analysis and synthesis methods that are based on the data obtained during the statistical study of the mechanical engineering area; • systematization methods that are based on collection of information from scientific papers and making conclusion about necessity for obtaining the latest data on the development of the mechanical engineering industry; • dialectical method that is focused on content study for the concepts of “marketing research” and “marketing system” in modern market conditions as well as improvement of technical means capabilities and rapidly growing competition.

24.5 Marketing for Education: Defining the Content Many authors note that the capabilities of machine building and enterprises located in Russia are unique. Among the largest engineering companies, there are 267 industrial enterprises including both large and small businesses that were recognized as competitive by the end of 2021. They consist of a lot of trends represented in the Fig. (24.1). The figure is based on the analysis of modern papers and represents the authors’ vision [9]. Machine-Tool contruction

7 18

9

Production Equipment Vehicles

3

Industrial Equipment

8

20

Production Automatione systems Mashinery and Equipment (Agriculture)

6

Instrument Marking

29

Machinery and Equipment (construction)

Fig. 24.1 Distribution of market shares of the Russian engineering cluster

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The conducted marketing research for each of the presented branches of mechanical engineering made it possible to identify the study and evaluate the classification of goods, and an overview for each product group was carried out. The positioning model of the machine-building and car industry in Russia predetermines a high level of decentralization of enterprises, which makes it difficult for the industry to communicate and integrate for other industries. The role of the regions in the development strategy of mechanical engineering and machine tool building in Russia can be considered as a processing center and a center for the formation of technological competence in accordance with the regional component. Based on the foregoing, the authors believe that when training personnel in the mechanical engineering area, it is important to simulate an employee competence model in mechanical engineering and car producing areas.

24.6 Our Results 24.6.1 Comparative Analysis of Engineering Market In the field of personnel training, as well as the marketing policy for the development of the engineering industry, it is advisable to study and assess the market for manufactured products. It is necessary to analyze the technologies used and apply them in the processes of training the graduates for successful career development. Studying the features of market development in the context of digital transformations [9, 10], the existing and forecast indicators of manufactured engineering products were analyzed. The study period was taken from 2015 to 2025. The products of eight countries were analyzed and they were chosen randomly by the authors. The peculiarity of the choice of countries was influenced by the fact that the growth prospects of the analyzed engineering products are most favorable in developing countries. Thus, we can conclude that in the global engineering industry, there will be a strengthening position of developing countries, especially China (Table 24.1). Table 24.1 Forecast of the release of conditionally clean engineering products (in billions) [10]

Country

2015

2020

2025

Brazil

18.8

22.6

27.2

China

248.0

329.4

410.1

India

19.3

26.0

34.4

Japan

75.4

81.0

86.3

Russia

14.9

17.6

20.8

United States

115.5

129.7

144.9

EU

178.3

193.2

204.7

278 Table 24.2 Average annual growth rate of engineering products (forecast), % [10]

A. A. Sherstobitova et al.

Country

2015–2020

2020–2025

Brazil

3.8

3.7

China

5.8

4.5

India

6.1

5.7

Japan

1.4

1.3

Russia

3.4

3.4

United States

2.4

2.2

EU

1.6

1.2

Table 24.1 reflects the author’s vision of the processed results based on the analysis of data from the source [10]. The analysis of the data in Table 24.1 showed the growth of engineering products will be expected to increase by 8% by 2025 in comparison with the existing indicators at the end of 2022. This corresponds to an annual increase in output of 3.8%. (author’s vision). Table 24.2 shows the calculated average annual growth rates of engineering products (forecast), %. The table shows that the Russian market is in stagnation. China has enough potential to become one of key leaders at the world machine building market, but Russia should look for ways to develop the production systems. As a result of the study, the following conclusions were drawn regarding the solution of the issues for organization and strategy of enterprises in the engineering industry: engineering is a leader in comparison with other industries in the application and development of high technologies and innovations. Therefore, when training engineering staff, it is important to rely on such new digital transformation technologies as artificial intelligence, the Internet of Things, cloud computing technologies, computer-aided design and programming software packages, and other smart technologies and platforms. It is necessary to make a competency model for a specialist who would have the skills of marketing market analysis, as well as designing and planning future directions for the development of mechanical engineering (Table 24.2).

24.6.2 Competitive Model for Technical Education: New Approach A competency model of requirements for university graduates was simulated based on the outcomes of analytical research and analysis of the stakeholders’ requirements for the mechanical engineering specialists graduating for the Russian labor market. Figure 24.2 represents this competence model (the authors’ vision). The model has been implemented and tested at Togliatti State University in 2022–2023. Table 24.3 presents the results of evaluation and testing.

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Fig. 24.2 New competitive model for technical education

Table 24.3 The skills assessment at university Skills

Expert assessment

2022

2023

New smart technology for industry

18.9

61.2

74.5

CAD/CAM/CAE skills

16.3

36.5

48.2

Catia 3D skills

14.7

68.3

76.4

ISO standards skills

14.2

39.8

64.6

Internet of things (IoT) skills

8.1

17.2

29.1

Cyber-physical systems skills

7.5

8.3

11.1

Big data skills

7.3

10.3

19.5

Cloud computing skills

7.2

54.2

55.4

Mobile-based systems

5.8

9.6

27.3

The first column of the table reveals the results of the expert assessment, which were obtained in the process of assessing the students’ skills and mastering at the Faculty of Mechanical Engineering during their employer-sponsored training at the university. In the papers by S. Gudkova, this issue has been considered for many times [6, 7]. Here, the estimates are reflected in the % ratio of the required 100% completion of the skills base for effective activity at Avtovaz company which is considered to be the flagship both for the car industry in Russia and region. The second column reflects the level of these competencies among students at the starting point of their training, and the third column reflects the achieved learning outcomes as of the assessment date (03/01/2023). The employer-sponsored training programs are conducted in English, and the choice of technologies for them is justified by the needs of car production. Figure 24.3 represents the results among graduates of the Machine Building Department (the authors’ vision).

