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.

Undergraduates enrolled in large, active learning courses must self-regulate their learning (self-regulated learning [SRL]) by appraising tasks, making plans, setting goals, and enacting and monitoring strategies. SRL researchers have relied on self-report and learner-mediated methods during academic tasks studied in laboratories and now collect digital event data when learners engage with technology-based tools in classrooms. Inferring SRL processes from digital events and testing their validity is challenging. We aligned digital and verbal SRL event data to validate digital events as traces of SRL and used them to predict achievement in lab and course settings. In Study 1, we sampled a learning task from a biology course into a laboratory setting. Enrolled students (N = 48) completed the lesson using digital resources (e.g., online textbook, course site) while thinking aloud weeks before it was taught in class. Analyses confirmed that 10 digital events reliably co-occurred >= 70% of the time with verbalized task definition and strategy use macroprocesses. Some digital events co-occurred with multiple verbalized SRL macroprocesses. Variance in occurrence of validated digital events was limited in lab sessions, and they explained statistically nonsignificant variance in learners' performance on lesson quizzes. In Study 2, lesson-specific digital event data from learners (N = 307) enrolled in the course (but not in Study 1) predicted performance on lesson-specific exam items, final exams, and course grades. Validated digital events also predicted final exam and course grades in the next semester (N = 432). Digital events can be validated to reflect SRL processes and scaled to explain achievement in naturalistic undergraduate education settings.

Despite the effectiveness of active learning, STEM learners often face challenges in navigating diverse activities and resources.Addressing this, we investigated how students with varying adherence to high-structure active learning exhibited distinct SRL processes during think-aloud protocols.Using epistemic network analysis, we identified differences in students' verbalized SRL processes between groups who differed in their level of adherence.Future work will be broadened to explore the relationship between student adherence and learning outcomes.