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Collection Highlights

Smart Education and e Learning 2019 Vladimir L. Uskov

E-Learning, E-Education, and Online Training: Third

International Conference, eLEOT 2016, Dublin, Ireland,
August 31 – September 2, 2016, Revised Selected Papers 1st
Edition Giovanni Vincenti

e Learning e Education and Online Training Shuai Liu

Distance Learning E Learning and Blended Learning in

Mathematics Education Jason Silverman
Ludic, Co-design and Tools Supporting Smart Learning
Ecosystems and Smart Education: Proceedings of the 5th
International Conference on Smart Learning Ecosystems and
Regional Development Óscar Mealha

Energetic nanomaterials : synthesis, characterization, and

application 1st Edition Vladimir E Zarko

Statistics for Management and Economics 11th Edition G E R

ALD KELLER

Smart Cities First International Conference Smart CT 2016

Málaga Spain June 15 17 2016 Proceedings 1st Edition
Enrique Alba

E Learning and Games 10th International Conference

Edutainment 2016 Hangzhou China April 14 16 2016 Revised
Selected Papers 1st Edition Abdennour El Rhalibi
Smart Innovation, Systems and Technologies 59

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

Smart Education
and e-Learning 2016

123
Smart Innovation, Systems and Technologies

Volume 59

Series editors
Robert James Howlett, KES International, Shoreham-by-sea, UK
e-mail: rjhowlett@kesinternational.org

Lakhmi C. Jain, University of Canberra, Canberra, Australia;

Bournemouth University, UK;
KES International, UK
e-mails: jainlc2002@yahoo.co.uk; Lakhmi.Jain@canberra.edu.au
About this Series

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.

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

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

Smart Education
and e-Learning 2016

123
Editors
Vladimir L. Uskov Lakhmi C. Jain
Computer Science and Information Systems, University of Canberra
InterLabs Research Institute Canberra
Bradley University Australia
Peoria, IL
USA and

Robert J. Howlett Bournemouth University

KES International Poole
Shoreham-by-sea UK
UK
and

KES International
Shoreham-by-sea
UK

ISSN 2190-3018 ISSN 2190-3026 (electronic)

Smart Innovation, Systems and Technologies
ISBN 978-3-319-39689-7 ISBN 978-3-319-39690-3 (eBook)
DOI 10.1007/978-3-319-39690-3

Library of Congress Control Number: 2016941070

© Springer International Publishing Switzerland 2016

This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part
of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,
recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission
or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar
methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this
publication does not imply, even in the absence of a specific statement, that such names are exempt from
the relevant protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this
book are believed to be true and accurate at the date of publication. Neither the publisher nor the
authors or the editors give a warranty, express or implied, with respect to the material contained herein or
for any errors or omissions that may have been made.

Printed on acid-free paper

This Springer imprint is published by Springer Nature

The registered company is Springer International Publishing AG Switzerland
Preface

Smart Education and e-Learning (SEEL) are emerging and rapidly growing areas
that represent an integration of smart objects and systems, smart technologies, smart
environments, smart features or smartness levels, smart pedagogy, smart learning
and teaching analytics, various branches of computer science and computer engi-
neering, state-of-the-art smart educational software and/or hardware systems. This
is the main reason that in June 2013, a group of enthusiastic and visionary scholars
from all over the world arrived with the idea to organize a new professional event
that would provide an excellent opportunity for faculty, scholars, Ph.D. students,
administrators, and practitioners to meet well-known experts and discuss innovative
ideas, findings and outcomes of research projects, and best practices in smart
education and e-learning.
The main research, design and development topics in SEEL area include, but are
not limited to, (1) conceptual frameworks for Smart Education (SmE) and Smart
e-Learning (SeL), (2) infrastructure, main characteristics and features of Smart
Universities (SmU) and Smart Classrooms (SmC), (3) SmU-wide software, hard-
ware, security, safety, communication, collaboration and management systems,
(4) SmE analytics, (5) innovative learning and teaching strategies as components of
smart pedagogy, (6) SeL strategies, approaches and environments, (7) smart learner
modelling, (8) assessment and quality assurance in SmE and SeL, (9) social, cul-
tural and ethical dimensions and challenges of SmE and SeL, (10) applications of
various innovative technologies–Internet of Things, cloud computing, Ambient
Intelligence (AmI), smart agents, sensors, wireless sensor networks,
context-awareness technology, etc.—and smart software/hardware systems in uni-
versities and classrooms, and numerous other topics. We hope that active and open
discussion of those topics within SEEL professional research and academic com-
munities will help us to (a) organize mutually beneficial partnerships, stimulate
national and international research, design and development projects in SEEL area,
(b) propose innovative pedagogy, teaching and learning strategies, standards and
policies in SEEL, (c) identify tangible and intangible benefits of SEEL.