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New smart technology for industy 80 61.2 Mobile-based systems CAD/CAM/CAE skills 60 Cloud computing skills

Big Data skills Cyber-physical systems skills

40 20 0

Catia 3D skills

ISO Standards skills Internet of things (IoT) skills

Fig. 24.3 Results among graduates of the machine building department

The model has been tested at Togliatti State University in 2022 and in 2023. The analysis of the obtained results according to the assessment of the JSC AvtoVAZ reveals the high level of the personnel training. When hiring graduates who have graduated from the employer-sponsored training, 98% of them are hired and have good opportunities for their career development in car industry.

24.7 Conclusion 1. Machine building plays a key role in the implementation of innovative solutions, products, high-tech equipment, and production processes in relation to other sectors of the economy. In the context of digital transformation, we need staff who can implement innovative technologies in different production areas. 2. The conducted marketing analysis identified the most required areas and practical skills of digital transformation in higher education for machine-building industries. 3. The higher school organized targeted training of students who master new smart and IoT technologies by prior arrangement with the management of industrial enterprises of the city. 4. Assessment of the level of training of target personnel showed a good prospect for further application of this approach.

24.8 Future Steps 1. Looking for new directions of digital transformation in the tested area. 2. Development of new employer-sponsored training programs for engineers in car and other industries.

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References 1. Winkelhaus, S., Grosse, E.H.: Logistics 4.0: a systematic review towards a new logistics system. Int. J. Prod. Res. Taylor & Francis J. 58(1), 18–43 (2020) 2. Lu, Y., Liu, C., Wang, K.I.-K., Huang, H., Xu, X.: Digital Twin-driven smart manufacturing: connotation, reference model, applications and research issues. Robot. Comput.-Integrat. Manuf. 61 (2020). https://doi.org/10.1016/j.rcim.2019.101837 3. Lugert, A., Batz, A., Winkler, H.: Empirical assessment of the future adequacy of value stream mapping in manufacturing industries. J. Manuf. Technol. Manag. 29(5), 886–906 (2018). https:/ /doi.org/10.1108/JMTM-11-2017-0236 4. Sherstobitova, A., Gudkova, S., Kazieva, B., Kaziev, K., Kaziev, V., Yakusheva, T.: University innovative networking in digital age: theory and simulation. in: smart education and e-learning. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Innovation, Systems and Technologies, vol.240, pp. 293–299 (2021) 5. Uskov, V. L., Bakken, J., Aluri, L., Rachakonda, R., Rayala, N., Uskova, M.: Smart pedagogy: innovative teaching and learning strategies in engineering education. In: II IEEE World Engineering Education Conference—EDUNINE 2018. 11–14 March 2018, Buenos Aires: IEEE (2018). https://ieeexplore.ieee.org/document/8450962 6. Gudkova, S.A., Yakusheva, T.S., Sherstobitova, A.A., Burenina, I.: Modeling, selection, and teaching staff training at higher school (2019). https://doi.org/10.1007/978-981-13-8260-4_54. Retrieved from www.scopus.com 7. Gudkova, S.A., Glukhova, L.V., Filippova, O.A., Syrotyuk, S.D., Krayneva, R.K.: Validating development indicators for smart university: quality function deployment. Smart Innov. Syst. Technol. 240, 241–252 (2021) 8. Strielkowski, W., Guliyeva, A., Rzayeva, U., Korneeva, E., Sherstobitova, A.: Mathematical modeling of intellectual capital and business efficiency of small and medium enterprises. Mathematics (9), 1–21 (2021). https://doi.org/10.3390/math9182305 9. Abdrakhmanova, G., Vishnevskiy, K., Gokhberg, L., et al.: Digital Economy Indicators in the Russian Federation: 2021: Data Book. National Research University Higher School of Economics. Moscow: HSE (2021). https://doi.org/10.17323/978-5-7598-2385-8 10. IMF World Economic Outlook, Goldman Sacks. URL: https://www.imf.org/en/News/Articles/ 2023/01/31/tr-13123-world-economic-outlook-upda

Chapter 25

Digital Technologies for Quality Management in Integrated Production Anna A. Sherstobitova , Elena N. Korneeva , Raisa K. Krayneva , Manchuk T. Bayetova , and Azyk A. Orozonova

Abstract The modern companies need quality management system. This system is based on sophisticated industry standards and many additional customer-specific requirements. For example, technologists need to know and be able to apply a set of methods in production activities within the APQP as well as the new approaches and documents required in production process management. One solution to the issues of information support for the application of these methods is the need to create databases with good practices for solving the problems identified. This will reduce the overall cost of development and implementation of quality systems with information support. The article reveals some models of decision support based on digitalization tools in the conditions of existing integrated production facilities.

25.1 Introduction Digitalization is a modern trend for economic development, so the issues of digitalization are also relevant for the sphere of industrial production. Digital transformations are becoming an inevitable process in industrial development. One of the factors influencing the digitalization processes is the integration IIIA transformations in the production sphere. The explanation for the increase in the share of digital production with the growth of integration interactions is reasonable, because digital and integration transformations lead to a special kind of integration A. A. Sherstobitova (B) · E. N. Korneeva Togliatti State University, Togliatti, Russia e-mail: [email protected] E. N. Korneeva · R. K. Krayneva Financial University Under the Government of the Russian Federation, Moscow, Russia M. T. Bayetova University of International Business, Almaty, Republic of Kazakhstan A. A. Orozonova Kyrgyz National University Named After Jusup Balasagyn, Bishkek, Kyrgyzstan © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_26

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and digital effects: increased competitiveness, creation of unified integration and digital platforms, increased sustainability of industrial enterprises and the exchange of digital resources. However, the efficiency of integrated industries largely depends on the standardization of their activities. Therefore, the article deals with the interaction of the three components: standardization, digitalization and integration on the basis of industrial enterprises of the automotive industry.

