  Numerous studies demonstrate the importance of self-regulation during
learning by problem-solving. Recent work in learning analytics has largely
examined students' use of SRL concerning overall learning gains. Limited
research has related SRL to in-the-moment performance differences among
learners. The present study investigates SRL behaviors in relationship to
learners' moment-by-moment performance while working with intelligent tutoring
systems for stoichiometry chemistry. We demonstrate the feasibility of labeling
SRL behaviors based on AI-generated think-aloud transcripts, identifying the
presence or absence of four SRL categories (processing information, planning,
enacting, and realizing errors) in each utterance. Using the SRL codes, we
conducted regression analyses to examine how the use of SRL in terms of
presence, frequency, cyclical characteristics, and recency relate to student
performance on subsequent steps in multi-step problems. A model considering
students' SRL cycle characteristics outperformed a model only using
in-the-moment SRL assessment. In line with theoretical predictions, students'
actions during earlier, process-heavy stages of SRL cycles exhibited lower
moment-by-moment correctness during problem-solving than later SRL cycle
stages. We discuss system re-design opportunities to add SRL support during
stages of processing and paths forward for using machine learning to speed
research depending on the assessment of SRL based on transcription of
think-aloud data.
