Join us @ LAK21

We are delighted to be presenting our work at LAK 21.  LAK21 is organised by the Society for Learning Analytics Research (SoLAR) with location host University of California, Irvine. This year, the conference will be fully online from April 12-16, 2021.

Attend our sessions to know more about our work:

Full Paper:

[1] van der Graaf, J., Molenaar, Lim L., I., Fan, Y.,Kilgour, J., Moore, J., Dasevic, D. & Bannert, M. (2021, April). Do Instrumentation Tools Capture Self-Regulated Learning? In Proceedings of the 11th International Conference on Learning Analytics & Knowledge (pp. 438-448).

Abstract: Researchers have been struggling with the measurement of SelfRegulated Learning (SRL) for decades. Instrumentation tools have been proposed to help capture SRL processes that are difficult to capture. The aim of the present study was to improve the measurement of SRL by embedding instrumentation tools in a learning environment and validating the measurement of SRL with these instrumentation tools using think aloud. Synchronizing log data and concurrent think-aloud data helped identify which SRL processes were captured by particular instrumentation tools. One tool was associated with a single SRL process: the timer co-occurred with monitoring. Other tools co-occurred with a number of SRL processes, i.e., the highlighter and note-taker captured superficial writing down, organizing, and monitoring, whereas the search and planner tools revealed planning and monitoring. When specific learner actions with the tool were analyzed, a clearer picture emerged of the relation between the highlighter and note taker and SRL processes. By aligning log data with think-aloud data, we showed that instrumentation tool use indeed reflects SRL processes.

The main contribution is that this paper is the first to show that SRL processes that are difficult to measure by trace data can indeed be captured by instrumentation tools such as high cognition and metacognition. Future challenges are to collect and process log data in real-time with learning analytic techniques to measure ongoing SRL processes and support learners during learning with personalized SRL scaffolds.


[2] Singh, S., Rakovic, M., Lim, K.P., van der Graaf, J., Fan, Y.,Kilgour, J., Bannert, M., Molenaar, I., Gasevic, D. & Moore, J. (2021, April). Using Enhanced Learner-facing Visual Interfaces to support Self-regulated Learning. In Companion Proceedings of the 11th International Conference on Learning Analytics & Knowledge (pp. 661-63).

Abstract: Visualisations provide a rapid way for learners to see and understand their learning metrics. Yet few learner-facing interfaces have been developed to support learners’ self-regulation. This paper proposes the application profile of personalised visual interfaces to support learners in self-regulated learning (SRL). Our design is theoretically based and empirically driven, and utilises trace data from multiple channels to provide clear actionable recommendations for learners to improve regulation. Guided by a quasi-experimental study in a university context, we survey the critical learning processes in SRL, describe the environment to collect multimodal and multichannel data about those processes, and suggest visualizations that can rely upon these data sources— to prompt learners to engage in metacognitive monitoring of their learning and adapt their learning. We conclude with future directions of contributing to the interdisciplinary efforts of addressing the use of cognitive and meta-cognitive data traces and visualisations to foster self-regulation to support optimal and successful learning.

See you there !