25.2 Literature Review The most striking example illustrating the need for intelligent and digital methods in quality management is the information support of quality management processes based on ISO/TS 16,949 standard in the automotive industry [1–3]. ISO/TS 16,949:2009 “Quality management systems, namely the particular requirements for the application of ISO 9001:2008 for automotive production and relevant service part organizations”. ISO/TS 16,949 was prepared by the IATF International Automotive Working Group and the Japan Automobile Manufacturers Association (JAMA) with the support of ISO Technical Committee TC 176 ISO. The standard is based on the structure of ISO 9001:2008 and includes additions specific to the automotive industry. The predecessor of ISO/TS 16,949 is the American QS-9000 standard. A specific feature of the application of ISO/TS 16,949 is the requirement for organizations to use a range of tools. Only in ISO 9001 standard there are more than 20 elements or sections, which can have information support in the form of appropriate IT-systems. In addition to requirements ISO 9001, for example, in standard ISO/TS 16,949 application of following methods is necessary: • • • • • •

Advanced product quality planning (APQP); Analysis of potential defects in new projects (FMEA); Measurement system analysis (MSA); Statistical process control (SPC); The process of solving problems with product quality (8D); Equipment maintenance support systems (TPM), etc.

The issues of quality management of production activity from the standpoint of standardization requirements are given much attention in the papers and studies of Russian [1–5] and foreign authors [6–8]. Let us consider the APQP process (Advanced product quality planning), which is used by companies to put into production new products, taking into account all the necessary actions in the field of quality assurance as a project and already serial processes. As it is known APQP process consists of 5 stages which are carried out step by step. The basic purposes of these stages and their description in standard ISO/TS 16,949 are represented in Table 25.1.

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Table 25.1 Linking APQP steps and ISO/TS 16,949 standard points [3] Step

Purpose

ISO/TS 16,949 points

Step 1. Planning, conceptualization and quality assurance plan

Ensuring a clear understanding of customer requirements and expectations

7.1, 7.2.1–7.2.2, 7.3.1

Step 2. Product design and development

Comprehensive analysis of product design (prototype) and feasibility of development

7.3.2–7.3.4, 7.3.7

Step 3. Process design and development

Design and development of an efficient production system to ensure that customer requirements and expectations are met

7.3.2–7.3.4, 7.3.7

Step 4. Product and process validation

Confirm that products and processes meet customer 7.3.5, 7.3.6, requirements before launching the series 8.2.3.1

Step 5. Production and improvement activities

Maintaining and improving product and process quality

7.5, 7.6, 8

In accordance with the table, we see that the precise organization of all project works and both hard and soft skills to apply the whole set of methods have one of the main priorities and software is the most important element of any quality management system. It is quite difficult to create such software package, that’s why there are not so many such software solutions in the market, although you can find several quite competitive software products in certain areas.

25.3 Software for Quality Management: A Review There are many software products in the market to support the methods directly mentioned in Table 25.1, although most of them are foreign-made and are based on English content, which is difficult to use by employees in the production area (Table 25.2). The demand for software for production depends on several factors including the following issues: • availability of the Russian interface and the methodology of its application; availability of samples or examples of application on the base of the suggested methodology; • ability to change the output forms (reports, protocols) to suit your current needs; • affordable price and possibility of updating. Therefore, those software products that take into account these factors will be in the highest demand. But in addition to these factors, the technology of creating knowledge bases for each method of quality management with the possibility of using this knowledge base as a training or methodological module, a module for

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Table 25.2 Market information systems to support quality methods (excerpt) Information systems

Methods Possibilities ISO/TS 16,949

Attestator

SPC, MSA

Analysis of the state and possibilities of technological and other processes with the use of SPC methods (control cards, indexes, histograms). Measuring systems analysis (MSA)

Minitab 16

SPC, MSA

A huge selection of different modules from simple statistical estimates, analysis of variations, statistical management of processes, graphs to experiment planning

Quality companion 3

FMEA, APQP

A wide range of software products for product and process design from the same manufacturer as Minitab 16

QiBox

APQP, Combines in one solution all the necessary tools to meet ANPQP, the requirements of ISO/TS 16,949 standard and tools for PPAP project management in accordance with the quality management procedures of new products

STATISTICA Quality Control (QC)

SPC, MSA

Allows to realize all technical stages of quality control and analysis of technological processes, planning of production experiments

Wonderware QI analyst software

SPC

Real-time network SPC system allows using all SPC tools, as well as to store information in the database and perform any reports

Q-DAS—solara, SPC, procella, qs-STAT, destra MSA

solara–measurement system analysis (MSA); procella–process control; qs-STAT–process evaluation; destra–statistical software package with many different methods of statistical analysis

Windchill quality solutions products

FMEA

A software product consisting of a whole line of programs for reliability analysis and risk management that can be integrated into one system

Xfmea

FMEA

Xfmea makes it easy to manage and report on all types of FMEA and FMECA–also part of a fairly large set of reliability analysis and risk management programs

storing and searching effective solutions for the corresponding method, and also a module for binding and storing all documents should be implemented [9–11].

