Revealing Data Feature Differences Between System- and Learner-Initiated Self-regulated Learning Processes Within Hypermedia

Self-regulated learning (SRL) with advanced learning technologies has shown to significantly augment learners’ performance across contexts. Yet studies find learners lack sufficient SRL skills to successfully implement strategies (e.g., judgments of learning, note taking, self-testing, etc.). Current research does not fully explain how and why this failure of effective strategy deployment occurs. We used principle component analysis (PCA) on process data (i.e., log files) from 190 undergraduates learning with MetaTutor, a hypermedia-based intelligent tutoring system, to explore underlying patterns in the frequency of strategy deployment occurring with and without pedagogical agent scaffolding to better understand any underlying structures of system- and learner-initiated cognitive and metacognitive SRL strategy use. Results showed that the system’s underlying architecture deploys processes corresponding to both the phases of learning and type of effort allocation according to Winne’s (2018) Information Processing Theory of SRL. However, learner-initiated processes for those who received scaffolding only displayed strategy deployment that corresponded to the type of effort allocation required of the processes (i.e., more effortful constructionist processes like note-taking versus short canned responses for judgements of learning). Additionally, results suggest all learners deploy strategies based on the familiarity of processes. Regression models using these principle components outperformed raw frequency models for capturing post-test learning performance across all participants.