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.

翻译：大量研究表明，自我调节在通过问题解决的学习过程中至关重要。近期学习分析领域的研究主要考察学生在自我调节学习行为与整体学习收益之间的关系，而将自我调节学习与学习者实时表现差异相关联的研究有限。本研究探讨了学生在使用化学计量学智能辅导系统时，自我调节学习行为与逐时刻表现之间的关系。我们基于人工智能生成的出声思维转录文本，论证了标注自我调节学习行为的可行性，识别了每条话语中四种自我调节学习类别（信息处理、规划、执行和错误发现）的存在与否。通过自我调节学习编码，我们进行了回归分析，考察自我调节学习在出现频率、周期性特征及新近性方面与学生在多步骤问题后续步骤中的表现之间的关系。考虑学生自我调节学习循环特征的模型优于仅使用实时自我调节学习评估的模型。与理论预测一致，学生在自我调节学习循环早期以过程为主的阶段中的行为，其逐时刻正确率低于自我调节学习循环后期阶段。我们讨论了系统重新设计的机会，以在信息处理阶段增加对自我调节学习的支持，以及利用机器学习加速基于出声思维数据转录的自我调节学习评估研究的路径。