Interpreting Predictive Learning Sequences in a College Math Course through a Self-Regulated Learning Framework

Abstract

Digital traces have been used to measure self-regulated learning (SRL), yet the validity of inferences made about these traces has often been questioned. Recently, researchers have used multiple channels of data — including digital traces, verbalizations, and self-reports — to validate inferences about individual SRL events. Research on the validation of inferences about sequences of multiple SRL events remains limited; however, investigating these sequences has the potential to refine SRL theories. To study the validation of sequences of SRL events, we collected multimodal data from 49 undergraduates completing a math task in a lab setting. Participants were asked to think aloud while interacting with different digital platforms. Then, we used sequence pattern mining to identify the digital events most predictive of post-test scores. Next, we used student verbalizations during the learning process to validate the inferences about what those predictive sequences reflected. Sequences representing learner conscientiousness predicted better performance; sequences that included pausing and rewinding videos predicted poorer performance. Some learner verbalizations co-occurred with digital events and consistently aligned with SRL processes, providing validity evidence for SRL sequences. Heterogeneity in verbal-to-digital trace alignment emerged and will require methodological advances to validate the sequences specific to individuals and task conditions.