Utilizing Learning Analytics to Support Study Success

32 Pages·2019·0.489 MB·English

by  Dirk Ifenthaler, Dana-Kristin Ma

Dirk Ifenthaler · Dana-Kristin Mah
Jane Yin-Kim Yau Editors
Utilizing
Learning
Analytics to
Support Study
Success
Utilizing Learning Analytics to Support Study
Success
Dirk Ifenthaler • Dana-Kristin Mah
Jane Yin-Kim Yau
Editors
Utilizing Learning Analytics
to Support Study Success
[email protected]
BUTUH LENGKAP HUB
Editors
Dirk Ifenthaler Dana-Kristin Mah
University of Mannheim University of Mannheim
Mannheim, BW, Germany Mannheim, BW, Germany
Curtin University
Perth, WA, Australia
Jane Yin-Kim Yau
University of Mannheim
Mannheim, BW, Germany
ISBN 978-3-319-64791-3 ISBN 978-3-319-64792-0 (eBook)
https://doi.org/10.1007/978-3-319-64792-0
Library of Congress Control Number: 2018968406
© Springer Nature Switzerland AG 2019
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Preface
Advances in educational technology have enabled opportunities to provide insight
into how learners engage within the learning environment provided. The resulting
availability of vast amounts of educational data can represent how students interact
with higher education resources, and further analysis may provide useful insights
into learning behaviour, processes, and outcomes. From a holistic point of view,
learning analytics use static and dynamic educational information from digital
learning environments, administrative systems, and social platforms for real-time
modelling, prediction, and optimization of learning processes, learning environ-
ments, and educational decision-making. Accordingly, learning analytics are
expected to provide benefits for all stakeholders (e.g. students, teachers, designers,
administrators) in the higher education arena.
In particular, students may benefit from learning analytics through personalized
and adaptive support of their learning journey. For example, students often enter
higher education academically unprepared and with unrealistic perceptions and
expectations of academic competencies for their studies. Both the inability to cope
with academic requirements and unrealistic perceptions and expectations of univer-
sity life, in particular with regard to academic competencies, are important factors
for leaving the institution prior to degree completion.
Still, research in learning analytics and how they support students at higher edu-
cation institutions is scarce. Therefore, this edited volume Utilizing Learning
Analytics to Support Study Success aims to provide insights into how educational
data and digital technologies contribute towards successful learning and teaching
scenarios. We organized the chapters included in this edited volume into three major
parts: (I) Theoretical and Technological Perspectives Linking Learning Analytics
and Study Success, (II) Issues and Challenges for Implementing Learning Analytics,
and (III) Learning Analytics Case Studies – Practices and Evidence, and closing
with an Epilogue.
In Part I, the first chapter, the authors discuss how learning theories and learning
analytics are important components of educational research and concludes by pro-
posing an iterative loop for educational research employing learning analytics in
which learning theories guide data collection and analyses (Jacqueline Wong,
v
vi Preface
Martine Baars, Björn B. de Koning, Tim van der Zee, Dan Davis, Mohammad
Khalil, Geert-Jan Houben, Fred Paas, Chap. 1). The next chapter presents a critical
reflection on empirical evidence linking study success and learning analytics.
Findings are reported and discussed focussing on positive evidence on the use of
learning analytics to support study success, insufficient evidence on the use of
learning analytics, and link between learning analytics and intervention measures to
facilitate study success (Dirk Ifenthaler, Dana-Kristin Mah, Jane Yin-Kim Yau,
Chap. 2). The next chapter describes how the Study Support Centre (SSC) at Aalen
UAS assists first-year students of all faculties and, in particular, improves their
mathematical skills (Miriam Hommel, Armin Egetenmeier, Ulrike Maier, Chap. 3).
The following chapter shows how a prompting application has been implemented
into an existing university environment by adding a plug-in to the local digital learn-
ing platform which injects user-centric prompts to specific objects within their digi-
tal learning environment. The solution is used to perform various educational
research studies, focussing on effects of prompting for self-regulated learning
(Daniel Klasen, Dirk Ifenthaler, Chap. 4). The final chapter of the first part explores
cognitive and motivational differences between students who drop out and students
who persist. From their findings, the authors consider the monitoring and analysing
of error streaks as a promising way for the design of adaptive instructional interven-
tions in courses where the students have to programme code (Anja Hawlitschek, Till
Krenz, Sebastian Zug, Chap. 5).
In Part II, the first chapter focusses on a practical tool that can be used to identify
risks and challenges that arise when implementing learning analytics initiatives and
discuss how to approach these to find acceptable solutions (Philipp Leitner, Markus
Ebner, Martin Ebner, Chap. 6). Next, the LAPS project is introduced, which is able
to analyse progressions of former students and to make statements on possible risks
for currently enrolled students by using machine learning techniques. The chapter
provides insights into how the project is technically developed and how it can be
used in consultation situations (Mathias Hinkelmann, Tobias Jordine, Chap. 7). The
argument that precourse data could be valuable resources for learning analytics is
explored in the following chapter. The authors discuss the difficulties of collecting
data from open web-based learning environments, from missing data to interactions
between cognitive and meta-cognitive variables (Katja Derr, Reinhold Hübl,
Mohammed Zaki Ahmed, Chap. 8). The next chapter addresses issues and chal-
lenges for implementing writing analytics in higher education through theoretical
considerations that emerge from the literature review and an example application
(Duygu Bektik, Chap. 9). Then, a collaborative research project is presented which
explores the short-term and long-term effects, risks, and benefits of the use of
mobile learning analytics in students’ daily life (Luisa Seiler, Matthias Kuhnel, Dirk
Ifenthaler, Andrea Honal, Chap. 10). The following chapter reviews three categories
