Susan Bull (University of Birmingham), Blandine Ginon (University
of Birmingham),Judy Kay (University of Sydney), Michael Kickmeier-Rust,
(TU Graz), Matthew Johnson (University of Birmingham),
The first objective of this
workshop is to bring together people working in learning analytics
generally, open learner models, and learning dashboards, to
raise awareness of projects and approaches across research
areas, that are relevant to learning analytics for learners.
The second objective of this workshop will be to organise
a state of the art review on learning analytics for learners
for the Journal of Learning Analytics, to be co-authored with
key participants at the workshop. This will aim to cover the
themes in the first objective, but may be adapted to incorporate
expertise and interests of workshop participants. Finally,
we aim to continue supporting research on learning analytics
for learners in future workshops, and would like to gather
suggestions for key topics for the next LAL proposal. This
will allow us to foster and stimulate ideas in areas of interest
to participants that may not yet be mature enough for inclusion
in the review article or workshop proceedings.
- Simon Buckingham Shum, University of Technology, Sydney,
- Eva Durall, Aalto University, Finland
- Albrecht Fortenbacher, HTW Berlin, Germany
- Alyssa Friend Wise, Simon Fraser University, Canada
- Dragan Gasevic, University of Edinburgh, UK
- Dai Griffiths, University of Bolton, UK
- Sharon Hsiao, Arizona State University, USA
- Stéphanie Jean-Daubias, University Claude Bernard
of Lyon, France
- Symeon Retalis, University of Piraeus, Greece
- Ravi Vatrapu, Copenhagen Business School, Denmark
- Dr. Marek Hatala, Simon Fraser University, Surrey, Canada
With the arrival of ‘big
data’ in education, the potential was recognised for
learning analytics to track students’ learning, to reveal
patterns in their learning, or to identify at-risk students,
in addition to guiding reform and supporting educators in
improving teaching and learning processes . Learning Analytics
dashboards have been used at all levels, including institutional,
regional and national level . In classroom use, while learning
visualisations are often based on counts of activity data
or interaction patterns, there is increasing recognition that
learning analytics relate to learning, and should therefore
provide pedagogically useful information . While increasing
numbers of technology-enhanced learning applications are embracing
the potential of learning analytics at the classroom level,
often these are aimed at teachers. However, learners can also
benefit from learning analytics data (e.g.,).
Open learner models have long
been showing learners information about their learning, often
with the aim of encouraging metacognitive behaviours such
as reflection, planning, self-assessment and self-directed
learning . Benefits of showing learning data to learners
for such purposes are now also being investigated in learning
analytics (e.g. ,). Nevertheless, despite a few exceptions,
there is limited reference to both open learner models and
learning analytics in the same publications. One of the aims
of this workshop, therefore, is to raise awareness of the
overlap, as well as differences, in approaches to, and purposes
of visualising learning data in these two fields. In particular,
we will bring our substantial expertise in open learner model
research, together with our more recent experience in learning
analytics for learners, to help facilitate discussion and
exchange of experiences amongst participants.
We would like to bring our understanding
of work related to learning analytics for learners to the
learning analytics community, to learn from others’
work on learning analytics for learners, and together build
a vision for learning analytics to directly support and facilitate
the learning process for learners.
1. Siemens, G. & Long P.
2011. Penetrating the Fog: Analytics in Learning and Education.
EDUCAUSE Review 46 (5), 30-38.
2. West, D. 2012. Big Data for Education: Data Mining, Data
Analytics, and Web Dashboards. Governance Studies at Brookings.
3. Gaševic, D., Dawson, S., & Siemens, G. (2015).
Let’s Not Forget: Learning Analytics are about Learning.
Techtrends, 59(1), 64-71.
4. Ferguson, R. and Buckingham Shum, S. (2012). Social Learning
Analytics: Five Approaches. LAK 2012. 23–33.
5. Vozniuk, A., Govaerts, S., & Gillet, D. 2013. Towards
Portable Learning Analytics Dashboards. ICALT 2013, IEEE,
6. Bull, S. & Kay, J. 2013. Open Learner Models as Drivers
for Metacognitive Processes. In R. Azevedo & V. Aleven
(eds), International Handbook of Metacognition and Learning
Technologies, Springer New York, 349-365.
7. Dawson, S., Macfadyen, L., Risko E., Foulsham, T. &
Kingstone, A. 2012. Using Technology to Encourage Self-Directed
Learning: The Collaborative Lecture Annotation System (CLAS).
8. Durall, E. & Gros, B. 2014. Learning Analytics as a
Meta-cognitive Tool. Proceedings of CSEDU 2014, 380-384.