CEUR proceedings are now available
Important dates
Motivation
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
[1]. Learning Analytics dashboards have been used at all levels, including institutional,
regional and national level [2]. 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 [3]. 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.[4],[5]).
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 [6]. Benefits
of showing learning data to learners for such purposes are now also being investigated
in learning analytics (e.g. [7],[8]). 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.
Dissemination activities
- Summary of workshop contributions and outcomes on workshop website
- CEUR Workshop Proceedings for refereed papers
- Joint state-of-the-art journal article between some of the organisers
and workshop participants
- Planning included for future workshop(s) on this theme
- Summary of workshop outcomes plus link to detail sent to SoLAR and AIED
mailing lists, they key fields where research overlaps (plus any lists recommended
at workshop)
- Social networking during the workshop (Twitter, Facebook), YouTube video
to be put together showing learning analytics examples from various talks
and demos, with explanations
References
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. 1-10.
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, 412-416.
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). ASCILITE 2012.
8. Durall, E. & Gros, B. 2014. Learning Analytics as a Meta-cognitive
Tool. Proceedings of CSEDU 2014, 380-384.
