Temporal learning analytics to explore traces of self-regulated learning behaviors and their associations with learning performance, cognitive load, and student engagement in an asynchronous online course

• DOI: 10.3389/fpsyg.2022.1096337
• Publication Date: 2023-01-23T14:29:12Z
• Authors: Jerry Chih-Yuan Sun; Yiming Liu; Xi Lin; Xiao Hu
• Keywords: Self-Regulated Learning; Learning Analytics; Cognitive Load; Student Engagement; Online discussion

Self-regulated learning (SRL) plays a critical role in asynchronous online courses. In recent years, attention has been focused on identifying student subgroups with different patterns of SRL behaviors and comparing their performance. However, there is limited research leveraging traces to detect examine the subgroup differences cognitive load engagement. The current study tracked engagement 101 graduate students SRL-enabling tools integrated into an course. According recorded behaviors, this identified two distinct subgroups, using sequence analysis cluster analysis: high (H-SRL) low (L-SRL) groups. H-SRL group showed lower extraneous higher performance, germane load, than L-SRL did. Additionally, articulated compared temporal between combining lag sequential epistemic network analysis. results revealed that both groups followed three phases self-regulation but performed off-task behaviors. preferred activating mastery goals improve ethical knowledge, whereas choosing performance-avoidance pass unit tests. invested more time management notetaking, engaged surface approaches. This offers researchers theoretical methodological insights. our findings help inform practitioners about how design deploy personalized interventions