v
vi Preface

The inaugural international KES conference on Smart Technology-based Edu-

cation and Training (STET) has been held at Chania, Crete, Greece, during 18–20
June 2014. The 2nd international KES conference on Smart Education and
e-Learning took place in Sorrento, Italy, during 17–19 June 2015. This book
contains the contributions presented at the 3rd international KES conference on
Smart Education and e-Learning, which took place in Puerto de la Cruz, Tenerife,
Spain, during 15–17 June 2016. Book chapters, a total of 56 peer-reviewed chap-
ters, are grouped into several parts, as follows: Part I—Smart University: Con-
ceptual Modelling, Part II—Smart Education: Research and Case Studies, Part III—
Smart e-Learning, Part IV—Smart Education: Software and Hardware Systems, and
Part V—Smart Technology as a Resource to Improve Education and Professional
Training.
We would like to thank scholars who dedicated a lot of efforts and time to make
SEEL international conference a great success: Dr. Luis Anido (Spain), Dr. Elena
Barbera (Spain), Dr. Claudio da Rocha Brito (Brazil), Dr. Dumitru Burdescu
(Romania), Dr. Nunzio Casalino (Italy), Prof. Melany Ciampi (Brazil), Mr. Marc
Fleetham (UK), Dr. Ekaterina Prasolova-Førland (Norway), Dr. Mikhail Fominykh
(Norway), Dr. Brian Garner (Australia), Prof. Natalya Gerova (Russia), Dr.
Jean-Pierre Gerval (France), Dr. Karsten Henke (Germany), Dr. Alexander Ivan-
nikov (Russia), Dr. Marina Lapenok (Russia), Dr. Aleksandra Klasnja-Milicevic
(Serbia), Prof. Andrew Nafalski (Australia), Dr. Enn Õunapuu (Estonia), Dr. Elvira
Popescu (Romania), Dr. Valeri Pougatchev (Jamaica), Prof. Jerzy Rutkowski
(Poland), Dr. Danguole Rutkauskiene (Lithuania), Prof. Adriana Burlea Schiopoiu
(Romania), Prof. Masanori Takagi (Japan), Dr. Gara Miranda Valladares (Spain),
Dr. Heinz-Dietrich Wuttke (Germany), and Dr. Larisa Zaiceva (Latvia).
We are indebted to many international collaborating organizations that made
SEEL international conference possible, specifically the following: KES Interna-
tional (UK), InterLabs Research Institute, Bradley University (USA), Institut
Superieur de l’Electronique et du Numerique ISEN-Brest (France), Silesian
University of Technology (Poland), and Multimedia Apps D&R Center, University
of Craiova (Romania).
It is our sincere hope that this book will serve as a useful source of valuable data
and information, and provide a baseline of further progress and inspiration for
research projects and advanced developments in SEEL area.

June 2016 Vladimir L. Uskov

Robert J. Howlett
Lakhmi C. Jain
Contents

Part I Smart University: Conceptual Modeling

Smart University Taxonomy: Features, Components, Systems. . . . . . . . 3
Vladimir L. Uskov, Jeffrey P. Bakken, Akshay Pandey, Urvashi Singh,
Mounica Yalamanchili and Archana Penumatsa
Smart Universities, Smart Classrooms and Students
with Disabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Jeffrey P. Bakken, Vladimir L. Uskov, Archana Penumatsa
and Aishwarya Doddapaneni
Innovative Approaches Toward Smart Education at National
Institute of Technology, Gifu College . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Nobuyuki Ogawa and Akira Shimizu
University Smart Guidance Counselling . . . . . . . . . . . . . . . . . . . . . . . . 39
Elena Belskaya, Evgeniia Moldovanova, Svetlana Rozhkova,
Olga Tsvetkova and Mariya Chervach
Teacher’s Role in a Smart Learning
Environment—A Review Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Blanka Klimova
Toward Smart Value Co-education . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Vincenzo Loia, Gennaro Maione, Aurelio Tommasetti, Carlo Torre,
Orlando Troisi and Antonio Botti
Smart University Management Based on Process Approach
and IT-Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
Boris Pozdneev, Filip Busina and Alexander Ivannikov
A Formal Algebraic Approach to Modeling Smart University
as an Efficient and Innovative System . . . . . . . . . . . . . . . . . . . . . . . . . 83
Natalia A. Serdyukova, Vladimir I. Serdyukov, Vladimir L. Uskov,
Vladimir V. Ilyin and Vladimir A. Slepov

vii
viii Contents

Conceptual Framework for Feedback Automation in SLEs. . . . . . . . . . 97

Estefanía Serral and Monique Snoeck
Development of Smart-System of Distance Learning of Visually
Impaired People on the Basis of the Combined of OWL Model. . . . . . . 109
Galina Samigulina and Assem Shayakhmetova

Part II Smart Education: Research and Case Studies

Data Mining of Students’ Behaviors in Programming Exercises . . . . . . 121
Toshiyasu Kato, Yasushi Kambayashi and Yasushi Kodama
Social Network Sites as a Good Support for Study Purposes. . . . . . . . . 135
Blanka Klimova, Petra Poulova and Lucie Ptackova
Design and Application of MOOC “Methods and Algorithms
of Graph Theory” on National Platform of Open Education
of Russian Federation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
Lyubov Lisitsyna and Eugeniy Efimchik
A Framework for Human Learning Ability Study Using
Simultaneous EEG/fNIRS and Portable EEG for Learning
and Teaching Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
Boonserm Kaewkamnerdpong
A Conceptual Framework for Knowledge Creation Based on
Constructed Meanings Within Mentor-Learner Conversations . . . . . . . 167
Farshad Badie
Inductive Teaching and Problem-Based Learning as Significant
Training Tools in Electrical Engineering . . . . . . . . . . . . . . . . . . . . . . . 179
Fredy Martínez, Holman Montiel and Edwar Jacinto
A Supporting Service in Teacher Training: Virtual Newspaper. . . . . . . 189
Nuray Zan and Burcu Umut Zan
Participation in State R&D Projects Jointly with Industrial
Enterprises: Factor in Employability Improving
of University Graduates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
Alexey Margun, Artem Kremlev, Aleksandr Shchukin,
Konstantin Zimenko and Dmitry Bazylev
On-Line Formative Assessment: Teaching Python in French CPGE . . . 213
François Kany
PageRank Algorithm to Improve the Peer-Led Team Learning
Pedagogical Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225
Nazar Zaki
Contents ix

Developing Computational Thinking Abilities Instead of Digital

Literacy in Primary and Secondary School Students . . . . . . . . . . . . . . 235
Eduardo Segredo, Gara Miranda, Coromoto León and Anthea Santos
Innovations in Subjects Knowledge Technologies . . . . . . . . . . . . . . . . . 247
Petra Poulova and Ivana Simonova
Economic Aspects of the Introduction of the SMART Technology
into Kindergartens and Primary Schools—Czech National
and Local Scene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
Libuse Svobodova and Miloslava Cerna