25.4 Ensuring the Life Cycle of Products by ISO Standards The well-known quality loop (Fig. 25.1), which reflects the interrelation of product life cycle stages, will help to understand the scope of necessary competences and required software products to support the enterprise management system. This loop

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1 12 2 11 Designing 10

3

Operation

4 9

Manufacture 5 8

7

1 - Marketing, 7.1, 7.2 2 - Product development, 7.3 3 - Shopping, 7.4 4 - Preparation of production processes, 7.3, 7.6 5 - Production, 7.5 6 - Inspection and testing, 7.5, 7.6, 8.1-8.3

6

7 - Packaging and storage, 7.5 8 - Sales and distribution 9 - Transfer to operation 10 - Maintenance, technical support, 7.5 11 - Operation, 8.2 12 – Disposal

Fig. 25.1 Quality loop by [2]

is almost fully reflected in the paragraphs of the ISO/TS 16,949 standard, specifically in sections 7 and 8 and their description: The presented scheme is static and does not reflect the dynamics of product development. Therefore, the authors of the article agree with modern trends that it is better to represent the model of development for any product on the basis of the quality spiral (Fig. 25.2), where each step means the degree of improvement for the product. The spiral is formed due to customer satisfaction–the higher it is, the longer the spiral will be, and hence the life span of a certain brand in the market [4]. The authors believe that it is necessary to distinguish between the development of the product and the development of enterprise management. System development depends on the development of its individual elements (for example, the use of new methods in product design and production, analysis and management of processes), as well as the systems that ensure their interaction and management (for example, new methods of staff motivation, management of information resources). Product development can also take place within an unchanged system–by improving and modernizing the design, using new materials, etc. According to the authors’ point of view, the most valuable trend is obtaining a new product, including as a result of the development of systems of design, production, control, implementation and others. Ideally, these two processes should complement each other. Only then we get a constantly improving innovative product, we can stay ahead of the market, maintaining a high level of competitiveness of the enterprise.

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1.1 2.1 1

12

3.1

2 11 3

Designing 10

4.1

Operation 4 Manufacture

9

5 8 7

6

1.1 - Marketing on a new coil; 2.1 - Product development on a new coil; 3.1 - New coil purchases; 4.1 - Preparation of production processes on a new turn Fig. 25.2 Quality loop (the authors vision)

25.5 IT for Product Quality: Simulation Each element of the quality spiral may develop independently, i.e., their levels of development may differ significantly, or not at all. The unevenness of the development of individual elements affects the overall development of the product. The degree of development of individual elements of the quality loop and the product as a whole can be assessed by using the point system and radar chart (Fig. 25.3), which can serve as a model of a spiral that shows the dynamics and degree of development. The most important changes occur in the production phase, because it is considered to be as the longest one and it requires significant investments in training new personnel, advanced digital skills, equipment, tools and accessories. Less change occurs during the project phase, but it is usually quite short in comparison with the production phase. Changes in this stage are less frequent, but they are more complicated, because they require more time and more knowledge about innovative processes. Each stage of the product or service lifecycle is accompanied by an analysis for different types of information ensuring the communication and interaction process both within the organization between different departments, and between suppliers and its stakeholders including state structures.

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12 - Disposal 11 - Operation

10 - Maintenance, technical suppor

1 - Marketing 10 8 6 4 2 0

289

2 - Product development 3 - Shopping 4 - Preparation of production processes

9 - Transfer to operation

5 - Production

8 - Sales and distribution

6 - Inspection and testing

7 - Packaging and storage 1-type product life 2-type product life

Fig. 25.3 The radar product development schedule (the author visions)

On the other hand, information affects the entire management system of the organization within the subsystems of quality management, ecology and labor protection, as well as other more traditional management subsystems–technical, financial, legal, etc. The leading competitive organizations try to have information systems to support the above subsystems, but the most developed and available information systems are used for accounting, law data and production management. Information systems to support quality management and other similar systems are less common among manufacturing enterprises.

25.6 Our Results 25.6.1 Standards for Integrated Production Systems: Analysis and Case Study According to the analysis of modern approaches and management trends to integration processes one of the main reasons for the insufficient use of management tools in the management of complex production systems is that the introduction and implementation of standardization into the production process is fragmented and quite slow. Newly introduced standards go through a lengthy process of adaptation to the needs of certain production activities. The figure shows (see Fig. 25.4) the main reasons that reveal and explain the “slow” activity on the need to quickly adapt the new requirements of the standards to the integration interaction. This figure was

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9,6

100%

Insufficient knowledge of decision support skills based on GOST requirements, including GOST R ISO 9001-2015

8,3 12,4

80%

10,8 60%

Inadequate knowledge of information asset protection skills in integrated structures Insufficient level of practical knowledge of staff of information support standards and process approach

13,5 7,3

40%

Insufficient information support of compliance with TR requirements

16,4

Insufficient level of interrelation between TR system requirements and internal standards of the enterprise 20%

21,7

0% 2018

Low level of standardization of integration processes

Insufficient knowledge of staff in the application of existing standards

Fig. 25.4 Reasons for low assessment of compliance with the requirements of the standardization system [12, 13]

compiled on the basis of data from a survey of suppliers and consumers of the automotive industry presented by JCS AVTOVAZ, which is considered to be the flagship and significant enterprise for the region of the article’ authors. The study of the economic efficiency of the engineering and car industry in the Samara region, conducted by the authors, revealed that the total losses for the reasons indicated in the Fig. 25.4 for the period 2018–2021 years amounted to more than 20%, and the main losses were related with the following issues: the insufficient information security of integrated production structures (9.6%), the lack of skills to protect the information assets of the enterprise (12.4%), and the low level of standardization of integration processes (16.4%). It should be noted that in the presented classification there is no motivation to reduce various kinds of losses due to the identified non-compliance with the requirements and standards of the current information protection profiles. In this regard, this article discusses the features for the development of companies on the basis of the quality management tools and modern software.

25.6.2 Information Security Profiles for Integrated Production Systems: Simulation A review of the prerequisites for studying the problems and regulatory documents considered in the article showed that the issue under consideration is relevant for the localization of the authors of the article.

