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Numerous studies demonstrate the importance of self-regulation duringlearning by problem-solving. Recent work in learning analytics has largelyexamined students' use of SRL concerning overall learning gains. Limitedresearch has related SRL to in-the-moment performance differences amonglearners. The present study investigates SRL behaviors in relationship tolearners' moment-by-moment performance while working with intelligent tutoringsystems for stoichiometry chemistry. We demonstrate the feasibility of labelingSRL behaviors based on AI-generated think-aloud transcripts, identifying thepresence or absence of four SRL categories (processing information, planning,enacting, and realizing errors) in each utterance. Using the SRL codes, weconducted regression analyses to examine how the use of SRL in terms ofpresence, frequency, cyclical characteristics, and recency relate to studentperformance on subsequent steps in multi-step problems. A model consideringstudents' SRL cycle characteristics outperformed a model only usingin-the-moment SRL assessment. In line with theoretical predictions, students'actions during earlier, process-heavy stages of SRL cycles exhibited lowermoment-by-moment correctness during problem-solving than later SRL cyclestages. We discuss system re-design opportunities to add SRL support duringstages of processing and paths forward for using machine learning to speedresearch depending on the assessment of SRL based on transcription ofthink-aloud data.

Using Think-Aloud Data to Understand Relations between Self-Regulation Cycle Characteristics and Student Performance in Intelligent Tutoring Systems