Part III Smart e-Learning

An Integrative Approach of E-Learning: From Consumer
to Prosumer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269
Adriana Schiopoiu Burlea and Dumitru Dan Burdescu
Using E-Learning in Teaching Economics at Universities
of the Czech Republic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281
Roman Svoboda, Martina Jarkovská, Karel Šrédl, Lucie Severová
and Lenka Kopecká
The Gamification Model for E-Learning Participants Engagement . . . . 291
Danguole Rutkauskiene, Daina Gudoniene, Rytis Maskeliunas
and Tomas Blazauskas
RLCP-Compatible Virtual Laboratories in Computer Science. . . . . . . . 303
Evgeniy A. Efimchik, Elena N. Cherepovskaya and Andrey V. Lyamin
Combining Science with Art to Educate and Motivate Patients
Prior to Colorectal Cancer Screening. . . . . . . . . . . . . . . . . . . . . . . . . . 315
Piet C. de Groen, Shreepali Patel, Mariana Lopez, Michael Szewczynski
and Rob Toulson
Tangible Interfaces for Cognitive Assessment and Training
in Children: LogicART . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329
Fabrizio Ferrara, Michela Ponticorvo, Andrea Di Ferdinando
and Orazio Miglino
On the Process of Mobile-Assisted Teaching and Learning
at FIM UHK—Analysis and Reflection . . . . . . . . . . . . . . . . . . . . . . . . 339
Ivana Simonova and Petra Poulová
Assessment of Outcomes in Collaborative Project-Based Learning
in Online Courses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351
Dmitrii A. Ivaniushin, Andrey V. Lyamin and Dmitrii S. Kopylov
x Contents

Vagrant Virtual Machines for Hands-On Exercises

in Massive Open Online Courses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363
Thomas Staubitz, Maximilian Brehm, Johannes Jasper,
Thomas Werkmeister, Ralf Teusner, Christian Willems,
Jan Renz and Christoph Meinel
Sequencing Educational Contents Using Clustering
and Ant Colony Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375
María José Franco Lugo, Christian von Lücken
and Esteban Ramírez Espinoza
Students’ Assessment Preferences in ESP in the Smart
Learning Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387
Ivana Simonova
Smart Technologies in Psycho-Oriented Strategies
of Adaptive Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397
Alexander Gein, Dmitriy Istomin and Andrey Sheka
Elderly People and Their Use of Smart Technologies:
Benefits and Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405
Blanka Klimova

Part IV Smart Education: Software and Hardware Systems

Smart Classroom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415
Jean-Pierre Gerval and Yann Le Ru
Creating a Smart Virtual Reality Simulator for Sports
Training and Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423
Emil Moltu Staurset and Ekaterina Prasolova-Førland
Using Embedded Robotic Platform and Problem-Based
Learning for Engineering Education . . . . . . . . . . . . . . . . . . . . . . . . . . 435
Fredy Martínez, Holman Montiel and Henry Valderrama
Learning Object Assembly Based on Learning Styles . . . . . . . . . . . . . . 447
Aldo Ramirez-Arellano, Juan Bory-Reyes
and Luis Manuel Hernández-Simón
Transmedia Storytelling for Social Integration of Children
with Cognitive Disabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463
Miguel Gea, Xavier Alaman and Pilar Rodriguez
Application of Semantic Web Technologies to Facilitate Use
of E-Learning System on Mobile Devices . . . . . . . . . . . . . . . . . . . . . . . 473
Boban Vesin, Aleksandra Klašnja-Milićević and Mirjana Ivanović
Contents xi

Applications of Innovative “Active Learning” Strategy

in “Control Systems” Curriculum . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485
Dmitry Bazylev, Alexander Shchukin, Alexey Margun,
Konstantin Zimenko, Artem Kremlev and Anton Titov
Learning Strategies for Cryptography Using Embedded Systems . . . . . 495
Edwar Jacinto, Fredy Martínez and Fernando Martínez
Introducing Smart Technologies for Teaching and Learning
of Fundamental Disciplines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507
Svetlana Rozhkova, Valentina Rozhkova and Mariya Chervach
Redefining Knowledge Management Education with the Support
of Personal Knowledge Management Devices . . . . . . . . . . . . . . . . . . . . 515
Ulrich Schmitt
Interactive Educational System Based on Generative Approach,
and the Problem of Answer Checking . . . . . . . . . . . . . . . . . . . . . . . . . 527
Vladimir G. Danilov and Ilya S. Turuntaev

Part V Smart Technology as a Resource to Improve Education

and Professional Training
Smart Technologies in Foreign Language Students’ Autonomous
Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541
Natalya Gerova, Marina Lapenok and Irina Sheina
Formation of the Individual Learning Path in the Information
and Educational School Environment . . . . . . . . . . . . . . . . . . . . . . . . . 553
Marina V. Lapenok, Olga M. Patrusheva, Natalya G. Tagiltseva,
Lada V. Matveyeva, Natalya V. Gerova and Valentina V. Makeeva
Information Education in Russia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563
Sergey A. Beshenkov, Etery V. Mindzaeva, Elena V. Beshenkova,
Margarita I. Shutikova and Irina I. Trubina
Implementation of the Internet for Educational Purposes . . . . . . . . . . . 573
Irena Robert, Lora Martirosyan, Natalya Gerova, Vasilina Kastornova,
Iskandar Mukhametzyanov and Alla Dimova
Assessment of Levels of Formation of Competence of Students
as Users of Information and Communication Technology
in the Field of Health Care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585
Iskandar Mukhametzyanov and Alla Dimova
Intelligent System of Training and Control of Knowledge,
Based on Adaptive Semantic Models . . . . . . . . . . . . . . . . . . . . . . . . . . 595
Tamara Shikhnabieva and Sergey Beshenkov
xii Contents

Use of Electronic Mind Maps for Creation of Flexible

Educational Information Environments . . . . . . . . . . . . . . . . . . . . . . . . 605
Marina Mamontova, Boris Starichenko, Sergey Novoselov
and Margarita Kusova
Exploiting the Characteristics of Lecturers Based on Faculty
Performance Evaluation Forms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 617
Thuc-Doan Do, Thuy-Van T. Duong and Ngoc-Phien Nguyen
Teaching Big Data Technology Practices in Cloud Environment . . . . . . 631
Alexander Shmid, Boris Pozin and Mikhail Ageykin

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641

 Part I
Smart University: Conceptual Modeling
Smart University Taxonomy: Features,
Components, Systems

Vladimir L. Uskov, Jeffrey P. Bakken, Akshay Pandey,

Urvashi Singh, Mounica Yalamanchili and Archana Penumatsa

Abstract Smart education creates unique and unprecedented opportunities for

academic and training organizations in terms of higher standards and innovative
approaches to (1) learning and teaching strategies—smart pedagogy, (2) unique
highly technological services to local on-campus and remote/online students,
(3) set-ups of innovative smart classrooms with easy local/remote student-to-faculty
interaction and local/remote student-to-student collaboration, (4) design and
development of Web-based rich multimedia learning content with interactive pre-
sentations, video lectures, Web-based interactive quizzes and tests, and instant
knowledge assessment. This paper presents the outcomes of an ongoing research
project aimed to create smart university taxonomy and identify main features,
components, technologies and systems of smart universities that go well beyond
those in a traditional university with predominantly face-to-face classes and
learning activities.