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Regulatory knowledge 100 Production Automation

80

Digital skills

60

Stakeholders

40 20 Qualimetric method

Marketing analysis skills

0

Design skills

Cluster analysis skills

TSU

Skills in mathematical modeling and forecasting

Fig. 25.5 The model of assessment of the level knowledge

Only in recent years, the requirements of standards in higher education institutions have appeared on the formation of the terminological base of basic concepts, the construction of competencies, the totality of knowledge, skills and abilities in the subject area of managing information security protection processes [11–13] in the production structure. The figure shows (Fig. 25.5) an assessment of the level of preparedness of university students for building the “Information Protection Profile” model that meets the requirements of stakeholders. The assessment was carried out in 2022.

25.7 Conclusions 1. The need for information security profiles for production systems was identified based on the analysis of external environment requirements in accordance with the quality loop (Fig. 25.2); 2. The description of the security problem includes a list of identified threats from the external and internal environment, corresponding to all points of the quality loop, developed by the authors; The obtained skills of building security profiles will significantly reduce the risks of loss of information assets at enterprises in the conditions of mutually beneficial cooperation of integrated production. This is the practical value of the results of the study. Future research trends. In the future, the authors of the study intend to develop the research in the following directions 1. An Assessment Objectives Tree and an Assessment Object Risk Tree are constructed according to the stages of the quality loop;

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2. The logical link “Requirements Tree”–“Decision Tree”, reflects the functional requirements for digital trust and security, and corresponds to the information protection measures taken for all stages of the quality loop in the product manufacturing process.

References 1. Albukhitan, S.: Developing digital transformation strategy for manufacturing. Procedia Comput. Sci. 664–671 (2020) 2. Panyukov, D.I.: Key aspects of implementation of the automotive quality standard ISO/TS 16949. In: Panyukov, D.I., Kozlovskiy, V.N. (eds.) Automotive Industry vol. 9, pp. 1–5 (2014) 3. Kozlovsky, V.N.: Information support of quality management methods. Kozlovsky, V.N., Panyukov, D.I., Yunak, G.L. (eds.) Science—Industry and Service vol. 9-2, pp. 362–368 (2015) 4. Becker, W., Schmid, O., Botzkowski, T.: Role of CDOs in the digital transformation of SMEs and LSEs. An empirical analysis. In: Hawaii International Conference on System Sciences, pp. 4534–4543 (2018) 5. Kristoffersen, E., Blomsma, F., Mikalef, P., Li, J.: The smart circular economy: A digitalenabled circular strategies framework for manufacturing companies. J. Bus. Res. 120, 241–261 (2020) 6. Nonaka, I., Teece, D.J.: Managing Industrial Knowledge: Creation, Transfer and Utilization, 344 p. Sage Publications, Thousand Oaks, California (2001) 7. Hobday, M.: Systems integration: a core capability of the modern corporation. In: Hobday, M., Davies, A., Prencipe, A. (eds.) Industrial and Corporate Change, vol. 14, pp. 1109–1143 (2020) 8. Sherstobitova, A.A., Glukhova, L.V., Gudkova, S.A., Korneeva, E.N., Filippova, O.A., Lyubivaya, T.G.: The concept of transition from smart university to smart business in digital economic environment. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning 2021. KES-SEEL 2021. Smart Innovation, Systems and Technologies, vol 240. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-2834-4_23 9. Glukhova, L.V., Mitrofanova, Y.S.: Digitalization of economy and peculiarities of its application in the activity of integrated production structures. In: Vestnik Povolzhsky State University of Service Section “Economics” 14 c (2017) 10. Glukhova, L.V., Syrotyuk, S.D., Sherstobitova, A.A., Gudkova, S.A.: Identification of key factors for a development of smart organization. In: Smart Innovation, Systems and Technologies, vol. 144, pp. 595–607 (2019) 11. Glukhova, L.V.: Concept of standardization of the managers’ activity in the conditions of the digital economy. In: Glukhova, L.V., Nemtsev, A.D. (eds) Vestnik of the Volga University named after V.N. Tatishchev, vol. 2, no. 1, pp. 165–175 (2018) 12. Glukhova, L.V.; Koroleva, E.I.: Standardization and information support of the integrated production structure (in Russian). In: Research Azimuth: Economics and Management, vol. 44, pp. 127–131 (2016) 13. Berdnikova, L.F., Sherstobitova, A.A., Schnaider, O.V., Mikhalenok, N.O., Medvedeva, O.E.: Smart university: assessment models for resources and economic potential. In: Smart Innovation, Systems and Technologies, vol. 144, pp. 583–593 (2019)

Chapter 26

Innovative Marketing Strategy for Industry Anna A. Sherstobitova, Elena V. Kargina, Varvara V. Danshina, and Olga A. Filippova

Abstract This paper proposes the author’s innovative approach to the simulation of Internet marketing strategy in enterprises of the Business-to-Business (B2B) segment. The PEST analysis, i.e., analysis of Political, Economic, Social and Technological factors that could affect a business now and in the future–has been performed. The survey of experts in marketing was also carried out and the necessary competencies and skills of staff for the implementation of marketing strategies were identified and a set of Strengths, Weaknesses, Opportunities and Threats (SWOT) for staff was revealed. The top 10 factors influencing the period of “search engine optimization” (SEO) promotion of the web site in search engines were presented. The implementation of the project on the implementation of marketing strategy MIX, tested at the university during the training course for graduates of the economic profile is described.

