Self-regulated learning is a crucial skill that learners need in order to be successful in an online learning environment. However, existing platforms oer little support for the development of such skills. Learning dashboards are learning analytics tools built with the purpose of making learners aware of their learning performance and behaviour and triggering self-reflection, yet most of the existing dashboards follow a "one size fits all" philosophy disregarding individual differences between learners. Throughout this PhD project, we aim to research and develop learning dashboard designs that cater to the individual needs of learners in their SRL skill development while closing the gap between learning analytics and learning sciences.

1. Offering adaptive support for self-regulated
learning with learning analytics dashboards
Ioana Jivet
Welten Institute
Open University of the Netherlands
ioana.jivet@ou.nl

2. Outline
§ Problem statement
§ Goals
§ Approach and methodology
§ Contribution

3. Source: https://www.flickr.com/photos/bruneluniversity/12926707974/

4. Learning analytics dashboards
“single displays that aggregate different indicators about learner(s),
learning process(es) and/or learning context(s) into one or multiple
visualisations”
Schwendimann, B. A., Rodriguez-Triana, M. J., Vozniuk, A., Prieto, L. P., Boroujeni, M. S., Holzer, A., ... & Dillenbourg,
P. (2017). Perceiving learning at a glance: A systematic literature review of learning dashboard research. IEEE
Transactions on Learning Technologies, 10(1), 30-41.

5. Learning analytics dashboards
Aljohani, N. R., & Davis, H. C. (2013). Learning analytics and formative assessment to provide immediate
detailed feedback using a student centered mobile dashboard. NGMAST 2013, pp. 262-267. IEEE.

6. Learning analytics dashboards
Tan, J. P. L., Yang, S., Koh, E., & Jonathan, C. (2016). Fostering 21st century literacies through a collaborative critical
reading and learning analytics environment: user-perceived benefits and problematics. LAK’16. pp. 430-434. ACM.

7. Learning analytics dashboards
Corrin, L., & de Barba, P. (2014). Exploring students’ interpretation of feedback delivered through learning analytics
dashboards. ASCILITE 2014. pp. 629-633.

8. Problem statement
Page 8
• Few dashboards for learners
• Little grounding in learning sciences
• No consistent support for self-regulated learning
• “One size fits all” design
• Strong focus on competition with peers as a motivational factor for
achievement
Jivet, I., Scheffel, M., Drachsler, H. & Specht, M. (2017). Awareness is not enough.
Pitfalls of learning analytics dashboards in the educational practice. EC-TEL 2017.

9. Goals
Page 9
i. develop a learning analytics dashboard that supports learners’
self-regulated learning by providing individualised feedback
based on learners’ preferences for different learning approaches
(competitive learning or knowledge mastery)
ii. assess the effects of delivering individualised feedback via this
dashboard on learners’ performance

10. What degree of adaptation do learning
analytics dashboards need in order to
effectively support individual preferences for
learners in self-regulated learning?

11. Approach – SRL cycle
Page 11
Adapted from Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into practice, 41(2), 64-70.

12. Approach – Learning approaches and motivation
Based on the Achievement Goal Model - Elliot, A. J., & McGregor, H. A. (2001). A 2×2
achievement goal framework. Journal of personality and social psychology, 80(3), 501.
Mastery
focus on acquiring skills and
competencies, learning the
material and mastering
knowledge.
Competition
focus on demonstrating their
ability by measuring skill in
comparison to others

13. Research questions

14. Iterative design

15. Evaluation of the dashboard
Setting: live experiments in MOOCs on edX and Open edX
Methodology: randomised controlled trials
– Live experiments in MOOCs
– Treatment groups with adapted versions of the dashboard
Evaluation data:
– User feedback gathered with EFLA1
– Activity trace logs and final grades
Evaluation criteria:
– User perception
– Engagement, behaviour, performance
1 http://www.laceproject.eu/evaluation-framework-for-la/

16. Contribution and originality
• Developing a learning analytics dashboard that supports learners’
throughout the whole self-regulated learning cycle
• Challenging the assumption that fostering competition through
comparison with peers is a legitimate way of motivating all learners
towards achievement
• Having a strong international component due to the fact that the
resulting dashboard prototypes will be evaluated in live-running
MOOCs developed within the Competen-SEA project and MOOCs
offered by TU Delft on the edX platform

17. Feedback
• Approach/RQs: detailed RQs mapped with the experiments and the SRL cycle
• Originality
• Background: other tools to support SRL
– Open student models
– Adaptive navigation support
• Methodology: detailed data collection, evaluation metrics, measurement of
SRL improvement, potential pitfalls