Keywords Smart university ⋅ Smartness features ⋅ Smart university components ⋅

⋅
Systems Smart pedagogy

1 Introduction

The “smart university” (SmU) concept and several related concepts, such as smart
learning environment, smart campus, smart education, smart e-learning, smart
training, and smart classrooms were introduced just several years ago; they are in
permanent evolution and improvement since that time [1, 2].

V.L. Uskov (✉) ⋅ A. Pandey ⋅ U. Singh ⋅ M. Yalamanchili ⋅ A. Penumatsa

Department of Computer Science and Information Systems, InterLabs Research Institute,
Bradley University, Peoria, Illinois, USA
e-mail: uskov@fsmail.bradley.edu; uskov@bradley.edu
J.P. Bakken
The Graduate School, Bradley University, Peoria, Illinois, USA
e-mail: jbakken@fsmail.bradley.edu

© Springer International Publishing Switzerland 2016 3

V.L. Uskov et al. (eds.), Smart Education and e-Learning 2016,
Smart Innovation, Systems and Technologies 59,
DOI 10.1007/978-3-319-39690-3_1
4 V.L. Uskov et al.

Smart education is rapidly gaining popularity among the world’s best univer-
sities because modern, sophisticated smart technologies, smart systems and smart
devices create unique and unprecedented opportunities for academic and training
organizations in terms of higher standards and innovative approaches to (1) edu-
cation, learning and teaching strategies, (2) unique services to local on-campus and
remote/online students, (3) set-ups of highly technological smart classrooms with
easy local/remote student-to-faculty interaction and local/remote student-to-student
collaboration, (4) design and development of Web-based rich multimedia learning
content with interactive presentations, video lectures, Web-based interactive quiz-
zes and tests, instant knowledge assessment, etc. Additionally, “the analysts fore-
cast the global smart education market to grow at a CAGR of 15.45 % during the
period 2016–2020” [3]. “Markets and Markets forecasts the global smart education
& learning market to grow from $105.23 Billion in 2015 to $446.85 Billion in
2020, at a Compound Annual Growth Rate (CAGR) of 24.4 %” [4].
Therefore, it is necessary to perform active research and obtain a clear under-
standing of what main features, components, technologies, software, hardware,
pedagogy, faculty, etc. will be required by SmUs in the near future.

2 Smart University: Literature Review

Recently, various creative researchers and developers began presenting their vision
of SmU concepts and principles; a brief summary of several remarkable publica-
tions on such concepts is given below.
Smart University. Tikhomirov’s [5] vision is that “Smart University is a con-
cept that involves a comprehensive modernization of all educational processes. …
The smart education is able to provide a new university, where a set of ICT and
faculty leads to an entirely new quality of the processes and outcomes of the
educational, research, commercial and other university activities. … The concept of
Smart in education area entails the emergence of technologies such as smart boards,
smart screens and wireless Internet access from everywhere”.
Smart Learning Environment. Hwang [6] presented a concept of smart
learning environments “… that can be regarded as the technology-supported
learning environments that make adaptations and provide appropriate support (e.g.,
guidance, feedback, hints or tools) in the right places and at the right time based on
individual learners’ needs, which might be determined via analyzing their learning
behaviors, performance and the online and real-world contexts in which they are
situated. … (1) A smart learning environment is context-aware; that is, the learner’s
situation or the contexts of the real-world environment in which the learner is
located are sensed… (2) A smart learning environment is able to offer instant and
adaptive support to learners by immediate analyses of the needs of individual
learners from different perspectives… (3) A smart learning environment is able to
adapt the user interface (i.e., the ways of presenting information) and the subject
Smart University Taxonomy: Features, Components, Systems 5

contents to meet the personal factors (e.g., learning styles and preferences) and
learning status (e.g., learning performance) of individual learners”.
Smart Education. IBM [7] defines smart education as follows: “A smart,
multi-disciplinary student-centric education system—linked across schools, tertiary
institutions and workforce training, using: (1) adaptive learning programs and
learning portfolios for students, (2) collaborative technologies and digital learning
resources for teachers and students, (3) computerized administration, monitoring
and reporting to keep teachers in the classroom, (4) better information on our
learners, (5) online learning resources for students everywhere”.
Cocoli et al. [8] described smart education as follows: “Education in a smart
environment supported by smart technologies, making use of smart tools and smart
devices, can be considered smart education… . In this respect, we observe that
novel technologies have been widely adopted in schools and especially in uni-
versities, which, in many cases, exploit cloud and grid computing, Next Generation
Network (NGN) services and portable devices, with advanced applications in
highly interactive frameworks … smart education is just the upper layer, though the
most visible one, and other aspects must be considered such as: (1) communication;
(2) social interaction; (3) transport; (4) management (administration and courses);
(5) wellness (safety and health); (6) governance; (7) energy management; (8) data
storage and delivery; (9) knowledge sharing; (10) IT infrastructure”.
Smart Campus. Kwok [9] defines intelligent campus (i-campus) “… a new
paradigm of thinking pertaining to a holistic intelligent campus environment which
encompasses at least, but not limited to, several themes of campus intelligence, such
as holistic e-learning, social networking and communications for work collabora-
tion, green and ICT sustainability with intelligent sensor management systems,
protective and preventative health care, smart building management with automated
security control and surveillance, and visible campus governance and reporting”.
Xiao [10] envisions smart campus as follows: “Smart campus is the outcome of
the application of integrating the cloud computing and the internet of things. …The
application framework of smart campus is a combination of IoT and cloud com-
puting based on the high performance computing and internet”.
Smart Teachers. Abueyalaman [11] argues “A smart campus depends on an
overarching strategy involving people, facilities, and ongoing faculty support as
well as effective use of technology…. A smart campus deploys smart teachers and
gives them smart tools and ongoing support to do their jobs while assessing their
pedagogical effectiveness using smart evaluation forms”.
Smart Learning Communities. Adamko et al. [12] describe features of smart
learning community applications as follows: “… the requirements of the smart
community applications are the following: (1) sensible—the environment is sensed
by sensors; (2) connectable—networking devices bring the sensing information to
the web; (3) accessible—the information is published on the web, and accessible to
the users; (4) ubiquitous—the users can get access to the information through the
web, but more importantly in mobile any time and any place; (5) sociable—a user can
publish the information through his social network; (6) sharable—not just the data,
6 V.L. Uskov et al.

