Thousands of learners are taking massive Open Online courses (MOOCs) in search of self-improvement opportunities. However, the learning process often involves a struggle with confusion, which can adversely affect the course participation experience, leading to dropout halfway through. At the same time, it is impossible for the instructors to recognize and respond “confusion” posts accurately and timely, due to the large volume of registrants. This study aims to develop a robust and interpretable NLP model to identify "confusion" posts that require instructors' immediate attention and to dig deeper into the origin causes of confusion posts according to the type of confusion. We find that posts identified as “confusion” are manifestations of different learner affects pertaining to their informational needs–primarily related to cognitive and social processes. Furthermore, we evaluated the model with a 13% improvement in F1 value compared to previous studies and successfully applied it across unseen domains.

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.