Learning Analytics
J.UCS Special Issue
Martin Ebner
(Department Social Learning, Computer and Information Services
Graz University of Technology, Graz, Austria
martin.ebner@tugraz.at)
Kinshuk
(Faculty of Science and Technology, Athabasca University, Canada
kinshuk@athabascau.ca)
David Wohlhart
(Private University of Teacher Education KPH Graz, Austria
david.wohlhart@kphgraz.at)
Behnam Taraghi
(Department Social Learning, Information Technology Services,
Graz University of Technology, Austria
b.taraghi@tugraz.at)
Vive Kumar
(Faculty of Science and Technology, Athabasca University, Canada
vive@athabascau.ca)
1 Introduction
Already back in 2006 Retalis et al. [Retalis, 06] proposed their first
thoughts on Learning Analytics (LA) and considered interaction
analysis as a promising way to better understand the learner's
behavior. A couple of years later, further activities were organized;
especially Siemens and Long [Siemens, 11] predicted that the most
important factor shaping the future of higher education would be big
data and analytics. Just few months later, the Horizon Report
[Horizon, 11] also described Learning Analytics as a big trend for the
forthcoming years. Since then a number of conferences (for example LAK
111, LAK 12, ...) have been organized and different projects have
been started as well as the topic has been rising on Google
trends2. The number of research publications has also increased
arbitrarily in different directions; for instance to define the
upcoming research field [Siemens, 12] [Duval, 11] [Elias, 11]
[Drachsler, 12], to gather practical experiences [Schön, 12]
[Ebner, 13a] or simply to confine LA from other topics (especially from
Educational Data Mining (EDM)) [Baker, 12].
----
1 Learning Analytics and Knowledge Conference
2 http://www.google.com/trends/explore#q=Learning%20Analytics (last visited October 2014)
2 Special Issue on Learning Analytics
As mentioned above, the analysis and discovery of relations between
human learning and contextual factors that influence these relations
have been one of the contemporary and critical global challenges
facing researchers in a number of areas, particularly in education,
psychology, sociology, information systems, and
computing. Traditionally, these relations concern learner performance
and the effectiveness of the learning context from a summative point
of view. Be it the assessment marks distribution in a classroom
context or the mined pattern of best practices in an apprenticeship
context, analysis and discovery have always addressed the elusive
causal question about the need to best serve learners' learning
efficiency and the need to make informed choices on a learning
context's instructional effectiveness.
Learning efficiency encompasses any and all aspects that concern
"learning" of individual learners or groups of
learners. Examples of learning efficiency aspects include learning
style, metacognitive scaffolds, peer interactions, self-regulation,
co-regulation, social networking, and other learning-oriented
activities and characteristics associated with learners.
Instructional effectiveness encompasses any and all aspects that
concern enhancement of targeted as well as inadvertent "support for
learning". Examples of instructional effectiveness include
pedagogy, andragogy, peer evaluation, software-agent-oriented
guidance, lectures, content, presentation of content, instructional
design, learning objects and other resources, assessment structures,
open learning, and so on.
With the advent of new technologies such as eye-tracking, activities
monitoring, video analysis, content analysis, sentiment analysis and
interaction analysis, the world of learning analytics has emerged as a
vast research area with strong potential in various forms of formal,
informal and non-formal learning opportunities. This special issue
focuses on these research dimensions and aims to foster discussion on
both individual impacts of these dimensions and their
interdependencies.
3 Contributions of the special issue
The special issue got huge attention due to the fact that Learning
Analytics is a big topic in the field of Technology Enhanced Learning
these days. Nevertheless a careful peer-review-process reduced the
number of contributions to finally eight:
3.1 The Procrastination Related Indicators in e-Learning Platforms
This paper by Paule-Ruiz, Riestra-González, Sánchez-Santillán
and Pérez-Pérez discusses the use of indicators in e-learning
systems. By establishing the importance of providing visual support as
feedback during learning process, the authors consider indicators as
tools to both help instructors and learners in planning and
development of learning strategies, and help system in generating
recommendations ad decisions. The analysis of the indicators and
associated learning analytics in actual e-learning platforms led to
better understanding of the effective use of indicators and the
influence of academic procrastination in the learning performance.
3.2 Dropout prediction and reduction in Distance Education courses
with the Learning Analytics Multitrail approach
Cambruzzi, Rigo and Barbosa look at dropout rates in distance
education courses using learning analytics approaches. Authors
developed a system to deal with dropout problem in such courses at
university level; using Multitrail approach, which represents and
manipulate data from several sources and formats. The system
incorporates various tools for data visualization, dropout
predictions, support to pedagogical actions and textual
analysis. Experiments using the system demonstrated dropout prediction
with 87% precision, which enabled implementation of specific
pedagogical action resulting in 11% average reduction in dropout
rates.