but the object itself must be accessible and addressable; (7) visible/augmented—
make the hidden information seen by retrofitting the physical environment”.
Smart Classrooms. An overview of smart classrooms of the first generations
and requirements for second generation smart classrooms is available [13].

3 Research Project Goal and Objectives

The performed analysis of these and multiple additional existing publications and
reports relevant to (1) smart systems, (2) smart technologies, (3) smart devices,
(4) smart universities, (5) smart campuses, (6) smart classrooms, and (7) smart
learning environments undoubtedly shows that “smart university” as a topic should
be in the center of multiple research, design and development projects in upcoming
years. It is expected that, in the near future, SmU concepts, features,
hardware/software solutions and technologies will have a significant role and be
actively deployed by leading academic intuitions—smart universities in the world.
Project Goal. The overall goal of the ongoing multi-aspect research project is to
create a taxonomy of a smart university, i.e. to identify and classify a SmU’s main
(1) features, (2) components (smart classrooms, technological resources—systems
and technologies, human resources, financial resources, services, etc.), (3) relations
(links) between components, (4) interfaces, (5) inputs, (6) outputs, and
(7) limits/constraints. The premise it that to-be-developed SmU taxonomy will
(1) enable us to identify and predict most effective software, hardware, pedagogy,
teaching/learning activities, services, etc. for the next generation of a university—
smart university, and (2) help traditional universities to understand, identify and
evaluate paths for a transformation into a smart university.
Project Objectives. The objectives of this project were to identify an SmU’s
main (1) features, (2) components, and (3) systems that go well beyond those in a
traditional university with predominantly face-to-face classes and learning activi-
ties. Due to limited space, we present a summary of up-to-date research outcomes
below.

4 Research Project Outcomes

4.1 Smart University: Distinctive Features

Our vision of SmUs is based on the idea that SmUs—as a smart system—should
implement and demonstrate significant maturity at various “smartness” levels or
smart features, including (1) adaptation, (2) sensing (awareness), (3) inferring
(logical reasoning), (4) self-learning, (5) anticipation, and (6) self-organization and
re-structuring (Table 1).
Smart University Taxonomy: Features, Components, Systems 7

Table 1 SmU distinctive features (that go well beyond features of a traditional university)
SmU smartness levels Details Possible examples (limited to 3)
Adaptation SmU ability to automatically modify • SmU easy adaptation to new style
its business functions, of learning and/or teaching
teaching/learning strategies, (learning-by-doing, flipped
administrative, safety, physical, classrooms, etc.) and/or courses
behavioral and other characteristics, (MOOCs, SPOCs, open education
etc. to better operate and perform its and/or life-long learning for
main business functions (teaching, retirees, etc.)
learning, safety, management, • SmU easy adaptation to needs of
maintenance, control, etc.) students with disabilities
(text-to-voice or voice-to-text
systems, etc.)
• SmU easy network adaptation to
new technical platforms (mobile
networking, tablets, mobile
devices with iOS and Android
operating systems, etc.)
Sensing (awareness) SmU ability to automatically use • Various sensors of a Local Action
various sensors and identify, Services (LAS) system to get data
recognize, understand and/or regarding power use, lights,
become aware of various events, temperature, humidity, safety,
processes, objects, phenomenon, etc. security, etc.
that may have impact (positive or • Smart card (or biometrics) readers
negative) on SmU’s operation, to open doors to mediated lecture
infrastructure, or well-being of its halls, computer labs, smart
components—students, faculty, classrooms and activate
staff, resources, properties, etc. features/software/hardware that
are listed in user’s profile
• Face, voice, gesture recognition
systems and corresponding
devices to retrieve and process
data about students’ class
attendance, class activities, etc.
Inferring (logical SmU ability to automatically make • Student Analytics System
reasoning) logical conclusion(s) on the basis of (SAS) to create (update) a profile
raw data, processed information, of each local or remote student
observations, evidence, based on his/her interaction,
assumptions, rules, and logic activities, technical skills, etc.
reasoning • Local Action Services
(LAS) campus-wide system to
analyze data from multiple sensors
and make conclusions (for ex:
activate actuators and close/lock
doors in all campus buildings
and/or labs, turn off lights, etc.)
• SAS can recommend
administrators take certain
pro-active measures regarding a
student
(continued)
8 V.L. Uskov et al.

Table 1 (continued)
SmU smartness levels Details Possible examples (limited to 3)
Self-learning SmU ability to automatically obtain, • Learning from active use of
acquire or formulate new or modify innovative software/hardware
existing knowledge, experience, or systems—Web-lecturing systems,
behavior to improve its operation, class recording systems, flipped
business functions, performance, class systems, etc.
effectiveness, etc. (A note: • Learning from anonymous
Self-description, self-discovery and Opinion Mining System (OMS)
self-optimization features are a part
• Learning from different types of
of self-learning)
classes—MOOCs, blended,
online, SPOCs, etc.
Anticipation SmU ability to automatically think • Campus-wide Safety System
or reason to predict what is going to (CSS) to anticipate, recognize and
happen, how to address that event, act accordingly in case of various
or what to do next events on campus
• Enrollment Management System
to predict, anticipate, and control
variations on student enrollment
• University-wide Risk
Management System (snow days,
tornado, electricity outage, etc.)
Self-organization and SmU ability automatically to change • Automatic configuration of
configuration, its internal structure (components), systems, performance parameters,
re-structuring, and self-regenerate and self-sustain in sensors, actuators and features in a
recovery purposeful (non-random) manner smart classroom in accordance
under appropriate conditions but with instructor’s profile
without an external agent/entity. • Streaming server automatic
(A note: Self-protection, closedown and recovery in case of
self-matchmaking, and self-healing temp electrical outage
are a part of self-organization)
• Automatic re-configuration of
wireless sensor network
(WSN) because nodes may join or
leave spontaneously (i.e. evolving
network typology),
university-wide cloud computing
(with multiple clients and
services), etc.