26.1 Introduction An important component of enterprise development in today’s economy is the use of flexible marketing strategy, providing an adaptation to changes of the external environment. To analyze any situation on the modern market it is required to have skills of big data analysis, skills of using modern clever technologies and means of communication. The article offers the author’s vision of using modern Internet marketing tools to attract new potential customers, consumers of manufacturing products. The digital age has revolutionized the way businesses work and communicate with their customers. The introduction and implementation of Internet marketing A. A. Sherstobitova (B) · E. V. Kargina · V. V. Danshina Togliatti State University, Togliatti, Russia e-mail: [email protected] O. A. Filippova Volga Region State University of Service, Togliatti, Russia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1_27

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has supported businesses to expand their customer base faster and more effectively than ever before. Thanks to Internet marketing tools, today’s businesses have a unique opportunity to expand their potential customer base through tools such as “search engine optimization” (SEO). The main benefit of such strategies for today’s businesses is the ability to target the right demographic and pinpoint those customers who convert into sales. Having used these analytics, you can determine the “ideal future customer profile” and create the right ads to reach the right audience. This will bring to the enterprise to more benefits and profits while reducing the cost of the advertising campaign. The authors consider that the training of future graduates in higher education who know how to apply modern smart technologies will support modern business and companies. Internet marketing technology is considered to be one of such technologies.

26.2 Literature Review One of the important approaches to the development of adaptation mechanisms is marketing strategy. The well-known scientist Layton [1] in the studies focuses on marketing systems as complex adaptive systems. The similar trends are represented in scientific papers by some other authors [2], who were engaged in considering the systems of measurement for marketing efficiency as well as the company performance. They noted the important role of marketing strategy alignment with market dynamism. Therefore, adaptation processes are also important. These same conclusions are also held by a range of authors [3–6]. The analysis of theory [7, 8] has shown that currently the means of increasing the investment attractiveness and competitiveness of commercial structures are situational marketing. In this connection one of the perspective trends is marketing planning for strategic preferences in specific situation [9, 10]. In general, based on the analysis of various sources, it can be concluded that nowadays the “marketing mix” strategy directed to the consumer can be widely used. Therefore, to solve various situational problems requiring big data analysis and intelligent analysis, it is preferable to apply and deal with the Internet marketing, while working with different target groups of consumers through the website and its optimal promotion (SEO). This conclusion is drawn from some modern trends [11–16].

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26.3 Problem Statement The increasing role of competition in the domestic and global markets, an unstable market situation, sanctions, the peculiarities of state regulation make new conditions for enterprises. Creating a flexible marketing strategy, taking into consideration all features of the market and which is able to quickly adaptation and adjustment to the requirements of the external environment is the general trend for the enterprise. The introduction of new methods for the marketing strategy fully justifies the expediency and necessity of revision and modification of existing concepts and methods. The suggested authors’ approach to marketing strategy is based on the conducted marketing research and provides qualitatively expanding frameworks of marketing interaction of subjects in the B2B segment and ensuring a significant increase of efficiency and economic effect of activity at the businesses and enterprises due to introduction and use of new marketing tools and smart technologies. Such innovative tools include mix-marketing strategy and Internet marketing.

26.3.1 Research Issues For the purpose of further scientific and practical development of innovative approach to the creation and mastering the marketing strategy of enterprises the authors consider that the main issues of the research in the article are the following: • analysis of the necessary components of the employee, whose functional duties include the development, promotion and optimization of the web site; • identification of internal and external environment factors providing the greatest impact on businesses in the context of digital transformation; • evaluating the possibilities of developing a mix-marketing strategy based on Internet marketing.

26.3.2 Research Objective In accordance with the designated problem area, the authors identified the following research objectives: • to substantiate the need for the required competencies for Internet marketing: • to propose a model of marketing strategy for the enterprise.

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26.3.3 Research Methods In order to solve the problems of organizing the improvement of marketing and sales activities of machine-building enterprises, the following methods were used in this project: 1. Systematization method. Scientific literature [12–16] review has been conducted based on systematization method; it was concluded about the importance and necessity of adapting the marketing system in B2B enterprises through SEO. 2. Dialectical method. A retrospective analysis of the concept of “marketing strategy” in modern market conditions was conducted based on dialectical method. 3. Competency approach. The required competencies for Internet marketing have been identified on the basis of this method. 4. The PEST analysis, i.e., analysis of Political, Economic, Social and Technological factors, was chosen as the main method of research.

26.4 Results 26.4.1 The PEST Analysis of the Organization’s Activity A group of companies of the “Machine Building” industry was analyzed. Analytical studies were performed by the authors of the article. The results of statistical processing were averaged and are presented below in the author’s interpretation. To find possible threats, strategic uncertainties and alternatives, the analysis of the external environment of the enterprise were carried out on the basis of PEST analysis (Table 26.1). The list of factors is drawn up in tabular form, which indicates degree of manifestation (strength of influence of the factor on the activity of the enterprise). The identified factors should be ranked, taking into account the positive (+) or negative (−) influence: estimate the probability of occurrence of a factor from 0.1 to 1 (0.1 is little probably, 1 has already occurred); estimate the influence of the factor on the activity of the enterprise from 1 to 10 (1—low influence, 10—critical influence); calculate a weighted estimate by multiplying the probability of the occurrence of the factor by the strength of its impact; make a rating in descending order for all identified factors. It is advisable to present the results of PEST analysis of factors in the form of a table with grouping by factors, indicating the probability of occurrence, degree of influence and the final weighted evaluation. The results of the PEST analysis show that despite the negative impact of social (− 0.6), economic (− 1.5) and political factors (− 1.6), there is a positive advantage to build a marketing strategy through the further development of technological factors (+ 1.2).

