2025/11/26 by Linyu Yu, Peter F. Halpin, Matthew L. Bernacki +3 · 1 voice · 1 citation
Psychology · #Innovative Teaching and Learning Methods #Learning Styles and Cognitive Differences #Visual and Cognitive Learning Processes
paper · pdf · doi:10.18608/jla.2025.8865
openalex publication_date 2025/11/26 · openalex created_date 2025/11/27 · openalex updated_date 2026/06/26
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.