4.2 Smart University: Distinctive Main Components

SmUs may have numerous components of a traditional university; however, it must

have multiple additional components to implement and maintain SmU distinctive
features that are described in Table 1. Based on our vision of SmUs and outcomes
of our research, the SmU main distinctive components should include at least those
that are described in Table 2 below.
Smart University Taxonomy: Features, Components, Systems 9

Table 2 SmU main components and main distinctive sub-components (that go well beyond
components of a traditional university)
SmU components SmU distinctive sub-components (that go well beyond those in a traditional
university)
Software systems • Web-lecturing systems (with video capturing and computer screen capturing
functions) for learning content development pre-class activities
• Smart classroom in-class activities recording systems
• Smart cameraman software systems
• Systems for seamless collaborative learning (of both local and remote students) in
smart classroom and sharing learning content/documents
• Collaborative Web-based audio/video one-to-one and many-to-many
communication systems
• Systems to host, join, form and evaluate group discussions (including both local
and remote students)
• Systems to replay automatically recorded class activities and lectures for post-class
review and activities (by both local and remote students)
• Repositories of digital learning content and online (Web) resources, learning
portals
• Smart learning analytics and smart teaching analytics systems
• Speaker/instructor motion tracking systems
• Speech/voice recognition systems
• Speech-to-text systems
• Text-to-voice synthesis systems
• Face recognition systems
• Emotion recognition systems
• Gesture (activity) recognition systems
• Context (situation) awareness systems
• Automatic translation systems (from/to English language)
• Intelligent cyber-physical systems (for safety and security)
• Various smart software agents
• Power/light/HVAC consumption monitoring system(s)
Technology • Internet-of-Things technology
• Cloud computing technology
• Web-lecturing technology
• Collaborative and communication technologies
• Ambient intelligence technology
• Smart agents technology
• Smart data visualization technology
• Augmented and virtual reality technology
• Computer gaming (serious gaming) technology
• Remote (virtual) labs
• 3D visualization technology
• Wireless sensor networking technology
• RFID (radio frequency identification) technology
• Location awareness technologies (indoor and outdoor)
• Sensor technology (motion, temperature, light, humidity, etc.)
Hardware/equipment • Panoramic video cameras
• Ceiling-mounted projectors (in some cases, 3D projectors)
• SMART boards and/or interactive white boards
• Smart pointing devices
• Controlled and self-activated microphones and speakers
• Interconnected big screen monitors or TVs (“smart learning cave”)
• Interconnected laptops or desktop computers
• Smart card readers
• Biometric-based access control devices
• Robotic controllers and actuators
(continued)
10 V.L. Uskov et al.

Table 2 (continued)
SmU components SmU distinctive sub-components (that go well beyond those in a traditional
university)
Smart curricula • Adaptive programs of study—major and minor programs, concentration and
certificate programs with variable structures adaptable to types of
students/learners, smart pedagogy, etc.
• Adaptive courses, lessons and learning modules with variable components and
structure suitable for various types of teaching—face-to-face, blended, online,
types of students/learners, smart pedagogy, etc.
Students, learners, • Students and/or learners with blended or flexible learning
faculty • Fully remote (or fully online) students and/or learners
• Life-long learners (retirees) in open education
• Students with disabilities
• Smart faculty (smart instructors)
Smart pedagogy Active utilization and, if needed, adaptable combination of the following innovative
types of pedagogy (teaching strategies):
• Learning-by-doing (including active use of virtual labs)
• Collaborative learning
• e-Books
• Learning analytics
• Adaptive teaching
• Student-generated learning content
• Serious games- and gamification-based learning
• Flipped classroom
• Project-based learning
• Bring-Your-Own-Device
• Smart robots (robotics) based learning
Smart classrooms Smart classrooms with corresponding technologies, software hardware systems, and
smart pedagogy for smart education

4.3 Smart University: Distinctive Software Systems

As a part of this research project, for several classes of selected software systems, in
Table 2 we
(1) analyzed about 10–15 existing systems usually—including both open source
and commercial systems—by means of (a) review of system’s functions and
features, (b) review of system’s demo version, (c) installation and testing of
the systems, and (d) review of users and analysts’ feedback,
(2) identified a list of main functions of those systems—functions to be required
by SmUs, and (3) evaluated and ranked those systems. A brief summary of our
research outcomes for selected classes of software systems for SmUs is pre-
sented in Table 3 below. A detailed list of references to all analyzed and
mentioned below systems is available at Towards Smart University project
web site at Bradley University at [14].
Smart University Taxonomy: Features, Components, Systems 11

Table 3 Selected classes of software systems to be used by SmUs [14]