3.3 Learning Analytics for the Academic: An Action Perspective
Dix and Leavesley sketch the big picture of the application of
learning analytics in academic education. Their tour d'horizon
starts with the quest for actionable analytics fitting into the
pattern of requirements and necessities of academic life of students,
academics and their institutional context. The authors identify the
crucial role of (lack of) time in a task structure with multiple
competing goals and analyze the conditions under which the
availability of learning analytics can influence learning and teaching
by triggering action. The presented framework based around academic
timescales starting off with daily routines and ending with multi-year
curricular development processes focuses on strategies for
synchronizing the recognition of need with the potential for execution
in teaching and learning interventions.
3.4 Learning Analytics at "Small" Scale: Exploring A Complexity-Grounded Model for Assessment Automation
Goggins, Xing, Chen, Chen and Wadholm argue that summative evaluation
of learning in small collaborative groups neglects the quality of
learning centered interaction in the group and does not assist in-time
scaffolding and intervention by teachers. Hence they propose and
explore an automatic assessment model for technology-mediated small
group learning that takes into account a range of events:
communication, tool selection, whiteboard actions and system
events. These data are processed on the basis of a simple,
theoretically sound rule set that focuses on the development of the
learning process. By applying Tree Augmented Naïve Bayes
Classifiers they are able to do fully automatic formative tracking of
the interactive learning process that can easily be monitored and
interpreted by teachers.
3.5 Towards a Learning-Aware Application Guided by Hierarchical Classification of Learner Profiles
Taraghi, Saranti, Ebner, Müller and Großmann describe the use of
implicit feedback, based on learner's answering behavior in the
Android application UnlockYourBrain for mathematics learning. An
analytical approach is introduced in the paper for modeling a learner
profile on the basis of such answering behavior. Similar learner's
profiles are grouped together to construct learning behavior clusters
using hierarchical clustering. Authors conclude that building
awareness about learners' behaviors is critical for future
learning-aware applications.
3.6 Development of the Learning Analytics Dashboard to Support Students’ Learning Performance
Park and Joa present the Learning Analytics Dashboard (LAD) application
that shows online behavior patterns of students in virtual learning
environment. The application tracks students' log files to mine
large amounts of data to find meaning and visualize the results. While
a usability test on an early version of LAD did not find any
significant impact on students' learning achievements, it did
reveal that visualized information had impact on students'
understanding level and the overall satisfaction with LAD contributed
to both students' degree of understanding and their perceived
change of behavior.
3.7 A Visual Analytics Method for Score Estimation in Learning Courses
In order to foster self-awareness of students,
de-la-Fuente-Valentïn, Pardo, Löpez Hernändez and Burgos
describe a visual analytics technique that enables students to compare
their learning performance to that of others. The presented method is
based on similarity measures between students' behavior and the
relation to their final grade, under the assumption that the students
who behave similarly are graded similarly. The approach is validated
then with an empirical evaluation.
3.8 Learning Analytics for English Language Teaching
In this work by Volk, Kellner and Wohlhart the
collected data from an online language-learning platform is
analyzed. The presented results comprise usage behavior over the whole
school year as well as user activities in different school types among
different Austrian provinces on a large scale. Furthermore the
efficiency and effectiveness of different type of exercises are
explored.
4 Program Committee
We express our gratitude to the program
committee for their valuable work on reviewing all contributions and
giving detailed feedback. Thank you for your expertise:
- Dietrich Albert (Graz University of Technology, Austria)
- Ulrike Cress (University Tübingen, Germany)
- Shane Dawson (University of South Australia, Australia)
- Michael Derntl (RWTH Aachen University, Germany)
- Margarete Grimus (Graz University of Technology, Austria)
- Andreas Holzinger (Medical University Graz, Austria)
- Michael Kickmeier-Rust (Graz University of Technology, Austria)
- Siu Cheung Kong (The Hong Kong Institute of Education, Hong Kong)
- Michael Kopp (University of Graz, Austria)
- Yanyan Li (Beijing Normal University, China)
- Felix Mödritscher (Vienna University of Economics and Business, Austria)
- Alexander Pohl (University of Munich, Germany)
- Mimi Recker (Utah State University, USA)
- Martin Schön (Graz University of Technology, Austria)
- Sandra Schön (Salzburg Research, Austria)
- Patrick Schweighofer (University of Applied Sciences, Campus02, Austria)
- Philip Tsang (Caritas Institute of Higher Education Hong Kong, Hong Kong)
- Katrien Verbert (Eindhoven University of Technology, Belgium)
Martin Ebner
Kinshuk
David Wohlhart
Behnam Taraghi
Vive Kumar
Athabasca (Canada) & Graz (Austria)
November 2014
References
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