How do learners practice? – theory-informed sequence analyses to investigate self-regulated learning processes and their link to achievement

Learners’ academic achievement relates to self-regulatory skills, which are especially relevant in digital learning environments. Whereas digital practice is increasingly used in K-12 education, the temporal dynamics of self-regulated learning (SRL) behaviors during digital practice, their achievement link, and the impact of individual differences remain underexplored. This study, therefore, examines theory-grounded and data-driven SRL-related behavioral patterns using trace data from N = 448 7th-grade English learners. Extracted, SRL-annotated sequences revealed that learners more frequently exhibit SRL-indicative than non-SRL-indicative behaviors. Using these sequences, we derived three learner clusters: Students displaying the most effort-related behaviors achieved the highest performance, whereas those showing the least effort performed poorly in subsequent tests. Interestingly, persistence behaviors followed an inverse trend. Additionally, clusters differed significantly in prior knowledge and motivation. This theory-informed analysis provides insights into how students practice digitally, enriches further system development, and deepens our understanding of how SRL behaviors are linked to achievement.