of algorithms in light of their application to assessment and student success. The
authors discuss an implementation of these algorithms through a new set of digital
tools, designed to support a community of practice in problem-based instruction
(Philippe J. Giabbanelli, Andrew A. Tawfik, Vishrant K. Gupta, Chap. 11). In the
final chapter of the second part, the researchers studied archival data from online
Preface vii
undergraduate course registrants through mining a dataset to determine trends and
patterns of student success, as determined by the final grade earned in the online
courses (Ellina Chernobilsky, Susan Hayes, Chap. 12).
In Part III, the authors of the first chapter present a teacher-friendly “learning
analytics lifecycle” that seeks to address challenges and critically assess the adop-
tion and impact of a unique solution in the form of an learning analytics platform
that is designed to be adaptable by teachers to diverse contexts (Natasha Arthars,
Mollie Dollinger, Lorenzo Vigentini, Danny Y.-T. Liu, Elsuida Kondo, Deborah
M. King, Chap. 13). Next, the presented study identifies key predictors of persis-
tence and achievement amongst students enrolled in an online English language
course. The study is framed in Deci and Ryan’s self-determination theory (SDT)
and uses data from a precourse student readiness survey, LMS log files, and a course
Facebook page (Danny Glick, Anat Cohen, Eitan Festinger, Di Xu, Qiujie Li, Mark
Warschauer, Chap. 14). The following chapter presents a study which investigates
how participants in a massive open online course (MOOC) designed for working
professionals interacted with various key course components of the MOOC and the
usage patterns connected to participants’ profiles and perceptions (Min Liu, Wenting
Zou, ChengLu Li, Yi Shi, Zilong Pan, Xin Pan, Chap. 15). The final chapter of this
part reports a case study focussing on a capstone unit in business at a university in
Western Australia. Instructors used learning analytics of average weekly eTest
scores, overall average eTest scores, a benchmark assessment score, and study mode
extracted from learning management system (LMS) reports to target areas where
assessment integrity could be improved (Michael Baird, Lesley Sefcik, Steve Steyn,
Chap. 16).
The edited volume closes with an Epilogue reflecting on the contributions of this
edited volume and identifying future research and directions in learning analytics
to enhance study success (Dana-Kristin Mah, Jane Yin-Kim Yau, Dirk Ifenthaler,
Chap. 17).
Without the assistance of experts in the field of learning analytics, the editors
would have been unable to prepare this volume for publication. We wish to thank
our board of reviewers for their tremendous help with both reviewing the chapters
and linguistic editing.
Mannheim, BW, Germany Dirk Ifenthaler
Dana-Kristin Mah
Jane Yin-Kim Yau
Contents
Part I Theoretical and Technological Perspectives
Linking Learning Analytics and Study Success
1 Educational Theories and Learning Analytics:
From Data to Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Jacqueline Wong, Martine Baars, Björn B. de Koning,
Tim van der Zee, Dan Davis, Mohammad Khalil,
Geert-Jan Houben, and Fred Paas
2 Utilising Learning Analytics for Study Success:
Reflections on Current Empirical Findings . . . . . . . . . . . . . . . . . . . . . . 27
Dirk Ifenthaler, Dana-Kristin Mah, and Jane Yin-Kim Yau
3 Supporting Stakeholders with Learning Analytics
to Increase Study Success . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Miriam Hommel, Armin Egetenmeier, and Ulrike Maier
4 Implementing Learning Analytics into Existing
Higher Education Legacy Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Daniel Klasen and Dirk Ifenthaler
5 When Students Get Stuck: Adaptive Remote Labs
as a Way to Support Students in Practical
Engineering Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
Anja Hawlitschek, Till Krenz, and Sebastian Zug
Part II Issues and Challenges for Implementing Learning Analytics
6 Learning Analytics Challenges to Overcome
in Higher Education Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
Philipp Leitner, Markus Ebner, and Martin Ebner
ix
x Contents
7 The LAPS Project: Using Machine Learning
Techniques for Early Student Support . . . . . . . . . . . . . . . . . . . . . . . . . . 105
Mathias Hinkelmann and Tobias Jordine
8 Monitoring the Use of Learning Strategies
in a Web-Based Pre-course in Mathematics . . . . . . . . . . . . . . . . . . . . . . 119
Katja Derr, Reinhold Hübl, and Mohammed Zaki Ahmed
9 Issues and Challenges for Implementing Writing
Analytics at Higher Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
Duygu Bektik
10 Digital Applications as Smart Solutions
for Learning and Teaching at Higher Education Institutions . . . . . . . . 157
Luisa Seiler, Matthias Kuhnel, Dirk Ifenthaler, and Andrea Honal
11 Learning Analytics to Support Teachers’ Assessment
of Problem Solving: A Novel Application for Machine
Learning and Graph Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
Philippe J. Giabbanelli, Andrew A. Tawfik, and Vishrant K. Gupta
12 Utilizing Learning Analytics in Small Institutions:
A Study of Performance of Adult Learners in Online Classes . . . . . . . 201
Ellina Chernobilsky and Susan Hayes
Part III Learning Analytics Case Studies: Practices and Evidence
13 Empowering Teachers to Personalize Learning Support . . . . . . . . . . . 223
Natasha Arthars, Mollie Dollinger, Lorenzo Vigentini,
Danny Y.-T. Liu, Elsuida Kondo, and Deborah M. King
14 Predicting Success, Preventing Failure . . . . . . . . . . . . . . . . . . . . . . . . . . 249
Danny Glick, Anat Cohen, Eitan Festinger, Di Xu,
Qiujie Li, and Mark Warschauer
15 Using Learning Analytics to Examine Relationships
Between Learners’ Usage Data with Their Profiles
and Perceptions: A Case Study of a MOOC Designed
for Working Professionals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275
Min Liu, Wenting Zou, Chenglu Li, Yi Shi, Zilong Pan,
and Xin Pan
16 Learning Analytics Leading to Remote Invigilation
for eTests: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295
Michael Baird, Lesley Sefcik, Steve Steyn, and Connie Price
17 Epilogue: Future Directions on Learning Analytics
to Enhance Study Success . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313
Dana-Kristin Mah, Jane Yin-Kim Yau, and Dirk Ifenthaler
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323
About the Editors
Dirk Ifenthaler is Professor and Chair of Learning, Design, and Technology at the

