A mixed method approach to studying self-regulated learning in MOOCs: Combining trace data with interviews

Abstract

To be successful in online education, learners should be able to self-regulate their learning due to the autonomy offered to them. Accurate measurement of learners’ selfregulated learning (SRL) in online education is necessary to determine which learners are in need of support and how to best offer support. Trace data is gathered automatically and unobtrusively during online education, and is therefore considered a valuable source to measure learners’ SRL. However, measuring SRL with trace data is challenging for two main reasons. First, without information on the how and why of learner behaviour it is difficult to interpret trace data correctly. Second, SRL activities outside of the online learning environment are not captured in trace data. To address these two challenges, we propose a mixed method approach with a sequential design. Such an approach is novel for the measurement of SRL. We present a pilot study in which we combined trace data with interview data to analyse learners’ SRL in online courses. In the interview, cued retrospective reporting was conducted by presenting learners with visualizations of their trace data. In the second part of the interview, learners’ activities outside of the online course environment were discussed. The results show that the mixed-method approach is indeed a promising approach to address the two described challenges. Suggestions for future research are provided, and include methodological considerations such as how to best visualize trace data for cued retrospective recall.