Proceedings of the Annual Meeting of the Cognitive Science Society

Self-regulated learning influences students' learning behaviors and is a significant academic performance factor. Resource management strategy based on self-regulated learning theory is an important indicator for students to demonstrate Self-regulated learning. However, current self-regulated learning and resource management strategy assessments still rely on subjective evaluations and self-assessments, which are time-consuming and laborious. Therefore, we propose a novel method combined with mobile sensing by collecting detailed learning strategy subscales and objective mobile sensing data from 211 college students to explore a new approach to assessing self-regulated learning and resource management strategy. We are the first to propose a mobile sensing approach for assessing self-regulated learning and learning strategies. The method studies the associations between the learning strategy subscales and these daily behavior patterns and presents features for behavior patterns from mobile sensing data. Our study helps to reveal new forms of assessing self-regulated learning and opens the way for personalized interventions.