Class of systems Open-source systems Commercial systems Our choice
(1-best)
In-class activities recording • Opencast • Panopto 1—Opencast
systems • ClassX • Echo360 Lect. Cap. 1—Panopto
• Kaltura • Camtasia Studio 2—Kaltura
• openEyA • Mediasite Lecture C. 2—Mediasite
• Lecture Record.x2 • Tegrity 3—ClassX
• VSDC Video Ed. • Valt 3—Echo360 L.C.
• CamStudio • Adobe Presenter 11
• SameView • YuLa Lecture/Room
C.
Instructor-to-remote • Skype • WebEx Meeting 1—Hangouts
students audio/video Center
conferencing systems • BigBlueButton • TurboMeeting 1—BlackBoard C.
(one-to-many,
• Open meetings • Adobe Connect 2—
many-to-many)
BigBlueButton
• DimDim • Citrix 2—Adobe
connect
• Mconf • Netop Vision ME 3—Skype
• BlueJeans • AB Tutor 3—GlobalMeet
• Jitsi • SoftLink
• Hangouts • LAN School
• JoinMe • GoToMeeting
• MeetingBurner • GlobalMeet
• WebHuddle • AnyMeeting
• Zoom • BlackBoard Collabor.
Web lecturing systems for • InterLabs • Camtasia Studio 1—CamStudio
pre-class learning content • ActivePresenter • Adobe Presenter 11 1—Camtasia
development activities Stud.
• Jing • Movavi Studio V7 2—Ezvid Scr.Rec.
• Webinaria • CamVerse 1.95 2—Adobe Pres 11
• Rylstim • WM Recorder Bundle 3—Screen-O-Mat.
• IceCream screen • Debut Video Capture 3—Movavi Stud.
rec.
• CamStudio • Fraps3.5.99
• Screen-O-Matic • Snagit 12
• Flash Back Exp. • 1AVCapture
Rec.
• Ezvid Screen Rec. • ScreenPresso
Instructor motion tracking • Motion • Qualisys 1—Motion
systems • Genious Vis. NVR • Bosh Security 1—Bosh Security
• iSpy • Honeywell Mot.Sens. 2—Voodoo C.T.
• OptiTrack • Camera Viewer Pro 2—Qualisys
• Zoneminder • Netcam Studio 3—OptiTrack
• Voodoo Camera Tr. 3—Netcam Studio
(continued)
12 V.L. Uskov et al.

Table 3 (continued)
Class of systems Open-source systems Commercial systems Our choice
(1-best)
Speech/voice recognition • HDecode • Dragon Natur.Sp. 1—Jasper
systems • JULIUS • IBM ViaVoice 1—Dragon N.S.
• KALDI • LH Voice Express 2—CSLU TK
• CMU Sphinx • Briana 2—Naunce Rec.
• SHoUT Toolkit • Kurzweil 3000 3—CMU Sphinx
• SIMON • IVR with SR 3—ViaTalk
• eSpeak • Tazti
• Jasper • Speechlogger
• EmacSpeak • iSpeech Translator
• MARF • Rubidium
• IVONA • ViaTalk
• CSLU Toolkit • ClapCommander
• iListen • Naunce Recognizer
Gesture recognition systems • OpenGesture • GestureTek 1—GRT
• GRT • Cognitec 1—Myo
• GR Engine • Omek 2—HandVu
• iGesture • PointGrab 2—GestureTek
• HandVu • SoftKinetic 3—iGesture
• LinHand • Myo 3—Rithmio
• GestureWorks • Rithmio
Face recognition systems • OpenBR • Cognitec FaceVACS 1- OpenBR
• OpenCV • EmoVu 1—FaceVACS
• Skybiometry • Kairos 2—FaceMark
• FaceMark • Eyeface 2—EmoVu
• Libface • Rekognition 3—Liccv
• Libccv • Face++ 3—Kairos
Collaborative learning • Cynapse • Mikogo 1—Cynapse
systems • Voki • Socrative 1—Socrative
• Storybirds • Weebly 2—Sakai
• Moodle • Edmodo 2—ClassDojo
• Sakai • ClassDojo 3—Moodle
Context/situation awareness • SARA 1—Qognify
systems • Magitti 2—Magitti
• Qognify 3—SARA

5 Conclusions

The performed research, and obtained research findings and outcomes enabled us to
make the following conclusions:
(1) Leading academic intuitions all over the world are investigating ways to
transform the traditional university into a smart university and benefit from the
Smart University Taxonomy: Features, Components, Systems 13

advantages of a smart university. Smart University concepts, principles,

technologies, systems, and pedagogy will be essential parts of multiple
research, design and development projects in upcoming years.
(2) It is necessary to create a taxonomy of a smart university, i.e. to identify and
classify SmU main (1) features, (2) components (smart classrooms, techno-
logical resources—systems and technologies, human resources, financial
resources, services, etc.), (3) relations (links) between components, (4) inter-
faces, (5) inputs, (6) outputs, and (7) limits/constraints. The premise it that
to-be-developed SmU taxonomy will (1) enable us to identify and predict most
effective software, hardware, pedagogy, teaching/learning activities, services,
etc. for the next generation of a university—smart university, and (2) help
traditional universities to understand, identify and evaluate paths for a trans-
formation into a smart university.
(3) Our vision of SmUs is based on the idea that SmUs—as a smart system—
should implement and demonstrate significant maturity at various “smartness”
levels or distinctive smart features, including (1) adaptation, (2) sensing
(awareness), (3) inferring (logical reasoning), (4) self-learning, (5) anticipa-
tion, and (6) self-organization and re-structuring—the corresponding research
outcomes are presented in Table 1.
(4) Based on our vision of SmUs, the identified SmU main components are
presented in Table 2, and multiple analyzed and ranked software systems of
selected classes to be used by SmU in Table 3.
Based on obtained research findings and outcomes, and developed SmU fea-
tures, components and systems, the future steps in this research project are to
(a) implement, test, validate, and analyze various identified software and hardware
systems, technologies and smart pedagogy in smart classroom environment,
(b) perform summative and formative evaluations of local and remote students and
gather sufficient data on the quality of SmU main components—hardware, soft-
ware, technologies, services, etc.).

Acknowledgments The authors would like to thank Ms. Colleen Heinemann, Mr. Rajat Palod,
Mr. Srinivas Karri, Ms. Supraja Talasila, Mr. Siva Margapuri, Ms. Aishwarya Doddapaneni, Mr.
Harsh Mehta, Mr. Priynk Bondili, Ms. Divya Doddi, and Ms. Rekha Kondamudi—the research
associates of the InterLabs Research Institute and/or graduate students of the Department of
Computer Science and Information Systems at Bradley University—for their valuable contribu-
tions into this research project.
This research is partially supported by grant REC # 1326809 at Bradley University [14].

References

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edu/Smart_Education_Project/Systems
Smart Universities, Smart Classrooms
and Students with Disabilities

Jeffrey P. Bakken, Vladimir L. Uskov, Archana Penumatsa

and Aishwarya Doddapaneni

Abstract To better educate in-classroom and remote students we will need to

approach education and how we teach various types of students differently. In
addition, students these days are more technological than ever and are demanding
new and innovative ways to learn. One of the most promising approaches is based
on design and development of smart universities and smart classrooms. This paper
presents the up-to-date outcomes of research project that is aimed on analysis of
students with disabilities and how they might benefit from smart software and
hardware systems, and smart technology.

