On Personalized Adaptation of Learning Environments

126 Pages·2017·4.46 MB·English

by  Gavriushenko, Mariia

J Y V Ä S K Y L Ä S T U D I E S I N C O M P U T I N G
272
Mariia Gavriushenko
On Personalized Adaptation
of Learning Environments
JYVÄSKYLÄ STUDIES IN COMPUTING 272
Mariia Gavriushenko
On Personalized Adaptation
of Learning Environments
Esitetään Jyväskylän yliopiston informaatioteknologian tiedekunnan suostumuksella
julkisesti tarkastettavaksi yliopiston Agora-rakennuksen Alfa-salissa
joulukuun 19. päivänä 2017 kello 12.
Academic dissertation to be publicly discussed, by permission of
the Faculty of Information Technology of the University of Jyväskylä,
in building Agora, Alfa hall, on December 19, 2017 at 12 o’clock noon.
UNIVERSITY OF JYVÄSKYLÄ
JYVÄSKYLÄ 2017
On Personalized Adaptation
of Learning Environments
JYVÄSKYLÄ STUDIES IN COMPUTING 272
Mariia Gavriushenko
On Personalized Adaptation
of Learning Environments
UNIVERSITY OF JYVÄSKYLÄ
JYVÄSKYLÄ 2017
Editors
Timo Männikkö
Faculty of Information Technology, University of Jyväskylä
Pekka Olsbo, Ville Korkiakangas
Publishing Unit, University Library of Jyväskylä
Cover picture by Mariia Gavriushenko and Oleksiy Khriyenko.
Permanent link to this publication: http://urn.f/URN:ISBN:978-951-39-7287-5
URN:ISBN:978-951-39-7287-5
ISBN 978-951-39-7287-5 (PDF)
ISBN 978-951-39-7286-8 (nid.)
ISSN 1456-5390
Copyright © 2017, by University of Jyväskylä
Jyväskylä University Printing House, Jyväskylä 2017
ABSTRACT
Gavriushenko, Mariia
On Personalized Adaptation of Learning Environments
Jyväskylä: University of Jyväskylä, 2017, 60 p.(+included articles)
(Jyväskylä Studies in Computing
ISSN 1456-5390; 272)
ISBN 978-951-39-7286-8 (nid.)
ISBN 978-951-39-7287-5 (PDF)
Finnish summary
Diss.
This work is devoted to the development of personalized training systems. A
major problem in learning environments is applying the same approach to all
students: i.e., teaching materials, time for their mastering, and a training pro-
gram that is designed in the same way for everyone. Although, each student is
individual, has his own skills, ability to assimilate the material, his preferences
and other. Recently, recommendation systems, of which the system of personal-
ized learning is a part, have become widespread in the learning environments.
On the one hand, this shift is due to mathematical approaches, such as machine
learning and data mining, that are used in such systems while, on the other hand,
the requirements of technological standards "validated" by the World Wide Web
Consortium (W3C). According to this symbiosis of mathematical methods and
advanced technologies, it is possible to implement a system that has several ad-
vantages: identifying current skill levels, building individual learning trajecto-
ries, tracking progress, and recommending relevant learning material.
The analysis of feedback, academic advising, and recommendation systems
underlies the proposed idea. The conducted research demonstrates how to make
learning environments more adaptive to the users according to their knowledge
base, behavior, preferences, and abilities. In this research, a model of a learn-
ing ecosystem based on the knowledge and skills annotations is presented. This
model is a general model of the lifelong learning process. Second, this thesis fo-
cuses on the creation of tools for personalized assessment, recommendation, and
advising. Third, it is concentrated on developing an adaptive learning game for
children, which takes into account the differing perception of words by students
during training.
Keywords: personalized learning, adaptive learning, learning environment, Se-
mantic Web, recommendation system, academic advising
Author Mariia Gavriushenko
Faculty of Information Technology
University of Jyväskylä
Jyväskylä, Finland
Supervisors Professor Dr. Pekka Neittaanmäki
Faculty of Information Technology
University of Jyväskylä
Jyväskylä, Finland
Professor Dr. Tommi Kärkkäinen
Faculty of Information Technology
University of Jyväskylä
Jyväskylä, Finland
Dr. Oleksiy Khriyenko
Faculty of Information Technology
University of Jyväskylä
Jyväskylä, Finland
Professor Dr. Marja Kankaanranta
Faculty of Information Technology
