Big Data and Learning Analytics in Higher Education: Current Theory and Practice

287 Pages·2017·15.64 MB·English

by  Ben Kei Daniel (eds.)

Ben Kei Daniel Editor
Big Data and
Learning
Analytics in
Higher Education
Current Theory and Practice
Big Data and Learning Analytics
in Higher Education

Ben Kei Daniel
Editor
Big Data and Learning
Analytics in Higher
Education
Current Theory and Practice
Editor
Ben Kei Daniel
University of Otago
Dunedin , New Zealand
ISBN 978-3-319-06519-9 ISBN 978-3-319-06520-5 (eBook)
DOI 10.1007/978-3-319-06520-5
Library of Congress Control Number: 2016947402
© Springer International Publishing Switzerland 2017
T his work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of
the material is concerned, specifi cally the rights of translation, reprinting, reuse of illustrations, recitation,
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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 specifi c statement, that such names are exempt from the relevant
protective laws and regulations and therefore free for general use.
T he 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
This book is dedicated to one of my brothers,
Mr. Lowo Joseph Daniel, who is thrilled
with the idea of big data and analytics.
I hope this book will inspire you Lowo to
follow your dreams along this route.

Foreword
E ducational data science (EDS) is an emergent interdisciplinary fi eld of inquiry,
which brings together computer science, education, statistics, and other social sci-
ences to examine and understand social and technical phenomena. EDS researchers
and practitioners utilize various sets of procedures and techniques to gather, orga-
nize, manipulate, and interpret rich educational data sources. EDS also presents
techniques for merging voluminous and diverse data sources together, ensuring con-
sistency of these data sets, and creating unifi ed visualizations to aid in understand-
ing of complex data. Further, in this fi eld, educational data scientists build
mathematical models and use them to communicate insights/fi ndings to other edu-
cational specialists and scientists in their team and if required to nonexpert
stakeholders.
As a subdiscipline of data science, EDS originated from discussions held during
several workshops between years 2000 and 2007, mainly from the Educational Data
Mining (EDM) Conference in 2008. EDM itself as a fi eld of research is concerned
with developing methods for exploring increasingly large-scale educational data, to
better understand students and the settings in which they learn.
In the last years, other two international conferences were held, focusing on EDS
themes. Learning and Knowledge Analytics (LAK2011) was the fi rst conference,
followed by Learning at Scale (L@S) in 2014. Lately, conferences in this area focus
discourse on exploring the impact of big data and learning analytics in fostering
learning and teaching and in engaging the growing community of researchers, prac-
titioners, and learners within higher education to build tools, procedures, and tech-
niques to explore and solve complex learning problems.
T his book introduces the reader to two current topics in EDS, big data and learn-
ing analytics. Learning analytics examines the challenges of collecting, analyzing,
and reporting data with the specifi c intent of improving student learning. Big data
(BD), on the other hand, offers the potential to tackle a wide range of issues that
appear when collecting and working with a large volume, variety, and velocity of
data. Specifi cally, applying BD in education or learning at scale enables researchers
to work with large numbers of students, where “large” is preferably thousands of
students but can also apply to hundreds in in-person settings.
vii
viii Foreword
T his book contains 15 chapters written by 32 international authors. It is orga-
nized into two parts: big data and learning analytics. Chapters cover both introduc-
tion, theory, limitations, methods, techniques, ethical considerations, recent trends,
future research, case studies, and examples. They provide a comprehensive and full
understanding of the current state of big data and learning analytics within higher
education. The book is written in a simple style, making it accessible to under-
graduate students and to other readers who might be interested in learning about big
data and learning analytics within the realm of higher education. Because of the
simplicity of presentation and illustrative nature of the issues presented, the book
can serve both as an introductory text and as an advanced text for students, policy-
makers, and researchers interested in exploring issues related to these two current
hot topics of EDS.
University of Cordoba Cristóbal Romero
Córdoba , Spain
Acknowledgments

My research is funded by the Department of Higher Education Development Centre
(HEDC), University of Otago, through the Performance-Based Research Fund
(PBRF). I want to thank the university and the government of New Zealand for
funding my research. I want to thank all contributors for sharing their research.
Thanks go to my colleague, Russell Butson, for contributing early ideas that led to
the development and maturity of the book. I want to express my sincere apprecia-
tion to the members of the Springer technical and editorial team, especially to Ms.
Anitha Chellamuthu and Lakshmi Narayanan R of SPi Technologies for overseeing
the project, as well as to the anonymous reviewers for their constructive insights
that have signifi cantly helped in improving the quality of work presented in this
book. Thanks go to my wife Michelle Daniel, family, and friends for their continu-
ous support while working on the book. Finally, to all who have contributed or
inspired me in one way or another, while working on the project, I thank you!
Educational Technology Ben Kei Daniel
Higher Education Development Centre (HEDC)
University of Otago
Dunedin , New Zealand
ix