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

• Published in: Computers in human behavior 2019-11, Vol.100, p.345-357
• Main Authors: Rienties, Bart, Tempelaar, Dirk, Nguyen, Quan, Littlejohn, Allison
• Format: Article
• Language: English
• Subjects: Blended learning; Cluster analysis; Complexity; Emotions; Learning; Learning analytics; Learning dispositions; Mathematics; Self-regulated learning; Students; Temporal analytics
• ISSN: 0747-5632; 1873-7692
• DOI: 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 explores 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 academic performance, self-regulated learning strategies, epistemic learning emotions, and activity learning emotions. 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 the 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. •With arrival of learning analytics there are new venues for understanding impact Self-Regulated Learning (SRL).•In blended learning environment, temporal analytics explored when, what, and how 1035 students were solving 429 tasks.•SRL learning dispositions linked with learning processes and academic performance.•Primary predictor of success was when students were engaged with mathematics.