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Table 26.1 PEST analysis results (the authors’ vision) Factor

Threat (−)/Possibility (+)

Probability of occurrence

Importance

Effect

P1. International sanctions

−

0.25

8

− 2.0

P2. Import Substitution Policy

−

0.15

6

− 0.9

P3. State support of the industry

+

0.30

9

+ 2.7

P4. Decrease in the value − of the ruble, leading to higher prices

0.20

7

− 1.4

Total

1

30

− 1.6

Political Factors

Economic Factors E1. Economic sanctions

−

0.20

7

− 1.4

E2. Mastering new markets

+

0.40

9

+ 3.6

E3. Inflation rate

−

0.15

8

− 1.2

E4. Decrease in the volume of sales

−

0.25

10

− 2.5

1

34

− 1.5

Total Social Factors S1. Shortage of qualified personnel in the field of promotion of goods on the market

−

0.35

10

− 3.5

S2. Changes in legislation supporting innovation activities

−

0.15

8

− 1.2

(continued)

For this purpose, according to the results of PEST analysis, the SWOT analysis was performed and some opportunities were identified (Fig. 26.1).

26.4.2 The SWOT Analysis for Staff Activity The authors have analyzed the staff opportunities and potential trends for the organization on the basis of SWOT analysis. Figure 26.1 represents the results of SWOT analysis to identify the prospects to make innovations at the enterprise. Taking into account the identified opportunities, the development strategy of the enterprise was considered.

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Table 26.1 (continued) Factor

Threat (−)/Possibility (+)

Probability of occurrence

S3. Increase of labor motivation in the conditions of digital transformation

+

0.20

7

+ 1.4

S4. Changing consumer preferences in sales

+

0.30

9

+ 2.7

34

− 0.6

1

Total

Importance

Effect

Technological Factors T1. Development of the machinery market in Russia

+

0.2

7

+ 1.4

T2. Smart Technology & + EdTech support

0.3

9

+ 2.7

T3. Progress in some areas of production

−

0.15

6

− 0.9

T4. Insufficient skills to form an ideal customer profile

−

0.25

8

− 2.0

30

+ 1.2

1

Total Strengths

Opportunities

• The company's team is engaged in innovation. • The staff is constantly working to improve their knowledge. • There is a digital transformation of equipment and technology. • Manufactured products are in demand in the domestic market. The company has its own website. Weaknesses

• Improving the quality of interaction with the external environment to develop new markets • Improving the quality of the production process by mastering new smart technologies and EdTech platforms. • Modernization and promotion of our own website, allowing us to switch to online ordering Threats

• Insufficient level of mastery of modern marketing tools • Decrease of sales • Competition for the production of finished products

• Rising prices of materials and components. • Unstable economic situation in the country • Unstable political situation • Low wages and low level of motivation.

Fig. 26.1 The results of the analysis of strengths and weaknesses of the enterprise staff activity

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26.4.3 Marketing Mix Model with Customer Focus The main activities of the mechanical engineering enterprise on the example of AVTOVAZ JSC, which is considered to be the flagship company for the authors’ region, are listed below. Marketing. The company produces high-tech industrial products with a high degree of uniqueness. This makes it necessary to establish contacts between the seller and the buyer long before production begins. High level of product complexity, as well as coordination of some technical parameters with each particular customer makes possible to produce cars “by preliminary order”. This necessitates the need to build a profile of the ideal buyer in order to learn about his preferences and form an advertising campaign specifically for those interested in purchasing the finished product. Production. Production capacities of JSC AVTOVAZ enable to produce qualitative production, demanded on the domestic and foreign markets. The company cooperates with foreign partners, and the quality control becomes very important. The main goal of the quality policy is to make sure that engineering, production and technologies meet the highest international requirements and standards. The quality control system guarantees the customers specified quality standards at all stages of production. External logistics at AVTOVAZ is represented by a well-developed system of transport logistics, in which all logistics decisions are made on the basis of smart technologies and existing automated production control systems. Therefore, the corporation’s top management pays great attention to processes of professional development of its employees through training in new smart technologies and implemented digital transformations in production. The development of the marketing strategy of the power engineering enterprise is determined by the presence of high technical and personnel potential for research and development work. Figure 26.2 shows the author’s vision of the model for marketing strategy aimed at further development of sales. The marketing service of the enterprise together with the specialists of higher education institution who researched scientific approaches to formation and development of new marketing tools proposed to include such tools into the model of sustainable competitive advantage without “looking back” and focusing on the needs of the target market, consumer behavior and in-depth analysis of expectations as much as possible. On this basis, it seems possible to make fundamental adjustments to the proposed model, fixing and designating a place for the four elements of Fig. 26.2 The model for marketing mix (the authors’ vision)

Marketing Production External Logistics Product sales

Internet Marketing

The Perfect Buyer Profile

Marketing Mix Strategy: 4C&7P&SEO

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80 The basic concept of competence development

70 60 50

Marketing concept based on the 7P approach

40 30 20

The "customer-oriented marketing concept" implementing the 4C approach

10 0 2020

2021

2022

Fig. 26.3 Consumer-driven marketing mix model (The authors’ vision)

socially-oriented marketing of the concept 4C (Convenience, Communication, Cost and Consume). This is a fundamental approach and a clear and measurable benefit. According to this concept, at the heart of the formation of competitive advantage and the development strategy of the organization is the consumer, the satisfaction of his needs, both latent and explicit. The proposed model for simulating sustainable competitive advantage reflects the relationship between the elements of the marketing mix concept 7P and the socially responsible marketing mix 4C. The detailed and substantive elaboration of each block of the two approaches allows the emerging challenges of target marketing to be elaborated and the right decisions to be taken in a timely manner. Figure 26.3 reflects the author’s vision. The dynamics of approaches to the application of marketing strategies in the enterprise is shown. Obviously, the most popular is the 4C strategy. However, nowadays Internet marketing strategy is added to it. The training for mastering the new concepts is aimed at integrating these strategies. The training for mastering the new concepts.

