Unpacking the intertemporal impact of self-regulation in a blended mathematics environment

Rienties, Bart; Tempelaar, Dirk; Nguyen, Quan and Littlejohn, Allison (2019). Unpacking the intertemporal impact of self-regulation in a blended mathematics environment. Computers in Human Behavior, 100 pp. 345–357.

DOI: https://doi.org/10.1016/j.chb.2019.07.007


With the arrival of fine-grained log-data and the emergence of learning analytics, there may be new avenues to explore how Self-Regulated Learning (SRL) can provide a lens to how students learn in blended and online environments. In particular, recent research has found that the notion of time may be an essential but complex concept through which students make (un)conscious and self-regulated decisions as to when, what, and how to study. This study explored distinct clusters of behavioural engagement in an online e-tutorial called Sowiso at different time points (before tutorials, before quizzes, before exams), and their associations with self-regulated learning strategies, epistemic learning emotions, activity learning emotions, and academic performance. Using a cluster analysis on trace data of 1035 students practicing 429 online exercises in Sowiso, we identified four distinct cluster of students (e.g. early mastery, strategic, exam-driven, and inactive). Further analyses revealed significant differences between these four clusters in their academic performance, step-wise cognitive processing strategies, external self-regulation strategies, epistemic learning emotions and activity learning emotions. Our findings took a step forward towards personalised and actionable feedback in learning analytics by recognizing the complexity of how and when students engage in learning activities over time, and supporting educators to design early and theoretically informed interventions based on learning dispositions.

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