Examining change in students’ self-regulated learning patterns after a formative assessment using process mining techniques

Abstract

The growing popularity of e-learning platforms, such as learning management systems, has foregrounded the role of self-regulated learning (SRL) in student success. In many e-learning environments, students typically complete learning assignments outside of school hours with little or no instructor support, which requires students to be highly self-regulated. The current study used trace data from a Moodle platform to examine both the temporal pattern of students' SRL behaviors and changes in high- and low-performing students' SRL behaviors following a formative in-course exam. This study employed repeated-measures ANOVA, multivariate ANOVA, Fuzzy miner, and pMineR on 122,167 event logs from 527 undergraduate students. Findings revealed that students engaged in a loosely sequenced recursive SRL cycle. Following the formative assessment, each group made different adjustments to their SRL processes. High-performing students exhibited more SRL behaviors and developed more structured and interconnected SRL patterns. Low-performing students displayed a smaller increase in SRL behaviors while maintaining their established SRL patterns. Findings from this study could provide a more in-depth theoretical understanding of the nature of SRL cycles. Furthermore, students’ adjustment of SRL patterns in response to assessment may be informative for practitioners to assist students in enhancing their SRL through formative feedback.