26.4.4 Internet Marketing Model and SEO The top 10 factors affecting the period of SEO promotion of the site in search engines are shown in Table 26.2. It shows the author’s processing of the results of a survey of experts in the field of Internet marketing. The results are arranged in descending order of factor importance. It is difficult to predict trends in the development of Internet marketing in 2023, but according to the results of the conference networking. Entrepreneurs, which took place in Samara region, revealed 6 basic directions, in which it is recommended to strengthen the training of graduates in the university of “Applied Computer Science”

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Table 26.2 The expert assessment Actor

Expert evaluation of importance Share, %

Rank

Site promotion

24.9

Purpose of site

13.2

2

Theme of the site and level of competition

12.6

3

Promotion region

11.1

4

Size of the site

10.4

5

8.2

6

Pessimizing factors

1

Budget of the site

7.1

7

Whether the site has been promoted before

5.1

8

Search engine activity

4.3

9

SEO team experience

3.1

10

Table 26.3 The new trends in marketing Trend

Comments

Expert assessment, %

Artificial Intelligence (AI) and automation

AI is already in use

33.2

Voice search

It is already in use. With the development of Siri, Alexa voice assistants the need will increase

12.4

Video marketing

It is used for a long time

21.6

Interactive content

Includes tests, games, and surveys and it is still developing and evolving

17.2

Mobile marketing

Increasing with the growth of mobile devices

10.3

Social media

They are used to promote the brand and developed intensively

5.3

and “Information Security”. Table 26.3 shows the authors‘ vision of the prospective areas. The data was obtained as a result of expert survey and statistical processing of the results. New training courses are currently being developed in this area.

26.5 Conclusion Innovative marketing strategy is considered by the authors for the automotive industry in the region based on the following existing marketing tools:

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• the 4C marketing concept, which gist is to increase communication with the customer as well as increasing their level of loyalty. This can help the company to understand its segment market better and increase the sales of its products; • the 7P marketing concept, which extends the basic 4P marketing concept (product, price, place, promotion) by focusing on people, processes and physical evidence; • Internet marketing, the concept of evidential feedback to the consumer of a service and which is implemented through new intelligent and digital platforms and technologies. Future Trends 1. Training the university graduates for mastering their marketing skills through the promotion of company websites in their grade area to a higher level. 2. Monitoring and evaluating the effectiveness of the marketing mix strategy used for the tested company. 3. Developing the concentrated marketing strategy for the tested company.

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15. Begley, S.: Simple Guide to Website Keyword Placement (2020). Retrieved from: https://www. vapromag.co.uk/simple-guide-to-website-keyword-placement/ 16. King, M.: The Future is Mobile SEO: Can You Adapt? (2019). Retrieved from: https://alkries. com/mobile-seo-is-the-future-heres-how-you-can-adapt-to-it

Author Index

A Afonichkina, Ekaterina A., 133 Aksinina, Olga S., 173 Anisimova, Iuliia A., 153 Azarova, Svetlana P., 35

B Bakken, Jeffrey P., 71, 89, 111 Bayetova, Manchuk T., 283 Berdnikova, Leyla F., 133, 153, 173 Borkovcová, Anna, 235

C Casalino, Nunzio, 121 ˇ Cerná, Miloslava, 235

D Dalmasso, Erik A., 71, 89, 111 Danshina, Varvara V., 293 Dayneko, Marina V., 143

E Elena A. Borgardt, 173

Gennaro, Alessandro, 121 Glukhova, Lyudmila V., 163, 197, 263 Gnatishina, Elizaveta I., 133 Gudkov, Anton A., 197 Gudkova, Svetlana A., 35, 143, 183, 197, 263 I Igoshina, Natalya A., 153 K Kabanov, Pavel A., 173 Kalashnikova, Irina V., 133 Kargina, Elena V., 251, 273, 293 Katalnikova, Sabina, 55 Kato, Takumi, 45 Kazieva, Bella V., 163, 221 Kaziev, Valery M., 163 Khramova, Elena A., 133 Khristoforova, Irina V., 183 Korneeva, Elena N., 35, 183, 283 Koroleva, Elena I., 163 Krayneva, Raisa K., 35, 183, 283 Kusminova, Olga A., 209 Kusnetsova, Olga A., 221

F Filippova, Olga A., 263, 293

L Lugovkina, Oksana A., 173 Lyubov K. Shamina, 153

G Garcia, Jose, 23

M Magnaghi, Elisabetta, 121

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. L. Uskov et al. (eds.), Smart Education and e-Learning—Smart University, Smart Innovation, Systems and Technologies 355, https://doi.org/10.1007/978-981-99-2993-1

305

306 Malashchenko, Marina V., 143 Malyarovskaya, Anastasia Yu., 153 Marco De, Marco, 121 Munirova, Yuliya S., 263

Author Index Shchepotkina-Marinina, Elena V., 173 Sherstobitova, Anna A., 163, 197, 221, 251, 273, 283, 293 Shishkina, Svetlana I., 209 Sotskova, Svetlana I., 133 Syrotyuk, Svetlana D., 197, 263

N Nesmeyanova, Natalya A., 273

O Omarova, Aizhan, 183 Orozonova, Azyk A., 283

P Palferova, Sabina Sh., 197 Pocklington, David, 79, 99 Polteva, Tatiana V., 221 Poulová, Petra, 235 Prasolova-Førland, Ekaterina, 23 Prokofyeva, Natalya, 55

S Samuratova, Aigul U., 35 Semrjakovs, Andrejs, 55 Serdyukova, Natalia A., 209 Serdyukov, Vladimir I., 209 Shamir, Haya, 79, 99 Shanogina, Slavyana O., 273

T Treshina, Inga V., 143

U Uskov, Vladimir, 3

V Vasilchuk, Andrei S., 153 Vasilyeva, Svetlana E., 251 Veglianti, Eleonora, 121

Y Yakusheva, Tatiana S., 143 Yoder, Erik, 79, 99

Z Ziborova, Viktorija, 55 Zolotareva, Ekaterina N., 251