University of Jyväskylä
Jyväskylä, Finland
Reviewers Assistant Professor Yuji Tou
Tokyo Institute of Technology
Tokyo, Japan
Associate Professor Francesco Guerra
University of Modena and Reggio Emilia
Modena, Italy
Opponent Academy Research Fellow Kristian Kiili
Tampere University of Technology
Tampere, Finland
ACKNOWLEDGEMENTS
While I am writing this text, my thesis is ready and I am happy to be at the final
stage of my Ph.D. studies.
I would like to express my deepest appreciation to my Ph.D. Supervisor
Prof. Pekka Neittaanmäki for giving me a possibility to work for the univer-
sity, for believing in me, for supporting and guiding me throughout this research
work.
In addition, my deepest appreciation goes to my co-supervisors Prof. Tommi
Kärkkäinen and Dr. Oleksiy Khriyenko for finding time for regular meetings
and fruitful discussions, for inspiring me with nice ideas and ways to implement
them, for great support in writing papers and mentoring this work, as well as for
friendly attitude.
I am grateful to the reviewers of my dissertation Prof. Francesco Guerra and
Assistant Prof. Yuji Tou for their constructive comments. It is an honour to have
Docent Kristian Kiili as an opponent and I am very grateful to him.
I thank Prof. Vagan Terziyan and Dr. Olena Kaikova for organizing a stu-
dent exchange programme between the University of Jyväskylä in Finland and
Kharkiv National University of Radio and Electronics in Ukraine. And also for
giving me a possibility to complete my Master degree in Finland and start my
academic career at the University of Jyväskylä, as well as for great support and
creative discussions.
I would like to thank all the co-authors of the papers related to this work:
Prof. Pekka Neittaanmäki, Prof. Tommi Kärkkäinen, Prof. Marja Kankaanranta,
Dr Oleksiy Khriyenko, Dr. Mirka Saarela, Iida Porokuokka, Renny Lindberg, Ari
Tuhkala, Laura Karilainen. I am very grateful to them for being involved in this
topic, for discussing inspiring ideas, and developing them together.
I acknowledge the PhD program of the Faculty of Information Technology,
COMAS, and Department of Mathematical Information Technology for gener-
ously funding my PhD research. I thank these funding parties for financial sup-
port in conference participation and INFORTE courses.
I am also grateful to Prof. Heikki Lyyttinen for having an opportunity to
work on Adaptive Learning Games , which could help in children development
and better learning.
As well, I thank all my colleagues who supported me during my research
work, who gave me lots of hints and who shared with me their experiences, and,
of course, for the nice and kind atmosphere.
I thank my dear friend Oleg Zolotukhin for great support and help during
writing this work, and for providing very useful guidelines.
I would also like to thank Ukraine as my homeland, which had given me
an identity, education and vast life experience. I also would like to express my
gratitude towards Finland not only as a country where I have been living for last
six years, but also as a wonderful place where I got my second Master degree and
my job as a researcher.
I am grateful to my beloved boyfriend Andrii Gontarenko for the warmth,
motivation, support and belief in my success, as well as for always being there
for me when needed.
Finally, my biggest thanks to my loving parents and relatives for giving me
vision and motivation to do research, and for giving me strengths to accomplish
this wonderful and important step in my life.
Last, but not least, I wish to thank all my friends for their support and
friendship.
LIST OF ACRONYMS
AA Academic Advising
AL Adaptive Learning
DM Data Mining
EDM Educational Data Mining
ILSS Intelligent Learning Support System
KB Knowledge Base
LA Learning Analytics
LE Learning Environment
LG Learning Game
LMS Learning Management System
LO Learning Object
ML Machine Learning
OWL Web Ontology Language
PL Personalized Learning
PLE Personal Learning Environment
RDF Resource Description Framework
RDFS Resource Description Framework Schema
RS Recommendation System
SW Semantic Web
SWRL Semantic Web Rule Language
URI Uniform Resource Identifier
W3C World Wide Web Consortium
XML Extensible Markup Language