ECT Brownbag: Qiujie Li 

Using clickstream data to measure, understand, and support self-regulated learning in online courses 

When: Tuesday, November 10th, 10am – 11am EST
Zoom Link: https://nyu.zoom.us/j/91044407438

The ability to regulate one’s learning is essential for success in online courses. Clickstream data, the detailed and real-time record of students’ interaction with the online platforms, provide new opportunities to measure, understand, and support students’ self-regulation in virtual learning. Recent efforts have used clickstream data to create timely, fine-grained, and comprehensive measures of self-regulated learning (SRL) behaviors in online courses in an attempt to shed light on the process of SRL and to improve the identification of students who lack SRL skills and are at risk of low achievement. Studies have also attempted to support students’ self-regulation in virtual learning environments using the clickstream data to provide students with feedback on their learning behaviors. However, key questions remain: to what extent do these clickstream measures correspond to traditional self-reported measures about specific SRL constructs? How effective are behavioral feedback interventions on student learning process and performance outcomes and what are the operating mechanisms? In this talk, I will discuss two studies that attempted to answer these questions. The first study used the clickstream data collected from a learning management system to measure two aspects of SRL (i.e., time management and effort regulation) and triangulated clickstream measures with student self-reported data from before and after the course. The second study tested the effect of one behavioral feedback intervention on student outcomes and verified a proposed mechanism through a randomized controlled trial in a large online undergraduate course.

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• Using clickstream data to measure, understand, and support self-regulated learning in online courses