University of Mannheim, Germany; Adjunct Professor at Deakin University,
Australia; and UNESCO Deputy-Chair of Data Science in Higher Education
Learning and Teaching at Curtin University, Australia. His previous roles include
Professor and Director at Centre for Research in Digital Learning at Deakin
University, Australia; Manager of Applied Research and Learning Analytics at Open
Universities Australia, Australia; and Professor of Applied Teaching and Learning
Research at the University of Potsdam, Germany. He was a 2012 Fulbright Scholar-
in-Residence at the Jeannine Rainbolt College of Education at the University of
Oklahoma, USA. Professor Ifenthaler’s research focusses on the intersection of cog-
nitive psychology, educational technology, learning science, data analytics, and
organizational learning. He developed automated and computer-based methodolo-
gies for the assessment, analysis, and feedback of graphical and natural language
representations as well as simulation and game environments for teacher education.
His research outcomes include numerous co-authored books, book series, book
chapters, journal articles, and international conference papers as well as successful
grant funding in Australia, Germany, and the USA. Professor Ifenthaler is the
Editor-in-Chief of the Springer journal Technology, Knowledge and Learning (www.
springer.com/10758). Dirk is the past president for the AECT (Association for
Educational Communications and Technology) Design and Development Division;
past chair for the AERA (American Educational Research Association) Special
Interest Group Technology, Instruction, Cognition and Learning; and co- programme
chair for the international conference series on Cognition and Exploratory Learning
in the Digital Age (CELDA).
Dana-Kristin Mah is a researcher and consultant in the field of educational tech-

nologies and higher education. In her doctoral thesis, she concentrated on students’
first-year experience in higher education with a focus on academic competencies
and the potential of learning analytics and digital badges to enhance first-year
student retention. She is co-editor of the edited volume Foundations of Digital
Badges and Micro-credentials: Demonstrating and Recognizing Knowledge and
xi