⋅
Keywords Smart university Smart classroom ⋅
Learning disabilities ⋅ Visual
impairments ⋅
Hearing impairments ⋅
Speech and language disabilities ⋅ Smart
system

1 Introduction

Smart universities (SmU) and smart classrooms (SmC) can create multiple
opportunities for students to learn material in a variety of ways. In addition, they
can give access to materials in a variety of ways. Although not designed or even

J.P. Bakken (✉)

The Graduate School, Bradley University, Peoria, Illinois, USA
e-mail: jbakken@fsmail.bradley.edu
V.L. Uskov ⋅ A. Penumatsa ⋅ A. Doddapaneni
Department of Computer Science and Information Systems,
InterLabs Research Institute, Bradley University, Peoria, Illinois, USA
e-mail: uskov@fsmail.bradley.edu
A. Penumatsa
e-mail: apenumatsa@fsmail.bradley.edu
A. Doddapaneni
e-mail: adoddapaneni@fsmail.bradley.edu

© Springer International Publishing Switzerland 2016 15

V.L. Uskov et al. (eds.), Smart Education and e-Learning 2016,
Smart Innovation, Systems and Technologies 59,
DOI 10.1007/978-3-319-39690-3_2
16 J.P. Bakken et al.

conceptualized to benefit students with disabilities, this concept would definitely

have an impact on the learning and access to material for students with disabilities.

1.1 Smart Classrooms: Literature Review

Pishva and Nishantha define a SmC as an intelligent classroom for teachers

involved in distant education that enables teachers to use a real classroom type
teaching approach to teach distant students. “Smart classrooms integrate
voice-recognition, computer-vision, and other technologies, collectively referred to
as intelligent agents, to provide a tele-education experience similar to a traditional
classroom experience” [1].
Glogoric, Uzelac and Krco addressed the potential of using Internet-of-Things
(IoT) technology to build a SmC. “Combining the IoT technology with social and
behavioral analysis, an ordinary classroom can be transformed into a smart class-
room that actively listens and analyzes voices, conversations, movements, behavior,
etc., in order to reach a conclusion about the lecturers’ presentation and listeners’
satisfaction” [2].
Slotta, Tissenbaum and Lui described an infrastructure for SmC called the
Scalable Architecture for Interactive Learning (SAIL) that “employs learning
analytic techniques to allow students’ physical interactions and spatial positioning
within the room to play a strong role in scripting and orchestration” [3].
Koutraki, Efthymiou, and Grigoris developed a real-time, context-aware system,
applied in a SmC domain, which aims to assist its users after recognizing any
occurring activity. The developed system “…assists instructors and students in a
smart classroom, in order to avoid spending time in such minor issues and stay
focused on the teaching process” [4].
Given all the research available that focus on SmC, no literature was located that
dealt with analysis of possible impact of SmCs concepts, features and functionality
on students with disabilities.

1.2 Smart Universities: Literature Review

Primary focus of smart universities is in the education area, but they also drive the
change in other aspects such as management, safety, and environmental protection.
The availability of newer and newer technology reflects on how the relevant pro-
cesses should be performed in the current fast changing digital era. This leads to the
adoption of a variety of smart solutions in university environments to enhance the
quality of life and to improve the performances of both teachers and students.
Nevertheless, we argue that being smart is not enough for a modern university. In
fact, all universities should become smarter in order to optimize learning. By
Smart Universities, Smart Classrooms and Students … 17

“smarter university” we mean a place where knowledge is shared between

employees, teachers, students, and all stakeholders in a seamless way [5].
Aqeel-ur-Rehman et al. in [6] present the outcomes of their research on one
feature of future SmU—sensing with RFID (Radio frequency identification) tech-
nology; it should benefit students and faculty with identification, tracking, smart
lecture room, smart lab, room security, smart attendance taking, etc.
Lane and Finsel emphasize an importance of big data movement and how it
could help to build smarter universities. “Now is the time to examine how the Big
Data movement could help build smarter universities—in situations that can use the
huge amounts of data they generate to improve the student learning experience,
enhance the research enterprise, support effective community outreach, and advance
the campus’s infrastructure. While much of the cutting-edge research being done
with Big Data is happening at colleges and universities, higher education has yet to
turn the digital mirror on itself to innovate the academic enterprise” [7]. Big data
analytics systems will strongly support inferring feature of a SmU.
Al Shimmary et al. analyzed advantages of using RFID and WSN technology in
development of SmU. “The developed prototype shows how evolving technologies
of RFID and WSN can add in improving student’s attendance method and power
conservation” [8]. RFID, WSN as well as Internet-of-Things technology are
expected to be significant parts of a SmU and strongly support sending charac-
teristics of smart universities.
Doulai in [9] presents a developed system for a smart campus. This system “…
offers an integrated series of educational tools that facilitate students’ communi-
cation and collaboration along with a number of facilities for students’ study aids
and classroom management. The application of two technologies, namely dynamic
web-based instruction and real-time streaming, in providing support for “smart and
flexible campus” education is demonstrated. It is shown that the usage of
technology-enabled methods in university campuses results in a model that works
equally well for distance students and learners in virtual campuses”.
Yu et al. argue that “… with the development of wireless communication and
pervasive computing technology, smart campuses are built to benefit the faculty and
students, manage the available resources and enhance user experience with
proactive services. A smart campus ranges from a smart classroom, which benefits
the teaching process within a classroom, to an intelligent campus that provides lots
of proactive services in a campus-wide environment” [10]. The authors described 3
particular systems–Wher2Study, I-Sensing, and BlueShare—that provide sensing,
adaptation, and inferring smart features of a SmU.

1.3 Research Project Goal and Objectives

The performed analysis of these and multiple additional publications and reports
relevant to (1) SmU, (2) SmC, (3) smart learning environments (SmLE), (4) smart
technologies, and (5) smart systems undoubtedly shows that (a) SmU, (b) SmC,
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